system

Abstract

The system according to this embodiment aims to enable proposers to generate original ideas and exchange opinions with other proposers. [Solution] The system according to the embodiment comprises a collection unit, a reception unit, an evaluation unit, a proposal unit, and a coordination unit. The collection unit stores past ideas as knowledge. The reception unit receives ideas from proposers. The evaluation unit evaluates the ideas received by the reception unit by comparing them with past ideas. The proposal unit proposes new and unique ideas based on the ideas evaluated by the evaluation unit. The coordination unit holds an exchange of opinions meeting, bringing together proposers with high affinity to each other based on the ideas proposed by the proposal unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult for a proposer to come up with an original idea in a generative AI contest, and there is a risk that many similar ideas will be submitted.

[0005] The system according to the embodiment aims to enable a proposer to come up with an original idea and exchange opinions with other proposers.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a reception unit, an evaluation unit, a proposal unit, and a coordination unit. The collection unit stores past ideas as knowledge. The reception unit receives ideas from proposers. The evaluation unit evaluates the ideas received by the reception unit by comparing them with past ideas. The proposal unit proposes new, unique ideas based on the ideas evaluated by the evaluation unit. The coordination unit conducts an exchange of ideas meeting, bringing together proposers with similar ideas based on the ideas proposed by the proposal unit. [Effects of the Invention]

[0007] The system according to this embodiment allows proposers to generate original ideas and exchange opinions with other proposers. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applicable 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) The system according to an embodiment of the present invention is a system that evaluates the originality and value of ideas in a generative AI contest and promotes the exchange of opinions among proposers. This system accumulates a collection of ideas from past generative AI contests as knowledge, and proposers input their own ideas, which the generative AI then compares with past ideas to evaluate their originality and value. If an idea lacks originality, the generative AI proposes an idea with added unique features. Furthermore, for useful ideas, the system selects other proposers with similar ideas and holds an exchange of opinions meeting with these proposers. This mechanism gives proposers the opportunity to refine their ideas. For example, a collection of ideas from past generative AI contests is accumulated as knowledge. At this time, detailed information on each idea is stored in a database so that the generative AI can access it. This includes, for example, an overview of the idea, information on the proposer, and evaluation results. Next, the proposer inputs their own idea. The proposer inputs an overview and details of their idea and sends it to the generative AI. The generative AI analyzes the input idea and compares it with past ideas. The generative AI evaluates the originality and value by comparing it with past ideas. For example, it evaluates similarity and novelty numerically and provides feedback to the proposer. If an idea lacks originality, the generative AI will suggest ideas that add new uniqueness. For example, it may add new elements to the proposer's idea or suggest an approach from a different perspective. It will also select other idea proposers with similar ideas for useful ideas. The generative AI analyzes information on past ideas and proposers to identify proposers with similar interests. For example, it may select proposers who are interested in the same field or theme. It will then hold an exchange of ideas meeting with the proposers. The generative AI will send invitations to the selected proposers to the exchange of ideas meeting, providing them with an opportunity to refine their ideas with each other. For example, the exchange of ideas may take place in the form of an online meeting or workshop. This system allows proposers to have the opportunity to refine their ideas. Through the exchange of ideas among proposers, new perspectives and ideas are generated, making it possible to propose more original and valuable ideas.Furthermore, the evaluation and suggestions provided by the generating AI allow proposers to understand the strengths and weaknesses of their ideas and identify areas for improvement. This enables the system to efficiently evaluate proposers' ideas, add uniqueness, and facilitate idea refinement through discussion sessions.

[0029] The system according to this embodiment comprises a collection unit, a reception unit, an evaluation unit, a proposal unit, and a coordination unit. The collection unit stores past ideas as knowledge. For example, the collection unit stores a collection of ideas from past generation AI contests in a database so that the generation AI can access it. The reception unit receives input from proposers. For example, the reception unit receives input from proposers, including an outline and details of their ideas, and sends them to the generation AI. The evaluation unit evaluates the ideas received by the reception unit by comparing them with past ideas. For example, the evaluation unit analyzes the input ideas using the generation AI and evaluates their originality and value by comparing them with past ideas. The proposal unit proposes new ideas with added originality based on the ideas evaluated by the evaluation unit. For example, the proposal unit suggests that the generation AI add new elements to the proposer's idea or propose approaches from different perspectives. The coordination unit holds an exchange of ideas meeting, bringing together proposers with high affinity based on the ideas proposed by the proposal unit. For example, the coordination unit uses the generation AI to analyze information on past ideas and proposers, identifies proposers with high affinity, and sends invitations to the exchange of ideas meeting. As a result, the system according to this embodiment can efficiently evaluate the proposer's idea, add originality, and refine the idea by conducting opinion exchange meetings.

[0030] The collection unit accumulates past ideas as knowledge. Specifically, it stores collections of ideas from past generative AI contests in a database, making them accessible to the generative AI. The collection unit systematically classifies and tags these ideas, enabling the generative AI to efficiently search and reference them. For example, it adds metadata such as the idea's theme, technology field, and the problem it aims to solve, allowing for the rapid extraction of relevant ideas in response to search queries. The collection unit also accumulates additional information such as evaluation results and implementation status of ideas, enabling the generative AI to learn from past successes and failures. In this way, the collection unit provides a foundation for the generative AI to generate new ideas by utilizing past knowledge. Furthermore, the collection unit regularly updates the database, adding new ideas and evaluation results to maintain up-to-date information. In this way, the collection unit supports the generative AI in generating ideas that reflect the latest trends and technological developments.

[0031] The reception section allows proposers to input their ideas. Specifically, it provides an interface for proposers to input an overview and details of their ideas and send them to the generation AI. The reception section features a user-friendly input form, making it easy for proposers to input their ideas. For example, it includes fields for inputting the idea's title, overview, detailed description, expected effects, and relevant technologies and markets. The reception section also provides a function for proposers to upload images, diagrams, and reference materials to supplement their ideas. Furthermore, the reception section has a function to automatically check the content of the input ideas to ensure there is no missing information or inappropriate expression. In this way, the reception section supports proposers in inputting high-quality ideas, enabling the generation AI to analyze them accurately.

[0032] The evaluation department evaluates ideas received by the reception department by comparing them to past ideas. Specifically, a generative AI analyzes the input ideas and evaluates their originality and value by comparing them to past ideas. The evaluation department uses natural language processing technology to analyze the content of ideas and calculate similarity and relevance. For example, it extracts keywords and phrases from ideas and compares them to past ideas to evaluate their degree of originality. The evaluation department also evaluates ideas from the perspectives of feasibility, marketability, and technical difficulty. This includes a function in which the generative AI refers to past success and failure cases to predict the probability of success of the proposed idea. Furthermore, the evaluation department provides feedback on the evaluation results to the proposer, suggesting areas for improvement and points to strengthen the idea. In this way, the evaluation department provides the proposer with guidance to objectively evaluate their own idea and refine it.

[0033] The Proposal Department proposes new and unique ideas based on those evaluated by the Evaluation Department. Specifically, the Generative AI adds new elements to the proposer's idea or suggests approaches from different perspectives. The Proposal Department uses the Generative AI to refer to past ideas and evaluation results to generate new ideas that further develop the proposer's idea. For example, it may propose ways to create new value by combining different technologies or approaches to address different market needs for the proposer's idea. The Proposal Department also has a function where the Generative AI analyzes the proposer's idea from multiple angles, identifies potential challenges and risks, and proposes solutions. In this way, the Proposal Department provides specific advice to proposers to further develop their ideas and increase their feasibility. Furthermore, the Proposal Department stores the proposed new ideas in a database and uses it as a knowledge base for future idea generation.

[0034] The Coordination Department will organize an exchange of ideas meeting, bringing together proposers with similar ideas based on the ideas proposed by the Proposal Department. Specifically, a generation AI will analyze past ideas and proposer information to identify highly compatible proposers and send them invitations to the exchange of ideas meeting. The Coordination Department will provide an online platform to enable proposers to exchange ideas efficiently. For example, a dedicated exchange of ideas platform equipped with a video conferencing system and chat function will be provided, allowing proposers to exchange ideas in real time. The Coordination Department will also assign facilitators to support the progress of the exchange of ideas meeting and help ensure that the discussion proceeds smoothly. Furthermore, the Coordination Department will provide a function to record the content of the exchange of ideas meeting so that it can be referenced later. This will allow proposers to gain new perspectives and ideas through exchange of ideas with other proposers and further refine their own ideas. The Coordination Department will store the results of the exchange of ideas meeting in a database and use it as a knowledge base for future idea generation and evaluation.

