System

Abstract

The system according to the embodiment aims to appropriately scrutinize an idea that has occurred and transmit the idea to an appropriate department.SOLUTION: A system according to an embodiment includes a receiving unit, a reviewing unit, a visualizing unit, and a transmitting unit. The reception unit receives an input of an idea. The reviewing unit reviews the idea received by the receiving unit. The visualizer visualizes the idea scrutinized by the scrutinizer. The transmission unit transmits the idea visualized by the visualizer to an appropriate department.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, ideas and business improvement proposals that come up every day tend to get buried and not be used properly.

[0005] The system according to the embodiment aims to properly examine ideas that have been conceived and convey them to the appropriate department. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a scrutiny unit, a visualization unit, and a transmission unit. The reception unit receives input of ideas. The scrutiny unit scrutinizes the ideas received by the reception unit. The visualization unit visualizes the ideas scrutinized by the scrutiny unit. The transmission unit transmits the ideas visualized by the visualization unit to the appropriate department. [Effects of the Invention]

[0007] The system according to the embodiment can properly examine ideas that are conceived and transmit them to the appropriate department. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An idea sharing system according to an embodiment of the present invention efficiently outputs ideas and business improvement proposals conceived by users and promotes their sharing within a company. In the idea sharing system, users write ideas into a mobile app or other device the moment they come up with them. A generation AI then examines the input ideas, visualizes them, and transmits them to the appropriate department. This promotes the creation of new services and campaigns for customers. For example, in the idea sharing system, users write ideas into a mobile app or other device the moment they come up with them. To do this, the user simply enters a simple note or keywords. The generation AI then examines the input ideas. The generation AI analyzes the input information and understands the content of the idea. For example, based on the keyword "idea for a new marketing campaign," the generation AI infers the specific campaign content. This promotes the idea's realization. The ideas examined by the generation AI are visualized and transmitted to the appropriate department. For example, the generation AI provides visualized information to departments appropriate to the content of the idea, such as the marketing department or sales department. This promotes smooth idea sharing and promotes the creation of new services and campaigns for customers. In this way, the idea sharing system can promote the sharing of ideas within a company and strengthen the connection between the field and management departments. As a result, the idea sharing system can quickly and efficiently share ideas that users come up with, revitalizing the entire company. For example, an idea that a field employee comes up with can be quickly communicated to the management department, and concrete action can be taken to realize it, revitalizing the entire company.

[0029] An idea sharing system according to an embodiment includes a receiving unit, a scrutiny unit, a visualization unit, and a transmission unit. The receiving unit inputs ideas conceived by users. Ideas conceived by users include, but are not limited to, text input, voice input, and image input. The receiving unit, for example, allows users to input ideas using a mobile app. The receiving unit can also convert voice input into text data using voice recognition technology. The receiving unit can also convert image input into text data using image analysis technology. For example, the receiving unit accepts ideas by users inputting text into a mobile app. The receiving unit can also allow users to input ideas by voice and convert them into text data using voice recognition technology. The receiving unit can also allow users to upload images and convert them into text data using image analysis technology. The scrutiny unit uses a generation AI to scrutinize the ideas accepted by the receiving unit. The scrutiny is performed based on, for example, but not limited to, the accuracy, originality, and feasibility of the content. For example, the generation AI analyzes and scrutinizes the content of the idea using a text generation AI (e.g., LLM). The review unit can also review the content of the idea using a multimodal generation AI. The review unit can also use the generation AI to estimate specific implementation methods for the idea. For example, the text generation AI has learned a large amount of text data and has advanced natural language processing capabilities. The multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI analyzes the content of the idea and estimates specific implementation methods. The visualization unit uses the generation AI to visualize the idea reviewed by the review unit. Visualization can be performed using methods such as graphs, charts, and infographics, but is not limited to these examples. For example, the generation AI visually represents the idea using a text generation AI (e.g., LLM). The visualization unit can also visually represent the idea using a multimodal generation AI. The visualization unit can also visually represent the content of the idea using the generation AI.For example, text generation AI has learned large amounts of text data and has advanced natural language processing capabilities. Multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI visually represents the content of the idea. The communication department communicates the idea visualized by the visualization department to the appropriate department. The appropriate department may include, but is not limited to, the marketing department, development department, and management department. For example, the communication department may send the visualized idea by email. The communication department may also upload the visualized idea to an internal sharing system. The communication department may also present the visualized idea in a meeting. For example, the communication department may send the visualized idea by email to the appropriate department. The communication department may also upload the visualized idea to an internal sharing system and communicate it to the appropriate department. The communication department may also present the visualized idea in a meeting and communicate it to the appropriate department. As a result, the idea sharing system according to the embodiment allows users to quickly and efficiently share ideas they come up with, thereby revitalizing the entire company. Some or all of the above-described processing in the review unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the review unit may input ideas received by the reception unit into the generation AI and cause the generation AI to analyze the content of the ideas. Some or all of the above-described processing in the visualization unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the visualization unit may input ideas reviewed by the review unit into the generation AI and cause the generation AI to visualize the ideas. Some or all of the above-described processing in the communication unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the communication unit may input ideas visualized by the visualization unit into the AI ​​and cause the AI ​​to communicate the ideas to the appropriate departments.

[0030] The reception unit can write an idea into the mobile app the moment the user comes up with it. For example, the reception unit writes the idea into the mobile app the moment the user comes up with it. The moment the user comes up with an idea includes, but is not limited to, immediately after the idea comes into their head, during a meeting, or while traveling. For example, if the user comes up with an idea during a meeting, the user can input a note into the mobile app. Also, if the user comes up with an idea while traveling, the user can input the idea into the mobile app by voice. Also, if the user comes up with an idea at home, the user can upload an image to the mobile app. This allows the user to instantly input the idea they come up with. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the idea entered by the user into the mobile app into AI and have the AI ​​analyze the content of the idea.

[0031] The refining unit can analyze the input idea using the generation AI and understand the content of the idea. The refining unit can analyze the input idea using, for example, the generation AI and understand the content of the idea. Examples of the generation AI include, but are not limited to, GPT-4 (registered trademark) and Gemini. For example, the generation AI can analyze the input idea using GPT-4 and understand the content of the idea. The generation AI can also analyze the input idea using Gemini and understand the content of the idea. The generation AI can also analyze the input idea using Transformer and understand the content of the idea. This allows the generation AI to accurately understand the content of the idea. Some or all of the above-mentioned processing in the refining unit can be performed using the generation AI. For example, the refining unit can input the idea accepted by the acceptance unit into the generation AI and have the generation AI analyze the content of the idea.

[0032] The visualization unit can visually represent the scrutinized idea by the generation AI. The visualization unit, for example, visually represents the scrutinized idea by the generation AI. Methods of visual representation include, but are not limited to, 2D graphics, 3D models, animations, etc. For example, the generation AI visually represents the scrutinized idea using 2D graphics. The generation AI can also visually represent the scrutinized idea using 3D models. The generation AI can also visually represent the scrutinized idea using animations. In this way, ideas can be visually represented by the generation AI. Some or all of the above-described processing in the visualization unit is performed by the generation AI. For example, the visualization unit inputs the idea scrutinized by the scrutiny unit into the generation AI and causes the generation AI to visualize the idea.

[0033] The communication unit can communicate the visualized idea to the appropriate department. For example, the communication unit communicates the visualized idea to the appropriate department. The appropriate department may include, but is not limited to, the marketing department, development department, or management department. For example, the communication unit may communicate the visualized idea to the appropriate department by email. The communication unit may also upload the visualized idea to an internal shared system and communicate it to the appropriate department. The communication unit may also present the visualized idea at a meeting and communicate it to the appropriate department. In this way, the visualized idea can be communicated to the appropriate department. Some or all of the above-mentioned processing in the communication unit may be performed using AI or may be performed without using AI. For example, the communication unit may input the idea visualized by the visualization unit into AI and have the AI ​​communicate the idea to the appropriate department.

[0034] The scrutiny unit can develop specific methods for implementing the idea. The scrutiny unit, for example, uses a generation AI to develop specific methods for implementing the idea. Specific implementation methods include, but are not limited to, creating a prototype, conducting experiments, and collecting feedback. For example, the generation AI can propose a method for creating a prototype. The generation AI can also propose a method for conducting experiments. The generation AI can also propose a method for collecting feedback. This allows the idea to be made more concrete. Some or all of the above-mentioned processing in the scrutiny unit can be performed using the generation AI. For example, the scrutiny unit can input the idea received by the reception unit into the generation AI and have the generation AI execute a proposal for a specific method for implementing the idea.

