system

Abstract

The system according to this embodiment aims to enable users to generate original ideas based on a theme and receive feedback in real time. [Solution] The system according to the embodiment comprises a reception unit, a collection unit, a generation unit, and a feedback unit. The reception unit receives theme input from the user. The collection unit collects relevant information based on the theme received by the reception unit. The generation unit analyzes the information collected by the collection unit and generates ideas. The feedback unit provides real-time feedback on the ideas generated by the generation unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult for a user to generate an original idea based on a theme and obtain real-time feedback.

[0005] The system according to the embodiment aims to enable a user to generate an original idea based on a theme and obtain real-time feedback.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a collection unit, a generation unit, and a feedback unit. The reception unit receives theme input from the user. The collection unit collects relevant information based on the theme received by the reception unit. The generation unit analyzes the information collected by the collection unit and generates ideas. The feedback unit provides real-time feedback on the ideas generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment allows users to generate original ideas based on a theme and receive feedback in real time. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An idea generation agent system according to an embodiment of the present invention is a system that automatically generates and improves ideas using generation AI. When a user inputs a theme, the generation AI searches the web for relevant information, analyzes past winning ideas and the latest trends, and makes original and socially significant proposals. The generation AI provides real-time feedback on the proposed ideas, allowing the user to refine them to suit their own needs. This entire process is conducted in a conversational format, enabling the user to operate intuitively and deepen their own ideas. For example, if a user inputs a theme such as "new business ideas related to environmental protection," the generation AI searches the web for relevant information, analyzing past winning ideas and the latest trends. The generation AI generates ideas such as "new business models utilizing renewable energy" or "new technologies to reduce plastic waste." The generation AI provides real-time feedback on the proposed ideas, allowing the user to receive feedback and refine their ideas to suit their own needs. For example, the generation AI might provide feedback such as, "This idea meets market needs, but there are cost challenges." Based on this feedback, the user can improve their ideas. Through dialogue with the generation AI, the user can deepen their own ideas. For example, a user can ask the generative AI, "How can I further concretize this idea?" and receive advice from the AI. The idea generation agent system solves the idea generation challenges faced by companies. By generating new ideas quickly and efficiently using the generative AI, and then evaluating and improving them, high-quality proposals can be made in a short period of time. This system utilizes a large-scale language model and is also tailored to the specific needs of the Japanese market. A unique value proposition is that users can receive customized proposals based on their past success stories and industry trends. This system utilizes a large-scale language model. Specifically, it collects and analyzes relevant information based on themes and conditions received from the user and automatically generates ideas.Furthermore, the system continuously improves its suggestions by receiving feedback through interactive dialogue with users. This leverages advanced natural language processing capabilities and the unique expressive power of the Japanese language. As a result, the idea generation agent system can collect relevant information based on the user's theme, generate ideas, and provide feedback in real time.

[0029] The idea generation agent system according to this embodiment comprises a reception unit, a collection unit, a generation unit, and a feedback unit. The reception unit receives theme input from the user. For example, the reception unit can receive the theme entered by the user in text format. The reception unit can also accept themes using voice input. For example, the user enters a theme by voice, and the reception unit converts the voice into text and accepts it. Furthermore, the reception unit can estimate the user's emotions and adjust the timing of theme input based on the estimated user emotions. For example, if the user is feeling stressed, it can prompt them to enter a theme at a time when they can relax. The collection unit collects relevant information based on the themes received by the reception unit. For example, the collection unit can perform a web search using generative AI to collect relevant information. The collection unit can also analyze past winning ideas and the latest trends. For example, the collection unit can analyze past contest winning ideas using generative AI to grasp the latest trends. The generation unit analyzes the information collected by the collection unit and generates ideas. For example, the generation unit can make original and socially significant proposals using generative AI. The generation unit can also generate ideas that meet market needs using generative AI. For example, the generation unit analyzes market research data using generation AI and generates ideas that meet the needs. The feedback unit provides real-time feedback on the ideas generated by the generation unit. For example, the feedback unit can improve the ideas based on user needs using generation AI. The feedback unit can also use generation AI to point out cost issues with the ideas. For example, the feedback unit uses generation AI to perform a cost analysis of the ideas and point out cost issues. As a result, the idea generation agent system according to this embodiment can collect relevant information based on the user's theme, generate ideas, and provide real-time feedback.

[0030] The reception desk accepts theme input from users. For example, the reception desk can receive themes entered by users in text format. Specifically, users enter themes using a keyboard and send the text data to the system. The reception desk can also accept themes using voice input. For example, users enter themes by voice, and the reception desk converts the voice into text and accepts it. Using speech recognition technology, the system can accurately transcribe the user's speech into text and recognize it as a theme. Furthermore, the reception desk can estimate the user's emotions and adjust the timing of theme input based on the estimated emotions. For example, if the user is feeling stressed, it will prompt them to enter a theme at a time when they can relax. Emotion estimation uses technology that analyzes the user's voice tone, facial expressions, and input speed. This allows the system to support users in entering themes in the optimal state. The reception desk provides an intuitive and easy-to-use operation through its user interface, enabling users to enter themes smoothly. For example, it provides an interface that allows easy switching between voice input and text input, improving user convenience. The reception desk also has a function to automatically analyze the content of the entered themes and extract relevant keywords and categories. This allows the collection and generation units to process information efficiently.

[0031] The data collection unit gathers relevant information based on themes received by the reception unit. For example, the data collection unit can use generative AI to perform web searches and gather relevant information. Specifically, the generative AI extracts keywords related to the input theme and uses them to collect data from internet sources. For example, it collects relevant information from a variety of sources, such as academic papers, news articles, blog posts, and social media posts. The data collection unit can also analyze past winning ideas and the latest trends. For example, the data collection unit uses generative AI to analyze winning ideas from past contests and grasp the latest trends. The generative AI uses natural language processing technology to analyze the content and evaluation points of past ideas and compare them with current trends to extract useful information related to the theme. Furthermore, the data collection unit can access databases on specific industries and markets to understand market trends and consumer needs related to the theme. For example, it can refer to market research databases and industry reports to collect the latest market information related to the theme. This allows the data collection unit to gather multifaceted information related to the theme and provide the generative unit with rich data for generating ideas. The data collection unit also has a filtering function to organize the collected information and prioritize providing highly relevant information. This allows the generation unit to utilize the information efficiently.

[0032] The generation unit analyzes information collected by the collection unit and generates ideas. For example, the generation unit can use generative AI to make original and socially significant proposals. Specifically, the generative AI executes an algorithm to generate new ideas related to a theme based on the collected information. The generative AI utilizes natural language processing and machine learning technologies to analyze the collected information and generate original ideas related to the theme. The generation unit can also use generative AI to generate ideas that meet market needs. For example, the generation unit can use generative AI to analyze market research data and generate ideas that meet those needs. By analyzing collected market research data and understanding consumer preferences and trends, the generative AI generates ideas that match market needs related to the theme. Furthermore, the generation unit also has a function to evaluate the generated ideas and select the most promising ones. The generative AI sets criteria for evaluating the generated ideas and assesses the originality, feasibility, and social significance of each idea. This allows the generation unit to provide users with the most promising ideas. When presenting the generated ideas to users, the generation unit can also provide them in a visually easy-to-understand format. For example, an idea's outline can be visualized using diagrams and graphs to allow users to understand it intuitively. This enables the generation unit to effectively communicate the idea to the user.