[0035] The collection unit can store collections of ideas from past generation AI contests as knowledge. For example, the collection unit can store collections of ideas from past generation AI contests in a database, making them accessible to generation AI. This allows for the evaluation of proposers' ideas by accumulating past ideas as knowledge. Some or all of the above processing in the collection unit may be performed using AI, or not. For example, when storing past ideas in the database, the collection unit can use AI to classify and tag the ideas.

[0036] The evaluation unit can assess the originality and value of a proposer's idea by comparing it to past ideas. For example, the evaluation unit can use a generative AI to analyze the input idea and evaluate its originality and value by comparing it to past ideas. This allows the evaluation unit to grasp the originality and value of the proposer's idea by comparing it to past ideas. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can use a generative AI to numerically evaluate the similarity and novelty of ideas and provide feedback to the proposer.

[0037] The proposal department can propose ideas that add new uniqueness if the originality is lacking. For example, the proposal department might use generative AI to add new elements to the proposer's idea or suggest approaches from different perspectives. This allows the proposer's idea to be refined by proposing ideas that add new uniqueness, even if the originality is lacking. Some or all of the above-described processes in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department could propose a method for adding new elements to the proposer's idea using generative AI.

[0038] The coordination unit can organize an exchange of ideas meeting bringing together proposers with high affinity. For example, the coordination unit uses a generative AI to analyze past ideas and proposer information, identify proposers with high affinity, and send them invitations to the exchange of ideas meeting. This allows for the refinement of ideas by holding an exchange of ideas meeting with proposers with high affinity. Some or all of the above processing in the coordination unit may be performed using AI, for example, or without AI. For example, the coordination unit can use a generative AI to evaluate the affinity between proposers and select participants for the exchange of ideas meeting.

[0039] The proposal section can add new elements to the proposer's idea or suggest approaches from different perspectives. For example, the proposal section might use generative AI to add new elements to the proposer's idea or suggest approaches from different perspectives. This adds originality to the proposer's idea by adding new elements or suggesting approaches from different perspectives. Some or all of the above processing in the proposal section may be performed using AI, for example, or without AI. For example, the proposal section could suggest a method for adding new elements to the proposer's idea using generative AI.

[0040] The collection unit can analyze the proposer's past submission history when collecting past ideas and select the optimal collection method. For example, the collection unit can analyze the trends of ideas previously submitted by the proposer and prioritize the collection of related ideas. The collection unit can also prioritize the collection of highly-rated ideas based on the evaluation results of ideas previously submitted by the proposer. Furthermore, the collection unit can analyze the fields of ideas previously submitted by the proposer and prioritize the collection of ideas in the same field. In this way, the optimal collection method can be selected by analyzing the proposer's past submission history. Some or all of the above processing in the collection unit may be performed using AI, for example, or not using AI. For example, the collection unit can input the proposer's past submission history data into a generating AI and have the generating AI select the optimal collection method.

[0041] The collection unit can filter past ideas based on the proposer's current areas of interest. For example, the collection unit can prioritize collecting ideas related to areas the proposer is currently interested in. It can also prioritize collecting ideas related to projects the proposer is currently working on. Furthermore, the collection unit can prioritize collecting ideas in areas the proposer is interested in based on current trends. This allows for the collection of highly relevant ideas by filtering based on the proposer's current areas of interest. Some or all of the above processing in the collection unit may be performed using AI, for example, or not. For example, the collection unit can input the proposer's current areas of interest data into a generating AI and have the generating AI perform the filtering.

[0042] The collection unit can prioritize collecting highly relevant ideas by considering the proposer's geographical location information when collecting past ideas. For example, the collection unit can prioritize collecting ideas related to the area where the proposer is currently located. It can also prioritize collecting ideas related to areas the proposer has visited in the past. Furthermore, it can prioritize collecting ideas related to areas the proposer plans to visit in the future. In this way, highly relevant ideas can be collected by considering the proposer's geographical location information. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the proposer's geographical location information data into a generating AI and have the generating AI perform the collection of highly relevant ideas.

[0043] The collection unit can analyze the proposer's social media activity when collecting past ideas and collect relevant ideas. For example, the collection unit can prioritize collecting ideas related to topics the proposer has shown interest in on social media. It can also prioritize collecting ideas related to the content of posts from accounts the proposer follows on social media. Furthermore, the collection unit can prioritize collecting ideas related to the activities of groups and communities the proposer participates in on social media. In this way, relevant ideas can be collected by analyzing the proposer's social media activity. Some or all of the above processing in the collection unit may be performed using AI, for example, or not using AI. For example, the collection unit can input the proposer's social media activity data into a generating AI and have the generating AI perform the collection of relevant ideas.

[0044] The reception department can analyze the proposer's past idea submission history and select the optimal reception method. For example, the reception department can analyze the trends of ideas previously submitted by the proposer and prioritize the acceptance of related ideas. The reception department can also prioritize the acceptance of highly-rated ideas based on the evaluation results of ideas previously submitted by the proposer. Furthermore, the reception department can analyze the fields of ideas previously submitted by the proposer and prioritize the acceptance of ideas in the same field. In this way, the optimal reception method can be selected by analyzing the proposer's past idea submission history. Some or all of the above processing in the reception department may be performed using AI, for example, or not using AI. For example, the reception department can input the proposer's past submission history data into a generating AI and have the generating AI select the optimal reception method.

[0045] The reception department can filter ideas based on the proposer's current projects and areas of interest when receiving them. For example, the reception department can prioritize ideas related to projects the proposer is currently working on. It can also prioritize ideas related to areas the proposer is currently interested in. Furthermore, it can prioritize ideas in areas the proposer is interested in based on current trends. This allows for the reception of highly relevant ideas by filtering based on the proposer's current projects and areas of interest. Some or all of the above processing in the reception department may be performed using AI, for example, or not. For example, the reception department can input the proposer's current project and area of ​​interest data into a generating AI and have the generating AI perform the filtering.

[0046] The reception department can prioritize accepting highly relevant ideas by considering the proposer's geographical location information when receiving ideas. For example, the reception department can prioritize accepting ideas related to the area the proposer is currently in. It can also prioritize accepting ideas related to areas the proposer has visited in the past. Furthermore, it can prioritize accepting ideas related to areas the proposer plans to visit in the future. In this way, by considering the proposer's geographical location information, highly relevant ideas can be accepted. Some or all of the above processing in the reception department may be performed using AI, for example, or not using AI. For example, the reception department can input the proposer's geographical location information data into a generating AI and have the generating AI perform the task of accepting highly relevant ideas.

[0047] The reception department can analyze the proposer's social media activity when receiving ideas and accept relevant ideas. For example, the reception department can prioritize ideas related to topics the proposer has shown interest in on social media. It can also prioritize ideas related to the content of posts from accounts the proposer follows on social media. Furthermore, the reception department can prioritize ideas related to the activities of groups and communities the proposer participates in on social media. In this way, relevant ideas can be accepted by analyzing the proposer's social media activity. Some or all of the above processing in the reception department may be performed using AI, for example, or not. For example, the reception department can input the proposer's social media activity data into a generating AI and have the generating AI perform the task of accepting relevant ideas.

[0048] The evaluation unit can optimize its evaluation algorithm by referring to past evaluation data when evaluating ideas. For example, the evaluation unit optimizes the evaluation algorithm based on past evaluation data. The evaluation unit can also analyze past evaluation data and revise the evaluation criteria. Furthermore, the evaluation unit can improve the accuracy of the evaluation by referring to past evaluation data. In this way, the evaluation algorithm can be optimized by referring to past evaluation data. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without using AI. For example, the evaluation unit can input past evaluation data into a generating AI and have the generating AI perform the optimization of the evaluation algorithm.

[0049] The evaluation unit can consider the proposer's attribute information when evaluating an idea. For example, the evaluation unit can consider the proposer's field of expertise. It can also consider the proposer's years of experience. Furthermore, the evaluation unit can consider the proposer's past evaluation results. This allows for a more appropriate evaluation by considering the proposer's attribute information. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the proposer's attribute information data into a generating AI and have the generating AI perform the evaluation.

