system

Abstract

The system according to this embodiment aims to efficiently generate, organize, and visualize ideas. [Solution] The system according to the embodiment comprises an idea generation unit, an idea organization unit, and an idea visualization unit. The idea generation unit generates ideas. The idea organization unit organizes the ideas generated by the idea generation unit. The idea visualization unit visualizes the ideas organized by the idea organization unit.

Description

Technical Field

[0004]

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that idea generation tends to stagnate and it is troublesome to organize and prioritize ideas.

[0005] The system according to the embodiment aims to efficiently perform idea generation, organization, and visualization.

Means for Solving the Problems

[0006] The system according to the embodiment includes an idea generation unit, an idea organization unit, and an idea visualization unit. The idea generation unit generates ideas. The idea organization unit organizes the ideas generated by the idea generation unit. The idea visualization unit visualizes the ideas organized by the idea organization unit.

Effects of the Invention

[0007] The system according to this embodiment can efficiently generate, organize, and visualize ideas. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The idea generation support system according to an embodiment of the present invention is a system that utilizes AI to streamline the idea generation process and support collaboration in multinational teams. As support for idea generation, this idea generation support system uses AI to activate brainstorming in real time and provide new perspectives. This ensures that idea generation does not stagnate and that lively discussions take place. For example, the AI ​​analyzes past data and trends and proposes new ideas, stimulating the team's creativity. Next, for idea organization and classification, the AI ​​automatically groups ideas and suggests priorities. This makes idea organization and prioritization effortless. For example, the AI ​​analyzes the relationships and importance of ideas and presents the optimal grouping and prioritization. Furthermore, as visualization of insights, the AI ​​automatically generates mind maps and related information, providing visual understanding. This makes it easier to grasp the overall picture of ideas. For example, the AI ​​visually shows the relationships between ideas, enabling team members to understand intuitively. Additionally, as real-time translation and multilingual support, the AI ​​assists in collaboration in multiple languages ​​and appropriately explains cultural backgrounds. This eliminates language barriers and cultural differences in multinational teams. For example, AI can perform real-time translations to facilitate communication between members who speak different languages. Furthermore, as decision-making support, AI can use frameworks such as SWOT analysis to support decision-making, improving both the speed and quality of decision-making. For instance, AI can analyze the strengths, weaknesses, opportunities, and threats of each idea to support optimal decision-making. In addition, to improve session efficiency, AI can manage time and automatically generate meeting minutes, ensuring smooth discussions. This improves session efficiency. For example, AI can monitor the progress of discussions and generate meeting minutes at the appropriate time. Furthermore, as a whiteboard design aid, AI can optimize visual design and provide colorblind-friendly support, resulting in visually easy-to-understand designs. For example, AI can suggest designs that are considerate of members with color vision deficiencies. Finally, as a learning and feedback tool, AI generates suggestions for future improvements based on session analysis, enabling continuous improvement.For example, AI can analyze data from past sessions and suggest improvements for the next session. In this way, leveraging AI streamlines the idea generation process and supports collaboration within multinational teams. This promotes creative thinking and enables efficient collaboration. Thus, idea generation support systems can streamline the idea generation process and support collaboration within multinational teams.

[0029] The idea generation support system according to the embodiment comprises an idea generation unit, an idea organization unit, and an idea visualization unit. The idea generation unit generates ideas. The idea generation unit can, for example, use AI to activate brainstorming in real time and provide new perspectives. For example, the idea generation unit can analyze past data and trends and propose new ideas. The idea generation unit can also introduce knowledge from different fields and provide new perspectives. Furthermore, the idea generation unit can facilitate participant interaction and enable active discussion. For example, the idea generation unit can ask participants questions to elicit ideas. The idea generation unit can also aggregate participants' opinions and generate new ideas. The idea organization unit organizes the ideas generated by the idea generation unit. The idea organization unit can, for example, automatically group ideas using AI and propose priorities. For example, the idea organization unit can analyze the relevance and importance of ideas and present the optimal grouping and priorities. The idea organization unit can also categorize and organize ideas. Furthermore, the idea organization unit can evaluate ideas and determine their priorities. For example, the Idea Organization Unit evaluates the feasibility and impact of ideas and determines their priority. The Idea Visualization Unit visualizes the ideas organized by the Idea Organization Unit. The Idea Visualization Unit provides visual understanding by, for example, automatically generating mind maps and related information using AI. For example, the Idea Visualization Unit makes it possible for team members to intuitively understand by visually showing the relationships between ideas. The Idea Visualization Unit can also generate graphs and charts to make it easier to grasp the overall picture of the ideas. Furthermore, the Idea Visualization Unit can display detailed information about the ideas to deepen understanding. For example, the Idea Visualization Unit displays background information and related data for the ideas. As a result, the idea generation support system according to this embodiment can efficiently generate, organize, and visualize ideas.

[0030] The idea generation unit generates ideas. For example, it uses AI to activate brainstorming in real time and provide new perspectives. Specifically, the AI ​​uses natural language processing technology to analyze participants' statements and extract relevant keywords and topics. This allows the AI ​​to ask appropriate questions to participants and propose new ideas to deepen the discussion. For example, it can analyze past data and trends and present new ideas related to the current discussion. The AI ​​can also integrate knowledge from different fields to provide participants with new perspectives. For example, combining ideas from the technology field with ideas from the marketing field can generate innovative ideas. Furthermore, the idea generation unit facilitates participant interaction and enables lively discussions. The AI ​​analyzes participants' statements in real time and asks questions at the appropriate time according to the progress of the discussion. This allows participants to deepen their own thinking and consider the opinions of other participants. The AI ​​can also generate new ideas by aggregating participants' opinions and finding common themes and topics. For example, the AI ​​clusters participants' statements and proposes new ideas based on common themes. This allows the idea generation unit to generate ideas efficiently and effectively, improving the quality of brainstorming.

[0031] The Idea Organization Department organizes the ideas generated by the Idea Generation Department. For example, the Idea Organization Department automatically groups ideas and suggests priorities using AI. Specifically, the AI ​​analyzes the content of the generated ideas and groups them based on their relevance and importance. For instance, the AI ​​uses natural language processing to extract keywords from ideas and group similar ideas. The AI ​​can also evaluate the importance of ideas and suggest priorities. For example, the AI ​​evaluates the feasibility and impact of ideas and places the most important ideas at the top. Furthermore, the Idea Organization Department can categorize and organize ideas. The AI ​​analyzes the content of ideas and classifies them into appropriate categories, streamlining the organization process. For example, it can classify ideas into different categories such as technical ideas, marketing ideas, and operational ideas. The Idea Organization Department can also evaluate ideas and determine their priorities. The AI ​​evaluates the feasibility and impact of ideas and places the most important ideas at the top. This allows the Idea Organization Department to efficiently organize generated ideas and clearly define priorities. As a result, the Idea Organization Department can efficiently and effectively organize ideas, ensuring smooth project progress.

[0032] The Idea Visualization Unit visualizes the ideas organized by the Idea Organization Unit. For example, the Idea Visualization Unit uses AI to automatically generate mind maps and related information, providing visual understanding. Specifically, the AI ​​analyzes the relationships between organized ideas and generates mind maps. Mind maps visually represent the relationships between ideas, allowing team members to understand them intuitively. The AI ​​can also generate graphs and charts to facilitate a comprehensive understanding of the ideas. For example, it can generate graphs showing the importance and relevance of ideas, making it easier for team members to grasp the overall picture. Furthermore, the Idea Visualization Unit can display detailed information about ideas to deepen understanding. The AI ​​analyzes background information and related data and displays them visually, making it easier for team members to understand the details of the ideas. For example, displaying background information and related data makes it easier to evaluate the feasibility and impact of ideas. This allows the Idea Visualization Unit to efficiently and effectively visualize ideas and deepen team members' understanding. Additionally, the Idea Visualization Unit provides interactive functions, making it easier for team members to manipulate ideas. For example, by allowing mind maps and graphs to be manipulated using drag-and-drop, team members can more easily organize their ideas freely. This enables the idea visualization department to visualize ideas efficiently and effectively, deepening the understanding of team members.

[0033] The Translation Department provides real-time translation and multilingual support. For example, the Translation Department uses AI to perform real-time translations, facilitating communication between members who speak different languages. For instance, the Translation Department can translate conversations in real time and display them as text. It can also use speech recognition technology to convert speech to text and translate it. Furthermore, the Translation Department can perform translations that take cultural context into account, providing appropriate expressions. For example, it selects appropriate words and phrases based on cultural context. This allows the Translation Department to overcome language barriers and cultural differences in multinational teams. Some or all of the above processes in the Translation Department may be performed using AI, or not. For example, the Translation Department can perform translations using an AI model that translates conversations in real time.

[0034] The Decision Support Department assists in decision-making. For example, the Decision Support Department supports decision-making by using frameworks such as SWOT analysis with AI. For example, the Decision Support Department can analyze the strengths, weaknesses, opportunities, and threats of each idea to support optimal decision-making. The Decision Support Department can also provide appropriate frameworks to improve the speed and quality of decision-making. Furthermore, the Decision Support Department can visualize and deepen understanding of the decision-making process. For example, the Decision Support Department can display each step of the decision-making process in graphs and charts. This allows the Decision Support Department to improve the speed and quality of decision-making. Some or all of the above processes in the Decision Support Department may be performed using AI, for example, or not using AI. For example, the Decision Support Department can support decision-making by using an AI model that analyzes the strengths, weaknesses, opportunities, and threats of each idea.

[0035] The Efficiency Department handles time management and automatic generation of meeting minutes. For example, the Efficiency Department uses AI to monitor the progress of a session and generate meeting minutes at the appropriate time. For example, the Efficiency Department can monitor the progress of a discussion in real time and automatically generate meeting minutes. The Efficiency Department can also manage time and improve the efficiency of the session. Furthermore, the Efficiency Department can summarize the content of the meeting minutes and extract key points. For example, the Efficiency Department summarizes the content of the meeting minutes and lists the key points. This allows the Efficiency Department to improve the efficiency of the session. Some or all of the above processes in the Efficiency Department may be performed using AI, for example, or without AI. For example, the Efficiency Department can generate meeting minutes using an AI model that monitors the progress of a session and automatically generates meeting minutes.

[0036] The Design Support Department optimizes visual designs and provides colorblind-friendly solutions. For example, the Design Support Department uses AI to optimize visual designs and provide visually easy-to-understand designs. For instance, the Design Support Department can propose designs that are considerate of members with color blindness. The Design Support Department can also adjust the style and color scheme of designs to provide optimal visual expression. Furthermore, the Design Support Department can conduct usability tests to improve the usability of designs. For example, the Design Support Department can propose design improvements based on the results of usability tests. This allows the Design Support Department to provide visually easy-to-understand designs. Some or all of the above processes performed by the Design Support Department may be carried out using AI, for example, or without AI. For example, the Design Support Department can propose designs using an AI model that optimizes visual designs.

[0037] The Learning Department generates improvement suggestions from session analysis. For example, the Learning Department can use AI to analyze data from past sessions and suggest improvements for the next session. For example, the Learning Department can list areas for improvement in the next session based on data from past sessions. The Learning Department can also analyze the progress of a session and suggest efficient methods for conducting it. Furthermore, the Learning Department can collect feedback from session participants and incorporate improvements. For example, the Learning Department can suggest areas for improvement in the next session based on participant feedback. This enables the Learning Department to achieve continuous improvement. Some or all of the above processes in the Learning Department may be performed using AI, for example, or without AI. For example, the Learning Department can suggest improvements using an AI model that analyzes data from past sessions and generates improvement suggestions.

[0038] The generation unit can activate brainstorming in real time and provide new perspectives. For example, the generation unit can use AI to conduct brainstorming in real time and provide participants with new perspectives. For example, the generation unit can analyze past data and trends and propose new ideas. It can also introduce knowledge from different fields to provide new perspectives. Furthermore, the generation unit can facilitate participant interaction and conduct lively discussions. For example, the generation unit can ask participants questions to elicit ideas. It can also aggregate participants' opinions and generate new ideas. This allows the generation unit to conduct lively discussions without stalling in idea generation. Some or all of the above processes in the generation unit may be performed using AI, for example, or not. For example, the generation unit can provide new perspectives using an AI model that conducts brainstorming in real time.