[0035] The reception unit can analyze the user's past idea submission history and suggest an appropriate input method. The reception unit can, for example, analyze the user's past idea submission history and suggest an optimal input method. The past idea submission history includes, for example, but is not limited to, the submission date and time, the submission content, and the evaluation results. For example, the reception unit can automatically display as candidates the idea formats that the user has frequently input in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest an input method to be used in a specific time period based on the user's past input history. This makes it possible to suggest an optimal input method based on the user's past history. Some or all of the above-described processing in the reception unit can be performed using AI, or can be performed without using AI. For example, the reception unit can input the user's past idea submission history into AI and have the AI ​​suggest an optimal input method.

[0036] The reception unit can automatically complete the input content based on the user's current project or area of ​​interest when the user inputs an idea. For example, when the user inputs an idea, the reception unit automatically completes the input content based on the user's current project or area of ​​interest. Current projects and areas of interest include, but are not limited to, ongoing projects, research topics, and hobbies. For example, the reception unit automatically completes keywords related to the project the user is currently working on. The reception unit can also suggest related idea candidates based on the user's area of ​​interest. The reception unit can also automatically complete appropriate input content by referring to the user's past project history. This allows the input content to be automatically completed based on the user's project or area of ​​interest. Some or all of the above-described processing by the reception unit may be performed using AI, or may be performed without AI. For example, the reception unit can input data on the user's current project or area of ​​interest into AI and have the AI ​​perform automatic completion of the input content.

[0037] The reception unit can select the optimal input means depending on the user's input method when inputting an idea. For example, when inputting an idea, the reception unit selects the optimal input means depending on the user's input method. Input methods include, but are not limited to, voice input, text input, and image input. For example, if the user selects voice input, the reception unit converts the idea into text using voice recognition technology. Furthermore, if the user uploads an image, the reception unit can extract the idea using image analysis technology. Furthermore, if the user selects text input, the reception unit can provide an input assistance function to enable efficient idea input. This makes it possible to provide the optimal input means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using AI or without AI. For example, the reception unit can input data on the user's input method into AI and have the AI ​​select the optimal input means.

[0038] The reception unit can prioritize inputting highly relevant ideas based on the user's geographical location information when inputting ideas. For example, the reception unit prioritizes inputting highly relevant ideas based on the user's geographical location information when inputting ideas. Geographical location information includes, but is not limited to, GPS data, location information services, etc. For example, if the user is in a specific area, the reception unit can prioritize inputting ideas related to that area. Furthermore, if the user is on a business trip, the reception unit can prioritize inputting ideas related to the business trip destination. Furthermore, if the user is at home, the reception unit can prioritize inputting ideas related to the user's home. In this way, highly relevant ideas can be prioritized based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to AI and cause the AI ​​to prioritize inputting highly relevant ideas.

[0039] The reception unit can analyze the user's social media activity and input related ideas when an idea is input. For example, the reception unit can analyze the user's social media activity and input related ideas when an idea is input. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. For example, the reception unit can input related ideas based on content shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and input related ideas. The reception unit can also input related ideas based on the activity of the user's friends on social media. In this way, related ideas can be input based on the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input data on the user's social media activity into AI and have the AI ​​input related ideas.

[0040] The reception unit can customize the input method by reflecting the user's past feedback when inputting an idea. For example, the reception unit customizes the input method by reflecting the user's past feedback when inputting an idea. Past feedback includes, but is not limited to, user comments, evaluation scores, and improvement suggestions. For example, the reception unit suggests an optimal input method based on the user's past feedback. The reception unit can also analyze the user's past feedback and customize the input interface. The reception unit can also optimize the input procedure by referring to the user's past feedback. This allows the input method to be customized based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using AI or without AI. For example, the reception unit can input the user's past feedback data into AI and have the AI ​​customize the input method.

[0041] The review unit can adjust the level of detail of the review based on the importance of the idea during the review. For example, the review unit adjusts the level of detail of the review based on the importance of the idea during the review. Examples of the importance of an idea include, but are not limited to, scope of impact, feasibility, and innovativeness. For example, the review unit performs detailed review on ideas with high importance. The review unit can also perform standard review on ideas with medium importance. The review unit can also perform simplified review on ideas with low importance. In this way, the level of detail of the review can be adjusted based on the importance of the idea. Some or all of the above-mentioned processing in the review unit can be performed using a generation AI. For example, the review unit can input data on the importance of the idea into the generation AI and cause the generation AI to adjust the level of detail of the review.

[0042] The scrutiny unit can apply different scrutiny algorithms depending on the category of the idea during scrutiny. For example, the scrutiny unit applies different scrutiny algorithms depending on the category of the idea during scrutiny. Idea categories include, but are not limited to, technology categories, business categories, and creative categories. For example, the scrutiny unit applies a marketing-specialized scrutiny algorithm to marketing-related ideas. The scrutiny unit can also apply a technology-specialized scrutiny algorithm to technology-related ideas. The scrutiny unit can also apply a business efficiency-specialized scrutiny algorithm to business efficiency-related ideas. This makes it possible to apply different scrutiny algorithms depending on the category of the idea. Some or all of the above-mentioned processing in the scrutiny unit is performed using a generation AI. For example, the scrutiny unit can input data of idea categories into the generation AI and cause the generation AI to apply different scrutiny algorithms.

[0043] The review unit can improve the accuracy of the review by referring to the user's past review results during the review. For example, the review unit improves the accuracy of the review by referring to the user's past review results during the review. Past review results include, but are not limited to, review evaluations, feedback, and areas for improvement. For example, the review unit improves the accuracy of the review based on the review results of ideas previously submitted by the user. The review unit can also analyze the user's past review results and optimize the review algorithm. The review unit can also adjust the review criteria by referring to the user's past review results. This improves the accuracy of the review by referring to the user's past review results. Some or all of the above-mentioned processing in the review unit is performed using a generation AI. For example, the review unit can input data on the user's past review results into the generation AI and cause the generation AI to improve the accuracy of the review.

[0044] The review unit can determine the priority of the review based on the time of submission of the ideas during the review. For example, the review unit determines the priority of the review based on the time of submission of the ideas during the review. The time of submission includes, but is not limited to, for example, the submission date, the submission time, and the submission frequency. For example, the review unit prioritizes the review of recently submitted ideas. The review unit can also postpone ideas that were submitted earlier. The review unit can also adjust the order in which ideas are reviewed based on the time of submission. This makes it possible to determine the priority of the review based on the time of submission of the ideas. Some or all of the above-mentioned processing in the review unit can be performed using the generation AI. For example, the review unit can input data on the time of submission of ideas into the generation AI and have the generation AI determine the priority of the review.

[0045] The review unit can adjust the order of review based on the relevance of the ideas during review. For example, the review unit adjusts the order of review based on the relevance of the ideas during review. The relevance of the ideas includes, but is not limited to, for example, a common theme, common technical features, and business relevance. For example, the review unit prioritizes review of highly related ideas. The review unit can also postpone less related ideas. The review unit can also adjust the order of review based on the relevance of the ideas. This makes it possible to adjust the order of review based on the relevance of the ideas. Some or all of the above-described processing in the review unit can be performed using a generation AI. For example, the review unit can input data on the relevance of the ideas into the generation AI and have the generation AI adjust the order of review.

[0046] The review unit can adjust the review criteria according to the user's level of expertise during the review. For example, the review unit adjusts the review criteria according to the user's level of expertise during the review. Expertise level includes, but is not limited to, qualifications, years of experience, and past achievements. For example, the review unit applies strict review criteria to ideas from users with high expertise. The review unit can also apply flexible review criteria to ideas from users with low expertise. The review unit can also adjust the review criteria according to the user's level of expertise. This allows the review criteria to be adjusted according to the user's level of expertise. Some or all of the above-mentioned processing in the review unit can be performed using a generation AI. For example, the review unit can input data on the user's level of expertise into the generation AI and cause the generation AI to adjust the review criteria.

[0047] The visualizer can adjust the level of detail of the visualization based on the importance of the idea during visualization. For example, the visualizer adjusts the level of detail of the visualization based on the importance of the idea during visualization. Examples of the importance of an idea include, but are not limited to, scope of impact, feasibility, and innovativeness. For example, the visualizer generates a detailed visualization for an idea with high importance. The visualizer can also generate a standard visualization for an idea with medium importance. The visualizer can also generate a simplified visualization for an idea with low importance. This allows the level of detail of the visualization to be adjusted based on the importance of the idea. Some or all of the above-described processing in the visualizer can be performed using a generation AI. For example, the visualizer can input data on the importance of the idea into the generation AI and cause the generation AI to adjust the level of detail of the visualization.