[0050] The evaluation unit can consider the geographical distribution of proposers when evaluating ideas. For example, the evaluation unit may prioritize ideas related to the region where the proposer is currently located. It can also prioritize ideas related to regions the proposer has visited in the past. Furthermore, it can prioritize ideas related to regions the proposer plans to visit in the future. This allows for a more appropriate evaluation by considering the geographical distribution of proposers. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the proposer's geographical distribution data into a generating AI and have the generating AI perform the evaluation.

[0051] The evaluation unit can improve the accuracy of its evaluation by referring to relevant literature when evaluating ideas. For example, the evaluation unit can improve the accuracy of its evaluation based on relevant literature. The evaluation unit can also revise its evaluation criteria by referring to relevant literature. Furthermore, the evaluation unit can optimize its evaluation algorithm by referring to relevant literature. In this way, the accuracy of the evaluation can be improved by referring to relevant literature. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input relevant literature data into a generating AI and have the generating AI perform the task of improving the accuracy of the evaluation.

[0052] The proposal department can select the optimal proposal method by referring to past proposal data when adding new unique features. For example, the proposal department can select the optimal proposal method based on past proposal data. The proposal department can also analyze past proposal data and revise proposal methods. Furthermore, the proposal department can improve the accuracy of proposals by referring to past proposal data. In this way, the optimal proposal method can be selected by referring to past proposal data. Some or all of the above processes in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input past proposal data into a generation AI and have the generation AI perform the selection of the optimal proposal method.

[0053] The proposal function can customize the proposal content based on the proposer's current areas of interest when adding new unique features. For example, the proposal function can add new unique features related to the proposer's current areas of interest. It can also add new unique features related to the project the proposer is currently working on. Furthermore, the proposal function can add new unique features in areas of interest based on current trends. This allows for more appropriate proposals by customizing the proposal content based on the proposer's current areas of interest. Some or all of the above processing in the proposal function may be performed using AI, for example, or not. For example, the proposal function can input the proposer's current areas of interest data into a generating AI and have the generating AI perform the customization of the proposal content.

[0054] The proposal unit can select the optimal proposal method by considering the proposer's geographical location information when adding new unique features. For example, the proposal unit can add new unique features related to the region the proposer is currently in. It can also add new unique features related to regions the proposer has visited in the past. Furthermore, it can add new unique features related to regions the proposer plans to visit in the future. This allows the proposal unit to select the optimal proposal method by considering the proposer's geographical location information. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the proposer's geographical location information data into a generating AI and have the generating AI select the optimal proposal method.

[0055] The proposal unit can customize the proposal content by analyzing the proposer's social media activity when adding new unique features. For example, the proposal unit can add new unique features related to topics the proposer is interested in on social media. It can also add new unique features related to the content of posts from accounts the proposer follows on social media. Furthermore, the proposal unit can add new unique features related to the activities of groups and communities the proposer participates in on social media. This allows for more appropriate proposals by analyzing the proposer's social media activity. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not. For example, the proposal unit can input the proposer's social media activity data into a generating AI and have the generating AI customize the proposal content.

[0056] The coordination unit can select the optimal coordination method by referring to past opinion exchange meeting data when coordinating opinion exchange meetings. For example, the coordination unit selects the optimal coordination method based on past opinion exchange meeting data. The coordination unit can also analyze past opinion exchange meeting data and revise the coordination method. Furthermore, the coordination unit can improve the accuracy of coordination by referring to past opinion exchange meeting data. In this way, the optimal coordination method can be selected by referring to past opinion exchange meeting data. Some or all of the above processing in the coordination unit may be performed using AI, for example, or without using AI. For example, the coordination unit can input past opinion exchange meeting data into a generating AI and have the generating AI perform the selection of the optimal coordination method.

[0057] The coordination unit can customize the content of the discussion meetings based on the proposer's current areas of interest when coordinating them. For example, the coordination unit can prioritize coordinating discussion meetings related to areas the proposer is currently interested in. It can also prioritize coordinating discussion meetings related to projects the proposer is currently working on. Furthermore, the coordination unit can prioritize coordinating discussion meetings in areas the proposer is interested in based on current trends. This allows for more appropriate discussion meetings by customizing the content of the discussions based on the proposer's current areas of interest. Some or all of the above processing in the coordination unit may be performed using AI, for example, or not. For example, the coordination unit can input data on the proposer's current areas of interest into a generating AI and have the generating AI perform the customization of the discussion content.

[0058] The coordination unit can select the optimal coordination method when coordinating opinion exchange meetings, taking into account the proposer's geographical location information. For example, the coordination unit may prioritize coordinating opinion exchange meetings related to the region where the proposer is currently located. It can also prioritize coordinating opinion exchange meetings related to regions the proposer has visited in the past. Furthermore, it can prioritize coordinating opinion exchange meetings related to regions the proposer plans to visit in the future. In this way, the optimal coordination method can be selected by taking into account the proposer's geographical location information. Some or all of the above processing in the coordination unit may be performed using AI, for example, or without AI. For example, the coordination unit may input the proposer's geographical location information data into a generating AI and have the generating AI select the optimal coordination method.

[0059] The coordination unit can analyze the proposer's social media activity and customize the coordination content when coordinating opinion exchange meetings. For example, the coordination unit can prioritize scheduling opinion exchange meetings related to topics the proposer has shown interest in on social media. It can also prioritize scheduling opinion exchange meetings related to the content of accounts the proposer follows on social media. Furthermore, the coordination unit can prioritize scheduling opinion exchange meetings related to the activities of groups and communities the proposer participates in on social media. This allows for more appropriate opinion exchange meetings by analyzing the proposer's social media activity. Some or all of the above processing in the coordination unit may be performed using AI, for example, or not. For example, the coordination unit can input the proposer's social media activity data into a generating AI and have the generating AI perform the customization of the coordination content.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The collection unit can analyze the proposer's past submission history when collecting past ideas and select the optimal collection method. For example, it can analyze the trends of ideas previously submitted by the proposer and prioritize the collection of related ideas. It can also prioritize the collection of highly-rated ideas based on the evaluation results of ideas previously submitted by the proposer. Furthermore, it can analyze the fields of ideas previously submitted by the proposer and prioritize the collection of ideas in the same field. In this way, the optimal collection method can be selected by analyzing the proposer's past submission history. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the proposer's past submission history data into a generating AI and have the generating AI select the optimal collection method.

[0062] The evaluation unit can optimize its evaluation algorithm by referring to past evaluation data when evaluating ideas. For example, it can optimize the evaluation algorithm based on past evaluation data. It can also analyze past evaluation data and revise the evaluation criteria. Furthermore, it can improve the accuracy of the evaluation by referring to past evaluation data. In this way, the evaluation algorithm can be optimized by referring to past evaluation data. Some or all of the above processes in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input past evaluation data into a generating AI and have the generating AI perform the optimization of the evaluation algorithm.

[0063] The proposal department can select the optimal proposal method by referring to past proposal data when adding new unique features. For example, it can select the optimal proposal method based on past proposal data. It can also analyze past proposal data and revise proposal methods. Furthermore, it can improve the accuracy of proposals by referring to past proposal data. In this way, the optimal proposal method can be selected by referring to past proposal data. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department can input past proposal data into a generation AI and have the generation AI perform the selection of the optimal proposal method.

[0064] The coordination unit can select the optimal coordination method by referring to past opinion exchange meeting data when coordinating opinion exchange meetings. For example, it can select the optimal coordination method based on past opinion exchange meeting data. It can also analyze past opinion exchange meeting data and revise the coordination method. Furthermore, it can improve the accuracy of coordination by referring to past opinion exchange meeting data. In this way, the optimal coordination method can be selected by referring to past opinion exchange meeting data. Some or all of the above processing in the coordination unit may be performed using AI or not. For example, the coordination unit can input past opinion exchange meeting data into a generating AI and have the generating AI perform the selection of the optimal coordination method.