[0039] The organization function can automatically group ideas and suggest priorities. For example, it can use AI to automatically group ideas and suggest priorities. For instance, it can analyze the relevance and importance of ideas and present the optimal grouping and prioritization. It can also categorize and organize ideas. Furthermore, it can evaluate ideas and determine their priorities. For example, it can evaluate the feasibility and impact of ideas and determine their priorities. This allows the organization function to organize and prioritize ideas effortlessly. Some or all of the above processes in the organization function may be performed using AI, or not. For example, the organization function can use an AI model to analyze the relevance and importance of ideas and suggest grouping and prioritization.

[0040] The visualization unit can automatically generate mind maps and related information, providing a visual understanding. For example, the visualization unit can use AI to automatically generate mind maps and related information, providing a visual understanding. For instance, the visualization unit can visually represent the relationships between ideas, enabling team members to understand them intuitively. The visualization unit can also generate graphs and charts to facilitate grasping the overall picture of an idea. Furthermore, the visualization unit can display detailed information about an idea to deepen understanding. For example, the visualization unit can display background information and related data about an idea. This makes it easier to grasp the overall picture of the idea. Some or all of the above-described processes in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can provide a visual understanding using an AI model that automatically generates mind maps and related information.

[0041] The generation unit can analyze data from past brainstorming sessions and apply the most effective idea generation method. For example, the generation unit can use AI to analyze data from past brainstorming sessions and apply the most effective idea generation method. For example, the generation unit can analyze successful idea generation methods from past sessions and apply similar methods. The generation unit can also avoid methods that failed in past sessions and select effective methods. Furthermore, the generation unit can find and apply methods that are effective for a specific theme from past session data. For example, the generation unit applies the optimal idea generation method for a specific theme based on past session data. This allows the generation unit to generate ideas effectively by utilizing past data. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can generate ideas using an AI model that analyzes data from past brainstorming sessions and applies the most effective idea generation method.

[0042] The generation unit can provide customized ideas based on the user's area of ​​expertise and interests during idea generation. For example, the generation unit can use AI to provide customized ideas based on the user's area of ​​expertise and interests. For instance, the generation unit can generate ideas based on the latest research and trends related to the user's area of ​​expertise. Furthermore, the generation unit can generate engaging ideas based on the user's interests. Additionally, the generation unit can generate highly relevant ideas by referencing the user's past projects and achievements. For example, the generation unit can generate highly relevant ideas based on the user's past project data. This allows the generation unit to provide ideas tailored to the user's area of ​​expertise and interests. Some or all of the above-described processes in the generation unit may be performed using AI, or not. For example, the generation unit can generate ideas using an AI model that provides customized ideas based on the user's area of ​​expertise and interests.

[0043] The generation unit can generate region-specific ideas by considering the user's geographical location information during idea generation. For example, the generation unit can use AI to consider the user's geographical location information and generate region-specific ideas. For example, the generation unit can generate ideas based on popular trends and events in the user's region. The generation unit can also generate appropriate ideas by considering the culture and customs of the user's region. Furthermore, the generation unit can generate ideas that address specific problems and needs in the user's region. For example, the generation unit can generate ideas that serve as solutions to specific problems in the user's region. In this way, the generation unit can generate region-specific ideas. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can generate ideas using an AI model that generates region-specific ideas by considering the user's geographical location information.

[0044] The generation unit can analyze a user's social media activity and generate ideas that reflect relevant trends during idea generation. For example, the generation unit can use AI to analyze a user's social media activity and generate ideas that reflect relevant trends. For example, the generation unit can generate ideas based on the trends of influencers and brands that the user follows. The generation unit can also analyze the content and reactions to a user's social media posts and generate interesting ideas. Furthermore, the generation unit can generate highly relevant ideas by referring to the user's social media activity history. For example, the generation unit generates highly relevant ideas based on the user's social media activity. This allows the generation unit to generate ideas that reflect social media activity. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can generate ideas using an AI model that analyzes a user's social media activity and generates ideas that reflect relevant trends.

[0045] The organization unit can analyze the relevance of ideas and apply an appropriate grouping algorithm. For example, the organization unit can use AI to analyze the relevance of ideas and apply an appropriate grouping algorithm. For example, the organization unit can analyze the commonalities and differences of ideas and group highly relevant ideas. The organization unit can also analyze the importance and impact of ideas and perform optimal grouping. Furthermore, the organization unit can perform appropriate grouping based on the theme or category of ideas. For example, the organization unit can group highly relevant ideas based on the theme of the ideas. This allows the organization unit to group highly relevant ideas. Some or all of the above processes in the organization unit may be performed using AI, for example, or without AI. For example, the organization unit can organize ideas using an AI model that analyzes the relevance of ideas and applies an appropriate grouping algorithm.

[0046] The organization unit can dynamically adjust priorities based on the importance and impact of ideas. For example, the organization unit can use AI to analyze the importance and impact of ideas and dynamically adjust priorities. For example, the organization unit can analyze the importance of ideas and dynamically adjust priorities. The organization unit can also analyze the impact of ideas and dynamically adjust priorities. Furthermore, the organization unit can analyze the feasibility of ideas and dynamically adjust priorities. For example, the organization unit can evaluate the feasibility of ideas and dynamically adjust priorities. This allows the organization unit to provide priorities based on importance and impact. Some or all of the above processes in the organization unit may be performed using AI, for example, or without AI. For example, the organization unit can organize ideas using an AI model that analyzes the importance and impact of ideas and dynamically adjusts priorities.

[0047] The organization unit can perform optimal grouping by referring to the user's past idea submission history when organizing ideas. For example, the organization unit can use AI to refer to the user's past idea submission history and perform optimal grouping. For example, the organization unit can analyze the user's past idea submission history and group highly relevant ideas. The organization unit can also group ideas based on successful ideas from the user's past idea submission history. Furthermore, the organization unit can perform optimal grouping by referring to the user's past idea submission history. For example, the organization unit groups highly relevant ideas based on the user's past idea submission history. This allows the organization unit to perform optimal grouping by referring to past history. Some or all of the above processes in the organization unit may be performed using AI, for example, or without AI. For example, the organization unit can organize ideas using an AI model that performs optimal grouping by referring to the user's past idea submission history.

[0048] The organization function can provide customized groupings tailored to the user's industry and job when organizing ideas. For example, the organization function can use AI to provide customized groupings tailored to the user's industry and job. For instance, the organization function can provide a grouping method specific to the user's industry. It can also provide a grouping method tailored to the user's job. Furthermore, the organization function can provide optimal groupings by considering trends related to the user's industry and job. For example, the organization function provides optimal groupings based on trends related to the user's industry and job. This allows the organization function to provide industry- and job-specific groupings. Some or all of the above processes in the organization function may be performed using AI, or not. For example, the organization function can organize ideas using an AI model that provides customized groupings tailored to the user's industry and job.

[0049] The visualization unit can apply an appropriate mind map generation algorithm to visually represent the relationships between ideas. For example, the visualization unit can use AI to apply an appropriate mind map generation algorithm to visually represent the relationships between ideas. For example, the visualization unit can analyze the relationships between ideas and generate an optimal mind map. The visualization unit can also generate an optimal mind map considering the importance and impact of the ideas. Furthermore, the visualization unit can generate an optimal mind map based on the theme or category of the ideas. For example, the visualization unit can generate a mind map that visually shows highly related ideas based on the theme of the ideas. In this way, the visualization unit deepens understanding by visually representing the relationships between ideas. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can perform visualization using an AI model that applies an appropriate mind map generation algorithm to visually represent the relationships between ideas.

[0050] The visualization unit can adjust the level of detail of information according to the user's level of understanding during visualization. For example, the visualization unit can estimate the user's level of understanding using AI and adjust the level of detail of information accordingly. For example, if the user's level of understanding is low, the visualization unit will provide detailed information. Conversely, if the user's level of understanding is high, the visualization unit can also provide information that focuses on the key points. Furthermore, the visualization unit can dynamically adjust the level of detail of information according to the user's level of understanding. For example, the visualization unit can monitor the user's level of understanding in real time and adjust the level of detail of information accordingly. This allows the visualization unit to provide information with a level of detail appropriate to the user's level of understanding. Some or all of the above-described processes in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can perform visualization using an AI model that estimates the user's level of understanding and adjusts the level of detail of information accordingly.

[0051] The visualization unit can select the optimal display method when visualizing data, taking into account the user's device information. For example, the visualization unit can use AI to consider the user's device information and select the optimal display method. For example, if the user is using a smartphone, the visualization unit can provide a display method that matches the screen size. The visualization unit can also provide a display method optimized for larger screens if the user is using a tablet. Furthermore, if the user is using a smartwatch, the visualization unit can provide a concise and highly visible display method. For example, the visualization unit selects the optimal display method based on the user's device information. This allows the visualization unit to provide the optimal display method according to the device information. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can perform visualization using an AI model that selects the optimal display method considering the user's device information.

[0052] The visualization unit can provide appropriate visual representations when visualizing, taking into account the user's cultural background. For example, the visualization unit can use AI to consider the user's cultural background and provide appropriate visual representations. For example, the visualization unit can provide appropriate colors and designs based on the user's cultural background. The visualization unit can also use appropriate icons and symbols, taking into account the user's cultural background. Furthermore, the visualization unit can provide appropriate visual representations according to the user's cultural background. For example, the visualization unit provides appropriate visual representations based on the user's cultural background. In this way, the visualization unit can provide visual representations that are appropriate to the cultural background. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can perform visualizations using an AI model that provides appropriate visual representations considering the user's cultural background.

[0053] The translation unit can dynamically update its dictionary to appropriately translate technical terms and industry-specific vocabulary during translation. For example, the translation unit can use AI to dynamically update the dictionary to appropriately translate technical terms and industry-specific vocabulary during translation. For instance, the translation unit can dynamically update the dictionary if new technical terms appear during translation. Furthermore, the translation unit can periodically update the dictionary to appropriately translate industry-specific vocabulary. In addition, the translation unit can dynamically update the dictionary based on user feedback. For example, the translation unit can update the dictionary based on user feedback to improve translation accuracy. This allows the translation unit to appropriately translate technical terms and industry-specific vocabulary. Some or all of the above processes in the translation unit may be performed using AI, or not. For example, the translation unit can perform translations using an AI model that dynamically updates the dictionary to appropriately translate technical terms and industry-specific vocabulary.

[0054] The translation unit can select the optimal translation method by referring to the user's past translation history during translation. For example, the translation unit can use AI to refer to the user's past translation history and select the optimal translation method. For example, the translation unit can analyze the user's past translation history and select the optimal translation method. Furthermore, the translation unit can perform translations based on successful translation methods from the user's past translation history. In addition, the translation unit can select the optimal translation method by referring to the user's past translation history. For example, the translation unit selects the optimal translation method based on the user's past translation history. This allows the translation unit to provide the optimal translation method by referring to past translation history. Some or all of the above-described processes in the translation unit may be performed using AI, for example, or without AI. For example, the translation unit can perform translations using an AI model that selects the optimal translation method by referring to the user's past translation history.

[0055] The translation unit can reflect region-specific expressions by considering the user's geographical location during translation. For example, the translation unit can use AI to consider the user's geographical location and reflect region-specific expressions. For instance, the translation unit can reflect expressions specific to the user's region. Furthermore, the translation unit can use appropriate expressions by considering the culture and customs of the user's region. In addition, the translation unit can use expressions that address specific issues and needs in the user's region. For example, the translation unit can use appropriate expressions for specific issues in the user's region. This allows the translation unit to provide translations that reflect region-specific expressions. Some or all of the above processing in the translation unit may be performed using AI, for example, or without AI. For example, the translation unit can perform translations using an AI model that reflects region-specific expressions by considering the user's geographical location.

[0056] The translation department can analyze users' social media activity during the translation process and provide translations that reflect relevant trends. For example, the translation department can use AI to analyze users' social media activity and provide translations that reflect relevant trends. For instance, the translation department can translate based on trends of influencers and brands that users follow. The translation department can also analyze users' social media posts and reactions.

[0057] The decision support unit can analyze the strengths, weaknesses, opportunities, and threats of each idea in detail and support appropriate decision-making. For example, the decision support unit can use AI to analyze the strengths, weaknesses, opportunities, and threats of each idea in detail and support optimal decision-making. For example, the decision support unit can analyze the strengths of each idea and support optimal decision-making. Furthermore, the decision support unit can analyze the weaknesses of each idea and support optimal decision-making. In addition, the decision support unit can analyze the opportunities and threats of each idea and support optimal decision-making. For example, the decision support unit analyzes the opportunities and threats of each idea and supports optimal decision-making. Thus, the decision support unit can analyze the strengths, weaknesses, opportunities, and threats of each idea and support optimal decision-making. Some or all of the above processing in the decision support unit may be performed using AI, for example, or without AI. For example, the decision support unit can support decision-making using an AI model that analyzes the strengths, weaknesses, opportunities, and threats of each idea in detail.