[0033] The feedback unit provides real-time feedback on ideas generated by the generation unit. For example, the feedback unit can use generation AI to improve ideas based on user needs. Specifically, the generation AI analyzes user feedback and identifies which parts of the generated ideas need improvement. For example, if a user points out specific areas for improvement in a part of an idea, the generation AI revises the idea based on that feedback. The feedback unit can also use generation AI to identify cost issues with ideas. For example, the feedback unit uses generation AI to perform a cost analysis of an idea and identify cost issues. Based on the collected cost data, the generation AI can calculate the cost of realizing the idea and make suggestions for cost reduction. Furthermore, the feedback unit also has the function of collecting user feedback and evaluating generated ideas. For example, a user evaluates an idea, and the generation AI identifies areas for improvement based on the evaluation results. This allows the feedback unit to quickly improve ideas in accordance with user needs. The feedback unit makes it easy for users to provide feedback through a user interface. For example, it provides evaluation forms and comment sections so that users can intuitively input feedback. This allows the feedback unit to efficiently collect user feedback and improve the quality of the generated ideas.

[0034] The data collection unit can analyze past winning ideas and the latest trends using generative AI. For example, the data collection unit can use generative AI to analyze winning ideas from past contests and grasp the latest trends. For example, the data collection unit can use generative AI to extract commonalities and success factors of past winning ideas and use that as a basis to analyze the latest trends. The data collection unit can also use generative AI to collect the latest news articles and social media posts from the internet and grasp trends. For example, the data collection unit can use generative AI to extract relevant keywords and use that as a basis to analyze the latest trends. In this way, more useful information can be collected by analyzing past winning ideas and the latest trends. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on past winning ideas into generative AI, and the generative AI can analyze that data and extract trends.

[0035] The generation unit can make original and socially significant proposals using generation AI. For example, the generation unit can make original and socially significant proposals using generation AI. For example, the generation unit can propose new business models related to environmental protection using generation AI. The generation unit can also propose new technologies to reduce plastic waste using generation AI. For example, the generation unit can propose new business models utilizing renewable energy using generation AI. In this way, the generation unit can make original and socially significant proposals using generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data related to environmental protection into the generation AI, and the generation AI can generate original proposals based on that data.

[0036] The feedback unit can improve ideas based on user needs using generative AI. For example, the feedback unit can improve ideas based on user needs using generative AI. For example, the feedback unit can analyze user feedback using generative AI and improve ideas. The feedback unit can also use generative AI to help concretize ideas based on user needs. For example, the feedback unit can concretize ideas in a way that suits user needs using generative AI. This allows the generative AI to improve ideas based on user needs. Some or all of the above processes in the feedback unit may be performed using AI, or not using AI. For example, the feedback unit can input user feedback data into the generative AI, and the generative AI can improve ideas based on that data.

[0037] The generation unit can generate ideas that meet market needs using generation AI. For example, the generation unit can use generation AI to generate ideas that meet market needs. For example, the generation unit can use generation AI to analyze market research data and generate ideas that meet needs. The generation unit can also use generation AI to analyze consumer behavior data and generate ideas that meet market needs. For example, the generation unit can use generation AI to analyze consumer purchase history and generate ideas that meet needs. In this way, the generation AI can generate ideas that meet market needs. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input market research data into the generation AI, and the generation AI can generate ideas that meet market needs based on that data.

[0038] The feedback unit can use generative AI to identify cost-related issues with an idea. For example, the feedback unit uses generative AI to identify cost-related issues with an idea. For example, the feedback unit uses generative AI to perform a cost analysis of an idea and identify cost-related issues. The feedback unit can also use generative AI to evaluate the potential for cost reduction of an idea. For example, the feedback unit uses generative AI to propose methods for reducing the cost of an idea. This allows the generative AI to identify cost-related issues with an idea. Some or all of the above-described processes in the feedback unit may be performed using AI, or not using AI. For example, the feedback unit can input cost data of an idea into the generative AI, and the generative AI can identify cost-related issues based on that data.

[0039] The reception desk can analyze the user's past theme input history and select the optimal reception method. For example, the reception desk can analyze the trends of themes the user has entered in the past and suggest relevant themes. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can also predict and suggest themes to be used during specific time periods based on the user's past input history. For example, the reception desk can analyze the user's past input history and suggest themes relevant to specific time periods. In this way, the optimal reception method can be selected by analyzing the user's past theme input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past theme input history data into a generating AI, and the generating AI can select the optimal reception method based on that data.

[0040] The reception desk can filter themes based on the user's current projects and areas of interest when a theme is entered. For example, the reception desk can prioritize suggesting themes related to the projects the user is currently working on. For example, the reception desk can filter and display relevant themes based on the user's areas of interest. The reception desk can also suggest highly relevant themes by referring to the user's past project history. For example, the reception desk can analyze the user's past project history and suggest relevant themes. This allows the reception desk to suggest highly relevant themes by filtering based on the user's current projects and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input data on the user's current projects and areas of interest into a generating AI, which can then filter based on that data.

[0041] The reception desk can prioritize themes that are highly relevant to the user when they input a theme, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk will prioritize suggesting themes related to that region. For example, if the user is traveling, the reception desk will prioritize suggesting themes related to their travel destination. Also, if the user is at home, the reception desk can prioritize suggesting themes that can be done at home. For example, the reception desk will suggest highly relevant themes based on the user's geographical location. In this way, by considering the user's geographical location, it is possible to prioritize suggesting highly relevant themes. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location data into a generating AI, and the generating AI can suggest highly relevant themes based on that data.

[0042] The reception unit can analyze the user's social media activity when a theme is entered and accept relevant themes. For example, the reception unit can suggest themes related to topics the user has shown interest in on social media. For example, the reception unit can analyze the content of posts from accounts the user follows and suggest relevant themes. The reception unit can also suggest relevant themes by referring to the activities of online communities the user participates in. For example, the reception unit can suggest relevant themes based on the user's social media activity. In this way, relevant themes can be suggested by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's social media activity data into a generating AI, and the generating AI can suggest relevant themes based on that data.

[0043] The data collection unit can optimize its data collection algorithm by referring to past collected data during data collection. For example, the data collection unit can improve its data collection algorithm and increase accuracy based on previously collected data. For example, the data collection unit can extract specific patterns from past collected data and reflect them in the data collection algorithm. The data collection unit can also analyze past collected data and correct biases in the data collection algorithm. For example, the data collection unit can detect and correct biases in the data collection algorithm based on past collected data. This allows the data collection algorithm to be optimized and accuracy improved by referring to past collected data. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past collected data into a generating AI, and the generating AI can optimize the data collection algorithm based on that data.