[0065] The proposal department can customize the proposal content based on the proposer's current areas of interest when adding new unique features. For example, it can add new unique features related to the proposer's current areas of interest. It can also add new unique features related to the project the proposer is currently working on. Furthermore, it can add new unique features in areas of interest based on current trends. This allows for more appropriate proposals by customizing the proposal content based on the proposer's current areas of interest. Some or all of the above processing in the proposal department may be performed using AI or not. For example, the proposal department can input the proposer's current areas of interest data into a generating AI and have the generating AI perform the customization of the proposal content.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The collection unit stores past ideas as knowledge. For example, it stores a collection of ideas from past generative AI contests in a database so that generative AI can access them. Step 2: The reception desk receives the proposer's idea. For example, the proposer enters an overview and details of their idea and sends it to the generation AI. Step 3: The evaluation unit evaluates the ideas received by the reception unit by comparing them with past ideas. For example, a generation AI analyzes the input ideas and evaluates their originality and value by comparing them with past ideas. Step 4: The proposal team proposes new, original ideas based on the ideas evaluated by the evaluation team. For example, a generative AI might add new elements to the proposer's idea or suggest an approach from a different perspective. Step 5: The coordination department conducts an exchange of ideas meeting, bringing together proposers with high affinity to the ideas proposed by the proposal department. For example, a generation AI analyzes past ideas and proposer information to identify proposers with high affinity and sends them an invitation to the exchange of ideas meeting.

[0068] (Example of form 2) The system according to an embodiment of the present invention is a system that evaluates the originality and value of ideas in a generative AI contest and promotes the exchange of opinions among proposers. This system accumulates a collection of ideas from past generative AI contests as knowledge, and proposers input their own ideas, which the generative AI then compares with past ideas to evaluate their originality and value. If an idea lacks originality, the generative AI proposes an idea with added unique features. Furthermore, for useful ideas, the system selects other proposers with similar ideas and holds an exchange of opinions meeting with these proposers. This mechanism gives proposers the opportunity to refine their ideas. For example, a collection of ideas from past generative AI contests is accumulated as knowledge. At this time, detailed information on each idea is stored in a database so that the generative AI can access it. This includes, for example, an overview of the idea, information on the proposer, and evaluation results. Next, the proposer inputs their own idea. The proposer inputs an overview and details of their idea and sends it to the generative AI. The generative AI analyzes the input idea and compares it with past ideas. The generative AI evaluates the originality and value by comparing it with past ideas. For example, it evaluates similarity and novelty numerically and provides feedback to the proposer. If an idea lacks originality, the generative AI will suggest ideas that add new uniqueness. For example, it may add new elements to the proposer's idea or suggest an approach from a different perspective. It will also select other idea proposers with similar ideas for useful ideas. The generative AI analyzes information on past ideas and proposers to identify proposers with similar interests. For example, it may select proposers who are interested in the same field or theme. It will then hold an exchange of ideas meeting with the proposers. The generative AI will send invitations to the selected proposers to the exchange of ideas meeting, providing them with an opportunity to refine their ideas with each other. For example, the exchange of ideas may take place in the form of an online meeting or workshop. This system allows proposers to have the opportunity to refine their ideas. Through the exchange of ideas among proposers, new perspectives and ideas are generated, making it possible to propose more original and valuable ideas.Furthermore, the evaluation and suggestions provided by the generating AI allow proposers to understand the strengths and weaknesses of their ideas and identify areas for improvement. This enables the system to efficiently evaluate proposers' ideas, add uniqueness, and facilitate idea refinement through discussion sessions.

[0069] The system according to this embodiment comprises a collection unit, a reception unit, an evaluation unit, a proposal unit, and a coordination unit. The collection unit stores past ideas as knowledge. For example, the collection unit stores a collection of ideas from past generation AI contests in a database so that the generation AI can access it. The reception unit receives input from proposers. For example, the reception unit receives input from proposers, including an outline and details of their ideas, and sends them to the generation AI. The evaluation unit evaluates the ideas received by the reception unit by comparing them with past ideas. For example, the evaluation unit analyzes the input ideas using the generation AI and evaluates their originality and value by comparing them with past ideas. The proposal unit proposes new ideas with added originality based on the ideas evaluated by the evaluation unit. For example, the proposal unit suggests that the generation AI add new elements to the proposer's idea or propose approaches from different perspectives. The coordination unit holds an exchange of ideas meeting, bringing together proposers with high affinity based on the ideas proposed by the proposal unit. For example, the coordination unit uses the generation AI to analyze information on past ideas and proposers, identifies proposers with high affinity, and sends invitations to the exchange of ideas meeting. As a result, the system according to this embodiment can efficiently evaluate the proposer's idea, add originality, and refine the idea by conducting opinion exchange meetings.

[0070] The collection unit accumulates past ideas as knowledge. Specifically, it stores collections of ideas from past generative AI contests in a database, making them accessible to the generative AI. The collection unit systematically classifies and tags these ideas, enabling the generative AI to efficiently search and reference them. For example, it adds metadata such as the idea's theme, technology field, and the problem it aims to solve, allowing for the rapid extraction of relevant ideas in response to search queries. The collection unit also accumulates additional information such as evaluation results and implementation status of ideas, enabling the generative AI to learn from past successes and failures. In this way, the collection unit provides a foundation for the generative AI to generate new ideas by utilizing past knowledge. Furthermore, the collection unit regularly updates the database, adding new ideas and evaluation results to maintain up-to-date information. In this way, the collection unit supports the generative AI in generating ideas that reflect the latest trends and technological developments.

[0071] The reception section allows proposers to input their ideas. Specifically, it provides an interface for proposers to input an overview and details of their ideas and send them to the generation AI. The reception section features a user-friendly input form, making it easy for proposers to input their ideas. For example, it includes fields for inputting the idea's title, overview, detailed description, expected effects, and relevant technologies and markets. The reception section also provides a function for proposers to upload images, diagrams, and reference materials to supplement their ideas. Furthermore, the reception section has a function to automatically check the content of the input ideas to ensure there is no missing information or inappropriate expression. In this way, the reception section supports proposers in inputting high-quality ideas, enabling the generation AI to analyze them accurately.

[0072] The evaluation department evaluates ideas received by the reception department by comparing them to past ideas. Specifically, a generative AI analyzes the input ideas and evaluates their originality and value by comparing them to past ideas. The evaluation department uses natural language processing technology to analyze the content of ideas and calculate similarity and relevance. For example, it extracts keywords and phrases from ideas and compares them to past ideas to evaluate their degree of originality. The evaluation department also evaluates ideas from the perspectives of feasibility, marketability, and technical difficulty. This includes a function in which the generative AI refers to past success and failure cases to predict the probability of success of the proposed idea. Furthermore, the evaluation department provides feedback on the evaluation results to the proposer, suggesting areas for improvement and points to strengthen the idea. In this way, the evaluation department provides the proposer with guidance to objectively evaluate their own idea and refine it.

[0073] The Proposal Department proposes new and unique ideas based on those evaluated by the Evaluation Department. Specifically, the Generative AI adds new elements to the proposer's idea or suggests approaches from different perspectives. The Proposal Department uses the Generative AI to refer to past ideas and evaluation results to generate new ideas that further develop the proposer's idea. For example, it may propose ways to create new value by combining different technologies or approaches to address different market needs for the proposer's idea. The Proposal Department also has a function where the Generative AI analyzes the proposer's idea from multiple angles, identifies potential challenges and risks, and proposes solutions. In this way, the Proposal Department provides specific advice to proposers to further develop their ideas and increase their feasibility. Furthermore, the Proposal Department stores the proposed new ideas in a database and uses it as a knowledge base for future idea generation.

[0074] The Coordination Department will organize an exchange of ideas meeting, bringing together proposers with similar ideas based on the ideas proposed by the Proposal Department. Specifically, a generation AI will analyze past ideas and proposer information to identify highly compatible proposers and send them invitations to the exchange of ideas meeting. The Coordination Department will provide an online platform to enable proposers to exchange ideas efficiently. For example, a dedicated exchange of ideas platform equipped with a video conferencing system and chat function will be provided, allowing proposers to exchange ideas in real time. The Coordination Department will also assign facilitators to support the progress of the exchange of ideas meeting and help ensure that the discussion proceeds smoothly. Furthermore, the Coordination Department will provide a function to record the content of the exchange of ideas meeting so that it can be referenced later. This will allow proposers to gain new perspectives and ideas through exchange of ideas with other proposers and further refine their own ideas. The Coordination Department will store the results of the exchange of ideas meeting in a database and use it as a knowledge base for future idea generation and evaluation.

[0075] The collection unit can store collections of ideas from past generation AI contests as knowledge. For example, the collection unit can store collections of ideas from past generation AI contests in a database, making them accessible to generation AI. This allows for the evaluation of proposers' ideas by accumulating past ideas as knowledge. Some or all of the above processing in the collection unit may be performed using AI, or not. For example, when storing past ideas in the database, the collection unit can use AI to classify and tag the ideas.