[0058] The decision support unit can apply the most effective decision-making method by referring to past decision-making data. For example, the decision support unit can use AI to refer to past decision-making data and apply the most effective decision-making method. For example, the decision support unit can analyze past decision-making data and apply the most effective method. Furthermore, the decision support unit can make decisions based on successful methods derived from past decision-making data. In addition, the decision support unit can apply the optimal method by referring to past decision-making data. For example, the decision support unit applies the optimal decision-making method based on past decision-making data. This allows the decision support unit to provide the optimal decision-making method by referring to past decision-making data. Some or all of the above processes in the decision support unit may be performed using AI, for example, or without AI. For example, the decision support unit can support decision-making using an AI model that applies the most effective decision-making method by referring to past decision-making data.

[0059] The decision support unit can provide a customized framework tailored to the user's industry and job function during the decision-making process. For example, the decision support unit can use AI to provide a customized framework tailored to the user's industry and job function. For instance, the decision support unit can provide a decision-making framework specific to the user's industry. Furthermore, the decision support unit can provide a decision-making framework tailored to the user's job function. In addition, the decision support unit can provide an optimal decision-making framework by considering trends related to the user's industry and job function. For example, the decision support unit provides an optimal decision-making framework based on trends related to the user's industry and job function. This allows the decision support unit to provide a decision-making framework tailored to the industry and job function. Some or all of the above-described processes in the decision support unit may be performed using AI, or not. For example, the decision support unit can support decision-making using an AI model that provides a customized framework tailored to the user's industry and job function.

[0060] The decision support unit can reflect region-specific factors by considering the user's geographical location information when making decisions. For example, the decision support unit can use AI to consider the user's geographical location information and reflect region-specific factors. For example, the decision support unit can make decisions by considering factors specific to the user's region. The decision support unit can also make appropriate decisions by considering the culture and customs of the user's region. Furthermore, the decision support unit can make decisions that address specific problems and needs in the user's region. For example, the decision support unit can make appropriate decisions regarding specific problems in the user's region. In this way, the decision support unit can provide decisions that reflect region-specific factors. Some or all of the above processing in the decision support unit may be performed using AI, for example, or without AI. For example, the decision support unit can support decision-making using an AI model that reflects region-specific factors by considering the user's geographical location information.

[0061] The efficiency unit can monitor the progress of a session in real time and generate meeting minutes at the optimal time. For example, the efficiency unit can use AI to monitor the progress of a session in real time and generate meeting minutes at the optimal time. For example, the efficiency unit can monitor the progress of a session in real time and generate meeting minutes at the appropriate time. Furthermore, the efficiency unit can analyze the progress of a session and generate meeting minutes at the optimal time. In addition, the efficiency unit can dynamically adjust the timing of meeting minute generation based on the progress of the session. For example, the efficiency unit dynamically adjusts the timing of meeting minute generation based on the progress of the session. This allows the efficiency unit to generate meeting minutes at the optimal time according to the progress of the session. Some or all of the above processes in the efficiency unit may be performed using AI, or not. For example, the efficiency unit can generate meeting minutes using an AI model that monitors the progress of a session in real time and generates meeting minutes at the optimal time.

[0062] The efficiency unit can analyze past session data and apply the most effective time management methods. For example, the efficiency unit can use AI to analyze past session data and apply the most effective time management methods. Furthermore, the efficiency unit can perform time management based on successful methods derived from past session data. In addition, the efficiency unit can apply the optimal time management method by referring to past session data. For example, the efficiency unit applies the optimal time management method based on past session data. This allows the efficiency unit to provide the optimal time management method by utilizing past session data. Some or all of the above-described processes in the efficiency unit may be performed using AI, for example, or without AI. For example, the efficiency unit can perform time management using an AI model that analyzes past session data and applies the most effective time management method.

[0063] The efficiency unit can provide an optimal time allocation during time management, taking into account the user's schedule information. For example, the efficiency unit can use AI to consider the user's schedule information and provide an optimal time allocation. For example, the efficiency unit can provide an optimal time allocation based on the user's schedule information. Furthermore, the efficiency unit can perform appropriate time management, taking into account the user's schedule information. In addition, the efficiency unit can provide an optimal time allocation by referring to the user's schedule information. For example, the efficiency unit provides an optimal time allocation based on the user's schedule information. This allows the efficiency unit to provide an optimal time allocation according to the schedule information. Some or all of the above-described processes in the efficiency unit may be performed using AI, for example, or without AI. For example, the efficiency unit can perform time management using an AI model that provides an optimal time allocation considering the user's schedule information.

[0064] The efficiency unit can provide customized time management methods tailored to the user's industry and job. For example, the efficiency unit can use AI to provide customized methods tailored to the user's industry and job. For instance, the efficiency unit can provide time management methods specific to the user's industry. Furthermore, the efficiency unit can provide time management methods tailored to the user's job. In addition, the efficiency unit can provide optimal time management methods by considering trends related to the user's industry and job. For example, the efficiency unit provides optimal time management methods based on trends related to the user's industry and job. This allows the efficiency unit to provide time management methods tailored to the industry and job. Some or all of the above-described processes in the efficiency unit may be performed using AI, or not. For example, the efficiency unit can perform time management using an AI model that provides customized methods tailored to the user's industry and job.

[0065] The design support unit can propose designs that take into consideration members with color blindness when optimizing visual designs. For example, the design support unit can use AI to optimize visual designs and propose designs that take into consideration members with color blindness. For example, the design support unit can propose designs that use colors that are easy for members with color blindness to see. The design support unit can also use icons and symbols that are easy for members with color blindness to identify. Furthermore, the design support unit can use color palettes that take into consideration members with color blindness. For example, the design support unit can use a color palette that takes into consideration members with color blindness and propose designs. In this way, the design support unit can provide designs that take into consideration members with color blindness. Some or all of the above processes in the design support unit may be performed using AI, for example, or without AI. For example, the design support unit can perform designs using an AI model that optimizes visual designs and proposes designs that take into consideration members with color blindness.

[0066] The design support unit can apply the most effective design methods by referring to past design data. For example, the design support unit can use AI to refer to past design data and apply the most effective design methods. For example, the design support unit can analyze past design data and apply the most effective design methods. Furthermore, the design support unit can perform designs based on successful design methods derived from past design data. In addition, the design support unit can apply the optimal design method by referring to past design data. For example, the design support unit applies the optimal design method based on past design data. This allows the design support unit to provide the optimal design method by utilizing past design data. Some or all of the above-described processes in the design support unit may be performed using AI, for example, or without AI. For example, the design support unit can perform designs using an AI model that applies the most effective design method by referring to past design data.

[0067] The design assistance unit can provide the optimal design by considering the user's device information during the design assistance process. For example, the design assistance unit can use AI to consider the user's device information and provide the optimal design. For example, if the user is using a smartphone, the design assistance unit can provide a design that matches the screen size. Also, if the user is using a tablet, the design assistance unit can provide a design optimized for a larger screen. Furthermore, if the user is using a smartwatch, the design assistance unit can provide a simple and highly visible design. For example, the design assistance unit provides the optimal design based on the user's device information. This allows the design assistance unit to provide the optimal design according to the device information. Some or all of the above processing in the design assistance unit may be performed using AI, for example, or without AI. For example, the design assistance unit can perform design using an AI model that provides the optimal design by considering the user's device information.

[0068] The design assistance unit can provide appropriate visual representations while considering the user's cultural background during the design process. For example, the design assistance unit can use AI to consider the user's cultural background and provide appropriate visual representations. For instance, the design assistance unit can provide appropriate colors and designs based on the user's cultural background. Furthermore, the design assistance unit can use appropriate icons and symbols while considering the user's cultural background. In addition, the design assistance unit can provide appropriate visual representations according to the user's cultural background. For example, the design assistance unit provides appropriate visual representations based on the user's cultural background. This allows the design assistance unit to provide visual representations appropriate to the cultural background. Some or all of the above-described processes in the design assistance unit may be performed using AI, for example, or without AI. For example, the design assistance unit can perform design using an AI model that provides appropriate visual representations while considering the user's cultural background.

[0069] The learning unit can analyze past session data and generate optimal improvement suggestions for the next session. For example, the learning unit can use AI to analyze past session data and generate optimal improvement suggestions for the next session. The learning unit can also make improvement suggestions based on successful methods from past session data. Furthermore, the learning unit can generate optimal improvement suggestions by referring to past session data. For example, the learning unit generates optimal improvement suggestions based on past session data. This allows the learning unit to provide optimal improvement suggestions by utilizing past session data. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can make improvement suggestions using an AI model that analyzes past session data and generates optimal improvement suggestions for the next session.

[0070] The learning unit can optimize its learning algorithm by reflecting user feedback during the learning process. For example, the learning unit can use AI to reflect user feedback during the learning process and optimize the learning algorithm. For example, the learning unit can optimize the learning algorithm based on user feedback. The learning unit can also update the learning data by reflecting user feedback. Furthermore, the learning unit can apply the optimal learning algorithm by referring to user feedback. For example, the learning unit applies the optimal learning algorithm based on user feedback. In this way, the learning unit can provide the optimal learning algorithm by reflecting user feedback. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can perform learning using an AI model that optimizes the learning algorithm by reflecting user feedback.

[0071] The learning unit can provide customized learning methods tailored to the user's industry and job during the learning process. For example, the learning unit can use AI to provide customized learning methods tailored to the user's industry and job. For instance, the learning unit can provide learning methods specific to the user's industry. Furthermore, the learning unit can also provide learning methods tailored to the user's job. In addition, the learning unit can provide optimal learning methods by considering trends related to the user's industry and job. For example, the learning unit provides optimal learning methods based on trends related to the user's industry and job. This allows the learning unit to provide learning methods tailored to the industry and job. Some or all of the above-described processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can perform learning using an AI model that provides customized learning methods tailored to the user's industry and job.

[0072] The learning unit can reflect region-specific factors by considering the user's geographical location information during learning. For example, the learning unit can use AI to consider the user's geographical location information and reflect region-specific factors. For example, the learning unit can perform learning while considering factors specific to the user's region. The learning unit can also provide appropriate learning methods by considering the culture and customs of the user's region. Furthermore, the learning unit can provide learning methods that address specific problems and needs in the user's region. For example, the learning unit can provide appropriate learning methods for specific problems in the user's region. In this way, the learning unit can provide learning methods that reflect region-specific factors. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can perform learning using an AI model that reflects region-specific factors by considering the user's geographical location information.

[0073] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0074] The idea generation unit can provide customized ideas based on the user's area of ​​expertise and interests. For example, it can generate ideas based on the latest research and trends related to the user's area of ​​expertise. It can also generate engaging ideas based on the user's interests. Furthermore, it can generate highly relevant ideas by referring to the user's past projects and achievements. In this way, the idea generation unit can provide ideas that are tailored to the user's area of ​​expertise and interests.

[0075] The idea organization unit can perform optimal grouping by referring to the user's past idea submission history. For example, it can analyze the user's past idea submission history and group highly relevant ideas. It can also group ideas based on successful ideas from the user's past submission history. Furthermore, it can perform optimal grouping by referring to the user's past idea submission history. In this way, the idea organization unit can perform optimal grouping by referring to past history.

[0076] The idea visualization unit can select the optimal display method considering the user's device information. For example, if the user is using a smartphone, it can provide a display method that matches the screen size. If the user is using a tablet, it can provide a display method optimized for larger screens. Furthermore, if the user is using a smartwatch, it can provide a concise and highly visible display method. In this way, the idea visualization unit can provide the optimal display method according to the device information.

[0077] The translation department can take the user's geographical location into consideration and reflect region-specific expressions. For example, it can reflect expressions unique to the user's region. It can also use appropriate expressions considering the culture and customs of the user's region. Furthermore, it can use expressions that address specific issues and needs in the user's region. This allows the translation department to provide translations that reflect region-specific expressions.

[0078] The following briefly describes the processing flow for example form 1.