[0044] The data collection unit can apply different collection methods depending on the theme category when gathering information. For example, if the theme is environmental protection, the data collection unit can collect information from specialized environmental websites. For example, if the theme is business ideas, the data collection unit can collect information from business news websites. Furthermore, if the theme is technological innovation, the data collection unit can also collect information from technology-related papers and patent databases. For example, the data collection unit can search technology-related databases and collect relevant information. By applying different collection methods depending on the theme category, more appropriate information can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data corresponding to the theme category into a generating AI, and the generating AI can apply a collection method based on that data.

[0045] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of information related to that region. For example, if the user is traveling, the data collection unit will prioritize the collection of information related to the travel destination. Furthermore, if the user is at home, the data collection unit can prioritize the collection of information that can be done at home. For example, the data collection unit collects highly relevant information based on the user's geographical location. This allows for the priority collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location data into a generating AI, and the generating AI can collect highly relevant information based on that data.

[0046] The data collection unit can analyze the user's social media activity and collect relevant information during data collection. For example, the data collection unit can collect information related to topics the user is interested in on social media. For example, the data collection unit can analyze the content of posts from accounts the user follows and collect relevant information. The data collection unit can also collect relevant information by referring to the activities of online communities the user participates in. For example, the data collection unit can collect relevant information based on the user's social media activity. In this way, relevant information can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI, and the generating AI can collect relevant information based on that data.

[0047] The generation unit can adjust the level of detail generated based on the importance of the theme during idea generation. For example, for important themes, the generation unit generates ideas with detailed explanations. For general themes, the generation unit generates ideas with concise explanations. The generation unit can also provide ideas that can be generated quickly for urgent themes. For example, the generation unit adjusts the level of detail based on the importance of the theme. By adjusting the level of detail based on the importance of the theme, more appropriate ideas can be generated. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input theme importance data into a generation AI, and the generation AI can adjust the level of detail based on that data.

[0048] The generation unit can apply different generation algorithms depending on the theme category when generating ideas. For example, in the case of a theme related to environmental protection, the generation unit generates ideas based on environmental data. For example, in the case of a theme related to business ideas, the generation unit generates ideas based on business data. Furthermore, in the case of a theme related to technological innovation, the generation unit can also generate ideas based on technology-related data. For example, the generation unit applies different generation algorithms depending on the theme category. By applying different generation algorithms depending on the theme category, more appropriate ideas can be generated. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input theme category data into a generation AI, and the generation AI can apply a generation algorithm based on that data.

[0049] The generation unit can determine the generation priority based on the theme submission deadline when generating ideas. For example, the generation unit will prioritize generating ideas for themes with approaching deadlines. For example, the generation unit will postpone generating ideas for themes with distant submission deadlines. The generation unit can also generate ideas for themes with unknown submission deadlines, taking into account the balance with other themes. For example, the generation unit will determine the generation priority based on the theme submission deadline. This allows for the generation of ideas at a more appropriate time by determining the generation priority based on the theme submission deadline. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input theme submission deadline data into a generation AI, which can then determine the generation priority based on that data.

[0050] The generation unit can adjust the generation order based on the relevance of themes when generating ideas. For example, the generation unit will prioritize generating ideas for highly relevant themes. For example, it will postpone generating ideas for less relevant themes. The generation unit can also generate ideas for themes whose relevance is unclear, taking into account the balance with other themes. For example, the generation unit will adjust the generation order based on the relevance of themes. By adjusting the generation order based on the relevance of themes, ideas can be generated in a more appropriate order. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input theme relevance data into a generation AI, and the generation AI can adjust the generation order based on that data.

[0051] The feedback unit can adjust the level of detail in the feedback based on the importance of the idea. For example, the feedback unit provides detailed feedback for important ideas. For example, the feedback unit provides concise feedback for general ideas. The feedback unit can also provide rapid feedback for urgent ideas. For example, the feedback unit adjusts the level of detail in the feedback based on the importance of the idea. By adjusting the level of detail in the feedback based on the importance of the idea, it is possible to provide more appropriate feedback. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input idea importance data into a generating AI, and the generating AI can adjust the level of detail in the feedback based on that data.

[0052] The feedback unit can apply different feedback methods depending on the category of the idea during the feedback process. For example, in the case of an idea related to environmental protection, the feedback unit provides feedback based on environmental expertise. For example, in the case of a business idea, the feedback unit provides feedback based on business knowledge. Furthermore, in the case of an idea related to technological innovation, the feedback unit can also provide feedback based on technology knowledge. For example, the feedback unit applies different feedback methods depending on the category of the idea. This allows for the provision of more appropriate feedback by applying different feedback methods depending on the category of the idea. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input idea category data into a generating AI, and the generating AI can apply a feedback method based on that data.

[0053] The feedback unit can prioritize feedback based on the submission date of the idea. For example, it will prioritize feedback for ideas with approaching deadlines. For example, it will postpone feedback for ideas with distant submission dates. It can also provide feedback for ideas with unknown submission dates, taking into account the balance with other ideas. For example, it will prioritize feedback based on the submission date of the idea. This allows for feedback to be provided at a more appropriate time by prioritizing feedback based on the submission date. Some or all of the above processes in the feedback unit may be performed using AI, or not. For example, the feedback unit can input idea submission date data into a generating AI, which can then determine the priority of feedback based on that data.

[0054] The feedback unit can adjust the order of feedback based on the relevance of ideas during the feedback process. For example, the feedback unit will prioritize providing feedback on highly relevant ideas. For example, it will postpone providing feedback on less relevant ideas. The feedback unit can also provide feedback on ideas whose relevance is unclear, taking into account the balance with other ideas. For example, the feedback unit will adjust the order of feedback based on the relevance of ideas. This allows for the provision of feedback in a more appropriate order by adjusting the order of feedback based on the relevance of ideas. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input idea relevance data into a generating AI, and the generating AI can adjust the order of feedback based on that data.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The reception desk can analyze a user's past theme input history and select the most suitable reception method. For example, it can analyze trends in themes the user has previously entered and suggest related themes. Furthermore, the reception desk can predict and suggest themes that a user might use during specific time periods based on their past input history. This allows for the selection of the most optimal reception method by analyzing the user's past theme input history.

[0057] The generation unit can adjust the level of detail generated based on the importance of the theme during idea generation. For example, for important themes, it can generate ideas with detailed explanations. For general themes, it can generate ideas with concise explanations. By adjusting the level of detail based on the importance of the theme, it is possible to generate more appropriate ideas.

[0058] The reception desk can prioritize themes that are highly relevant to the user, taking into account their geographical location. For example, if a user is in a specific region, the system will prioritize suggesting themes related to that region. Similarly, if a user is traveling, the system can prioritize suggesting themes related to their travel destination. This allows the system to prioritize suggesting themes that are highly relevant by considering the user's geographical location.