[0076] The evaluation unit can assess the originality and value of a proposer's idea by comparing it to past ideas. For example, the evaluation unit can use a generative AI to analyze the input idea and evaluate its originality and value by comparing it to past ideas. This allows the evaluation unit to grasp the originality and value of the proposer's idea by comparing it to past ideas. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can use a generative AI to numerically evaluate the similarity and novelty of ideas and provide feedback to the proposer.

[0077] The proposal department can propose ideas that add new uniqueness if the originality is lacking. For example, the proposal department might use generative AI to add new elements to the proposer's idea or suggest approaches from different perspectives. This allows the proposer's idea to be refined by proposing ideas that add new uniqueness, even if the originality is lacking. Some or all of the above-described processes in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department could propose a method for adding new elements to the proposer's idea using generative AI.

[0078] The coordination unit can organize an exchange of ideas meeting bringing together proposers with high affinity. For example, the coordination unit uses a generative AI to analyze past ideas and proposer information, identify proposers with high affinity, and send them invitations to the exchange of ideas meeting. This allows for the refinement of ideas by holding an exchange of ideas meeting with proposers with high affinity. Some or all of the above processing in the coordination unit may be performed using AI, for example, or without AI. For example, the coordination unit can use a generative AI to evaluate the affinity between proposers and select participants for the exchange of ideas meeting.

[0079] The proposal section can add new elements to the proposer's idea or suggest approaches from different perspectives. For example, the proposal section might use generative AI to add new elements to the proposer's idea or suggest approaches from different perspectives. This adds originality to the proposer's idea by adding new elements or suggesting approaches from different perspectives. Some or all of the above processing in the proposal section may be performed using AI, for example, or without AI. For example, the proposal section could suggest a method for adding new elements to the proposer's idea using generative AI.

[0080] The collection unit can estimate the proposer's emotions and adjust the timing of collecting past ideas based on the estimated emotions. For example, if the proposer is stressed, the collection unit will collect past ideas during times when they can relax. It can also collect past ideas during times when the proposer is focused, when their concentration is at its highest. Furthermore, if the proposer is tired, the collection unit can collect past ideas after they have rested. This allows for efficient idea collection by adjusting the collection timing based on the proposer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the collection unit may be performed using AI, or not. For example, the collection unit can input the proposer's emotion data into the generative AI and have the generative AI adjust the collection timing.

[0081] The collection unit can analyze the proposer's past submission history when collecting past ideas and select the optimal collection method. For example, the collection unit can analyze the trends of ideas previously submitted by the proposer and prioritize the collection of related ideas. The collection unit can also prioritize the collection of highly-rated ideas based on the evaluation results of ideas previously submitted by the proposer. Furthermore, the collection unit can analyze the fields of ideas previously submitted by the proposer and prioritize the collection of ideas in the same field. In this way, the optimal collection method can be selected by analyzing the proposer's past submission history. Some or all of the above processing in the collection unit may be performed using AI, for example, or not using AI. For example, the collection unit can input the proposer's past submission history data into a generating AI and have the generating AI select the optimal collection method.

[0082] The collection unit can filter past ideas based on the proposer's current areas of interest. For example, the collection unit can prioritize collecting ideas related to areas the proposer is currently interested in. It can also prioritize collecting ideas related to projects the proposer is currently working on. Furthermore, the collection unit can prioritize collecting ideas in areas the proposer is interested in based on current trends. This allows for the collection of highly relevant ideas by filtering based on the proposer's current areas of interest. Some or all of the above processing in the collection unit may be performed using AI, for example, or not. For example, the collection unit can input the proposer's current areas of interest data into a generating AI and have the generating AI perform the filtering.

[0083] The collection unit can estimate the proposer's emotions and determine the priority of ideas to collect based on the estimated emotions. For example, if the proposer is relaxed, the collection unit will prioritize collecting ideas that can be evaluated in a relaxed state. If the proposer is excited, the collection unit can also prioritize collecting stimulating ideas that can maintain that excitement. Furthermore, if the proposer is tired, the collection unit can also prioritize collecting ideas that are easy to understand. This enables efficient idea collection by determining the priority of ideas to collect based on the proposer'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 collection unit may be performed using AI or not using AI. For example, the collection unit can input the proposer's emotion data into a generative AI and have the generative AI determine the priority of ideas to collect.

[0084] The collection unit can prioritize collecting highly relevant ideas by considering the proposer's geographical location information when collecting past ideas. For example, the collection unit can prioritize collecting ideas related to the area where the proposer is currently located. It can also prioritize collecting ideas related to areas the proposer has visited in the past. Furthermore, it can prioritize collecting ideas related to areas the proposer plans to visit in the future. In this way, highly relevant ideas can be collected by considering the proposer's geographical location information. Some or all of the above processing in the collection unit may be performed using AI, for example, or without AI. For example, the collection unit can input the proposer's geographical location information data into a generating AI and have the generating AI perform the collection of highly relevant ideas.

[0085] The collection unit can analyze the proposer's social media activity when collecting past ideas and collect relevant ideas. For example, the collection unit can prioritize collecting ideas related to topics the proposer has shown interest in on social media. It can also prioritize collecting ideas related to the content of posts from accounts the proposer follows on social media. Furthermore, the collection unit can prioritize collecting ideas related to the activities of groups and communities the proposer participates in on social media. In this way, relevant ideas can be collected by analyzing the proposer's social media activity. Some or all of the above processing in the collection unit may be performed using AI, for example, or not using AI. For example, the collection unit can input the proposer's social media activity data into a generating AI and have the generating AI perform the collection of relevant ideas.

[0086] The reception unit can estimate the proposer's emotions and adjust the timing of idea submission based on the estimated emotions. For example, if the proposer is feeling stressed, the reception unit will accept ideas during a time when they can relax. Similarly, if the proposer is focused, the reception unit can accept ideas during a time when their concentration is high. Furthermore, if the proposer is tired, the reception unit can accept ideas after they have rested. This allows for efficient idea submission by adjusting the timing based on the proposer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can input the proposer's emotion data into the generative AI and have the generative AI adjust the timing of submission.

[0087] The reception department can analyze the proposer's past idea submission history and select the optimal reception method. For example, the reception department can analyze the trends of ideas previously submitted by the proposer and prioritize the acceptance of related ideas. The reception department can also prioritize the acceptance of highly-rated ideas based on the evaluation results of ideas previously submitted by the proposer. Furthermore, the reception department can analyze the fields of ideas previously submitted by the proposer and prioritize the acceptance of ideas in the same field. In this way, the optimal reception method can be selected by analyzing the proposer's past idea submission history. Some or all of the above processing in the reception department may be performed using AI, for example, or not using AI. For example, the reception department can input the proposer's past submission history data into a generating AI and have the generating AI select the optimal reception method.

[0088] The reception department can filter ideas based on the proposer's current projects and areas of interest when receiving them. For example, the reception department can prioritize ideas related to projects the proposer is currently working on. It can also prioritize ideas related to areas the proposer is currently interested in. Furthermore, it can prioritize ideas in areas the proposer is interested in based on current trends. This allows for the reception of highly relevant ideas by filtering based on the proposer's current projects and areas of interest. Some or all of the above processing in the reception department may be performed using AI, for example, or not. For example, the reception department can input the proposer's current project and area of ​​interest data into a generating AI and have the generating AI perform the filtering.

[0089] The reception unit can estimate the proposer's emotions and determine the priority of ideas to accept based on the estimated emotions. For example, if the proposer is relaxed, the reception unit will prioritize ideas that can be evaluated in a relaxed state. If the proposer is excited, the reception unit may also prioritize stimulating ideas that can maintain that excitement. Furthermore, if the proposer is tired, the reception unit may also prioritize ideas that are easy to understand. This allows for efficient idea reception by prioritizing ideas based on the proposer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the proposer's emotion data into a generative AI and have the generative AI determine the priority of ideas to accept.

[0090] The reception department can prioritize accepting highly relevant ideas by considering the proposer's geographical location information when receiving ideas. For example, the reception department can prioritize accepting ideas related to the area the proposer is currently in. It can also prioritize accepting ideas related to areas the proposer has visited in the past. Furthermore, it can prioritize accepting ideas related to areas the proposer plans to visit in the future. In this way, by considering the proposer's geographical location information, highly relevant ideas can be accepted. Some or all of the above processing in the reception department may be performed using AI, for example, or not using AI. For example, the reception department can input the proposer's geographical location information data into a generating AI and have the generating AI perform the task of accepting highly relevant ideas.