[0079] Step 1: The idea generation unit generates ideas. For example, it can use AI to stimulate brainstorming in real time and provide new perspectives. It can analyze past data and trends to propose new ideas. It can also introduce knowledge from different fields to provide new perspectives. Furthermore, it can facilitate participant interaction and lively discussions. For example, it can ask participants questions to elicit ideas. It can also aggregate participants' opinions and generate new ideas. Step 2: The Idea Organization Department organizes the ideas generated by the Idea Generation Department. For example, it can automatically group ideas using AI and suggest priorities. It can analyze the relevance and importance of ideas and present the optimal grouping and priorities. It can also categorize and organize ideas. Furthermore, it can evaluate ideas and determine their priorities. For example, it can evaluate the feasibility and impact of ideas and determine their priorities. Step 3: The Idea Visualization Department visualizes the ideas organized by the Idea Organization Department. For example, it uses AI to automatically generate mind maps and related information, providing a visual understanding. By visually showing the relationships between ideas, it enables team members to understand intuitively. It can also generate graphs and charts to make it easier to grasp the overall picture of the ideas. Furthermore, it can display detailed information about the ideas to deepen understanding. For example, it can display background information and related data about the ideas.

[0080] (Example of form 2) The idea generation support system according to an embodiment of the present invention is a system that utilizes AI to streamline the idea generation process and support collaboration in multinational teams. As support for idea generation, this idea generation support system uses AI to activate brainstorming in real time and provide new perspectives. This ensures that idea generation does not stagnate and that lively discussions take place. For example, the AI ​​analyzes past data and trends and proposes new ideas, stimulating the team's creativity. Next, for idea organization and classification, the AI ​​automatically groups ideas and suggests priorities. This makes idea organization and prioritization effortless. For example, the AI ​​analyzes the relationships and importance of ideas and presents the optimal grouping and prioritization. Furthermore, as visualization of insights, the AI ​​automatically generates mind maps and related information, providing visual understanding. This makes it easier to grasp the overall picture of ideas. For example, the AI ​​visually shows the relationships between ideas, enabling team members to understand intuitively. Additionally, as real-time translation and multilingual support, the AI ​​assists in collaboration in multiple languages ​​and appropriately explains cultural backgrounds. This eliminates language barriers and cultural differences in multinational teams. For example, AI can perform real-time translations to facilitate communication between members who speak different languages. Furthermore, as decision-making support, AI can use frameworks such as SWOT analysis to support decision-making, improving both the speed and quality of decision-making. For instance, AI can analyze the strengths, weaknesses, opportunities, and threats of each idea to support optimal decision-making. In addition, to improve session efficiency, AI can manage time and automatically generate meeting minutes, ensuring smooth discussions. This improves session efficiency. For example, AI can monitor the progress of discussions and generate meeting minutes at the appropriate time. Furthermore, as a whiteboard design aid, AI can optimize visual design and provide colorblind-friendly support, resulting in visually easy-to-understand designs. For example, AI can suggest designs that are considerate of members with color vision deficiencies. Finally, as a learning and feedback tool, AI generates suggestions for future improvements based on session analysis, enabling continuous improvement.For example, AI can analyze data from past sessions and suggest improvements for the next session. In this way, leveraging AI streamlines the idea generation process and supports collaboration within multinational teams. This promotes creative thinking and enables efficient collaboration. Thus, idea generation support systems can streamline the idea generation process and support collaboration within multinational teams.

[0081] The idea generation support system according to the embodiment comprises an idea generation unit, an idea organization unit, and an idea visualization unit. The idea generation unit generates ideas. The idea generation unit can, for example, use AI to activate brainstorming in real time and provide new perspectives. For example, the idea generation unit can analyze past data and trends and propose new ideas. The idea generation unit can also introduce knowledge from different fields and provide new perspectives. Furthermore, the idea generation unit can facilitate participant interaction and enable active discussion. For example, the idea generation unit can ask participants questions to elicit ideas. The idea generation unit can also aggregate participants' opinions and generate new ideas. The idea organization unit organizes the ideas generated by the idea generation unit. The idea organization unit can, for example, automatically group ideas using AI and propose priorities. For example, the idea organization unit can analyze the relevance and importance of ideas and present the optimal grouping and priorities. The idea organization unit can also categorize and organize ideas. Furthermore, the idea organization unit can evaluate ideas and determine their priorities. For example, the Idea Organization Unit evaluates the feasibility and impact of ideas and determines their priority. The Idea Visualization Unit visualizes the ideas organized by the Idea Organization Unit. The Idea Visualization Unit provides visual understanding by, for example, automatically generating mind maps and related information using AI. For example, the Idea Visualization Unit makes it possible for team members to intuitively understand by visually showing the relationships between ideas. The Idea Visualization Unit can also generate graphs and charts to make it easier to grasp the overall picture of the ideas. Furthermore, the Idea Visualization Unit can display detailed information about the ideas to deepen understanding. For example, the Idea Visualization Unit displays background information and related data for the ideas. As a result, the idea generation support system according to this embodiment can efficiently generate, organize, and visualize ideas.

[0082] The idea generation unit generates ideas. For example, it uses AI to activate brainstorming in real time and provide new perspectives. Specifically, the AI ​​uses natural language processing technology to analyze participants' statements and extract relevant keywords and topics. This allows the AI ​​to ask appropriate questions to participants and propose new ideas to deepen the discussion. For example, it can analyze past data and trends and present new ideas related to the current discussion. The AI ​​can also integrate knowledge from different fields to provide participants with new perspectives. For example, combining ideas from the technology field with ideas from the marketing field can generate innovative ideas. Furthermore, the idea generation unit facilitates participant interaction and enables lively discussions. The AI ​​analyzes participants' statements in real time and asks questions at the appropriate time according to the progress of the discussion. This allows participants to deepen their own thinking and consider the opinions of other participants. The AI ​​can also generate new ideas by aggregating participants' opinions and finding common themes and topics. For example, the AI ​​clusters participants' statements and proposes new ideas based on common themes. This allows the idea generation unit to generate ideas efficiently and effectively, improving the quality of brainstorming.

[0083] The Idea Organization Department organizes the ideas generated by the Idea Generation Department. For example, the Idea Organization Department automatically groups ideas and suggests priorities using AI. Specifically, the AI ​​analyzes the content of the generated ideas and groups them based on their relevance and importance. For instance, the AI ​​uses natural language processing to extract keywords from ideas and group similar ideas. The AI ​​can also evaluate the importance of ideas and suggest priorities. For example, the AI ​​evaluates the feasibility and impact of ideas and places the most important ideas at the top. Furthermore, the Idea Organization Department can categorize and organize ideas. The AI ​​analyzes the content of ideas and classifies them into appropriate categories, streamlining the organization process. For example, it can classify ideas into different categories such as technical ideas, marketing ideas, and operational ideas. The Idea Organization Department can also evaluate ideas and determine their priorities. The AI ​​evaluates the feasibility and impact of ideas and places the most important ideas at the top. This allows the Idea Organization Department to efficiently organize generated ideas and clearly define priorities. As a result, the Idea Organization Department can efficiently and effectively organize ideas, ensuring smooth project progress.

[0084] The Idea Visualization Unit visualizes the ideas organized by the Idea Organization Unit. For example, the Idea Visualization Unit uses AI to automatically generate mind maps and related information, providing visual understanding. Specifically, the AI ​​analyzes the relationships between organized ideas and generates mind maps. Mind maps visually represent the relationships between ideas, allowing team members to understand them intuitively. The AI ​​can also generate graphs and charts to facilitate a comprehensive understanding of the ideas. For example, it can generate graphs showing the importance and relevance of ideas, making it easier for team members to grasp the overall picture. Furthermore, the Idea Visualization Unit can display detailed information about ideas to deepen understanding. The AI ​​analyzes background information and related data and displays them visually, making it easier for team members to understand the details of the ideas. For example, displaying background information and related data makes it easier to evaluate the feasibility and impact of ideas. This allows the Idea Visualization Unit to efficiently and effectively visualize ideas and deepen team members' understanding. Additionally, the Idea Visualization Unit provides interactive functions, making it easier for team members to manipulate ideas. For example, by allowing mind maps and graphs to be manipulated using drag-and-drop, team members can more easily organize their ideas freely. This enables the idea visualization department to visualize ideas efficiently and effectively, deepening the understanding of team members.

[0085] The Translation Department provides real-time translation and multilingual support. For example, the Translation Department uses AI to perform real-time translations, facilitating communication between members who speak different languages. For instance, the Translation Department can translate conversations in real time and display them as text. It can also use speech recognition technology to convert speech to text and translate it. Furthermore, the Translation Department can perform translations that take cultural context into account, providing appropriate expressions. For example, it selects appropriate words and phrases based on cultural context. This allows the Translation Department to overcome language barriers and cultural differences in multinational teams. Some or all of the above processes in the Translation Department may be performed using AI, or not. For example, the Translation Department can perform translations using an AI model that translates conversations in real time.

[0086] The Decision Support Department assists in decision-making. For example, the Decision Support Department supports decision-making by using frameworks such as SWOT analysis with AI. For example, the Decision Support Department can analyze the strengths, weaknesses, opportunities, and threats of each idea to support optimal decision-making. The Decision Support Department can also provide appropriate frameworks to improve the speed and quality of decision-making. Furthermore, the Decision Support Department can visualize and deepen understanding of the decision-making process. For example, the Decision Support Department can display each step of the decision-making process in graphs and charts. This allows the Decision Support Department to improve the speed and quality of decision-making. Some or all of the above processes in the Decision Support Department may be performed using AI, for example, or not using AI. For example, the Decision Support Department can support decision-making by using an AI model that analyzes the strengths, weaknesses, opportunities, and threats of each idea.

[0087] The Efficiency Department handles time management and automatic generation of meeting minutes. For example, the Efficiency Department uses AI to monitor the progress of a session and generate meeting minutes at the appropriate time. For example, the Efficiency Department can monitor the progress of a discussion in real time and automatically generate meeting minutes. The Efficiency Department can also manage time and improve the efficiency of the session. Furthermore, the Efficiency Department can summarize the content of the meeting minutes and extract key points. For example, the Efficiency Department summarizes the content of the meeting minutes and lists the key points. This allows the Efficiency Department to improve the efficiency of the session. Some or all of the above processes in the Efficiency Department may be performed using AI, for example, or without AI. For example, the Efficiency Department can generate meeting minutes using an AI model that monitors the progress of a session and automatically generates meeting minutes.

[0088] The Design Support Department optimizes visual designs and provides colorblind-friendly solutions. For example, the Design Support Department uses AI to optimize visual designs and provide visually easy-to-understand designs. For instance, the Design Support Department can propose designs that are considerate of members with color blindness. The Design Support Department can also adjust the style and color scheme of designs to provide optimal visual expression. Furthermore, the Design Support Department can conduct usability tests to improve the usability of designs. For example, the Design Support Department can propose design improvements based on the results of usability tests. This allows the Design Support Department to provide visually easy-to-understand designs. Some or all of the above processes performed by the Design Support Department may be carried out using AI, for example, or without AI. For example, the Design Support Department can propose designs using an AI model that optimizes visual designs.

[0089] The Learning Department generates improvement suggestions from session analysis. For example, the Learning Department can use AI to analyze data from past sessions and suggest improvements for the next session. For example, the Learning Department can list areas for improvement in the next session based on data from past sessions. The Learning Department can also analyze the progress of a session and suggest efficient methods for conducting it. Furthermore, the Learning Department can collect feedback from session participants and incorporate improvements. For example, the Learning Department can suggest areas for improvement in the next session based on participant feedback. This enables the Learning Department to achieve continuous improvement. Some or all of the above processes in the Learning Department may be performed using AI, for example, or without AI. For example, the Learning Department can suggest improvements using an AI model that analyzes data from past sessions and generates improvement suggestions.

[0090] The generation unit can activate brainstorming in real time and provide new perspectives. For example, the generation unit can use AI to conduct brainstorming in real time and provide participants with new perspectives. For example, the generation unit can analyze past data and trends and propose new ideas. It can also introduce knowledge from different fields to provide new perspectives. Furthermore, the generation unit can facilitate participant interaction and conduct lively discussions. For example, the generation unit can ask participants questions to elicit ideas. It can also aggregate participants' opinions and generate new ideas. This allows the generation unit to conduct lively discussions without stalling in idea generation. Some or all of the above processes in the generation unit may be performed using AI, for example, or not. For example, the generation unit can provide new perspectives using an AI model that conducts brainstorming in real time.

[0091] The organization function can automatically group ideas and suggest priorities. For example, it can use AI to automatically group ideas and suggest priorities. For instance, it can analyze the relevance and importance of ideas and present the optimal grouping and prioritization. It can also categorize and organize ideas. Furthermore, it can evaluate ideas and determine their priorities. For example, it can evaluate the feasibility and impact of ideas and determine their priorities. This allows the organization function to organize and prioritize ideas effortlessly. Some or all of the above processes in the organization function may be performed using AI, or not. For example, the organization function can use an AI model to analyze the relevance and importance of ideas and suggest grouping and prioritization.