[0059] The data collection unit can analyze a user's social media activity and collect relevant information during data collection. For example, it can collect information related to topics the user shows interest in on social media. It can also analyze the content of posts from accounts the user follows and collect relevant information. In this way, relevant information can be collected by analyzing the user's social media activity.

[0060] The generation unit can prioritize idea generation based on the theme submission deadline. For example, it can prioritize generating ideas for themes with approaching deadlines, and postpone idea generation for themes with later submission deadlines. This allows for idea generation at a more appropriate time by prioritizing generation based on the theme submission deadline.

[0061] The feedback system can adjust the order of feedback based on the relevance of the ideas. For example, highly relevant ideas will receive priority feedback, while less relevant ideas will receive feedback later. This allows for more appropriate feedback delivery by adjusting the order based on the relevance of the ideas.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The reception desk receives theme input from the user. For example, the reception desk can receive the theme entered by the user in text format. It can also accept themes using voice input, converting the voice to text for reception. Furthermore, the reception desk can estimate the user's emotions and adjust the timing of theme input based on the estimated emotions of the user. Step 2: The collection department gathers relevant information based on the themes received by the reception department. For example, the collection department can use generative AI to perform web searches and gather relevant information. It can also analyze past winning ideas and the latest trends. Step 3: The generation unit analyzes the information collected by the collection unit and generates ideas. For example, the generation unit can use generative AI to make original and socially meaningful proposals. It can also generate ideas that meet market needs. Step 4: The feedback unit provides real-time feedback on the ideas generated by the generation unit. For example, the feedback unit can use generation AI to improve the ideas based on user needs. It can also point out cost-related issues with the ideas.

[0064] (Example of form 2) An idea generation agent system according to an embodiment of the present invention is a system that automatically generates and improves ideas using generation AI. When a user inputs a theme, the generation AI searches the web for relevant information, analyzes past winning ideas and the latest trends, and makes original and socially significant proposals. The generation AI provides real-time feedback on the proposed ideas, allowing the user to refine them to suit their own needs. This entire process is conducted in a conversational format, enabling the user to operate intuitively and deepen their own ideas. For example, if a user inputs a theme such as "new business ideas related to environmental protection," the generation AI searches the web for relevant information, analyzing past winning ideas and the latest trends. The generation AI generates ideas such as "new business models utilizing renewable energy" or "new technologies to reduce plastic waste." The generation AI provides real-time feedback on the proposed ideas, allowing the user to receive feedback and refine their ideas to suit their own needs. For example, the generation AI might provide feedback such as, "This idea meets market needs, but there are cost challenges." Based on this feedback, the user can improve their ideas. Through dialogue with the generation AI, the user can deepen their own ideas. For example, a user can ask the generative AI, "How can I further concretize this idea?" and receive advice from the AI. The idea generation agent system solves the idea generation challenges faced by companies. By generating new ideas quickly and efficiently using the generative AI, and then evaluating and improving them, high-quality proposals can be made in a short period of time. This system utilizes a large-scale language model and is also tailored to the specific needs of the Japanese market. A unique value proposition is that users can receive customized proposals based on their past success stories and industry trends. This system utilizes a large-scale language model. Specifically, it collects and analyzes relevant information based on themes and conditions received from the user and automatically generates ideas.Furthermore, the system continuously improves its suggestions by receiving feedback through interactive dialogue with users. This leverages advanced natural language processing capabilities and the unique expressive power of the Japanese language. As a result, the idea generation agent system can collect relevant information based on the user's theme, generate ideas, and provide feedback in real time.

[0065] The idea generation agent system according to this embodiment comprises a reception unit, a collection unit, a generation unit, and a feedback unit. The reception unit receives theme input from the user. For example, the reception unit can receive the theme entered by the user in text format. The reception unit can also accept themes using voice input. For example, the user enters a theme by voice, and the reception unit converts the voice into text and accepts it. Furthermore, the reception unit can estimate the user's emotions and adjust the timing of theme input based on the estimated user emotions. For example, if the user is feeling stressed, it can prompt them to enter a theme at a time when they can relax. The collection unit collects relevant information based on the themes received by the reception unit. For example, the collection unit can perform a web search using generative AI to collect relevant information. The collection unit can also analyze past winning ideas and the latest trends. For example, the collection unit can analyze past contest winning ideas using generative AI to grasp the latest trends. The generation unit analyzes the information collected by the collection unit and generates ideas. For example, the generation unit can make original and socially significant proposals using generative AI. The generation unit can also generate ideas that meet market needs using generative AI. For example, the generation unit analyzes market research data using generation AI and generates ideas that meet the needs. The feedback unit provides real-time feedback on the ideas generated by the generation unit. For example, the feedback unit can improve the ideas based on user needs using generation AI. The feedback unit can also use generation AI to point out cost issues with the ideas. For example, the feedback unit uses generation AI to perform a cost analysis of the ideas and point out cost issues. As a result, the idea generation agent system according to this embodiment can collect relevant information based on the user's theme, generate ideas, and provide real-time feedback.

[0066] The reception desk accepts theme input from users. For example, the reception desk can receive themes entered by users in text format. Specifically, users enter themes using a keyboard and send the text data to the system. The reception desk can also accept themes using voice input. For example, users enter themes by voice, and the reception desk converts the voice into text and accepts it. Using speech recognition technology, the system can accurately transcribe the user's speech into text and recognize it as a theme. Furthermore, the reception desk can estimate the user's emotions and adjust the timing of theme input based on the estimated emotions. For example, if the user is feeling stressed, it will prompt them to enter a theme at a time when they can relax. Emotion estimation uses technology that analyzes the user's voice tone, facial expressions, and input speed. This allows the system to support users in entering themes in the optimal state. The reception desk provides an intuitive and easy-to-use operation through its user interface, enabling users to enter themes smoothly. For example, it provides an interface that allows easy switching between voice input and text input, improving user convenience. The reception desk also has a function to automatically analyze the content of the entered themes and extract relevant keywords and categories. This allows the collection and generation units to process information efficiently.

[0067] The data collection unit gathers relevant information based on themes received by the reception unit. For example, the data collection unit can use generative AI to perform web searches and gather relevant information. Specifically, the generative AI extracts keywords related to the input theme and uses them to collect data from internet sources. For example, it collects relevant information from a variety of sources, such as academic papers, news articles, blog posts, and social media posts. The data collection unit can also analyze past winning ideas and the latest trends. For example, the data collection unit uses generative AI to analyze winning ideas from past contests and grasp the latest trends. The generative AI uses natural language processing technology to analyze the content and evaluation points of past ideas and compare them with current trends to extract useful information related to the theme. Furthermore, the data collection unit can access databases on specific industries and markets to understand market trends and consumer needs related to the theme. For example, it can refer to market research databases and industry reports to collect the latest market information related to the theme. This allows the data collection unit to gather multifaceted information related to the theme and provide the generative unit with rich data for generating ideas. The data collection unit also has a filtering function to organize the collected information and prioritize providing highly relevant information. This allows the generation unit to utilize the information efficiently.