[0048] The visualization unit can apply different visualization techniques depending on the category of the idea when visualizing. For example, the visualization unit applies different visualization techniques depending on the category of the idea when visualizing. Idea categories include, but are not limited to, technology, business, and creative categories. For example, the visualization unit can apply a marketing-specialized visualization technique to marketing-related ideas. The visualization unit can also apply a technology-specialized visualization technique to technology-related ideas. The visualization unit can also apply a business efficiency-specialized visualization technique to business efficiency-related ideas. This allows different visualization techniques to be applied depending on the category of the idea. Some or all of the above-mentioned processing in the visualization unit is performed using a generation AI. For example, the visualization unit can input data of idea categories into the generation AI and cause the generation AI to apply different visualization techniques.

[0049] The visualization unit can improve the accuracy of visualization by referring to the user's past visualization results when visualizing. For example, the visualization unit can improve the accuracy of visualization by referring to the user's past visualization results when visualizing. Past visualization results include, but are not limited to, visualization evaluations, feedback, and improvements. For example, the visualization unit can improve the accuracy based on visualization results previously generated by the user. The visualization unit can also analyze the user's past visualization results and optimize the visualization algorithm. The visualization unit can also adjust the visualization expression method by referring to the user's past visualization results. This improves the accuracy of visualization by referring to the user's past visualization results. Some or all of the above-described processing in the visualization unit is performed using a generation AI. For example, the visualization unit can input data on the user's past visualization results into the generation AI and have the generation AI improve the accuracy of the visualization.

[0050] The visualization unit can determine the priority of visualization based on the submission date of the ideas during visualization. For example, the visualization unit determines the priority of visualization based on the submission date of the ideas during visualization. The submission date includes, but is not limited to, the submission date, submission time, and submission frequency. For example, the visualization unit prioritizes visualization of recently submitted ideas. The visualization unit can also postpone ideas that were submitted earlier. The visualization unit can also adjust the order of visualization based on the submission date. This allows the priority of visualization to be determined based on the submission date of the ideas. Some or all of the above-mentioned processing in the visualization unit is performed using a generation AI. For example, the visualization unit can input data on the submission date of the ideas into the generation AI and have the generation AI determine the priority of visualization.

[0051] The visualizer can adjust the order of visualization based on the relevance of ideas during visualization. For example, the visualizer adjusts the order of visualization based on the relevance of ideas during visualization. Examples of idea relevance include, but are not limited to, thematic similarity, commonalities in technology, and business relevance. For example, the visualizer prioritizes visualization of highly related ideas. The visualizer can also postpone less related ideas. The visualizer can also adjust the order of visualization based on the relevance of ideas. This allows the order of visualization to be adjusted based on the relevance of ideas. Some or all of the above-described processing in the visualizer can be performed using a generation AI. For example, the visualizer can input data on the relevance of ideas into the generation AI and cause the generation AI to adjust the order of visualization.

[0052] The visualization unit can adjust the use of technical terms in the visualization according to the user's level of expertise during visualization. For example, the visualization unit can adjust the use of technical terms in the visualization according to the user's level of expertise during visualization. Examples of expertise levels include, but are not limited to, qualifications, years of experience, and past achievements. For example, the visualization unit can generate visualizations that use a lot of technical terms for users with high levels of expertise. The visualization unit can also generate visualizations that are concise and easy to understand for users with low levels of expertise. The visualization unit can also adjust the use of technical terms in the visualization according to the user's level of expertise. This allows the use of technical terms in the visualization to be adjusted according to the user's level of expertise. Some or all of the above-described processing in the visualization unit can be performed using a generation AI. For example, the visualization unit can input data on the user's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terms.

[0053] The communication unit can adjust the level of detail of the communication based on the importance of the idea when communicating. For example, the communication unit adjusts the level of detail of the communication based on the importance of the idea when communicating. Examples of the importance of an idea include, but are not limited to, scope of impact, feasibility, and innovativeness. For example, the communication unit provides detailed communication for ideas with high importance. The communication unit can also provide standard communication for ideas with medium importance. The communication unit can also provide simplified communication for ideas with low importance. In this way, the level of detail of the communication can be adjusted based on the importance of the idea. Some or all of the above-mentioned processing in the communication unit may be performed using AI or without AI. For example, the communication unit can input data on the importance of the idea into AI and have the AI ​​adjust the level of detail of the communication.

[0054] The communication unit can apply different communication methods depending on the category of the idea when communicating. For example, the communication unit applies different communication methods depending on the category of the idea when communicating. Idea categories include, but are not limited to, technology categories, business categories, and creative categories. For example, the communication unit applies a marketing-specific communication method to marketing-related ideas. The communication unit can also apply a technology-specific communication method to technology-related ideas. The communication unit can also apply a business efficiency-specific communication method to business efficiency-related ideas. This makes it possible to apply different communication methods depending on the category of the idea. Some or all of the above-mentioned processing in the communication unit may be performed using AI, or may be performed without using AI. For example, the communication unit can input data on idea categories into AI and have the AI ​​apply different communication methods.

[0055] The communication unit can improve the accuracy of communication by referring to the user's past communication results when communicating. For example, the communication unit improves the accuracy of communication by referring to the user's past communication results when communicating. Past communication results include, but are not limited to, communication evaluations, feedback, and areas for improvement. For example, the communication unit improves the accuracy of communication based on the results of ideas previously communicated by the user. The communication unit can also analyze the user's past communication results and optimize the communication algorithm. The communication unit can also adjust the communication method by referring to the user's past communication results. This improves the accuracy of communication by referring to the user's past communication results. Some or all of the above-described processing in the communication unit may be performed using AI, or may be performed without using AI. For example, the communication unit can input data on the user's past communication results into AI and have the AI ​​improve the accuracy of communication.

[0056] The communication unit can determine the priority of transmission based on the time of submission of the ideas at the time of transmission. For example, the communication unit determines the priority of transmission based on the time of submission of the ideas at the time of transmission. The time of submission includes, but is not limited to, for example, the submission date, the submission time, and the submission frequency. For example, the communication unit prioritizes the transmission of recently submitted ideas. The communication unit can also postpone ideas that were submitted earlier. The communication unit can also adjust the order of transmission of ideas based on the time of submission. In this way, the priority of transmission can be determined based on the time of submission of the ideas. Some or all of the above-described processing in the communication unit may be performed using AI, or may be performed without using AI. For example, the communication unit can input data on the time of submission of ideas into AI and have the AI ​​determine the priority of transmission.

[0057] The communication unit can adjust the order of ideas to be transmitted based on the relevance of the ideas. For example, the communication unit adjusts the order of ideas to be transmitted based on the relevance of the ideas. Examples of idea relevance include, but are not limited to, a common theme, commonalities in technology, and business relevance. For example, the communication unit prioritizes the transmission of highly related ideas. The communication unit can also postpone less related ideas. The communication unit can also adjust the order of ideas to be transmitted based on the relevance of the ideas. This makes it possible to adjust the order of ideas to be transmitted based on the relevance of the ideas. Some or all of the above-described processing in the communication unit may be performed using AI, or may be performed without using AI. For example, the communication unit can input data on the relevance of ideas to AI and have the AI ​​adjust the order of ideas to be transmitted.

[0058] The communication unit can adjust the use of technical terms in the communication according to the user's level of expertise. For example, the communication unit adjusts the use of technical terms in the communication according to the user's level of expertise. Expertise levels include, but are not limited to, qualifications, years of experience, and past achievements. For example, the communication unit uses a lot of technical terms in the communication for users with high levels of expertise. The communication unit can also provide concise and easy-to-understand communication for users with low levels of expertise. The communication unit can also adjust the use of technical terms in the communication according to the user's level of expertise. This makes it possible to adjust the use of technical terms in the communication according to the user's level of expertise. Some or all of the above-described processing in the communication unit may be performed using AI or without AI. For example, the communication unit can input data on the user's level of expertise into AI and have the AI ​​adjust the use of technical terms.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] When a user inputs an idea, the reception unit can analyze the user's past idea submission history and suggest the optimal input method. For example, it can prioritize and suggest input methods (voice, text, etc.) that the user has frequently used in the past. It can also automatically display related keywords and templates based on the content and evaluation results of ideas that the user has submitted in the past. Furthermore, if a user tends to submit ideas during a specific time period, it can also suggest the optimal input method for that time period. This makes it possible to suggest the optimal input method based on the user's past history.