[0091] The reception department can analyze the proposer's social media activity when receiving ideas and accept relevant ideas. For example, the reception department can prioritize ideas related to topics the proposer has shown interest in on social media. It can also prioritize ideas related to the content of posts from accounts the proposer follows on social media. Furthermore, the reception department can prioritize ideas related to the activities of groups and communities the proposer participates in on social media. In this way, relevant ideas can be accepted by analyzing the proposer's social media activity. Some or all of the above processing in the reception department may be performed using AI, for example, or not. For example, the reception department can input the proposer's social media activity data into a generating AI and have the generating AI perform the task of accepting relevant ideas.

[0092] The evaluation unit can estimate the proposer's emotions and adjust the evaluation criteria for ideas based on the estimated emotions. For example, if the proposer is relaxed, the evaluation unit can set criteria that allow for evaluation in a relaxed state. If the proposer is excited, the evaluation unit can also set criteria that allow for maintaining that excitement. Furthermore, if the proposer is tired, the evaluation unit can set criteria that are easy to understand. This allows for efficient idea evaluation by adjusting the evaluation criteria based on the proposer'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 evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit can input the proposer's emotion data into the generative AI and have the generative AI perform the adjustment of the evaluation criteria.

[0093] The evaluation unit can optimize its evaluation algorithm by referring to past evaluation data when evaluating ideas. For example, the evaluation unit optimizes the evaluation algorithm based on past evaluation data. The evaluation unit can also analyze past evaluation data and revise the evaluation criteria. Furthermore, the evaluation unit can improve the accuracy of the evaluation by referring to past evaluation data. In this way, the evaluation algorithm can be optimized by referring to past evaluation data. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without using AI. For example, the evaluation unit can input past evaluation data into a generating AI and have the generating AI perform the optimization of the evaluation algorithm.

[0094] The evaluation unit can consider the proposer's attribute information when evaluating an idea. For example, the evaluation unit can consider the proposer's field of expertise. It can also consider the proposer's years of experience. Furthermore, the evaluation unit can consider the proposer's past evaluation results. This allows for a more appropriate evaluation by considering the proposer's attribute information. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the proposer's attribute information data into a generating AI and have the generating AI perform the evaluation.

[0095] The evaluation unit can estimate the proposer's emotions and adjust the display method of the evaluation results based on the estimated emotions of the proposer. For example, if the proposer is relaxed, the evaluation unit will display the evaluation results in a relaxed manner. The evaluation unit can also provide a display method that helps maintain the excitement if the proposer is excited. Furthermore, if the proposer is tired, the evaluation unit can provide a display method that is easy to understand. This allows for efficient display of evaluation results by adjusting the display method based on the proposer's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit can input the proposer's emotion data into the generative AI and have the generative AI adjust the display method of the evaluation results.

[0096] The evaluation unit can consider the geographical distribution of proposers when evaluating ideas. For example, the evaluation unit may prioritize ideas related to the region where the proposer is currently located. It can also prioritize ideas related to regions the proposer has visited in the past. Furthermore, it can prioritize ideas related to regions the proposer plans to visit in the future. This allows for a more appropriate evaluation by considering the geographical distribution of proposers. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the proposer's geographical distribution data into a generating AI and have the generating AI perform the evaluation.

[0097] The evaluation unit can improve the accuracy of its evaluation by referring to relevant literature when evaluating ideas. For example, the evaluation unit can improve the accuracy of its evaluation based on relevant literature. The evaluation unit can also revise its evaluation criteria by referring to relevant literature. Furthermore, the evaluation unit can optimize its evaluation algorithm by referring to relevant literature. In this way, the accuracy of the evaluation can be improved by referring to relevant literature. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input relevant literature data into a generating AI and have the generating AI perform the task of improving the accuracy of the evaluation.

[0098] The proposal unit can estimate the proposer's emotions and adjust the method of adding new uniqueness based on the estimated emotions. For example, if the proposer is relaxed, the proposal unit can suggest a method of adding new uniqueness in a relaxed state. If the proposer is excited, the proposal unit can also suggest a method of adding new uniqueness that helps maintain the excitement. Furthermore, if the proposer is tired, the proposal unit can suggest a method of adding new uniqueness that is easy to understand. This allows for efficient proposals by adjusting the method of adding new uniqueness based on the proposer'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 proposal unit may be performed using AI or not using AI. For example, the proposal unit can input the proposer's emotion data into the generative AI and have the generative AI adjust the method of adding new uniqueness.

[0099] The proposal department can select the optimal proposal method by referring to past proposal data when adding new unique features. For example, the proposal department can select the optimal proposal method based on past proposal data. The proposal department can also analyze past proposal data and revise proposal methods. Furthermore, the proposal department can improve the accuracy of proposals by referring to past proposal data. In this way, the optimal proposal method can be selected by referring to past proposal data. Some or all of the above processes in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can input past proposal data into a generation AI and have the generation AI perform the selection of the optimal proposal method.

[0100] The proposal function can customize the proposal content based on the proposer's current areas of interest when adding new unique features. For example, the proposal function can add new unique features related to the proposer's current areas of interest. It can also add new unique features related to the project the proposer is currently working on. Furthermore, the proposal function can add new unique features in areas of interest based on current trends. This allows for more appropriate proposals by customizing the proposal content based on the proposer's current areas of interest. Some or all of the above processing in the proposal function may be performed using AI, for example, or not. For example, the proposal function can input the proposer's current areas of interest data into a generating AI and have the generating AI perform the customization of the proposal content.

[0101] The proposal unit can estimate the proposer's emotions and determine the priority of proposals based on the estimated emotions. For example, if the proposer is relaxed, the proposal unit will prioritize proposals that can be evaluated in a relaxed state. If the proposer is excited, the proposal unit can also prioritize proposals that can maintain that excitement. Furthermore, if the proposer is tired, the proposal unit can prioritize proposals that are easy to understand. This allows for efficient proposals by prioritizing proposals based on the proposer's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the proposal unit may be performed using AI, or not. For example, the proposal unit can input the proposer's emotion data into a generative AI and have the generative AI determine the priority of proposals.

[0102] The proposal unit can select the optimal proposal method by considering the proposer's geographical location information when adding new unique features. For example, the proposal unit can add new unique features related to the region the proposer is currently in. It can also add new unique features related to regions the proposer has visited in the past. Furthermore, it can add new unique features related to regions the proposer plans to visit in the future. This allows the proposal unit to select the optimal proposal method by considering the proposer's geographical location information. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit can input the proposer's geographical location information data into a generating AI and have the generating AI select the optimal proposal method.

[0103] The proposal unit can customize the proposal content by analyzing the proposer's social media activity when adding new unique features. For example, the proposal unit can add new unique features related to topics the proposer is interested in on social media. It can also add new unique features related to the content of posts from accounts the proposer follows on social media. Furthermore, the proposal unit can add new unique features related to the activities of groups and communities the proposer participates in on social media. This allows for more appropriate proposals by analyzing the proposer's social media activity. Some or all of the above processing in the proposal unit may be performed using AI, for example, or not. For example, the proposal unit can input the proposer's social media activity data into a generating AI and have the generating AI customize the proposal content.

[0104] The adjustment unit can estimate the proposer's emotions and adjust the method of conducting the discussion based on the estimated emotions. For example, if the proposer is relaxed, the adjustment unit can suggest a method of conducting the discussion in a relaxed state. If the proposer is excited, the adjustment unit can also suggest a method of conducting the discussion in a way that helps maintain that excitement. Furthermore, if the proposer is tired, the adjustment unit can suggest a method of conducting the discussion in an easy-to-understand manner. By adjusting the method of conducting the discussion based on the proposer's emotions, an efficient discussion becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the adjustment unit may be performed using AI, for example, or not using AI. For example, the adjustment unit can input the proposer's emotion data into a generative AI and have the generative AI perform the adjustment of the method of conducting the discussion.

[0105] The coordination unit can select the optimal coordination method by referring to past opinion exchange meeting data when coordinating opinion exchange meetings. For example, the coordination unit selects the optimal coordination method based on past opinion exchange meeting data. The coordination unit can also analyze past opinion exchange meeting data and revise the coordination method. Furthermore, the coordination unit can improve the accuracy of coordination by referring to past opinion exchange meeting data. In this way, the optimal coordination method can be selected by referring to past opinion exchange meeting data. Some or all of the above processing in the coordination unit may be performed using AI, for example, or without using AI. For example, the coordination unit can input past opinion exchange meeting data into a generating AI and have the generating AI perform the selection of the optimal coordination method.