[0068] The generation unit analyzes information collected by the collection unit and generates ideas. For example, the generation unit can use generative AI to make original and socially significant proposals. Specifically, the generative AI executes an algorithm to generate new ideas related to a theme based on the collected information. The generative AI utilizes natural language processing and machine learning technologies to analyze the collected information and generate original ideas related to the theme. The generation unit can also use generative AI to generate ideas that meet market needs. For example, the generation unit can use generative AI to analyze market research data and generate ideas that meet those needs. By analyzing collected market research data and understanding consumer preferences and trends, the generative AI generates ideas that match market needs related to the theme. Furthermore, the generation unit also has a function to evaluate the generated ideas and select the most promising ones. The generative AI sets criteria for evaluating the generated ideas and assesses the originality, feasibility, and social significance of each idea. This allows the generation unit to provide users with the most promising ideas. When presenting the generated ideas to users, the generation unit can also provide them in a visually easy-to-understand format. For example, an idea's outline can be visualized using diagrams and graphs to allow users to understand it intuitively. This enables the generation unit to effectively communicate the idea to the user.

[0069] The feedback unit provides real-time feedback on ideas generated by the generation unit. For example, the feedback unit can use generation AI to improve ideas based on user needs. Specifically, the generation AI analyzes user feedback and identifies which parts of the generated ideas need improvement. For example, if a user points out specific areas for improvement in a part of an idea, the generation AI revises the idea based on that feedback. The feedback unit can also use generation AI to identify cost issues with ideas. For example, the feedback unit uses generation AI to perform a cost analysis of an idea and identify cost issues. Based on the collected cost data, the generation AI can calculate the cost of realizing the idea and make suggestions for cost reduction. Furthermore, the feedback unit also has the function of collecting user feedback and evaluating generated ideas. For example, a user evaluates an idea, and the generation AI identifies areas for improvement based on the evaluation results. This allows the feedback unit to quickly improve ideas in accordance with user needs. The feedback unit makes it easy for users to provide feedback through a user interface. For example, it provides evaluation forms and comment sections so that users can intuitively input feedback. This allows the feedback unit to efficiently collect user feedback and improve the quality of the generated ideas.

[0070] The data collection unit can analyze past winning ideas and the latest trends using generative AI. For example, the data collection unit can use generative AI to analyze winning ideas from past contests and grasp the latest trends. For example, the data collection unit can use generative AI to extract commonalities and success factors of past winning ideas and use that as a basis to analyze the latest trends. The data collection unit can also use generative AI to collect the latest news articles and social media posts from the internet and grasp trends. For example, the data collection unit can use generative AI to extract relevant keywords and use that as a basis to analyze the latest trends. In this way, more useful information can be collected by analyzing past winning ideas and the latest trends. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data on past winning ideas into generative AI, and the generative AI can analyze that data and extract trends.

[0071] The generation unit can make original and socially significant proposals using generation AI. For example, the generation unit can make original and socially significant proposals using generation AI. For example, the generation unit can propose new business models related to environmental protection using generation AI. The generation unit can also propose new technologies to reduce plastic waste using generation AI. For example, the generation unit can propose new business models utilizing renewable energy using generation AI. In this way, the generation unit can make original and socially significant proposals using generation AI. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data related to environmental protection into the generation AI, and the generation AI can generate original proposals based on that data.

[0072] The feedback unit can improve ideas based on user needs using generative AI. For example, the feedback unit can improve ideas based on user needs using generative AI. For example, the feedback unit can analyze user feedback using generative AI and improve ideas. The feedback unit can also use generative AI to help concretize ideas based on user needs. For example, the feedback unit can concretize ideas in a way that suits user needs using generative AI. This allows the generative AI to improve ideas based on user needs. Some or all of the above processes in the feedback unit may be performed using AI, or not using AI. For example, the feedback unit can input user feedback data into the generative AI, and the generative AI can improve ideas based on that data.

[0073] The generation unit can generate ideas that meet market needs using generation AI. For example, the generation unit can use generation AI to generate ideas that meet market needs. For example, the generation unit can use generation AI to analyze market research data and generate ideas that meet needs. The generation unit can also use generation AI to analyze consumer behavior data and generate ideas that meet market needs. For example, the generation unit can use generation AI to analyze consumer purchase history and generate ideas that meet needs. In this way, the generation AI can generate ideas that meet market needs. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input market research data into the generation AI, and the generation AI can generate ideas that meet market needs based on that data.

[0074] The feedback unit can use generative AI to identify cost-related issues with an idea. For example, the feedback unit uses generative AI to identify cost-related issues with an idea. For example, the feedback unit uses generative AI to perform a cost analysis of an idea and identify cost-related issues. The feedback unit can also use generative AI to evaluate the potential for cost reduction of an idea. For example, the feedback unit uses generative AI to propose methods for reducing the cost of an idea. This allows the generative AI to identify cost-related issues with an idea. Some or all of the above-described processes in the feedback unit may be performed using AI, or not using AI. For example, the feedback unit can input cost data of an idea into the generative AI, and the generative AI can identify cost-related issues based on that data.

[0075] The reception unit can estimate the user's emotions and adjust the timing of theme input based on the estimated emotions. For example, if the user is feeling stressed, the reception unit will prompt them to input a theme at a time when they can relax. For example, the reception unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. For example, the reception unit can calculate an emotion score based on changes in facial expressions and adjust the timing of theme input. The reception unit can also record the user's voice and estimate their emotions using voice analysis technology. For example, the reception unit can analyze the tone and speed of the voice, calculate an emotion score, and adjust the timing of theme input. The reception unit can also collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. For example, the reception unit can calculate an emotion score based on fluctuations in heart rate and adjust the timing of theme input. By adjusting the timing of theme input based on the user's emotions, it is possible to prompt them to input a theme at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI may be, but is not limited to, text-generating AI (e.g., LLM) or multimodal generative AI. Some or all of the processing described above in the reception area may be performed using AI, or not using AI. For example, the reception area may input image data of the user captured by a camera into the generative AI and have the generative AI perform the estimation of the user's emotions.

[0076] The reception desk can analyze the user's past theme input history and select the optimal reception method. For example, the reception desk can analyze the trends of themes the user has entered in the past and suggest relevant themes. For example, the reception desk can prioritize suggesting input methods (voice, text, etc.) that the user has used in the past. The reception desk can also predict and suggest themes to be used during specific time periods based on the user's past input history. For example, the reception desk can analyze the user's past input history and suggest themes relevant to specific time periods. In this way, the optimal reception method can be selected by analyzing the user's past theme input history. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the user's past theme input history data into a generating AI, and the generating AI can select the optimal reception method based on that data.

[0077] The reception desk can filter themes based on the user's current projects and areas of interest when a theme is entered. For example, the reception desk can prioritize suggesting themes related to the projects the user is currently working on. For example, the reception desk can filter and display relevant themes based on the user's areas of interest. The reception desk can also suggest highly relevant themes by referring to the user's past project history. For example, the reception desk can analyze the user's past project history and suggest relevant themes. This allows the reception desk to suggest highly relevant themes by filtering based on the user's current projects and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input data on the user's current projects and areas of interest into a generating AI, which can then filter based on that data.