[0061] When proceeding with the specific implementation method of an idea, the review unit can adjust the review criteria by reflecting the user's past feedback. For example, the review unit can analyze feedback on ideas previously submitted by the user and optimize evaluation criteria for feasibility and innovativeness. The review unit can also suggest specific implementation methods by referring to successful examples of ideas previously submitted by the user. Furthermore, the review unit can analyze failed examples of ideas previously submitted by the user and suggest measures to avoid similar failures. In this way, the review unit can adjust the review criteria based on the user's past feedback and proceed with the specific implementation method.

[0062] When visually expressing the scrutinized ideas, the visualization unit can adjust the use of technical terms in the visualization according to the user's level of expertise. For example, for a user with high expertise, a detailed visualization using a lot of technical terms can be generated. For a user with low expertise, a simple and easy-to-understand visualization can be generated. Furthermore, the visualization expression method can be customized according to the user's level of expertise. In this way, the use of technical terms in the visualization can be adjusted and visually expressed according to the user's level of expertise.

[0063] When communicating visualized ideas to the appropriate departments, the communication department can adjust the level of detail of the communication based on the importance of the idea. For example, detailed communication can be provided for ideas with high importance. Standard communication can be provided for ideas with medium importance. Furthermore, simple communication can be provided for ideas with low importance. In this way, the level of detail of the communication can be adjusted based on the importance of the idea, and it can be communicated to the appropriate departments.

[0064] When an idea is input, the reception unit can automatically complete the input content based on the user's current project or area of ​​interest. For example, it can automatically complete keywords related to the project the user is currently working on. It can also suggest related idea candidates based on the user's area of ​​interest. It can also automatically complete appropriate input content by referring to the user's past project history. This makes it possible to automatically complete input content based on the user's project or area of ​​interest, supporting efficient idea input.

[0065] During the review, the review department can apply different review algorithms depending on the category of the idea. For example, a marketing-specific review algorithm can be applied to marketing-related ideas. A technology-specific review algorithm can also be applied to technology-related ideas. Furthermore, a business efficiency-specific review algorithm can also be applied to business efficiency-related ideas. This allows different review algorithms to be applied depending on the category of the idea, making it possible to make an appropriate evaluation.

[0066] The processing flow of the first embodiment will be briefly explained below.

[0067] Step 1: The reception unit inputs an idea that the user has come up with. Ideas that the user has come up with can be text input, voice input, image input, etc. For example, the user inputs an idea using a mobile app. The reception unit can also convert voice input into text data using voice recognition technology. Furthermore, the reception unit can also convert image input into text data using image analysis technology. Step 2: The review unit uses a generation AI to review the ideas received by the reception unit. The review is based on the accuracy, originality, feasibility, etc. of the content. For example, the generation AI analyzes and reviews the content of the idea using a text generation AI (e.g., LLM). The review unit can also use a multimodal generation AI to review the content of the idea. Step 3: The visualization unit uses a generation AI to visualize the ideas vetted by the vetter. Visualization can be done using methods such as graphs, charts, and infographics. For example, the generation AI can use a text generation AI (e.g., LLM) to visually represent the ideas. The visualization unit can also use a multimodal generation AI to visually represent the ideas. Step 4: The communication department communicates the visualized ideas to the appropriate departments. These departments may include marketing, development, and management. The communication department sends the visualized ideas by email. The communication department can also upload the visualized ideas to an internal sharing system. The communication department can also present the visualized ideas at a meeting.

[0068] (Example 2) An idea sharing system according to an embodiment of the present invention efficiently outputs ideas and business improvement proposals conceived by users and promotes their sharing within a company. In the idea sharing system, users write ideas into a mobile app or other device the moment they come up with them. A generation AI then examines the input ideas, visualizes them, and transmits them to the appropriate department. This promotes the creation of new services and campaigns for customers. For example, in the idea sharing system, users write ideas into a mobile app or other device the moment they come up with them. To do this, the user simply enters a simple note or keywords. The generation AI then examines the input ideas. The generation AI analyzes the input information and understands the content of the idea. For example, based on the keyword "idea for a new marketing campaign," the generation AI infers the specific campaign content. This promotes the idea's realization. The ideas examined by the generation AI are visualized and transmitted to the appropriate department. For example, the generation AI provides visualized information to departments appropriate to the content of the idea, such as the marketing department or sales department. This promotes smooth idea sharing and promotes the creation of new services and campaigns for customers. In this way, the idea sharing system can promote the sharing of ideas within a company and strengthen the connection between the field and management departments. As a result, the idea sharing system can quickly and efficiently share ideas that users come up with, revitalizing the entire company. For example, an idea that a field employee comes up with can be quickly communicated to the management department, and concrete action can be taken to realize it, revitalizing the entire company.

[0069] An idea sharing system according to an embodiment includes a receiving unit, a scrutiny unit, a visualization unit, and a transmission unit. The receiving unit inputs ideas conceived by users. Ideas conceived by users include, but are not limited to, text input, voice input, and image input. The receiving unit, for example, allows users to input ideas using a mobile app. The receiving unit can also convert voice input into text data using voice recognition technology. The receiving unit can also convert image input into text data using image analysis technology. For example, the receiving unit accepts ideas by users inputting text into a mobile app. The receiving unit can also allow users to input ideas by voice and convert them into text data using voice recognition technology. The receiving unit can also allow users to upload images and convert them into text data using image analysis technology. The scrutiny unit uses a generation AI to scrutinize the ideas accepted by the receiving unit. The scrutiny is performed based on, for example, but not limited to, the accuracy, originality, and feasibility of the content. For example, the generation AI analyzes and scrutinizes the content of the idea using a text generation AI (e.g., LLM). The review unit can also review the content of the idea using a multimodal generation AI. The review unit can also use the generation AI to estimate specific implementation methods for the idea. For example, the text generation AI has learned a large amount of text data and has advanced natural language processing capabilities. The multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI analyzes the content of the idea and estimates specific implementation methods. The visualization unit uses the generation AI to visualize the idea reviewed by the review unit. Visualization can be performed using methods such as graphs, charts, and infographics, but is not limited to these examples. For example, the generation AI visually represents the idea using a text generation AI (e.g., LLM). The visualization unit can also visually represent the idea using a multimodal generation AI. The visualization unit can also visually represent the content of the idea using the generation AI.For example, text generation AI has learned large amounts of text data and has advanced natural language processing capabilities. Multimodal generation AI can handle multiple modalities, including not only text but also images and audio. The generation AI visually represents the content of the idea. The communication department communicates the idea visualized by the visualization department to the appropriate department. The appropriate department may include, but is not limited to, the marketing department, development department, and management department. For example, the communication department may send the visualized idea by email. The communication department may also upload the visualized idea to an internal sharing system. The communication department may also present the visualized idea in a meeting. For example, the communication department may send the visualized idea by email to the appropriate department. The communication department may also upload the visualized idea to an internal sharing system and communicate it to the appropriate department. The communication department may also present the visualized idea in a meeting and communicate it to the appropriate department. As a result, the idea sharing system according to the embodiment allows users to quickly and efficiently share ideas they come up with, thereby revitalizing the entire company. Some or all of the above-described processing in the review unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the review unit may input ideas received by the reception unit into the generation AI and cause the generation AI to analyze the content of the ideas. Some or all of the above-described processing in the visualization unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the visualization unit may input ideas reviewed by the review unit into the generation AI and cause the generation AI to visualize the ideas. Some or all of the above-described processing in the communication unit may be performed, for example, using an AI, or may be performed without using an AI. For example, the communication unit may input ideas visualized by the visualization unit into the AI ​​and cause the AI ​​to communicate the ideas to the appropriate departments.

[0070] The reception unit can write an idea into the mobile app the moment the user comes up with it. For example, the reception unit writes the idea into the mobile app the moment the user comes up with it. The moment the user comes up with an idea includes, but is not limited to, immediately after the idea comes into their head, during a meeting, or while traveling. For example, if the user comes up with an idea during a meeting, the user can input a note into the mobile app. Also, if the user comes up with an idea while traveling, the user can input the idea into the mobile app by voice. Also, if the user comes up with an idea at home, the user can upload an image to the mobile app. This allows the user to instantly input the idea they come up with. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the idea entered by the user into the mobile app into AI and have the AI ​​analyze the content of the idea.