[0092] The visualization unit can automatically generate mind maps and related information, providing a visual understanding. For example, the visualization unit can use AI to automatically generate mind maps and related information, providing a visual understanding. For instance, the visualization unit can visually represent the relationships between ideas, enabling team members to understand them intuitively. The visualization unit can also generate graphs and charts to facilitate grasping the overall picture of an idea. Furthermore, the visualization unit can display detailed information about an idea to deepen understanding. For example, the visualization unit can display background information and related data about an idea. This makes it easier to grasp the overall picture of the idea. Some or all of the above-described processes in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can provide a visual understanding using an AI model that automatically generates mind maps and related information.

[0093] The generation unit can estimate the user's emotions and adjust the type and content of the ideas it generates based on the estimated emotions. For example, the generation unit can use AI to estimate the user's emotions and adjust the type and content of the ideas based on the estimated emotions. For example, if the user is stressed, the generation unit can generate relaxing ideas. Also, if the user is excited, the generation unit can generate challenging and stimulating ideas. Furthermore, if the user is tired, the generation unit can generate simple and actionable ideas. For example, the generation unit can monitor the user's emotions in real time and adjust the type and content of the ideas in response to changes in emotions. This allows the generation unit to generate ideas that are appropriate for 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-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can generate ideas using an AI model that estimates the user's emotions and adjusts the type and content of ideas based on those emotions.

[0094] The generation unit can analyze data from past brainstorming sessions and apply the most effective idea generation method. For example, the generation unit can use AI to analyze data from past brainstorming sessions and apply the most effective idea generation method. For example, the generation unit can analyze successful idea generation methods from past sessions and apply similar methods. The generation unit can also avoid methods that failed in past sessions and select effective methods. Furthermore, the generation unit can find and apply methods that are effective for a specific theme from past session data. For example, the generation unit applies the optimal idea generation method for a specific theme based on past session data. This allows the generation unit to generate ideas effectively by utilizing past data. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can generate ideas using an AI model that analyzes data from past brainstorming sessions and applies the most effective idea generation method.

[0095] The generation unit can provide customized ideas based on the user's area of ​​expertise and interests during idea generation. For example, the generation unit can use AI to provide customized ideas based on the user's area of ​​expertise and interests. For instance, the generation unit can generate ideas based on the latest research and trends related to the user's area of ​​expertise. Furthermore, the generation unit can generate engaging ideas based on the user's interests. Additionally, the generation unit can generate highly relevant ideas by referencing the user's past projects and achievements. For example, the generation unit can generate highly relevant ideas based on the user's past project data. This allows the generation unit to provide ideas tailored to the user's area of ​​expertise and interests. Some or all of the above-described processes in the generation unit may be performed using AI, or not. For example, the generation unit can generate ideas using an AI model that provides customized ideas based on the user's area of ​​expertise and interests.

[0096] The generation unit can estimate the user's emotions and determine the priority of ideas to generate based on the estimated emotions. For example, the generation unit can use AI to estimate the user's emotions and determine the priority of ideas based on the estimated emotions. For example, if the user is stressed, the generation unit will prioritize relaxing ideas. The generation unit can also prioritize challenging ideas if the user is excited. Furthermore, if the user is tired, the generation unit can prioritize easy and actionable ideas. For example, the generation unit can monitor the user's emotions in real time and determine the priority of ideas according to changes in emotions. This allows the generation unit to determine the priority of ideas according to 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 AI, for example, or not using AI. For example, the generation unit can generate ideas using an AI model that estimates the user's emotions and determines the priority of ideas based on those emotions.

[0097] The generation unit can generate region-specific ideas by considering the user's geographical location information during idea generation. For example, the generation unit can use AI to consider the user's geographical location information and generate region-specific ideas. For example, the generation unit can generate ideas based on popular trends and events in the user's region. The generation unit can also generate appropriate ideas by considering the culture and customs of the user's region. Furthermore, the generation unit can generate ideas that address specific problems and needs in the user's region. For example, the generation unit can generate ideas that serve as solutions to specific problems in the user's region. In this way, the generation unit can generate region-specific ideas. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can generate ideas using an AI model that generates region-specific ideas by considering the user's geographical location information.

[0098] The generation unit can analyze a user's social media activity and generate ideas that reflect relevant trends during idea generation. For example, the generation unit can use AI to analyze a user's social media activity and generate ideas that reflect relevant trends. For example, the generation unit can generate ideas based on the trends of influencers and brands that the user follows. The generation unit can also analyze the content and reactions to a user's social media posts and generate interesting ideas. Furthermore, the generation unit can generate highly relevant ideas by referring to the user's social media activity history. For example, the generation unit generates highly relevant ideas based on the user's social media activity. This allows the generation unit to generate ideas that reflect social media activity. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can generate ideas using an AI model that analyzes a user's social media activity and generates ideas that reflect relevant trends.

[0099] 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 emotions. For example, if the organization unit is stressed, it can provide a simple grouping method. It can also provide a more detailed grouping method if the user is relaxed. Furthermore, if the organization unit is excited, it can provide a visually stimulating grouping method. For example, the organization unit can monitor the user's emotions in real time and adjust the grouping method according to changes in emotions. This allows the organization unit to provide a grouping method that is appropriate for 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 organization unit may be performed using AI, for example, or without AI. For example, the organization unit can use an AI model that estimates the user's emotions and adjusts the grouping method based on those emotions to organize ideas.

[0100] The organization unit can analyze the relevance of ideas and apply an appropriate grouping algorithm. For example, the organization unit can use AI to analyze the relevance of ideas and apply an appropriate grouping algorithm. For example, the organization unit can analyze the commonalities and differences of ideas and group highly relevant ideas. The organization unit can also analyze the importance and impact of ideas and perform optimal grouping. Furthermore, the organization unit can perform appropriate grouping based on the theme or category of ideas. For example, the organization unit can group highly relevant ideas based on the theme of the ideas. This allows the organization unit to group highly relevant ideas. Some or all of the above processes in the organization unit may be performed using AI, for example, or without AI. For example, the organization unit can organize ideas using an AI model that analyzes the relevance of ideas and applies an appropriate grouping algorithm.

[0101] The organization unit can dynamically adjust priorities based on the importance and impact of ideas. For example, the organization unit can use AI to analyze the importance and impact of ideas and dynamically adjust priorities. For example, the organization unit can analyze the importance of ideas and dynamically adjust priorities. The organization unit can also analyze the impact of ideas and dynamically adjust priorities. Furthermore, the organization unit can analyze the feasibility of ideas and dynamically adjust priorities. For example, the organization unit can evaluate the feasibility of ideas and dynamically adjust priorities. This allows the organization unit to provide priorities based on importance and impact. Some or all of the above processes in the organization unit may be performed using AI, for example, or without AI. For example, the organization unit can organize ideas using an AI model that analyzes the importance and impact of ideas and dynamically adjusts priorities.

[0102] The organization unit can estimate the user's emotions and adjust the display order of ideas based on the estimated emotions. For example, the organization unit can use AI to estimate the user's emotions and adjust the display order of ideas based on the estimated emotions. For example, if the user is feeling stressed, the organization unit will display important ideas first. Also, if the user is relaxed, the organization unit can display ideas containing detailed information. Furthermore, if the user is excited, the organization unit can display visually stimulating ideas. For example, the organization unit can monitor the user's emotions in real time and adjust the display order of ideas according to changes in emotions. This allows the organization unit to provide a display order that is appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI 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 AI, for example, or without AI. For example, the organization unit can organize ideas using an AI model that estimates the user's emotions and adjusts the display order of ideas based on those emotions.

[0103] The organization unit can perform optimal grouping by referring to the user's past idea submission history when organizing ideas. For example, the organization unit can use AI to refer to the user's past idea submission history and perform optimal grouping. For example, the organization unit can analyze the user's past idea submission history and group highly relevant ideas. The organization unit can also group ideas based on successful ideas from the user's past idea submission history. Furthermore, the organization unit can perform optimal grouping by referring to the user's past idea submission history. For example, the organization unit groups highly relevant ideas based on the user's past idea submission history. This allows the organization unit to perform optimal grouping by referring to past history. Some or all of the above processes in the organization unit may be performed using AI, for example, or without AI. For example, the organization unit can organize ideas using an AI model that performs optimal grouping by referring to the user's past idea submission history.

[0104] The organization function can provide customized groupings tailored to the user's industry and job when organizing ideas. For example, the organization function can use AI to provide customized groupings tailored to the user's industry and job. For instance, the organization function can provide a grouping method specific to the user's industry. It can also provide a grouping method tailored to the user's job. Furthermore, the organization function can provide optimal groupings by considering trends related to the user's industry and job. For example, the organization function provides optimal groupings based on trends related to the user's industry and job. This allows the organization function to provide industry- and job-specific groupings. Some or all of the above processes in the organization function may be performed using AI, or not. For example, the organization function can organize ideas using an AI model that provides customized groupings tailored to the user's industry and job.

[0105] The visualization unit can estimate the user's emotions and adjust the style and color of the visualization based on the estimated emotions. For example, the visualization unit can use AI to estimate the user's emotions and adjust the style and color of the visualization based on the estimated emotions. For example, if the user is stressed, the visualization unit can provide a visualization with calm colors. The visualization unit can also provide a visualization with bright colors if the user is relaxed. Furthermore, if the user is excited, the visualization unit can provide a visually stimulating visualization. For example, the visualization unit can monitor the user's emotions in real time and adjust the style and color of the visualization according to changes in emotions. This allows the visualization unit to provide a visualization style and color that matches 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 AI, for example, or without AI. For example, the visualization unit can perform visualizations using an AI model that estimates the user's emotions and adjusts the visualization style and color scheme based on those emotions.

[0106] The visualization unit can apply an appropriate mind map generation algorithm to visually represent the relationships between ideas. For example, the visualization unit can use AI to apply an appropriate mind map generation algorithm to visually represent the relationships between ideas. For example, the visualization unit can analyze the relationships between ideas and generate an optimal mind map. The visualization unit can also generate an optimal mind map considering the importance and impact of the ideas. Furthermore, the visualization unit can generate an optimal mind map based on the theme or category of the ideas. For example, the visualization unit can generate a mind map that visually shows highly related ideas based on the theme of the ideas. In this way, the visualization unit deepens understanding by visually representing the relationships between ideas. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can perform visualization using an AI model that applies an appropriate mind map generation algorithm to visually represent the relationships between ideas.

[0107] The visualization unit can adjust the level of detail of information according to the user's level of understanding during visualization. For example, the visualization unit can estimate the user's level of understanding using AI and adjust the level of detail of information accordingly. For example, if the user's level of understanding is low, the visualization unit will provide detailed information. Conversely, if the user's level of understanding is high, the visualization unit can also provide information that focuses on the key points. Furthermore, the visualization unit can dynamically adjust the level of detail of information according to the user's level of understanding. For example, the visualization unit can monitor the user's level of understanding in real time and adjust the level of detail of information accordingly. This allows the visualization unit to provide information with a level of detail appropriate to the user's level of understanding. Some or all of the above-described processes in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can perform visualization using an AI model that estimates the user's level of understanding and adjusts the level of detail of information accordingly.

[0108] The visualization unit can estimate the user's emotions and adjust the display order of the visualizations based on the estimated emotions. For example, the visualization unit can use AI to estimate the user's emotions and adjust the display order of the visualizations based on the estimated emotions. For example, if the user is feeling stressed, the visualization unit can display important information first. Also, if the user is relaxed, the visualization unit can provide a display order that includes detailed information. Furthermore, if the user is excited, the visualization unit can display visually stimulating information first. For example, the visualization unit can monitor the user's emotions in real time and adjust the display order of the visualizations according to changes in emotions. This allows the visualization unit to provide a display order that is appropriate for 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 AI, for example, or without AI. For example, the visualization unit can perform visualizations using an AI model that estimates the user's emotions and adjusts the display order of the visualizations based on those emotions.

[0109] The visualization unit can select the optimal display method when visualizing data, taking into account the user's device information. For example, the visualization unit can use AI to consider the user's device information and select the optimal display method. For example, if the user is using a smartphone, the visualization unit can provide a display method that matches the screen size. The visualization unit can also provide a display method optimized for larger screens if the user is using a tablet. Furthermore, if the user is using a smartwatch, the visualization unit can provide a concise and highly visible display method. For example, the visualization unit selects the optimal display method based on the user's device information. This allows the visualization unit to provide the optimal display method according to the device information. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can perform visualization using an AI model that selects the optimal display method considering the user's device information.