[0078] The reception desk can estimate the user's emotions and determine the priority of topics to accept based on the estimated emotions. For example, if the user is excited, the reception desk will prioritize suggesting challenging topics. For example, if the user is relaxed, the reception desk will prioritize suggesting topics that can be approached in a relaxed state. Also, if the user is stressed, the reception desk can prioritize suggesting topics that can reduce stress. For example, the reception desk estimates the user's emotions and determines the priority of topics based on the estimated emotions. This allows for the prioritization of more appropriate topics based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input user emotion data into a generative AI, which can then determine the priority of topics based on that data.

[0079] The reception desk can prioritize themes that are highly relevant to the user when they input a theme, taking into account the user's geographical location. For example, if the user is in a specific region, the reception desk will prioritize suggesting themes related to that region. For example, if the user is traveling, the reception desk will prioritize suggesting themes related to their travel destination. Also, if the user is at home, the reception desk can prioritize suggesting themes that can be done at home. For example, the reception desk will suggest highly relevant themes based on the user's geographical location. In this way, by considering the user's geographical location, it is possible to prioritize suggesting highly relevant themes. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can input the user's geographical location data into a generating AI, and the generating AI can suggest highly relevant themes based on that data.

[0080] The reception unit can analyze the user's social media activity when a theme is entered and accept relevant themes. For example, the reception unit can suggest themes related to topics the user has shown interest in on social media. For example, the reception unit can analyze the content of posts from accounts the user follows and suggest relevant themes. The reception unit can also suggest relevant themes by referring to the activities of online communities the user participates in. For example, the reception unit can suggest relevant themes based on the user's social media activity. In this way, relevant themes can be suggested by analyzing the user's social media activity. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the user's social media activity data into a generating AI, and the generating AI can suggest relevant themes based on that data.

[0081] The data collection unit can estimate the user's emotions and adjust the scope of information collected based on the estimated emotions. For example, if the user is relaxed, the data collection unit will collect a wide range of information and provide diverse perspectives. For example, if the user is in a hurry, the data collection unit will focus on collecting only important information. Also, if the user is excited, the data collection unit can prioritize collecting stimulating information. For example, the data collection unit estimates the user's emotions and adjusts the scope of information based on the estimated emotions. This allows for the collection of more appropriate information by adjusting the scope of information collected based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into a generative AI, which can then adjust the scope of information based on that data.

[0082] The data collection unit can optimize its data collection algorithm by referring to past collected data during data collection. For example, the data collection unit can improve its data collection algorithm and increase accuracy based on previously collected data. For example, the data collection unit can extract specific patterns from past collected data and reflect them in the data collection algorithm. The data collection unit can also analyze past collected data and correct biases in the data collection algorithm. For example, the data collection unit can detect and correct biases in the data collection algorithm based on past collected data. This allows the data collection algorithm to be optimized and accuracy improved by referring to past collected data. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past collected data into a generating AI, and the generating AI can optimize the data collection algorithm based on that data.

[0083] The data collection unit can apply different collection methods depending on the theme category when gathering information. For example, if the theme is environmental protection, the data collection unit can collect information from specialized environmental websites. For example, if the theme is business ideas, the data collection unit can collect information from business news websites. Furthermore, if the theme is technological innovation, the data collection unit can also collect information from technology-related papers and patent databases. For example, the data collection unit can search technology-related databases and collect relevant information. By applying different collection methods depending on the theme category, more appropriate information can be collected. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input data corresponding to the theme category into a generating AI, and the generating AI can apply a collection method based on that data.

[0084] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is excited, the data collection unit will prioritize collecting the latest trending information. For example, if the user is relaxed, the data collection unit will collect a wide range of information in a balanced manner. Also, if the user is stressed, the data collection unit can prioritize collecting information that can alleviate stress. For example, the data collection unit estimates the user's emotions and determines the priority of information based on the estimated emotions. This allows for the collection of more appropriate information by prioritizing information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into a generative AI, and the generative AI can determine the priority of information based on that data.

[0085] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location during data collection. For example, if the user is in a specific region, the data collection unit will prioritize the collection of information related to that region. For example, if the user is traveling, the data collection unit will prioritize the collection of information related to the travel destination. Furthermore, if the user is at home, the data collection unit can prioritize the collection of information that can be done at home. For example, the data collection unit collects highly relevant information based on the user's geographical location. This allows for the priority collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location data into a generating AI, and the generating AI can collect highly relevant information based on that data.

[0086] The data collection unit can analyze the user's social media activity and collect relevant information during data collection. For example, the data collection unit can collect information related to topics the user is interested in on social media. For example, the data collection unit can analyze the content of posts from accounts the user follows and collect relevant information. The data collection unit can also collect relevant information by referring to the activities of online communities the user participates in. For example, the data collection unit can collect relevant information based on the user's social media activity. In this way, relevant information can be collected by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity data into a generating AI, and the generating AI can collect relevant information based on that data.

[0087] The generation unit can estimate the user's emotions and adjust the way the generated ideas are presented based on the estimated emotions. For example, if the user is relaxed, the generation unit will present ideas in a soft manner. For example, if the user is in a hurry, the generation unit will present ideas in a concise and to-the-point manner. The generation unit can also present ideas in a visually stimulating manner if the user is excited. For example, the generation unit estimates the user's emotions and adjusts the way the ideas are presented based on the estimated emotions. By adjusting the way the generated ideas are presented based on the user's emotions, ideas can be presented in a more appropriate manner. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input user emotion data into the generation AI, and the generation AI can adjust the way the ideas are presented based on that data.

[0088] The generation unit can adjust the level of detail generated based on the importance of the theme during idea generation. For example, for important themes, the generation unit generates ideas with detailed explanations. For general themes, the generation unit generates ideas with concise explanations. The generation unit can also provide ideas that can be generated quickly for urgent themes. For example, the generation unit adjusts the level of detail based on the importance of the theme. By adjusting the level of detail based on the importance of the theme, more appropriate ideas can be generated. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input theme importance data into a generation AI, and the generation AI can adjust the level of detail based on that data.

[0089] The generation unit can apply different generation algorithms depending on the theme category when generating ideas. For example, in the case of a theme related to environmental protection, the generation unit generates ideas based on environmental data. For example, in the case of a theme related to business ideas, the generation unit generates ideas based on business data. Furthermore, in the case of a theme related to technological innovation, the generation unit can also generate ideas based on technology-related data. For example, the generation unit applies different generation algorithms depending on the theme category. By applying different generation algorithms depending on the theme category, more appropriate ideas can be generated. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input theme category data into a generation AI, and the generation AI can apply a generation algorithm based on that data.