[0071] The refining unit can analyze the input idea using the generation AI and understand the content of the idea. The refining unit can analyze the input idea using, for example, the generation AI and understand the content of the idea. Examples of the generation AI include, but are not limited to, GPT-4 and Gemini. For example, the generation AI can analyze the input idea using GPT-4 and understand the content of the idea. The generation AI can also analyze the input idea using Gemini and understand the content of the idea. The generation AI can also analyze the input idea using Transformer and understand the content of the idea. This allows the generation AI to accurately understand the content of the idea. Some or all of the above-mentioned processing in the refining unit can be performed using the generation AI. For example, the refining unit can input the idea accepted by the acceptance unit into the generation AI and have the generation AI analyze the content of the idea.

[0072] The visualization unit can visually represent the scrutinized idea by the generation AI. The visualization unit, for example, visually represents the scrutinized idea by the generation AI. Methods of visual representation include, but are not limited to, 2D graphics, 3D models, animations, etc. For example, the generation AI visually represents the scrutinized idea using 2D graphics. The generation AI can also visually represent the scrutinized idea using 3D models. The generation AI can also visually represent the scrutinized idea using animations. In this way, ideas can be visually represented by the generation AI. Some or all of the above-described processing in the visualization unit is performed by the generation AI. For example, the visualization unit inputs the idea scrutinized by the scrutiny unit into the generation AI and causes the generation AI to visualize the idea.

[0073] The communication unit can communicate the visualized idea to the appropriate department. For example, the communication unit communicates the visualized idea to the appropriate department. The appropriate department may include, but is not limited to, the marketing department, development department, or management department. For example, the communication unit may communicate the visualized idea to the appropriate department by email. The communication unit may also upload the visualized idea to an internal shared system and communicate it to the appropriate department. The communication unit may also present the visualized idea at a meeting and communicate it to the appropriate department. In this way, the visualized idea can be communicated to the appropriate department. Some or all of the above-mentioned processing in the communication unit may be performed using AI or may be performed without using AI. For example, the communication unit may input the idea visualized by the visualization unit into AI and have the AI ​​communicate the idea to the appropriate department.

[0074] The scrutiny unit can develop specific methods for implementing the idea. The scrutiny unit, for example, uses a generation AI to develop specific methods for implementing the idea. Specific implementation methods include, but are not limited to, creating a prototype, conducting experiments, and collecting feedback. For example, the generation AI can propose a method for creating a prototype. The generation AI can also propose a method for conducting experiments. The generation AI can also propose a method for collecting feedback. This allows the idea to be made more concrete. Some or all of the above-mentioned processing in the scrutiny unit can be performed using the generation AI. For example, the scrutiny unit can input the idea received by the reception unit into the generation AI and have the generation AI execute a proposal for a specific method for implementing the idea.

[0075] The reception unit can estimate the user's emotions and customize the idea input interface based on the estimated user emotions. The reception unit, for example, estimates the user's emotions and customizes the idea input interface based on the estimated user emotions. Methods for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, if the user is stressed, the reception unit can provide a simple interface and minimize input steps. If the user is relaxed, the reception unit can provide detailed input options and suggest a customizable input method. If the user is in a hurry, the reception unit can prioritize voice input to enable quick idea input. This allows an input interface to be provided that corresponds to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the reception unit may be performed using AI or without AI. For example, the reception unit can input the user's emotional data into the AI ​​and have the AI ​​estimate the emotion.

[0076] The reception unit can analyze the user's past idea submission history and suggest an appropriate input method. The reception unit can, for example, analyze the user's past idea submission history and suggest an optimal input method. The past idea submission history includes, for example, but is not limited to, the submission date and time, the submission content, and the evaluation results. For example, the reception unit can automatically display as candidates the idea formats that the user has frequently input in the past. The reception unit can also preferentially suggest input methods (voice, text, etc.) that the user has used in the past. The reception unit can also predict and suggest an input method to be used in a specific time period based on the user's past input history. This makes it possible to suggest an optimal input method based on the user's past history. Some or all of the above-described processing in the reception unit can be performed using AI, or can be performed without using AI. For example, the reception unit can input the user's past idea submission history into AI and have the AI ​​suggest an optimal input method.

[0077] The reception unit can automatically complete the input content based on the user's current project or area of ​​interest when the user inputs an idea. For example, when the user inputs an idea, the reception unit automatically completes the input content based on the user's current project or area of ​​interest. Current projects and areas of interest include, but are not limited to, ongoing projects, research topics, and hobbies. For example, the reception unit automatically completes keywords related to the project the user is currently working on. The reception unit can also suggest related idea candidates based on the user's area of ​​interest. The reception unit can also automatically complete appropriate input content by referring to the user's past project history. This allows the input content to be automatically completed based on the user's project or area of ​​interest. Some or all of the above-described processing by the reception unit may be performed using AI, or may be performed without AI. For example, the reception unit can input data on the user's current project or area of ​​interest into AI and have the AI ​​perform automatic completion of the input content.

[0078] The reception unit can select the optimal input means depending on the user's input method when inputting an idea. For example, when inputting an idea, the reception unit selects the optimal input means depending on the user's input method. Input methods include, but are not limited to, voice input, text input, and image input. For example, if the user selects voice input, the reception unit converts the idea into text using voice recognition technology. Furthermore, if the user uploads an image, the reception unit can extract the idea using image analysis technology. Furthermore, if the user selects text input, the reception unit can provide an input assistance function to enable efficient idea input. This makes it possible to provide the optimal input means depending on the user's input method. Some or all of the above-described processing in the reception unit may be performed using AI or without AI. For example, the reception unit can input data on the user's input method into AI and have the AI ​​select the optimal input means.

[0079] The reception unit can estimate the user's emotions and determine the priority of the input ideas based on the estimated user emotions. The reception unit, for example, estimates the user's emotions and determines the priority of the input ideas based on the estimated user emotions. Methods for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, the reception unit can set the priority of an idea to high if the user is excited. The reception unit can also set the priority of an idea to medium if the user is relaxed. The reception unit can also set the priority of an idea to low if the user is stressed. This allows the priority of ideas to be determined based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without AI. For example, the reception unit can input the user's emotion data into AI and have the AI ​​perform emotion estimation.

[0080] The reception unit can prioritize inputting highly relevant ideas based on the user's geographical location information when inputting ideas. For example, the reception unit prioritizes inputting highly relevant ideas based on the user's geographical location information when inputting ideas. Geographical location information includes, but is not limited to, GPS data, location information services, etc. For example, if the user is in a specific area, the reception unit can prioritize inputting ideas related to that area. Furthermore, if the user is on a business trip, the reception unit can prioritize inputting ideas related to the business trip destination. Furthermore, if the user is at home, the reception unit can prioritize inputting ideas related to the user's home. In this way, highly relevant ideas can be prioritized based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to AI and cause the AI ​​to prioritize inputting highly relevant ideas.

[0081] The reception unit can analyze the user's social media activity and input related ideas when an idea is input. For example, the reception unit can analyze the user's social media activity and input related ideas when an idea is input. Social media activity includes, but is not limited to, the content of posts, the number of likes, and the number of followers. For example, the reception unit can input related ideas based on content shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and input related ideas. The reception unit can also input related ideas based on the activity of the user's friends on social media. In this way, related ideas can be input based on the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input data on the user's social media activity into AI and have the AI ​​input related ideas.

[0082] The reception unit can customize the input method by reflecting the user's past feedback when inputting an idea. For example, the reception unit customizes the input method by reflecting the user's past feedback when inputting an idea. Past feedback includes, but is not limited to, user comments, evaluation scores, and improvement suggestions. For example, the reception unit suggests an optimal input method based on the user's past feedback. The reception unit can also analyze the user's past feedback and customize the input interface. The reception unit can also optimize the input procedure by referring to the user's past feedback. This allows the input method to be customized based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using AI or without AI. For example, the reception unit can input the user's past feedback data into AI and have the AI ​​customize the input method.

[0083] The scrutiny unit can estimate the user's emotions and adjust the scrutiny criteria for ideas based on the estimated user emotions. The scrutiny unit, for example, estimates the user's emotions and adjusts the scrutiny criteria for ideas based on the estimated user emotions. Methods for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, if the user is excited, the scrutiny unit can scrutinize the idea by emphasizing its innovativeness. Furthermore, if the user is relaxed, the scrutiny unit can scrutinize the idea by emphasizing its feasibility. Furthermore, if the user is stressed, the scrutiny unit can scrutinize the idea by emphasizing its simplicity. This allows the scrutiny criteria for ideas to be adjusted based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the scrutiny unit is performed using the generation AI. For example, the scrutiny unit can input the user's emotional data into the generation AI and cause the generation AI to adjust the scrutiny criteria based on the emotion.