[0106] The coordination unit can customize the content of the discussion meetings based on the proposer's current areas of interest when coordinating them. For example, the coordination unit can prioritize coordinating discussion meetings related to areas the proposer is currently interested in. It can also prioritize coordinating discussion meetings related to projects the proposer is currently working on. Furthermore, the coordination unit can prioritize coordinating discussion meetings in areas the proposer is interested in based on current trends. This allows for more appropriate discussion meetings by customizing the content of the discussions based on the proposer's current areas of interest. Some or all of the above processing in the coordination unit may be performed using AI, for example, or not. For example, the coordination unit can input data on the proposer's current areas of interest into a generating AI and have the generating AI perform the customization of the discussion content.

[0107] The coordination unit can estimate the proposer's emotions and determine the priority of opinion exchange sessions based on the estimated emotions. For example, if the proposer is relaxed, the coordination unit will prioritize opinion exchange sessions that allow for relaxed evaluation. If the proposer is excited, the coordination unit can also prioritize opinion exchange sessions that help maintain that excitement. Furthermore, if the proposer is tired, the coordination unit can prioritize opinion exchange sessions that are easy to understand. This allows for efficient opinion exchange sessions by determining the priority of opinion exchange sessions based on the proposer'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 coordination unit may be performed using AI, or not using AI. For example, the coordination unit can input the proposer's emotion data into a generative AI and have the generative AI determine the priority of opinion exchange sessions.

[0108] The coordination unit can select the optimal coordination method when coordinating opinion exchange meetings, taking into account the proposer's geographical location information. For example, the coordination unit may prioritize coordinating opinion exchange meetings related to the region where the proposer is currently located. It can also prioritize coordinating opinion exchange meetings related to regions the proposer has visited in the past. Furthermore, it can prioritize coordinating opinion exchange meetings related to regions the proposer plans to visit in the future. In this way, the optimal coordination method can be selected by taking into account the proposer's geographical location information. Some or all of the above processing in the coordination unit may be performed using AI, for example, or without AI. For example, the coordination unit may input the proposer's geographical location information data into a generating AI and have the generating AI select the optimal coordination method.

[0109] The coordination unit can analyze the proposer's social media activity and customize the coordination content when coordinating opinion exchange meetings. For example, the coordination unit can prioritize scheduling opinion exchange meetings related to topics the proposer has shown interest in on social media. It can also prioritize scheduling opinion exchange meetings related to the content of accounts the proposer follows on social media. Furthermore, the coordination unit can prioritize scheduling opinion exchange meetings related to the activities of groups and communities the proposer participates in on social media. This allows for more appropriate opinion exchange meetings by analyzing the proposer's social media activity. Some or all of the above processing in the coordination unit may be performed using AI, for example, or not. For example, the coordination unit can input the proposer's social media activity data into a generating AI and have the generating AI perform the customization of the coordination content.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The proposal unit can estimate the proposer's emotions and customize the proposal content based on those emotions. For example, if the proposer is relaxed, it can make a proposal that is easy to accept in a relaxed state. If the proposer is excited, it can make a stimulating proposal that will help maintain that excitement. Furthermore, if the proposer is tired, it can make a proposal that is easy to understand. By customizing the proposal content based on the proposer's emotions, more effective proposals can be made. Emotion estimation can be achieved using, for example, an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can input the proposer's emotion data into a generative AI and have the generative AI customize the proposal content.

[0112] The evaluation unit can estimate the proposer's emotions and adjust the evaluation criteria based on the estimated emotions. For example, if the proposer is relaxed, the evaluation unit can set criteria that allow for evaluation in a relaxed state. If the proposer is excited, the evaluation unit can set criteria that help maintain that excitement. Furthermore, if the proposer is tired, the evaluation unit can set criteria that are easy to understand. This allows for efficient idea evaluation by adjusting the evaluation criteria based on the proposer's emotions. Emotion estimation can be achieved using, for example, an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input the proposer's emotion data into a generative AI and have the generative AI adjust the evaluation criteria.

[0113] The adjustment unit can estimate the proposer's emotions and adjust the method of conducting the opinion exchange meeting based on the estimated emotions. For example, if the proposer is relaxed, it can suggest a method of conducting the opinion exchange meeting in a relaxed state. If the proposer is excited, it can suggest a method of conducting the opinion exchange meeting in a way that helps maintain that excitement. Furthermore, if the proposer is tired, it can suggest a method of conducting the opinion exchange meeting in an easy-to-understand manner. By adjusting the method of conducting the opinion exchange meeting based on the proposer's emotions, an efficient opinion exchange meeting becomes possible. Emotion estimation is achieved, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input the proposer's emotion data into a generative AI and have the generative AI perform the adjustment of the method of conducting the opinion exchange meeting.

[0114] The collection unit can estimate the proposer's emotions and adjust the timing of collecting past ideas based on the estimated emotions. For example, if the proposer is feeling stressed, past ideas can be collected during times when they can relax. If the proposer is focused, past ideas can be collected during times when their concentration is high. Furthermore, if the proposer is tired, past ideas can be collected after they have rested. This allows for efficient idea collection by adjusting the collection timing based on the proposer's emotions. Emotion estimation can be achieved using, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the proposer's emotion data into the generative AI and have the generative AI adjust the collection timing.

[0115] The reception unit can estimate the proposer's emotions and adjust the timing of idea submission based on the estimated emotions. For example, if the proposer is stressed, ideas can be submitted during a time when they can relax. If the proposer is focused, ideas can be submitted during a time when their concentration is at its highest. Furthermore, if the proposer is tired, ideas can be submitted after they have rested. This allows for efficient idea submission by adjusting the timing of submission based on the proposer's emotions. Emotion estimation can be achieved using, for example, an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI or not. For example, the reception unit can input the proposer's emotion data into the generative AI and have the generative AI adjust the timing of submission.

[0116] The collection unit can analyze the proposer's past submission history when collecting past ideas and select the optimal collection method. For example, it can analyze the trends of ideas previously submitted by the proposer and prioritize the collection of related ideas. It can also prioritize the collection of highly-rated ideas based on the evaluation results of ideas previously submitted by the proposer. Furthermore, it can analyze the fields of ideas previously submitted by the proposer and prioritize the collection of ideas in the same field. In this way, the optimal collection method can be selected by analyzing the proposer's past submission history. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the proposer's past submission history data into a generating AI and have the generating AI select the optimal collection method.

[0117] The evaluation unit can optimize its evaluation algorithm by referring to past evaluation data when evaluating ideas. For example, it can optimize the evaluation algorithm based on past evaluation data. It can also analyze past evaluation data and revise the evaluation criteria. Furthermore, it can improve the accuracy of the evaluation by referring to past evaluation data. In this way, the evaluation algorithm can be optimized by referring to past evaluation data. Some or all of the above processes in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input past evaluation data into a generating AI and have the generating AI perform the optimization of the evaluation algorithm.

[0118] The proposal department can select the optimal proposal method by referring to past proposal data when adding new unique features. For example, it can select the optimal proposal method based on past proposal data. It can also analyze past proposal data and revise proposal methods. Furthermore, it can improve the accuracy of proposals by referring to past proposal data. In this way, the optimal proposal method can be selected by referring to past proposal data. Some or all of the above processes in the proposal department may be performed using AI or not. For example, the proposal department can input past proposal data into a generation AI and have the generation AI perform the selection of the optimal proposal method.

[0119] The coordination unit can select the optimal coordination method by referring to past opinion exchange meeting data when coordinating opinion exchange meetings. For example, it can select the optimal coordination method based on past opinion exchange meeting data. It can also analyze past opinion exchange meeting data and revise the coordination method. Furthermore, it can improve the accuracy of coordination by referring to past opinion exchange meeting data. In this way, the optimal coordination method can be selected by referring to past opinion exchange meeting data. Some or all of the above processing in the coordination unit may be performed using AI or not. For example, the coordination unit can input past opinion exchange meeting data into a generating AI and have the generating AI perform the selection of the optimal coordination method.