[0090] The generation unit can estimate the user's emotions and adjust the length of the ideas it generates based on the estimated emotions. For example, if the user is in a hurry, the generation unit will generate short, concise ideas. For example, if the user is relaxed, the generation unit will generate longer ideas that include detailed explanations. The generation unit can also generate ideas with visually stimulating effects if the user is excited. For example, the generation unit estimates the user's emotions and adjusts the length of the ideas based on the estimated emotions. This allows for the generation of ideas of a more appropriate length by adjusting the length of the ideas generated based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using AI or not. For example, the generation unit can input user emotion data into the generation AI, which can then adjust the length of the ideas based on that data.

[0091] The generation unit can determine the generation priority based on the theme submission deadline when generating ideas. For example, the generation unit will prioritize generating ideas for themes with approaching deadlines. For example, the generation unit will postpone generating ideas for themes with distant submission deadlines. The generation unit can also generate ideas for themes with unknown submission deadlines, taking into account the balance with other themes. For example, the generation unit will determine the generation priority based on the theme submission deadline. This allows for the generation of ideas at a more appropriate time by determining the generation priority based on the theme submission deadline. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input theme submission deadline data into a generation AI, which can then determine the generation priority based on that data.

[0092] The generation unit can adjust the generation order based on the relevance of themes when generating ideas. For example, the generation unit will prioritize generating ideas for highly relevant themes. For example, it will postpone generating ideas for less relevant themes. The generation unit can also generate ideas for themes whose relevance is unclear, taking into account the balance with other themes. For example, the generation unit will adjust the generation order based on the relevance of themes. By adjusting the generation order based on the relevance of themes, ideas can be generated in a more appropriate order. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input theme relevance data into a generation AI, and the generation AI can adjust the generation order based on that data.

[0093] The feedback unit can estimate the user's emotions and adjust the way it expresses the feedback based on those emotions. For example, if the user is nervous, the feedback unit can provide feedback in gentle language. For example, if the user is relaxed, the feedback unit can provide detailed feedback. Also, if the user is in a hurry, the feedback unit can provide concise and to-the-point feedback. For example, the feedback unit estimates the user's emotions and adjusts the way it expresses the feedback based on those emotions. This allows for the provision of more appropriate feedback by adjusting the expression of the feedback based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input user emotion data into the generative AI, and the generative AI can adjust the way it expresses the feedback based on that data.

[0094] The feedback unit can adjust the level of detail in the feedback based on the importance of the idea. For example, the feedback unit provides detailed feedback for important ideas. For example, the feedback unit provides concise feedback for general ideas. The feedback unit can also provide rapid feedback for urgent ideas. For example, the feedback unit adjusts the level of detail in the feedback based on the importance of the idea. By adjusting the level of detail in the feedback based on the importance of the idea, it is possible to provide more appropriate feedback. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input idea importance data into a generating AI, and the generating AI can adjust the level of detail in the feedback based on that data.

[0095] The feedback unit can apply different feedback methods depending on the category of the idea during the feedback process. For example, in the case of an idea related to environmental protection, the feedback unit provides feedback based on environmental expertise. For example, in the case of a business idea, the feedback unit provides feedback based on business knowledge. Furthermore, in the case of an idea related to technological innovation, the feedback unit can also provide feedback based on technology knowledge. For example, the feedback unit applies different feedback methods depending on the category of the idea. This allows for the provision of more appropriate feedback by applying different feedback methods depending on the category of the idea. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input idea category data into a generating AI, and the generating AI can apply a feedback method based on that data.

[0096] The feedback unit can estimate the user's emotions and adjust the length of the feedback based on the estimated emotions. For example, if the user is in a hurry, the feedback unit will provide short, concise feedback. For example, if the user is relaxed, the feedback unit will provide detailed feedback. The feedback unit can also provide visually stimulating feedback if the user is excited. For example, the feedback unit estimates the user's emotions and adjusts the length of the feedback based on the estimated emotions. This allows for the provision of feedback of a more appropriate length by adjusting the length of the feedback based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input user emotion data into the generative AI, and the generative AI can adjust the length of the feedback based on that data.

[0097] The feedback unit can prioritize feedback based on the submission date of the idea. For example, it will prioritize feedback for ideas with approaching deadlines. For example, it will postpone feedback for ideas with distant submission dates. It can also provide feedback for ideas with unknown submission dates, taking into account the balance with other ideas. For example, it will prioritize feedback based on the submission date of the idea. This allows for feedback to be provided at a more appropriate time by prioritizing feedback based on the submission date. Some or all of the above processes in the feedback unit may be performed using AI, or not. For example, the feedback unit can input idea submission date data into a generating AI, which can then determine the priority of feedback based on that data.

[0098] The feedback unit can adjust the order of feedback based on the relevance of ideas during the feedback process. For example, the feedback unit will prioritize providing feedback on highly relevant ideas. For example, it will postpone providing feedback on less relevant ideas. The feedback unit can also provide feedback on ideas whose relevance is unclear, taking into account the balance with other ideas. For example, the feedback unit will adjust the order of feedback based on the relevance of ideas. This allows for the provision of feedback in a more appropriate order by adjusting the order of feedback based on the relevance of ideas. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input idea relevance data into a generating AI, and the generating AI can adjust the order of feedback based on that data.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] The reception desk can analyze a user's past theme input history and select the most suitable reception method. For example, it can analyze trends in themes the user has previously entered and suggest related themes. Furthermore, the reception desk can predict and suggest themes that a user might use during specific time periods based on their past input history. This allows for the selection of the most optimal reception method by analyzing the user's past theme input history.

[0101] The data collection unit can estimate the user's emotions and adjust the scope of information collected based on those emotions. For example, if the user is relaxed, it can collect a wide range of information to provide diverse perspectives. Conversely, if the user is in a hurry, it can focus on collecting only essential information. By adjusting the scope of information collected based on the user's emotions, more relevant information can be gathered.

[0102] The generation unit can adjust the level of detail generated based on the importance of the theme during idea generation. For example, for important themes, it can generate ideas with detailed explanations. For general themes, it can generate ideas with concise explanations. By adjusting the level of detail based on the importance of the theme, it is possible to generate more appropriate ideas.

[0103] The feedback unit can estimate the user's emotions and adjust the way feedback is expressed based on those emotions. For example, if the user is tense, it can provide feedback in gentle language. Conversely, if the user is relaxed, it can provide more detailed feedback. By adjusting the way feedback is expressed based on the user's emotions, it is possible to provide feedback in a more appropriate manner.

[0104] The reception desk can prioritize themes that are highly relevant to the user, taking into account their geographical location. For example, if a user is in a specific region, the system will prioritize suggesting themes related to that region. Similarly, if a user is traveling, the system can prioritize suggesting themes related to their travel destination. This allows the system to prioritize suggesting themes that are highly relevant by considering the user's geographical location.

[0105] The data collection unit can analyze a user's social media activity and collect relevant information during data collection. For example, it can collect information related to topics the user shows interest in on social media. It can also analyze the content of posts from accounts the user follows and collect relevant information. In this way, relevant information can be collected by analyzing the user's social media activity.