[0084] The review unit can adjust the level of detail of the review based on the importance of the idea during the review. For example, the review unit adjusts the level of detail of the review based on the importance of the idea during the review. Examples of the importance of an idea include, but are not limited to, scope of impact, feasibility, and innovativeness. For example, the review unit performs detailed review on ideas with high importance. The review unit can also perform standard review on ideas with medium importance. The review unit can also perform simplified review on ideas with low importance. In this way, the level of detail of the review can be adjusted based on the importance of the idea. Some or all of the above-mentioned processing in the review unit can be performed using a generation AI. For example, the review unit can input data on the importance of the idea into the generation AI and cause the generation AI to adjust the level of detail of the review.

[0085] The scrutiny unit can apply different scrutiny algorithms depending on the category of the idea during scrutiny. For example, the scrutiny unit applies different scrutiny algorithms depending on the category of the idea during scrutiny. Idea categories include, but are not limited to, technology categories, business categories, and creative categories. For example, the scrutiny unit applies a marketing-specialized scrutiny algorithm to marketing-related ideas. The scrutiny unit can also apply a technology-specialized scrutiny algorithm to technology-related ideas. The scrutiny unit can also apply a business efficiency-specialized scrutiny algorithm to business efficiency-related ideas. This makes it possible to apply different scrutiny algorithms depending on the category of the idea. Some or all of the above-mentioned processing in the scrutiny unit is performed using a generation AI. For example, the scrutiny unit can input data of idea categories into the generation AI and cause the generation AI to apply different scrutiny algorithms.

[0086] The review unit can improve the accuracy of the review by referring to the user's past review results during the review. For example, the review unit improves the accuracy of the review by referring to the user's past review results during the review. Past review results include, but are not limited to, review evaluations, feedback, and areas for improvement. For example, the review unit improves the accuracy of the review based on the review results of ideas previously submitted by the user. The review unit can also analyze the user's past review results and optimize the review algorithm. The review unit can also adjust the review criteria by referring to the user's past review results. This improves the accuracy of the review by referring to the user's past review results. Some or all of the above-mentioned processing in the review unit is performed using a generation AI. For example, the review unit can input data on the user's past review results into the generation AI and cause the generation AI to improve the accuracy of the review.

[0087] The review unit can estimate the user's emotions and determine the priority of review based on the estimated user emotions. The review unit, for example, estimates the user's emotions and determines the priority of review based on the estimated user emotions. Methods for estimating emotions include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, if the user is excited, the review unit can prioritize review of ideas. Also, if the user is relaxed, the review unit can prioritize review of ideas. Also, if the user is stressed, the review unit can postpone review of ideas. In this way, the priority of review can be determined based on the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the review unit is performed using the generation AI. For example, the scrutiny unit can input user emotion data into the generation AI and have the generation AI determine the priority of scrutiny based on emotion.

[0088] The review unit can determine the priority of the review based on the time of submission of the ideas during the review. For example, the review unit determines the priority of the review based on the time of submission of the ideas during the review. The time of submission includes, but is not limited to, for example, the submission date, the submission time, and the submission frequency. For example, the review unit prioritizes the review of recently submitted ideas. The review unit can also postpone ideas that were submitted earlier. The review unit can also adjust the order in which ideas are reviewed based on the time of submission. This makes it possible to determine the priority of the review based on the time of submission of the ideas. Some or all of the above-mentioned processing in the review unit can be performed using the generation AI. For example, the review unit can input data on the time of submission of ideas into the generation AI and have the generation AI determine the priority of the review.

[0089] The review unit can adjust the order of review based on the relevance of the ideas during review. For example, the review unit adjusts the order of review based on the relevance of the ideas during review. The relevance of the ideas includes, but is not limited to, for example, a common theme, common technical features, and business relevance. For example, the review unit prioritizes review of highly related ideas. The review unit can also postpone less related ideas. The review unit can also adjust the order of review based on the relevance of the ideas. This makes it possible to adjust the order of review based on the relevance of the ideas. Some or all of the above-described processing in the review unit can be performed using a generation AI. For example, the review unit can input data on the relevance of the ideas into the generation AI and have the generation AI adjust the order of review.

[0090] The review unit can adjust the review criteria according to the user's level of expertise during the review. For example, the review unit adjusts the review criteria according to the user's level of expertise during the review. Expertise level includes, but is not limited to, qualifications, years of experience, and past achievements. For example, the review unit applies strict review criteria to ideas from users with high expertise. The review unit can also apply flexible review criteria to ideas from users with low expertise. The review unit can also adjust the review criteria according to the user's level of expertise. This allows the review criteria to be adjusted according to the user's level of expertise. Some or all of the above-mentioned processing in the review unit can be performed using a generation AI. For example, the review unit can input data on the user's level of expertise into the generation AI and cause the generation AI to adjust the review criteria.

[0091] The visualization unit can estimate the user's emotion and adjust the visualization expression method based on the estimated user's emotion. The visualization unit, for example, estimates the user's emotion and adjusts the visualization expression method based on the estimated user's emotion. Methods for estimating emotion include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, the visualization unit can generate a visually stimulating visualization when the user is excited. The visualization unit can also generate a calm visualization when the user is relaxed. The visualization unit can also generate a simple, highly visible visualization when the user is stressed. This allows the visualization expression method to be adjusted based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the visualization unit is performed using the generation AI. For example, the visualization unit can input the user's emotional data into the generation AI and have the generation AI adjust the way the visualization is expressed based on the emotion.

[0092] The visualizer can adjust the level of detail of the visualization based on the importance of the idea during visualization. For example, the visualizer adjusts the level of detail of the visualization based on the importance of the idea during visualization. Examples of the importance of an idea include, but are not limited to, scope of impact, feasibility, and innovativeness. For example, the visualizer generates a detailed visualization for an idea with high importance. The visualizer can also generate a standard visualization for an idea with medium importance. The visualizer can also generate a simplified visualization for an idea with low importance. This allows the level of detail of the visualization to be adjusted based on the importance of the idea. Some or all of the above-described processing in the visualizer can be performed using a generation AI. For example, the visualizer can input data on the importance of the idea into the generation AI and cause the generation AI to adjust the level of detail of the visualization.

[0093] The visualization unit can apply different visualization techniques depending on the category of the idea when visualizing. For example, the visualization unit applies different visualization techniques depending on the category of the idea when visualizing. Idea categories include, but are not limited to, technology, business, and creative categories. For example, the visualization unit can apply a marketing-specialized visualization technique to marketing-related ideas. The visualization unit can also apply a technology-specialized visualization technique to technology-related ideas. The visualization unit can also apply a business efficiency-specialized visualization technique to business efficiency-related ideas. This allows different visualization techniques to be applied depending on the category of the idea. Some or all of the above-mentioned processing in the visualization unit is performed using a generation AI. For example, the visualization unit can input data of idea categories into the generation AI and cause the generation AI to apply different visualization techniques.

[0094] The visualization unit can improve the accuracy of visualization by referring to the user's past visualization results when visualizing. For example, the visualization unit can improve the accuracy of visualization by referring to the user's past visualization results when visualizing. Past visualization results include, but are not limited to, visualization evaluations, feedback, and improvements. For example, the visualization unit can improve the accuracy based on visualization results previously generated by the user. The visualization unit can also analyze the user's past visualization results and optimize the visualization algorithm. The visualization unit can also adjust the visualization expression method by referring to the user's past visualization results. This improves the accuracy of visualization by referring to the user's past visualization results. Some or all of the above-described processing in the visualization unit is performed using a generation AI. For example, the visualization unit can input data on the user's past visualization results into the generation AI and have the generation AI improve the accuracy of the visualization.

[0095] The visualization unit can estimate the user's emotion and adjust the length of the visualization based on the estimated user emotion. The visualization unit, for example, estimates the user's emotion and adjusts the length of the visualization based on the estimated user emotion. Methods for estimating emotion include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, if the user is in a hurry, the visualization unit can generate a short, to-the-point visualization. If the user is relaxed, the visualization unit can generate a longer visualization with detailed explanations. If the user is excited, the visualization unit can generate a visualization with visually stimulating effects. This allows the length of the visualization to be adjusted based on the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the visualization unit is performed using the generation AI. For example, the visualization unit can input the user's emotional data into the generation AI and cause the generation AI to adjust the length of the visualization based on the emotion.