[0110] The visualization unit can provide appropriate visual representations when visualizing, taking into account the user's cultural background. For example, the visualization unit can use AI to consider the user's cultural background and provide appropriate visual representations. For example, the visualization unit can provide appropriate colors and designs based on the user's cultural background. The visualization unit can also use appropriate icons and symbols, taking into account the user's cultural background. Furthermore, the visualization unit can provide appropriate visual representations according to the user's cultural background. For example, the visualization unit provides appropriate visual representations based on the user's cultural background. In this way, the visualization unit can provide visual representations that are appropriate to the cultural background. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can perform visualizations using an AI model that provides appropriate visual representations considering the user's cultural background.

[0111] The translation unit can estimate the user's emotions and adjust the tone and expression of the translation based on the estimated emotions. For example, the translation unit may use AI to estimate the user's emotions and adjust the tone and expression of the translation based on the estimated emotions. For example, if the user is nervous, the translation unit will translate in a calm tone. Also, if the user is relaxed, the translation unit may translate in a cheerful tone. Furthermore, if the user is excited, the translation unit may translate in an energetic tone. For example, the translation unit may monitor the user's emotions in real time and adjust the tone and expression of the translation according to changes in emotions. This allows the translation unit to provide a tone and expression of translation that is appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI 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 translation unit may be performed using AI, for example, or without AI. For example, the translation department can perform translations using an AI model that estimates the user's emotions and adjusts the tone and expression of the translation based on those emotions.

[0112] The translation unit can dynamically update its dictionary to appropriately translate technical terms and industry-specific vocabulary during translation. For example, the translation unit can use AI to dynamically update the dictionary to appropriately translate technical terms and industry-specific vocabulary during translation. For instance, the translation unit can dynamically update the dictionary if new technical terms appear during translation. Furthermore, the translation unit can periodically update the dictionary to appropriately translate industry-specific vocabulary. In addition, the translation unit can dynamically update the dictionary based on user feedback. For example, the translation unit can update the dictionary based on user feedback to improve translation accuracy. This allows the translation unit to appropriately translate technical terms and industry-specific vocabulary. Some or all of the above processes in the translation unit may be performed using AI, or not. For example, the translation unit can perform translations using an AI model that dynamically updates the dictionary to appropriately translate technical terms and industry-specific vocabulary.

[0113] The translation unit can select the optimal translation method by referring to the user's past translation history during translation. For example, the translation unit can use AI to refer to the user's past translation history and select the optimal translation method. For example, the translation unit can analyze the user's past translation history and select the optimal translation method. Furthermore, the translation unit can perform translations based on successful translation methods from the user's past translation history. In addition, the translation unit can select the optimal translation method by referring to the user's past translation history. For example, the translation unit selects the optimal translation method based on the user's past translation history. This allows the translation unit to provide the optimal translation method by referring to past translation history. Some or all of the above-described processes in the translation unit may be performed using AI, for example, or without AI. For example, the translation unit can perform translations using an AI model that selects the optimal translation method by referring to the user's past translation history.

[0114] The translation unit can estimate the user's emotions and determine translation priorities based on those emotions. For example, the translation unit might use AI to estimate the user's emotions and determine translation priorities based on those emotions. For instance, if the user is stressed, the translation unit might prioritize important translations. It could also prioritize detailed translations if the user is relaxed. Furthermore, if the user is excited, it could prioritize energetic translations. For example, the translation unit could monitor the user's emotions in real time and determine translation priorities in response to changes in those emotions. This allows the translation unit to provide translation priorities that align with the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the translation unit may be performed using AI or not. For example, the translation unit can perform translations using an AI model that estimates the user's emotions and determines translation priorities based on those emotions.

[0115] The translation unit can reflect region-specific expressions by considering the user's geographical location during translation. For example, the translation unit can use AI to consider the user's geographical location and reflect region-specific expressions. For instance, the translation unit can reflect expressions specific to the user's region. Furthermore, the translation unit can use appropriate expressions by considering the culture and customs of the user's region. In addition, the translation unit can use expressions that address specific issues and needs in the user's region. For example, the translation unit can use appropriate expressions for specific issues in the user's region. This allows the translation unit to provide translations that reflect region-specific expressions. Some or all of the above processing in the translation unit may be performed using AI, for example, or without AI. For example, the translation unit can perform translations using an AI model that reflects region-specific expressions by considering the user's geographical location.

[0116] The translation department can analyze users' social media activity during the translation process and provide translations that reflect relevant trends. For example, the translation department can use AI to analyze users' social media activity and provide translations that reflect relevant trends. For instance, the translation department can translate based on trends of influencers and brands that users follow. The translation department can also analyze users' social media posts and reactions.

[0117] The decision support unit can estimate the user's emotions and adjust the decision-making framework based on the estimated emotions. For example, the decision support unit can use AI to estimate the user's emotions and adjust the decision-making framework based on the estimated emotions. For example, if the user is stressed, the decision support unit can provide a simple decision-making framework. It can also provide a detailed decision-making framework if the user is relaxed. Furthermore, if the user is excited, the decision support unit can provide a visually stimulating decision-making framework. For example, the decision support unit can monitor the user's emotions in real time and adjust the decision-making framework in response to changes in emotions. This allows the decision support unit to provide a decision-making framework that is appropriate for the user's emotions. Emotion estimation can be performed, for example, by an emotion engine or by generation. Emotion estimation is implemented using an emotion estimation function, for example, by 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-described processes in the decision support unit may be performed, for example, using AI or not using AI. For example, the decision support unit can assist in decision-making by using an AI model that estimates the user's emotions and adjusts the decision-making framework based on those emotions.

[0118] The decision support unit can analyze the strengths, weaknesses, opportunities, and threats of each idea in detail and support appropriate decision-making. For example, the decision support unit can use AI to analyze the strengths, weaknesses, opportunities, and threats of each idea in detail and support optimal decision-making. For example, the decision support unit can analyze the strengths of each idea and support optimal decision-making. Furthermore, the decision support unit can analyze the weaknesses of each idea and support optimal decision-making. In addition, the decision support unit can analyze the opportunities and threats of each idea and support optimal decision-making. For example, the decision support unit analyzes the opportunities and threats of each idea and supports optimal decision-making. Thus, the decision support unit can analyze the strengths, weaknesses, opportunities, and threats of each idea and support optimal decision-making. Some or all of the above processing in the decision support unit may be performed using AI, for example, or without AI. For example, the decision support unit can support decision-making using an AI model that analyzes the strengths, weaknesses, opportunities, and threats of each idea in detail.

[0119] The decision support unit can apply the most effective decision-making method by referring to past decision-making data. For example, the decision support unit can use AI to refer to past decision-making data and apply the most effective decision-making method. For example, the decision support unit can analyze past decision-making data and apply the most effective method. Furthermore, the decision support unit can make decisions based on successful methods derived from past decision-making data. In addition, the decision support unit can apply the optimal method by referring to past decision-making data. For example, the decision support unit applies the optimal decision-making method based on past decision-making data. This allows the decision support unit to provide the optimal decision-making method by referring to past decision-making data. Some or all of the above processes in the decision support unit may be performed using AI, for example, or without AI. For example, the decision support unit can support decision-making using an AI model that applies the most effective decision-making method by referring to past decision-making data.

[0120] The decision support unit can estimate the user's emotions and determine decision priorities based on those emotions. For example, the decision support unit might use AI to estimate the user's emotions and determine decision priorities based on those emotions. For instance, if the user is feeling stressed, the decision support unit will prioritize important decisions. Later, a generative AI can support decision-making using an AI model that estimates the user's emotions and determines decision priorities based on those emotions.

[0121] The decision support unit can provide a customized framework tailored to the user's industry and job function during the decision-making process. For example, the decision support unit can use AI to provide a customized framework tailored to the user's industry and job function. For instance, the decision support unit can provide a decision-making framework specific to the user's industry. Furthermore, the decision support unit can provide a decision-making framework tailored to the user's job function. In addition, the decision support unit can provide an optimal decision-making framework by considering trends related to the user's industry and job function. For example, the decision support unit provides an optimal decision-making framework based on trends related to the user's industry and job function. This allows the decision support unit to provide a decision-making framework tailored to the industry and job function. Some or all of the above-described processes in the decision support unit may be performed using AI, or not. For example, the decision support unit can support decision-making using an AI model that provides a customized framework tailored to the user's industry and job function.

[0122] The decision support unit can reflect region-specific factors by considering the user's geographical location information when making decisions. For example, the decision support unit can use AI to consider the user's geographical location information and reflect region-specific factors. For example, the decision support unit can make decisions by considering factors specific to the user's region. The decision support unit can also make appropriate decisions by considering the culture and customs of the user's region. Furthermore, the decision support unit can make decisions that address specific problems and needs in the user's region. For example, the decision support unit can make appropriate decisions regarding specific problems in the user's region. In this way, the decision support unit can provide decisions that reflect region-specific factors. Some or all of the above processing in the decision support unit may be performed using AI, for example, or without AI. For example, the decision support unit can support decision-making using an AI model that reflects region-specific factors by considering the user's geographical location information.

[0123] The efficiency unit can estimate the user's emotions and adjust the time management method based on the estimated emotions. For example, the efficiency unit can use AI to estimate the user's emotions and adjust the time management method based on the estimated emotions. For example, if the user is feeling stressed, the efficiency unit can provide a simple time management method. The efficiency unit can also provide a detailed time management method if the user is relaxed. Furthermore, if the user is excited, the efficiency unit can provide a visually stimulating time management method. For example, the efficiency unit can monitor the user's emotions in real time and adjust the time management method according to changes in emotions. This allows the efficiency unit to provide a time management method that is appropriate for 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 efficiency unit may be performed using AI, for example, or without AI. For example, the efficiency unit can perform time management using an AI model that estimates the user's emotions and adjusts the time management method based on those emotions.

[0124] The efficiency unit can monitor the progress of a session in real time and generate meeting minutes at the optimal time. For example, the efficiency unit can use AI to monitor the progress of a session in real time and generate meeting minutes at the optimal time. For example, the efficiency unit can monitor the progress of a session in real time and generate meeting minutes at the appropriate time. Furthermore, the efficiency unit can analyze the progress of a session and generate meeting minutes at the optimal time. In addition, the efficiency unit can dynamically adjust the timing of meeting minute generation based on the progress of the session. For example, the efficiency unit dynamically adjusts the timing of meeting minute generation based on the progress of the session. This allows the efficiency unit to generate meeting minutes at the optimal time according to the progress of the session. Some or all of the above processes in the efficiency unit may be performed using AI, or not. For example, the efficiency unit can generate meeting minutes using an AI model that monitors the progress of a session in real time and generates meeting minutes at the optimal time.

[0125] The efficiency unit can analyze past session data and apply the most effective time management methods. For example, the efficiency unit can use AI to analyze past session data and apply the most effective time management methods. Furthermore, the efficiency unit can perform time management based on successful methods derived from past session data. In addition, the efficiency unit can apply the optimal time management method by referring to past session data. For example, the efficiency unit applies the optimal time management method based on past session data. This allows the efficiency unit to provide the optimal time management method by utilizing past session data. Some or all of the above-described processes in the efficiency unit may be performed using AI, for example, or without AI. For example, the efficiency unit can perform time management using an AI model that analyzes past session data and applies the most effective time management method.

[0126] The efficiency unit can estimate the user's emotions and determine the priority of meeting minutes based on the estimated emotions. For example, the efficiency unit can use AI to estimate the user's emotions and determine the priority of meeting minutes based on the estimated emotions. For example, if the user is feeling stressed, the efficiency unit will prioritize important meeting minutes. The efficiency unit can also prioritize detailed meeting minutes if the user is relaxed. Furthermore, if the user is excited, the efficiency unit can prioritize energetic meeting minutes. For example, the efficiency unit can monitor the user's emotions in real time and determine the priority of meeting minutes according to changes in emotions. This allows the efficiency unit to provide meeting minute priorities that correspond to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI 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 AI, for example, or without AI. For example, the efficiency unit can generate meeting minutes using an AI model that estimates user emotions and determines the priority of meeting minutes based on those emotions.

[0127] The efficiency unit can provide an optimal time allocation during time management, taking into account the user's schedule information. For example, the efficiency unit can use AI to consider the user's schedule information and provide an optimal time allocation. For example, the efficiency unit can provide an optimal time allocation based on the user's schedule information. Furthermore, the efficiency unit can perform appropriate time management, taking into account the user's schedule information. In addition, the efficiency unit can provide an optimal time allocation by referring to the user's schedule information. For example, the efficiency unit provides an optimal time allocation based on the user's schedule information. This allows the efficiency unit to provide an optimal time allocation according to the schedule information. Some or all of the above-described processes in the efficiency unit may be performed using AI, for example, or without AI. For example, the efficiency unit can perform time management using an AI model that provides an optimal time allocation considering the user's schedule information.