[0120] The proposal department can customize the proposal content based on the proposer's current areas of interest when adding new unique features. For example, it can add new unique features related to the proposer's current areas of interest. It can also add new unique features related to the project the proposer is currently working on. Furthermore, it can add new unique features in areas of interest based on current trends. This allows for more appropriate proposals by customizing the proposal content based on the proposer's current areas of interest. Some or all of the above processing in the proposal department may be performed using AI or not. For example, the proposal department can input the proposer's current areas of interest data into a generating AI and have the generating AI perform the customization of the proposal content.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The collection unit stores past ideas as knowledge. For example, it stores a collection of ideas from past generative AI contests in a database so that generative AI can access them. Step 2: The reception desk receives the proposer's idea. For example, the proposer enters an overview and details of their idea and sends it to the generation AI. Step 3: The evaluation unit evaluates the ideas received by the reception unit by comparing them with past ideas. For example, a generation AI analyzes the input ideas and evaluates their originality and value by comparing them with past ideas. Step 4: The proposal team proposes new, original ideas based on the ideas evaluated by the evaluation team. For example, a generative AI might add new elements to the proposer's idea or suggest an approach from a different perspective. Step 5: The coordination department conducts an exchange of ideas meeting, bringing together proposers with high affinity to the ideas proposed by the proposal department. For example, a generation AI analyzes past ideas and proposer information to identify proposers with high affinity and sends them an invitation to the exchange of ideas meeting.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the collection unit, reception unit, evaluation unit, proposal unit, and coordination unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and stores past ideas in the database 24. The reception unit is implemented by the control unit 46A of the smart device 14 and receives the proposer's idea. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the input idea, comparing it with past ideas to evaluate its originality and value. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and adds new elements to the proposer's idea or proposes an approach from a different perspective. The coordination unit is implemented by the specific processing unit 290 of the data processing unit 12 and conducts an exchange of opinions meeting bringing together proposers with similar affinities. 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.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the collection unit, reception unit, evaluation unit, proposal unit, and adjustment unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and stores past ideas in the database 24. The reception unit is implemented, for example, by the control unit 46A of the smart glasses 214 and receives the proposer's idea. The evaluation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and analyzes the input idea, comparing it with past ideas to evaluate its originality and value. The proposal unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and adds new elements to the proposer's idea or proposes an approach from a different perspective. The adjustment unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and conducts an exchange of opinions meeting bringing together proposers with similar affinity. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements described above, including the collection unit, reception unit, evaluation unit, proposal unit, and coordination unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and stores past ideas in the database 24. The reception unit is implemented by, for example, the control unit 46A of the headset terminal 314 and receives the proposer's idea. The evaluation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the input idea, comparing it with past ideas to evaluate its originality and value. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and adds new elements to the proposer's idea or proposes an approach from a different perspective. The coordination unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and conducts an exchange of opinions meeting bringing together proposers with high affinity. 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.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] Each of the multiple elements described above, including the collection unit, reception unit, evaluation unit, proposal unit, and adjustment unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the specific processing unit 290 of the data processing unit 12 and stores past ideas in the database 24. The reception unit is implemented by, for example, the control unit 46A of the robot 414 and receives the proposer's idea. The evaluation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the input idea, comparing it with past ideas to evaluate its originality and value. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and adds new elements to the proposer's idea or proposes an approach from a different perspective. The adjustment unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and conducts an exchange of ideas meeting bringing together proposers with similar affinities. 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] (Note 1) A collection department that accumulates past ideas as knowledge, A reception area where proposers input their ideas, An evaluation unit that evaluates the ideas received by the reception unit by comparing them with past ideas, A proposal unit proposes new, original ideas based on the ideas evaluated by the aforementioned evaluation unit, The system includes a coordination unit that conducts an exchange of opinions meeting bringing together proposers with similar ideas based on the ideas proposed by the aforementioned proposal unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Accumulate a collection of ideas from past generative AI contests as knowledge. The system described in Appendix 1, characterized by the features described herein. (Note 3) The evaluation unit, The proposer's idea is compared to past ideas to evaluate its originality and value. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, If originality is lacking, propose an idea that adds a new, unique touch. The system described in Appendix 1, characterized by the features described herein. (Note 5) The adjustment unit is, We will hold an exchange of ideas meeting bringing together proposers with similar affiliations. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, We add new elements to the proposer's idea or suggest approaches from a different perspective. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is The system estimates the proposer's emotions and adjusts the timing of collecting past ideas based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting past ideas, analyze the proposer's past submission history to select the most suitable collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting past ideas, filter them based on the proposer's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is Estimate the proposer's sentiments and prioritize the ideas to collect based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting past ideas, prioritize collecting highly relevant ideas by considering the geographical location of the proposer. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting past ideas, analyze the proposer's social media activity and gather relevant ideas. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is The system estimates the proposer's emotions and adjusts the timing of idea submission based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned reception unit is Analyze the proposer's past idea submission history and select the most suitable submission method. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned reception unit is When receiving ideas, we filter them based on the proposer's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned reception unit is The system estimates the proposer's emotions and prioritizes the ideas to be accepted based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned reception unit is When receiving ideas, we will prioritize accepting ideas that are highly relevant, taking into account the proposer's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned reception unit is When accepting ideas, we analyze the proposer's social media activity and accept relevant ideas. The system described in Appendix 1, characterized by the features described herein. (Note 19) The evaluation unit, Estimate the proposer's emotions and adjust the idea evaluation criteria based on the estimated proposer's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The evaluation unit, When evaluating ideas, the evaluation algorithm is optimized by referring to past evaluation data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The evaluation unit, When evaluating ideas, the proposer's attribute information should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 22) The evaluation unit, The system estimates the proposer's emotions and adjusts how the evaluation results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The evaluation unit, When evaluating ideas, the geographical distribution of the proposers should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 24) The evaluation unit, When evaluating ideas, refer to relevant literature to improve the accuracy of the evaluation. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, We estimate the proposer's emotions and adjust the method of adding new uniqueness based on the estimated proposer's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When adding new unique features, we select the optimal proposal method by referring to past proposal data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, When adding a new unique feature, customize the proposal based on the proposer's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, The system estimates the proposer's emotions and determines the priority of the proposals based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, When adding a new, unique feature, the optimal proposal method will be selected considering the proposer's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned proposal section is, When adding a new, unique element, we analyze the proposer's social media activity and customize the proposal accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 31) The adjustment unit is, Estimate the proposer's emotions and adjust the method of coordinating the exchange of opinions based on the estimated emotions of the proposer. The system described in Appendix 1, characterized by the features described herein. (Note 32) The adjustment unit is, When coordinating opinion exchange meetings, the optimal coordination method will be selected by referring to data from past opinion exchange meetings. The system described in Appendix 1, characterized by the features described herein. (Note 33) The adjustment unit is, When coordinating the exchange of opinions, customize the content of the discussion based on the proposer's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 34) The adjustment unit is, The system estimates the sentiments of the proposers and determines the priority of the discussion sessions based on the estimated sentiments of the proposers. The system described in Appendix 1, characterized by the features described herein. (Note 35) The adjustment unit is, When coordinating the exchange of opinions, the most suitable coordination method will be selected, taking into account the geographical location information of the proposer. The system described in Appendix 1, characterized by the features described herein. (Note 36) The adjustment unit is, When coordinating the exchange of opinions, analyze the proposer's social media activity to customize the coordination. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0195] 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 collection department that accumulates past ideas as knowledge, A reception area where proposers input their ideas, An evaluation unit that evaluates the ideas received by the reception unit by comparing them with past ideas, A proposal unit proposes new, original ideas based on the ideas evaluated by the aforementioned evaluation unit, The system includes a coordination unit that conducts an exchange of opinions meeting bringing together proposers with similar ideas based on the ideas proposed by the aforementioned proposal unit. A system characterized by the following features.

2. The aforementioned collection unit is We will accumulate a collection of ideas from past generative AI contests as knowledge. The system according to feature 1.

3. The evaluation unit, The proposer's idea is compared to past ideas to evaluate its originality and value. The system according to feature 1.

4. The aforementioned proposal section is, If originality is lacking, propose an idea that adds a new, unique touch. The system according to feature 1.

5. The adjustment unit is, We will hold an exchange of ideas meeting bringing together proposers with similar affiliations. The system according to feature 1.

6. The aforementioned proposal section is, We add new elements to the proposer's idea or suggest approaches from a different perspective. The system according to feature 1.

7. The aforementioned collection unit is The system estimates the proposer's emotions and adjusts the timing of collecting past ideas based on the estimated emotions. The system according to feature 1.

8. The aforementioned collection unit is When collecting past ideas, analyze the proposer's past submission history to select the most suitable collection method. The system according to feature 1.

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