[0096] The visualization unit can determine the priority of visualization based on the submission date of the ideas during visualization. For example, the visualization unit determines the priority of visualization based on the submission date of the ideas during visualization. The submission date includes, but is not limited to, the submission date, submission time, and submission frequency. For example, the visualization unit prioritizes visualization of recently submitted ideas. The visualization unit can also postpone ideas that were submitted earlier. The visualization unit can also adjust the order of visualization based on the submission date. This allows the priority of visualization to be determined based on the submission date of the ideas. Some or all of the above-mentioned processing in the visualization unit is performed using a generation AI. For example, the visualization unit can input data on the submission date of the ideas into the generation AI and have the generation AI determine the priority of visualization.

[0097] The visualizer can adjust the order of visualization based on the relevance of ideas during visualization. For example, the visualizer adjusts the order of visualization based on the relevance of ideas during visualization. Examples of idea relevance include, but are not limited to, thematic similarity, commonalities in technology, and business relevance. For example, the visualizer prioritizes visualization of highly related ideas. The visualizer can also postpone less related ideas. The visualizer can also adjust the order of visualization based on the relevance of ideas. This allows the order of visualization to be adjusted based on the relevance of ideas. Some or all of the above-described processing in the visualizer can be performed using a generation AI. For example, the visualizer can input data on the relevance of ideas into the generation AI and cause the generation AI to adjust the order of visualization.

[0098] The visualization unit can adjust the use of technical terms in the visualization according to the user's level of expertise during visualization. For example, the visualization unit can adjust the use of technical terms in the visualization according to the user's level of expertise during visualization. Examples of expertise levels include, but are not limited to, qualifications, years of experience, and past achievements. For example, the visualization unit can generate visualizations that use a lot of technical terms for users with high levels of expertise. The visualization unit can also generate visualizations that are concise and easy to understand for users with low levels of expertise. The visualization unit can also adjust the use of technical terms in the visualization according to the user's level of expertise. This allows the use of technical terms in the visualization to be adjusted according to the user's level of expertise. Some or all of the above-described processing in the visualization unit can be performed using a generation AI. For example, the visualization unit can input data on the user's level of expertise into the generation AI and cause the generation AI to adjust the use of technical terms.

[0099] The communication unit can estimate the user's emotion and adjust the communication method based on the estimated user's emotion. The communication unit, for example, estimates the user's emotion and adjusts the communication method based on the estimated user's emotion. Methods for estimating emotion include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, if the user is excited, the communication unit can select a quick and direct communication method. If the user is relaxed, the communication unit can select a communication method that includes detailed explanations. If the user is stressed, the communication unit can select a simple and highly visible communication method. This allows the communication method to be adjusted based on the user's emotion. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the communication unit may be performed using AI, or without AI. For example, the communication unit can input the user's emotion data into AI and have the AI ​​adjust the communication method based on the emotion.

[0100] The communication unit can adjust the level of detail of the communication based on the importance of the idea when communicating. For example, the communication unit adjusts the level of detail of the communication based on the importance of the idea when communicating. Examples of the importance of an idea include, but are not limited to, scope of impact, feasibility, and innovativeness. For example, the communication unit provides detailed communication for ideas with high importance. The communication unit can also provide standard communication for ideas with medium importance. The communication unit can also provide simplified communication for ideas with low importance. In this way, the level of detail of the communication can be adjusted based on the importance of the idea. Some or all of the above-mentioned processing in the communication unit may be performed using AI or without AI. For example, the communication unit can input data on the importance of the idea into AI and have the AI ​​adjust the level of detail of the communication.

[0101] The communication unit can apply different communication methods depending on the category of the idea when communicating. For example, the communication unit applies different communication methods depending on the category of the idea when communicating. Idea categories include, but are not limited to, technology categories, business categories, and creative categories. For example, the communication unit applies a marketing-specific communication method to marketing-related ideas. The communication unit can also apply a technology-specific communication method to technology-related ideas. The communication unit can also apply a business efficiency-specific communication method to business efficiency-related ideas. This makes it possible to apply different communication methods depending on the category of the idea. Some or all of the above-mentioned processing in the communication unit may be performed using AI, or may be performed without using AI. For example, the communication unit can input data on idea categories into AI and have the AI ​​apply different communication methods.

[0102] The communication unit can improve the accuracy of communication by referring to the user's past communication results when communicating. For example, the communication unit improves the accuracy of communication by referring to the user's past communication results when communicating. Past communication results include, but are not limited to, communication evaluations, feedback, and areas for improvement. For example, the communication unit improves the accuracy of communication based on the results of ideas previously communicated by the user. The communication unit can also analyze the user's past communication results and optimize the communication algorithm. The communication unit can also adjust the communication method by referring to the user's past communication results. This improves the accuracy of communication by referring to the user's past communication results. Some or all of the above-described processing in the communication unit may be performed using AI, or may be performed without using AI. For example, the communication unit can input data on the user's past communication results into AI and have the AI ​​improve the accuracy of communication.

[0103] The communication unit can estimate the user's emotion and determine the priority of communication based on the estimated user's emotion. The communication unit, for example, estimates the user's emotion and determines the priority of communication based on the estimated user's emotion. Methods for estimating emotion include, but are not limited to, facial expression recognition, voice analysis, and text analysis. For example, if the user is excited, the communication unit can prioritize communication of ideas. If the user is relaxed, the communication unit can also prioritize communication of ideas. If the user is stressed, the communication unit can postpone communication of ideas. This makes it possible to determine the priority of communication based on the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the communication unit may be performed using AI or without AI. For example, the transmission unit can input the user's emotional data into the AI ​​and have the AI ​​determine the priority of transmission based on the emotions.

[0104] The communication unit can determine the priority of transmission based on the time of submission of the ideas at the time of transmission. For example, the communication unit determines the priority of transmission based on the time of submission of the ideas at the time of transmission. The time of submission includes, but is not limited to, for example, the submission date, the submission time, and the submission frequency. For example, the communication unit prioritizes the transmission of recently submitted ideas. The communication unit can also postpone ideas that were submitted earlier. The communication unit can also adjust the order of transmission of ideas based on the time of submission. In this way, the priority of transmission can be determined based on the time of submission of the ideas. Some or all of the above-described processing in the communication unit may be performed using AI, or may be performed without using AI. For example, the communication unit can input data on the time of submission of ideas into AI and have the AI ​​determine the priority of transmission.

[0105] The communication unit can adjust the order of ideas to be transmitted based on the relevance of the ideas. For example, the communication unit adjusts the order of ideas to be transmitted based on the relevance of the ideas. Examples of idea relevance include, but are not limited to, a common theme, commonalities in technology, and business relevance. For example, the communication unit prioritizes the transmission of highly related ideas. The communication unit can also postpone less related ideas. The communication unit can also adjust the order of ideas to be transmitted based on the relevance of the ideas. This makes it possible to adjust the order of ideas to be transmitted based on the relevance of the ideas. Some or all of the above-described processing in the communication unit may be performed using AI, or may be performed without using AI. For example, the communication unit can input data on the relevance of ideas to AI and have the AI ​​adjust the order of ideas to be transmitted.