[0128] The efficiency unit can provide customized time management methods tailored to the user's industry and job. For example, the efficiency unit can use AI to provide customized methods tailored to the user's industry and job. For instance, the efficiency unit can provide time management methods specific to the user's industry. Furthermore, the efficiency unit can provide time management methods tailored to the user's job. In addition, the efficiency unit can provide optimal time management methods by considering trends related to the user's industry and job. For example, the efficiency unit provides optimal time management methods based on trends related to the user's industry and job. This allows the efficiency unit to provide time management methods tailored to the industry and job. Some or all of the above-described processes in the efficiency unit may be performed using AI, or not. For example, the efficiency unit can perform time management using an AI model that provides customized methods tailored to the user's industry and job.

[0129] The design assistance unit can estimate the user's emotions and adjust the design style and color scheme based on the estimated emotions. For example, the design assistance unit can use AI to estimate the user's emotions and adjust the design style and color scheme based on the estimated emotions. For example, if the user is feeling stressed, the design assistance unit can provide a design with calming colors. It can also provide a design with bright colors if the user is relaxed. Furthermore, if the user is excited, the design assistance unit can provide a visually stimulating design. For example, the design assistance unit can monitor the user's emotions in real time and adjust the design style and color scheme according to changes in emotions. This allows the design assistance unit to provide a design style and color scheme that matches 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 design assistance unit may be performed using AI, for example, or without AI. For example, the design support unit can perform design work using an AI model that estimates the user's emotions and adjusts the design style and color scheme based on those emotions.

[0130] The design support unit can propose designs that take into consideration members with color blindness when optimizing visual designs. For example, the design support unit can use AI to optimize visual designs and propose designs that take into consideration members with color blindness. For example, the design support unit can propose designs that use colors that are easy for members with color blindness to see. The design support unit can also use icons and symbols that are easy for members with color blindness to identify. Furthermore, the design support unit can use color palettes that take into consideration members with color blindness. For example, the design support unit can use a color palette that takes into consideration members with color blindness and propose designs. In this way, the design support unit can provide designs that take into consideration members with color blindness. Some or all of the above processes in the design support unit may be performed using AI, for example, or without AI. For example, the design support unit can perform designs using an AI model that optimizes visual designs and proposes designs that take into consideration members with color blindness.

[0131] The design support unit can apply the most effective design methods by referring to past design data. For example, the design support unit can use AI to refer to past design data and apply the most effective design methods. For example, the design support unit can analyze past design data and apply the most effective design methods. Furthermore, the design support unit can perform designs based on successful design methods derived from past design data. In addition, the design support unit can apply the optimal design method by referring to past design data. For example, the design support unit applies the optimal design method based on past design data. This allows the design support unit to provide the optimal design method by utilizing past design data. Some or all of the above-described processes in the design support unit may be performed using AI, for example, or without AI. For example, the design support unit can perform designs using an AI model that applies the most effective design method by referring to past design data.

[0132] The design assistance unit can estimate the user's emotions and determine design priorities based on those estimated emotions. For example, the design assistance unit might use AI to estimate the user's emotions and determine design priorities based on those emotions. For instance, if the user is stressed, the design assistance unit might prioritize important design elements. It could also prioritize detailed design elements if the user is relaxed. Furthermore, if the user is excited, it could prioritize visually stimulating design elements. For example, the design assistance unit could monitor the user's emotions in real time and determine design priorities in response to changes in those emotions. This allows the design assistance unit to provide design priorities that align with the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the design assistance unit may be performed using AI or not. For example, the design assistance unit can perform design using an AI model that estimates user emotions and determines design priorities based on those emotions.

[0133] The design assistance unit can provide the optimal design by considering the user's device information during the design assistance process. For example, the design assistance unit can use AI to consider the user's device information and provide the optimal design. For example, if the user is using a smartphone, the design assistance unit can provide a design that matches the screen size. Also, if the user is using a tablet, the design assistance unit can provide a design optimized for a larger screen. Furthermore, if the user is using a smartwatch, the design assistance unit can provide a simple and highly visible design. For example, the design assistance unit provides the optimal design based on the user's device information. This allows the design assistance unit to provide the optimal design according to the device information. Some or all of the above processing in the design assistance unit may be performed using AI, for example, or without AI. For example, the design assistance unit can perform design using an AI model that provides the optimal design by considering the user's device information.

[0134] The design assistance unit can provide appropriate visual representations while considering the user's cultural background during the design process. For example, the design assistance unit can use AI to consider the user's cultural background and provide appropriate visual representations. For instance, the design assistance unit can provide appropriate colors and designs based on the user's cultural background. Furthermore, the design assistance unit can use appropriate icons and symbols while considering the user's cultural background. In addition, the design assistance unit can provide appropriate visual representations according to the user's cultural background. For example, the design assistance unit provides appropriate visual representations based on the user's cultural background. This allows the design assistance unit to provide visual representations appropriate to the cultural background. Some or all of the above-described processes in the design assistance unit may be performed using AI, for example, or without AI. For example, the design assistance unit can perform design using an AI model that provides appropriate visual representations while considering the user's cultural background.

[0135] The learning unit can estimate the user's emotions and select training data based on the estimated emotions. For example, the learning unit can use AI to estimate the user's emotions and select training data based on the estimated emotions. For example, if the user is stressed, the learning unit will select training data that promotes relaxation. The learning unit can also select detailed training data if the user is relaxed. Furthermore, if the user is excited, the learning unit can select visually stimulating training data. For example, the learning unit can monitor the user's emotions in real time and select training data according to changes in emotions. This allows the learning unit to provide training data that is appropriate for 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-described processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can perform training using an AI model that estimates the user's emotions and selects training data based on those emotions.

[0136] The learning unit can analyze past session data and generate optimal improvement suggestions for the next session. For example, the learning unit can use AI to analyze past session data and generate optimal improvement suggestions for the next session. The learning unit can also make improvement suggestions based on successful methods from past session data. Furthermore, the learning unit can generate optimal improvement suggestions by referring to past session data. For example, the learning unit generates optimal improvement suggestions based on past session data. This allows the learning unit to provide optimal improvement suggestions by utilizing past session data. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can make improvement suggestions using an AI model that analyzes past session data and generates optimal improvement suggestions for the next session.

[0137] The learning unit can optimize its learning algorithm by reflecting user feedback during the learning process. For example, the learning unit can use AI to reflect user feedback during the learning process and optimize the learning algorithm. For example, the learning unit can optimize the learning algorithm based on user feedback. The learning unit can also update the learning data by reflecting user feedback. Furthermore, the learning unit can apply the optimal learning algorithm by referring to user feedback. For example, the learning unit applies the optimal learning algorithm based on user feedback. In this way, the learning unit can provide the optimal learning algorithm by reflecting user feedback. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can perform learning using an AI model that optimizes the learning algorithm by reflecting user feedback.

[0138] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, the learning unit might use AI to estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For instance, the learning unit might reduce the learning frequency if the user is stressed. It could also increase the learning frequency if the user is relaxed. Furthermore, it could adjust the learning frequency if the user is excited. For example, the learning unit could monitor the user's emotions in real time and adjust the learning frequency in response to changes in emotions. This allows the learning unit to provide a learning frequency that is appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the learning unit may be performed using AI or not. For example, the learning unit can provide a learning frequency that is appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, by using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can perform learning using an AI model that estimates the user's emotions and adjusts the learning frequency based on those emotions.

[0139] The learning unit can provide customized learning methods tailored to the user's industry and job during the learning process. For example, the learning unit can use AI to provide customized learning methods tailored to the user's industry and job. For instance, the learning unit can provide learning methods specific to the user's industry. Furthermore, the learning unit can also provide learning methods tailored to the user's job. In addition, the learning unit can provide optimal learning methods by considering trends related to the user's industry and job. For example, the learning unit provides optimal learning methods based on trends related to the user's industry and job. This allows the learning unit to provide learning methods tailored to the industry and job. Some or all of the above-described processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can perform learning using an AI model that provides customized learning methods tailored to the user's industry and job.

[0140] The learning unit can reflect region-specific factors by considering the user's geographical location information during learning. For example, the learning unit can use AI to consider the user's geographical location information and reflect region-specific factors. For example, the learning unit can perform learning while considering factors specific to the user's region. The learning unit can also provide appropriate learning methods by considering the culture and customs of the user's region. Furthermore, the learning unit can provide learning methods that address specific problems and needs in the user's region. For example, the learning unit can provide appropriate learning methods for specific problems in the user's region. In this way, the learning unit can provide learning methods that reflect region-specific factors. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can perform learning using an AI model that reflects region-specific factors by considering the user's geographical location information.

[0141] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0142] The idea generation unit can estimate the user's emotions and adjust the type and content of ideas based on those emotions. For example, if the user is stressed, it can generate relaxing ideas. If the user is excited, it can generate challenging and stimulating ideas. Furthermore, if the user is tired, it can generate simple and actionable ideas. In this way, the idea generation unit can provide ideas that are tailored to the user's emotions.

[0143] The idea organization function can estimate the user's emotions and adjust the idea grouping method based on those emotions. For example, if the user is stressed, it can provide a simple grouping method. If the user is relaxed, it can provide a more detailed grouping method. Furthermore, if the user is excited, it can provide a visually stimulating grouping method. In this way, the idea organization function can provide a grouping method that is tailored to the user's emotions.

[0144] The idea visualization unit can estimate the user's emotions and adjust the visualization style and color scheme based on those estimates. For example, if the user is stressed, it can provide a visualization with calming colors. If the user is relaxed, it can provide a visualization with bright colors. Furthermore, if the user is excited, it can provide a visually stimulating visualization. In this way, the idea visualization unit can provide a visualization style and color scheme that is appropriate for the user's emotions.

[0145] The translation unit can estimate the user's emotions and adjust the tone and expression of the translation based on those estimates. For example, if the user is nervous, the translation will be done in a calm tone. If the user is relaxed, the translation can be done in a cheerful tone. Furthermore, if the user is excited, the translation can be done in an energetic tone. In this way, the translation unit can provide a translation tone and expression that matches the user's emotions.

[0146] The decision support unit can estimate the user's emotions and adjust the decision-making framework based on those emotions. For example, if the user is stressed, it can provide a simple decision-making framework. If the user is relaxed, it can provide a more detailed decision-making framework. Furthermore, if the user is excited, it can provide a visually stimulating decision-making framework. In this way, the decision support unit can provide a decision-making framework that is tailored to the user's emotions.

[0147] The efficiency unit can estimate the user's emotions and adjust the time management method based on those emotions. For example, if the user is stressed, it can provide a simple time management method. If the user is relaxed, it can provide a more detailed time management method. Furthermore, if the user is excited, it can provide a visually stimulating time management method. In this way, the efficiency unit can provide a time management method that is tailored to the user's emotions.

[0148] The idea generation unit can provide customized ideas based on the user's area of ​​expertise and interests. For example, it can generate ideas based on the latest research and trends related to the user's area of ​​expertise. It can also generate engaging ideas based on the user's interests. Furthermore, it can generate highly relevant ideas by referring to the user's past projects and achievements. In this way, the idea generation unit can provide ideas that are tailored to the user's area of ​​expertise and interests.

[0149] The idea organization unit can perform optimal grouping by referring to the user's past idea submission history. For example, it can analyze the user's past idea submission history and group highly relevant ideas. It can also group ideas based on successful ideas from the user's past submission history. Furthermore, it can perform optimal grouping by referring to the user's past idea submission history. In this way, the idea organization unit can perform optimal grouping by referring to past history.

[0150] The idea visualization unit can select the optimal display method considering the user's device information. For example, if the user is using a smartphone, it can provide a display method that matches the screen size. If the user is using a tablet, it can provide a display method optimized for larger screens. Furthermore, if the user is using a smartwatch, it can provide a concise and highly visible display method. In this way, the idea visualization unit can provide the optimal display method according to the device information.

[0151] The translation department can take the user's geographical location into consideration and reflect region-specific expressions. For example, it can reflect expressions unique to the user's region. It can also use appropriate expressions considering the culture and customs of the user's region. Furthermore, it can use expressions that address specific issues and needs in the user's region. This allows the translation department to provide translations that reflect region-specific expressions.

[0152] The following briefly describes the processing flow for example form 2.