[0106] The generation unit can estimate the user's emotions and adjust the way the generated ideas are presented based on those emotions. For example, if the user is relaxed, the ideas will be presented in a softer style. Conversely, if the user is in a hurry, the ideas can be presented in a concise and to-the-point style. By adjusting the way the generated ideas are presented based on the user's emotions, the system can present ideas in a more appropriate manner.

[0107] The generation unit can prioritize idea generation based on the theme submission deadline. For example, it can prioritize generating ideas for themes with approaching deadlines, and postpone idea generation for themes with later submission deadlines. This allows for idea generation at a more appropriate time by prioritizing generation based on the theme submission deadline.

[0108] The feedback system can adjust the order of feedback based on the relevance of the ideas. For example, highly relevant ideas will receive priority feedback, while less relevant ideas will receive feedback later. This allows for more appropriate feedback delivery by adjusting the order based on the relevance of the ideas.

[0109] The feedback unit can estimate the user's emotions and adjust the length of the feedback based on those emotions. For example, if the user is in a hurry, it can provide short, concise feedback. Conversely, if the user is relaxed, it can provide more detailed feedback. By adjusting the length of feedback based on the user's emotions, it is possible to provide feedback of a more appropriate length.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The reception desk receives theme input from the user. For example, the reception desk can receive the theme entered by the user in text format. It can also accept themes using voice input, converting the voice to text for reception. Furthermore, the reception desk can estimate the user's emotions and adjust the timing of theme input based on the estimated emotions of the user. Step 2: The collection department gathers relevant information based on the themes received by the reception department. For example, the collection department can use generative AI to perform web searches and gather relevant information. It can also analyze past winning ideas and the latest trends. Step 3: The generation unit analyzes the information collected by the collection unit and generates ideas. For example, the generation unit can use generative AI to make original and socially meaningful proposals. It can also generate ideas that meet market needs. Step 4: The feedback unit provides real-time feedback on the ideas generated by the generation unit. For example, the feedback unit can use generation AI to improve the ideas based on user needs. It can also point out cost-related issues with the ideas.

[0112] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0113] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0114] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0115] Each of the multiple elements described above, including the reception unit, collection unit, generation unit, and feedback unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives theme input from the user. The collection unit is implemented by the specific processing unit 290 of the data processing device 12 and collects relevant information. The generation unit is implemented by the specific processing unit 290 of the data processing device 12 and generates ideas by analyzing the collected information. The feedback unit is implemented by the control unit 46A of the smart device 14 and provides real-time feedback on the generated ideas. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0118] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0119] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0120] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0122] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0123] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0124] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0125] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0126] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0127] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0128] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

[0130] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0131] Each of the multiple elements described above, including the reception unit, collection unit, generation unit, and feedback unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives theme input from the user. The collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects relevant information. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates ideas by analyzing the collected information. The feedback unit is implemented by the control unit 46A of the smart glasses 214 and provides real-time feedback on the generated ideas. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

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

[0135] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

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

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

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

[0139] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

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

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

[0142] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0144] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

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

[0146] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0147] Each of the multiple elements described above, including the reception unit, collection unit, generation unit, and feedback unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives theme input from the user. The collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects relevant information. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates ideas by analyzing the collected information. The feedback unit is implemented by the control unit 46A of the headset terminal 314 and provides real-time feedback on the generated ideas. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

[0151] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

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

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

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

[0155] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0156] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0157] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0158] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0159] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0160] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0161] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0162] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0163] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0164] Each of the multiple elements described above, including the reception unit, collection unit, generation unit, and feedback unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives theme input from the user. The collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and collects relevant information. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates ideas by analyzing the collected information. The feedback unit is implemented by the control unit 46A of the robot 414 and provides real-time feedback of the generated ideas. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0165] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0166] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0167] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0168] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0169] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0170] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0171] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0172] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0173] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0174] 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.

[0175] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0176] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0177] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0178] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0179] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0180] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0181] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0182] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0183] (Note 1) A reception desk that accepts theme input from users, A collection unit that collects relevant information based on the themes received by the aforementioned reception unit, A generation unit analyzes the information collected by the aforementioned collection unit and generates ideas, The system includes a feedback unit that provides real-time feedback of the ideas generated by the generation unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Generating AI analyzes past winning ideas and the latest trends. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Generative AI will make original and socially significant proposals. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned feedback unit is Generative AI improves ideas based on user needs. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is Generative AI generates ideas that meet market needs. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned feedback unit is Generative AI highlights the cost-related challenges of generating ideas. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of theme input based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is Analyze the user's past theme input history and select the optimal submission method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When entering a theme, filtering is performed based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and determines the priority of topics to accept based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When users enter a theme, the system prioritizes accepting themes that are highly relevant to their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When a theme is entered, the system analyzes the user's social media activity and accepts relevant themes. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is We estimate the user's emotions and adjust the scope of information collected based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned collection unit is When collecting information, the collection algorithm is optimized by referring to past collected data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned collection unit is When gathering information, apply different collection methods depending on the theme category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned collection unit is When collecting information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned collection unit is When gathering information, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 19) The generating unit is It estimates the user's emotions and adjusts how ideas are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is When generating ideas, adjust the level of detail based on the importance of the theme. The system described in Appendix 1, characterized by the features described herein. (Note 21) The generating unit is When generating ideas, different generation algorithms are applied depending on the theme category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is It estimates the user's emotions and adjusts the length of the ideas generated based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is When generating ideas, prioritize the generation based on the deadline for submitting the theme. The system described in Appendix 1, characterized by the features described herein. (Note 24) The generating unit is When generating ideas, adjust the order of generation based on the relevance of the theme. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned feedback unit is It estimates the user's emotions and adjusts how feedback is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned feedback unit is When providing feedback, adjust the level of detail based on the importance of the idea. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned feedback unit is When providing feedback, apply different feedback methods depending on the category of the idea. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned feedback unit is It estimates the user's emotions and adjusts the length of the feedback based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned feedback unit is When providing feedback, prioritize feedback based on when the idea was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned feedback unit is When giving feedback, adjust the order of feedback based on the relevance of the ideas. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0184] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that accepts theme input from users, A collection unit that collects relevant information based on the themes received by the aforementioned reception unit, A generation unit analyzes the information collected by the aforementioned collection unit and generates ideas, The system includes a feedback unit that provides real-time feedback of the ideas generated by the generation unit. A system characterized by the following features.

2. The aforementioned collection unit is Generative AI analyzes past winning ideas and the latest trends. The system according to feature 1.

3. The generating unit is Generative AI will produce original and socially significant proposals. The system according to feature 1.

4. The aforementioned feedback unit is Generative AI improves ideas based on user needs. The system according to feature 1.

5. The generating unit is Generative AI generates ideas that meet market needs. The system according to feature 1.

6. The aforementioned feedback unit is Generative AI points out cost-related challenges in generating ideas. The system according to feature 1.

7. The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of theme input based on the estimated user emotions. The system according to feature 1.

8. The aforementioned reception unit is Analyze the user's past theme input history and select the optimal submission method. The system according to feature 1.

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