[0106] The communication unit can adjust the use of technical terms in the communication according to the user's level of expertise. For example, the communication unit adjusts the use of technical terms in the communication according to the user's level of expertise. Expertise levels include, but are not limited to, qualifications, years of experience, and past achievements. For example, the communication unit uses a lot of technical terms in the communication for users with high levels of expertise. The communication unit can also provide concise and easy-to-understand communication for users with low levels of expertise. The communication unit can also adjust the use of technical terms in the communication according to the user's level of expertise. This makes it possible to adjust the use of technical terms in the communication according to the user's level of expertise. Some or all of the above-described processing in the communication unit may be performed using AI or without AI. For example, the communication unit can input data on the user's level of expertise into AI and have the AI ​​adjust the use of technical terms. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, scrutiny unit, visualization unit, and transmission unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the computer 36 of the smart device 14 and inputs ideas that the user has come up with. The scrutiny unit is realized by the specific processing unit 290 of the data processing device 12 and scrutinizes the idea using a generation AI. The visualization unit is realized by the specific processing unit 290 of the data processing device 12 and visualizes the scrutinized idea. The transmission unit is realized by the control unit 46A of the smart device 14 and transmits the visualized idea to the appropriate department. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, scrutiny unit, visualization unit, and transmission unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the computer 36 of the smart glasses 214 and inputs an idea that a user has come up with. The scrutiny unit is realized by the specific processing unit 290 of the data processing device 12 and scrutinizes the idea using a generation AI. The visualization unit is realized by the specific processing unit 290 of the data processing device 12 and visualizes the scrutinized idea. The transmission unit is realized by the control unit 46A of the smart glasses 214 and transmits the visualized idea to the appropriate department. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, review unit, visualization unit, and transmission unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the computer 36 of the headset type terminal 314, and inputs ideas that the user has come up with. The review unit is realized by the specific processing unit 290 of the data processing device 12, and reviews the ideas using a generation AI. The visualization unit is realized by the specific processing unit 290 of the data processing device 12, and visualizes the reviewed ideas. The transmission unit is realized by the control unit 46A of the headset type terminal 314, and transmits the visualized ideas to the appropriate department. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, scrutiny unit, visualization unit, and transmission unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the computer 36 of the robot 414, and inputs ideas that the user has come up with. The scrutiny unit is realized by the specific processing unit 290 of the data processing device 12, and scrutinizes the idea using a generation AI. The visualization unit is realized by the specific processing unit 290 of the data processing device 12, and visualizes the scrutinized idea. The transmission unit is realized by the control unit 46A of the robot 414, and transmits the visualized idea to the appropriate department.

[0107] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0108] When a user inputs an idea, the reception unit can analyze the user's past idea submission history and suggest the optimal input method. For example, it can prioritize and suggest input methods (voice, text, etc.) that the user has frequently used in the past. It can also automatically display related keywords and templates based on the content and evaluation results of ideas that the user has submitted in the past. Furthermore, if a user tends to submit ideas during a specific time period, it can also suggest the optimal input method for that time period. This makes it possible to suggest the optimal input method based on the user's past history.

[0109] When proceeding with the specific implementation method of an idea, the review unit can adjust the review criteria by reflecting the user's past feedback. For example, the review unit can analyze feedback on ideas previously submitted by the user and optimize evaluation criteria for feasibility and innovativeness. The review unit can also suggest specific implementation methods by referring to successful examples of ideas previously submitted by the user. Furthermore, the review unit can analyze failed examples of ideas previously submitted by the user and suggest measures to avoid similar failures. In this way, the review unit can adjust the review criteria based on the user's past feedback and proceed with the specific implementation method.

[0110] When visually expressing the scrutinized ideas, the visualization unit can adjust the use of technical terms in the visualization according to the user's level of expertise. For example, for a user with high expertise, a detailed visualization using a lot of technical terms can be generated. For a user with low expertise, a simple and easy-to-understand visualization can be generated. Furthermore, the visualization expression method can be customized according to the user's level of expertise. In this way, the use of technical terms in the visualization can be adjusted and visually expressed according to the user's level of expertise.

[0111] When communicating visualized ideas to the appropriate departments, the communication department can adjust the level of detail of the communication based on the importance of the idea. For example, detailed communication can be provided for ideas with high importance. Standard communication can be provided for ideas with medium importance. Furthermore, simple communication can be provided for ideas with low importance. In this way, the level of detail of the communication can be adjusted based on the importance of the idea, and it can be communicated to the appropriate departments.

[0112] The reception unit can estimate the user's emotions and customize the idea input interface based on the estimated user emotions. For example, if the user is feeling stressed, a simple interface can be provided to minimize the input steps. Alternatively, if the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to allow the user to input ideas quickly. In this way, an input interface can be provided that corresponds to the user's emotions.

[0113] The scrutiny unit can estimate the user's emotions and adjust the scrutiny criteria for ideas based on the estimated user's emotions. For example, if the user is excited, the scrutiny can be focused on the innovativeness of the ideas. If the user is relaxed, the scrutiny can be focused on the feasibility of the ideas. Furthermore, if the user is stressed, the scrutiny can be focused on the simplicity of the ideas. In this way, the scrutiny criteria for ideas can be adjusted based on the user's emotions, and appropriate evaluations can be made.

[0114] The visualization unit can estimate the user's emotions and adjust the visualization expression method based on the estimated user emotions. For example, if the user is excited, a visually stimulating visualization can be generated. If the user is relaxed, a calm visualization can be generated. Furthermore, if the user is stressed, a simple visualization with high visibility can be generated. In this way, the visualization expression method can be adjusted based on the user's emotions, and effective visual expression can be provided.

[0115] The communication unit can estimate the user's emotions and adjust the communication method based on the estimated user's emotions. For example, if the user is excited, a quick and direct communication method can be selected. If the user is relaxed, a communication method including detailed explanations can be selected. Furthermore, if the user is stressed, a simple and highly visible communication method can be selected. In this way, the communication method can be adjusted based on the user's emotions, allowing for effective information communication.

[0116] When an idea is input, the reception unit can automatically complete the input content based on the user's current project or area of ​​interest. For example, it can automatically complete keywords related to the project the user is currently working on. It can also suggest related idea candidates based on the user's area of ​​interest. It can also automatically complete appropriate input content by referring to the user's past project history. This makes it possible to automatically complete input content based on the user's project or area of ​​interest, supporting efficient idea input.

[0117] During the review, the review department can apply different review algorithms depending on the category of the idea. For example, a marketing-specific review algorithm can be applied to marketing-related ideas. A technology-specific review algorithm can also be applied to technology-related ideas. Furthermore, a business efficiency-specific review algorithm can also be applied to business efficiency-related ideas. This allows different review algorithms to be applied depending on the category of the idea, making it possible to make an appropriate evaluation.

[0118] The processing flow of the second embodiment will be briefly explained below.

[0119] Step 1: The reception unit inputs an idea that the user has come up with. Ideas that the user has come up with can be text input, voice input, image input, etc. For example, the user inputs an idea using a mobile app. The reception unit can also convert voice input into text data using voice recognition technology. Furthermore, the reception unit can also convert image input into text data using image analysis technology. Step 2: The review unit uses a generation AI to review the ideas received by the reception unit. The review is based on the accuracy, originality, feasibility, etc. of the content. For example, the generation AI analyzes and reviews the content of the idea using a text generation AI (e.g., LLM). The review unit can also use a multimodal generation AI to review the content of the idea. Step 3: The visualization unit uses a generation AI to visualize the ideas vetted by the vetter. Visualization can be done using methods such as graphs, charts, and infographics. For example, the generation AI can use a text generation AI (e.g., LLM) to visually represent the ideas. The visualization unit can also use a multimodal generation AI to visually represent the ideas. Step 4: The communication department communicates the visualized ideas to the appropriate departments. These departments may include marketing, development, and management. The communication department sends the visualized ideas by email. The communication department can also upload the visualized ideas to an internal sharing system. The communication department can also present the visualized ideas at a meeting.

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0123] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0124] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0125] 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.

[0126] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0127] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0128] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0130] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0131] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0132] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0133] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0134] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0135] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0136] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0137] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0138] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0139] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0140] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0141] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0142] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0143] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0144] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0146] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0147] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0148] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0149] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0151] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0152] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0153] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0154] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0157] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0158] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0159] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0160] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0161] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0162] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0163] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0164] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0165] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0166] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0167] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0168] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0169] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0170] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0171] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0172] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0173] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0174] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0175] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0176] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0177] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0178] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0179] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0180] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0181] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0182] 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.

[0183] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0184] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0185] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0186] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0187] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0188] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0189] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0190] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0191] [Explanation of symbols]

[0192] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives input of ideas; a review unit that reviews the ideas received by the reception unit; a visualization unit that visualizes the ideas reviewed by the review unit; a communication unit that communicates the ideas visualized by the visualization unit to the appropriate department. A system characterized by:

2. The reception unit Users can write ideas into the mobile app as soon as they come up with them.

2. The system of claim 1.

3. The inspection unit Generative AI analyzes input ideas and understands their content 2. The system of claim 1.

4. The visualization unit Generative AI creates visual representations of curated ideas 2. The system of claim 1.

5. The transmission unit is Communicate visualized ideas to the appropriate departments 2. The system of claim 1.

6. The inspection unit Proceed with concrete implementation of the idea 2. The system of claim 1.

7. The reception unit Estimating user emotions and customizing the idea input interface based on the estimated user emotions 2. The system of claim 1.

8. The reception unit Analyzes the user's past idea submission history and suggests the appropriate input method 2. The system of claim 1.

Images