[0153] Step 1: The idea generation unit generates ideas. For example, it can use AI to stimulate brainstorming in real time and provide new perspectives. It can analyze past data and trends to propose new ideas. It can also introduce knowledge from different fields to provide new perspectives. Furthermore, it can facilitate participant interaction and lively discussions. For example, it can ask participants questions to elicit ideas. It can also aggregate participants' opinions and generate new ideas. Step 2: The Idea Organization Department organizes the ideas generated by the Idea Generation Department. For example, it can automatically group ideas using AI and suggest priorities. It can analyze the relevance and importance of ideas and present the optimal grouping and priorities. It can also categorize and organize ideas. Furthermore, it can evaluate ideas and determine their priorities. For example, it can evaluate the feasibility and impact of ideas and determine their priorities. Step 3: The Idea Visualization Department visualizes the ideas organized by the Idea Organization Department. For example, it uses AI to automatically generate mind maps and related information, providing a visual understanding. By visually showing the relationships between ideas, it enables team members to understand intuitively. It can also generate graphs and charts to make it easier to grasp the overall picture of the ideas. Furthermore, it can display detailed information about the ideas to deepen understanding. For example, it can display background information and related data about the ideas.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] Each of the multiple elements described above, including the idea generation unit, idea organization unit, idea visualization unit, translation unit, decision support unit, efficiency improvement unit, design assistance unit, and learning unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the idea generation unit is implemented by the control unit 46A of the smart device 14, which activates brainstorming in real time and provides new perspectives. The idea organization unit is implemented by the specific processing unit 290 of the data processing unit 12, which automatically groups ideas and suggests priorities. The idea visualization unit is implemented by the control unit 46A of the smart device 14, which automatically generates mind maps and related information and provides visual understanding. The translation unit is implemented by the specific processing unit 290 of the data processing unit 12, which performs real-time translation and multilingual support. The decision support unit is implemented by the specific processing unit 290 of the data processing unit 12, which supports decision-making using frameworks such as SWOT analysis. The efficiency improvement unit is implemented, for example, by the control unit 46A of the smart device 14, and performs time management and automatic generation of meeting minutes. The design assistance unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and optimizes visual design and supports color blindness. The learning unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and generates suggestions for the next improvement from session analysis. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0158] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.).

[0170] 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.

[0171] 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.

[0172] 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.

[0173] Each of the multiple elements described above, including the idea generation unit, idea organization unit, idea visualization unit, translation unit, decision support unit, efficiency improvement unit, design assistance unit, and learning unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the idea generation unit is implemented by the control unit 46A of the smart glasses 214, which activates brainstorming in real time and provides new perspectives. The idea organization unit is implemented by the specific processing unit 290 of the data processing unit 12, which automatically groups ideas and suggests priorities. The idea visualization unit is implemented by the control unit 46A of the smart glasses 214, which automatically generates mind maps and related information and provides visual understanding. The translation unit is implemented by the specific processing unit 290 of the data processing unit 12, which performs real-time translation and multilingual support. The decision support unit is implemented by the specific processing unit 290 of the data processing unit 12, which supports decision-making using frameworks such as SWOT analysis. The efficiency improvement unit is implemented, for example, by the control unit 46A of the smart glasses 214, and performs time management and automatic generation of meeting minutes. The design assistance unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and optimizes visual design and supports color blindness. The learning unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and generates suggestions for the next improvement from session analysis. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0174] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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).

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.).

[0186] 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.

[0187] 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.

[0188] 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.

[0189] Each of the multiple elements described above, including the idea generation unit, idea organization unit, idea visualization unit, translation unit, decision support unit, efficiency improvement unit, design assistance unit, and learning unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the idea generation unit is implemented by the control unit 46A of the headset terminal 314, which activates brainstorming in real time and provides new perspectives. The idea organization unit is implemented by the specific processing unit 290 of the data processing unit 12, which automatically groups ideas and suggests priorities. The idea visualization unit is implemented by the control unit 46A of the headset terminal 314, which automatically generates mind maps and related information and provides visual understanding. The translation unit is implemented by the specific processing unit 290 of the data processing unit 12, which performs real-time translation and multilingual support. The decision support unit is implemented by the specific processing unit 290 of the data processing unit 12, which supports decision-making using frameworks such as SWOT analysis. The efficiency improvement unit is implemented, for example, by the control unit 46A of the headset terminal 314, and performs time management and automatic generation of meeting minutes. The design assistance unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and optimizes visual design and supports colorblindness. The learning unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and generates improvement suggestions from session analysis. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0190] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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).

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.).

[0203] 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.

[0204] 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.

[0205] 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.

[0206] Each of the multiple elements described above, including the idea generation unit, idea organization unit, idea visualization unit, translation unit, decision support unit, efficiency improvement unit, design assistance unit, and learning unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the idea generation unit is implemented by the control unit 46A of the robot 414, which activates brainstorming in real time and provides new perspectives. The idea organization unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which automatically groups ideas and suggests priorities. The idea visualization unit is implemented by, for example, the control unit 46A of the robot 414, which automatically generates mind maps and related information and provides visual understanding. The translation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which performs real-time translation and multilingual support. The decision support unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which supports decision-making using frameworks such as SWOT analysis. The efficiency improvement unit is implemented, for example, by the control unit 46A of the robot 414, and performs time management and automatic generation of meeting minutes. The design assistance unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and optimizes visual design and supports color blindness. The learning unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and generates suggestions for the next improvement from session analysis. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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."

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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.

[0225] (Note 1) An idea generation unit that generates ideas, An idea organizing unit that organizes the ideas generated by the aforementioned idea generation unit, An idea visualization unit visualizes the ideas organized by the aforementioned idea organization unit, Equipped with A system characterized by the following features. (Note 2) It has a translation department that provides real-time translation and multilingual support. The system described in Appendix 1, characterized by the features described herein. (Note 3) Equipped with a decision support unit to assist in decision-making. The system described in Appendix 1, characterized by the features described herein. (Note 4) It includes an efficiency unit that handles time management and automatic generation of meeting minutes. The system described in Appendix 1, characterized by the features described herein. (Note 5) It includes a design support unit that improves visual design and accommodates color blindness. The system described in Appendix 1, characterized by the features described herein. (Note 6) It includes a learning unit that generates suggestions for future improvements based on session analysis. The system described in Appendix 1, characterized by the features described herein. (Note 7) 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 8) The aforementioned editing unit, It automatically groups ideas and suggests priorities. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned visualization unit, Automatically generates mind maps and related information to provide visual understanding. The system described in Appendix 1, characterized by the features described herein. (Note 10) The generating unit is It estimates the user's emotions and adjusts the type and content of ideas generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is We analyze data from past brainstorming sessions and apply the most effective idea generation techniques. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is When generating ideas, we provide customized ideas based on the user's area of ​​expertise and interests. The system described in Appendix 1, characterized by the features described herein. (Note 13) 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 14) The generating unit is When generating ideas, the system takes into account the user's geographical location to generate region-specific ideas. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is During idea generation, we analyze users' social media activity and generate ideas that reflect relevant trends. The system described in Appendix 1, characterized by the features described herein. (Note 16) 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 17) The aforementioned editing unit, Analyze the relevance of ideas and apply an appropriate grouping algorithm. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned editing unit, Dynamically adjust priorities based on the importance and impact of ideas. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned editing unit, It estimates the user's emotions and adjusts the display order of ideas based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned editing unit, When organizing ideas, the system uses the user's past idea submission history to perform optimal grouping. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned editing unit, When organizing ideas, it provides customized groupings tailored to the user's industry and job function. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned visualization unit, It estimates the user's emotions and adjusts the visualization style and color scheme based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned visualization unit, To visually represent the relationships between ideas, apply an appropriate mind mapping algorithm. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned visualization unit, When visualizing the data, adjust the level of detail based on the user's level of understanding. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned visualization unit, It estimates the user's emotions and adjusts the display order of visualizations based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned visualization unit, When visualizing data, the optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned visualization unit, When visualizing data, provide appropriate visual representations that take into account the user's cultural background. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned translation department, It estimates the user's emotions and adjusts the tone and expression of the translation based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 29) The aforementioned translation department, During translation, the dictionary is dynamically updated to ensure that specialized terminology and industry-specific vocabulary are translated appropriately. The system described in Appendix 2, characterized by the features described herein. (Note 30) The aforementioned translation department, During translation, the system selects the optimal translation method by referring to the user's past translation history. The system described in Appendix 2, characterized by the features described herein. (Note 31) The aforementioned translation department, It estimates the user's emotions and determines translation priorities based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned translation department, During translation, the system takes the user's geographical location into account to reflect region-specific expressions. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned translation department, During translation, we analyze users' social media activity and provide translations that reflect relevant trends. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned decision support unit, It estimates user emotions and adjusts the decision-making framework based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 35) The aforementioned decision support unit, We analyze the strengths, weaknesses, opportunities, and threats of each idea in detail to support appropriate decision-making. The system described in Appendix 3, characterized by the features described herein. (Note 36) The aforementioned decision support unit, Referencing past decision-making data, apply the most effective decision-making method. The system described in Appendix 3, characterized by the features described herein. (Note 37) The aforementioned decision support unit, It estimates user emotions and prioritizes decision-making based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned decision support unit, We provide a customized framework tailored to the user's industry and job function during the decision-making process. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned decision support unit, When making decisions, consider the user's geographical location and reflect region-specific factors. The system described in Appendix 3, characterized by the features described herein. (Note 40) The aforementioned efficiency improvement unit is It estimates the user's emotions and adjusts the time management method based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 41) The aforementioned efficiency improvement unit is The session progress is monitored in real time, and meeting minutes are generated at the optimal time. The system described in Appendix 4, characterized by the features described herein. (Note 42) The aforementioned efficiency improvement unit is Analyze past session data and apply the most effective time management techniques The system according to appendix 4, characterized in that (Appendix 43) The efficiency improvement unit Estimate the user's emotion and determine the priority of the minutes of the meeting based on the estimated user's emotion The system according to appendix 4, characterized in that (Appendix 44) The efficiency improvement unit Provide optimal time allocation considering the user's schedule information during time management The system according to appendix 4, characterized in that (Appendix 45) The efficiency improvement unit Provide customized techniques according to the user's industry and position during time management The system according to appendix 4, characterized in that (Appendix 46) The design assistance unit Estimate the user's emotion and adjust the design style and color tone based on the estimated user's emotion The system according to appendix 5, characterized in that (Appendix 47) The design assistance unit Propose a design that takes into account members with color vision abnormalities during the optimization of visual design The system according to appendix 5, characterized in that (Appendix 48) The design assistance unit Refer to past design data and apply the most effective design techniques The system according to appendix 5, characterized in that (Appendix 49) The design assistance unit Estimate the user's emotion and determine the priority of the design based on the estimated user's emotion The system according to appendix 5, characterized in that (Appendix 50) The aforementioned design support unit is When assisting with design, we provide the optimal design by taking into account the user's device information. The system described in Appendix 5, characterized by the features described herein. (Note 51) The aforementioned design support unit is When assisting with design, we provide appropriate visual representations that take into account the user's cultural background. The system described in Appendix 5, characterized by the features described herein. (Note 52) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 6, characterized by the features described herein. (Note 53) The aforementioned learning unit, Analyze past session data and generate optimal improvement suggestions for the next session. The system described in Appendix 6, characterized by the features described herein. (Note 54) The aforementioned learning unit, During training, the learning algorithm is optimized by incorporating user feedback. The system described in Appendix 6, characterized by the features described herein. (Note 55) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 6, characterized by the features described herein. (Note 56) The aforementioned learning unit, During the learning process, we provide customized learning methods tailored to the user's industry and job responsibilities. The system described in Appendix 6, characterized by the features described herein. (Note 57) The aforementioned learning unit, During training, the system takes into account the user's geographical location and reflects region-specific factors. The system described in Appendix 6, characterized by the features described herein. [Explanation of symbols]

[0226] 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 that generates ideas, An idea organizing unit that organizes the ideas generated by the aforementioned idea generation unit, An idea visualization unit visualizes the ideas organized by the aforementioned idea organization unit, Equipped with A system characterized by the following features.

2. It has a translation department that provides real-time translation and multilingual support. The system according to feature 1.

3. Equipped with a decision support unit to assist in decision-making. The system according to feature 1.

4. It includes an efficiency unit that handles time management and automatic generation of meeting minutes. The system according to feature 1.

5. It includes a design support unit that improves visual design and accommodates color blindness. The system according to feature 1.

6. It includes a learning unit that generates suggestions for future improvements based on session analysis. The system according to feature 1.

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

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

Images