system

Abstract

The system according to this embodiment aims to achieve consistent review and provide stable feedback to applicants. [Solution] The system according to the embodiment comprises a reception unit, a review unit, a feedback unit, and a publication unit. The reception unit receives ideas from contest applicants. The review unit reviews the ideas received by the reception unit based on evaluation criteria and scores them. The feedback unit provides feedback to the applicants on the review results obtained by the review unit. The publication unit publishes the review results provided by the feedback unit on a dedicated website.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that in the examination of a contest, the feelings and fluctuations of the examiner affect the evaluation, and it is difficult to make a consistent evaluation.

[0005] The system according to the embodiment aims to achieve a consistent examination and provide stable feedback to the applicants.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a review unit, a feedback unit, and a publication unit. The reception unit receives ideas from contest applicants. The review unit reviews the ideas received by the reception unit based on evaluation criteria and assigns scores. The feedback unit provides feedback to the applicants based on the review results obtained by the review unit. The publication unit publishes the review results provided by the feedback unit on a dedicated website. [Effects of the Invention]

[0007] The system according to this embodiment can achieve consistent review and provide stable feedback to applicants. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The contest judging system according to an embodiment of the present invention is a system that uses generative AI to evaluate and score contest ideas. In this contest judging system, contest applicants submit ideas, and the generative AI evaluates and scores the submitted ideas based on evaluation criteria. The evaluation criteria include originality, profitability, social contribution, feasibility, and usefulness. The generative AI learns from past contest evaluation criteria and scoring results, as well as examples of AI applications in the world, and improves its evaluation criteria. The evaluation results are fed back to the applicants and published on a dedicated website. This allows applicants to know how their ideas were evaluated and to identify areas for improvement for future applications. In addition, opinions and questions regarding the evaluation results can be collected and used to improve the system's operation. Based on its use in generative AI contests, this contest judging system can be developed into various contest judging AIs, both internally and externally. For example, it is expected to be used in various fields, such as corporate idea contests and idea solicitations for regional revitalization projects. This contest judging system allows applicants to receive consistent evaluations, and the organizing committee can efficiently evaluate a large number of ideas. Furthermore, continuous learning and improvement are carried out through the publication of judging results and the collection of feedback, resulting in higher quality judging. For example, the contest judging system provides an online form for applicants to submit their ideas. Applicants can enter the necessary information into the form and submit their ideas. Next, the generative AI judges the submitted ideas based on evaluation criteria and scores them. For example, the generative AI evaluates the differences and novelty of ideas as a criterion for originality. It evaluates the projected revenue and cost performance as a criterion for profitability. It evaluates the social impact and environmental considerations as a criterion for social contribution. It evaluates the technical feasibility and resource requirements as a criterion for feasibility. It evaluates the satisfaction of user needs and actual use value as a criterion for usefulness. The generative AI judges and scores ideas based on these evaluation criteria. The judging results are fed back to the applicants. For example, the judging results include scores and comments based on each evaluation criterion.Applicants can learn how their ideas were evaluated through the judging results. The results are also published on a dedicated website, allowing other applicants and the general public to view them. Furthermore, opinions and questions regarding the judging results can be collected and used to improve the system. For example, feedback from applicants and judges can be gathered and used to improve evaluation criteria and the judging process. This allows the contest judging system to continuously learn and provide higher quality evaluations. Ultimately, the contest judging system can efficiently evaluate applicants' ideas and provide feedback and publish the results.

[0029] The contest judging system according to this embodiment comprises a reception unit, a judging unit, a feedback unit, and a publication unit. The reception unit receives ideas from contest applicants. The reception unit can receive ideas, for example, through an online form. The reception unit can also receive ideas via email or a dedicated application. Furthermore, the reception unit can save the ideas submitted by applicants to a database. For example, the reception unit automatically saves ideas submitted through an online form to the database. Ideas submitted via email can be manually registered in the database. Ideas submitted through a dedicated application are saved on the application's server. The judging unit evaluates the ideas received by the reception unit based on evaluation criteria and assigns scores. The judging unit evaluates ideas, for example, using a generative AI. The generative AI learns evaluation criteria such as originality, profitability, social contribution, feasibility, and usefulness, and performs evaluations. For example, the generative AI learns the evaluation criteria and scoring results of past contests and improves the evaluation criteria. The generative AI can also learn about AI application examples in the world and update the evaluation criteria. For example, the Generative AI learns the latest AI technologies and trends and reflects them in the evaluation criteria. The Feedback Department provides applicants with feedback on the evaluation results obtained by the Review Department. For example, the Feedback Department provides applicants with the reasons for the scoring along with the evaluation results. For example, the Feedback Department provides applicants with scoring results and comments based on each evaluation criterion. The Feedback Department can also notify applicants of the evaluation results via email or a dedicated app. For example, the Feedback Department sends the evaluation results via email so that applicants can check the results. When notifying applicants of the evaluation results via a dedicated app, the app's notification function is used to display the results. The Public Department publishes the evaluation results that have been fed back by the Feedback Department on a dedicated website. For example, the Public Department posts the evaluation results on a dedicated website so that other applicants and the general public can view them. For example, the Public Department posts the evaluation results on a specific page of the dedicated website so that viewers can search for the results. The Public Department can also collect opinions and questions regarding the evaluation results and use them to improve operations.For example, the public section can set up a feedback form on a dedicated website to collect opinions and questions from applicants and viewers. This allows the contest judging system according to the embodiment to efficiently review applicants' ideas and provide feedback and make them public.

[0030] The reception department accepts ideas from contest applicants. For example, ideas can be submitted via an online form. Specifically, the online form is designed as an easily accessible webpage where applicants can enter the necessary information and submit their ideas. The form includes fields for idea title, summary, detailed description, and the ability to upload related materials. The reception department can also accept ideas via email or a dedicated app. For email submissions, applicants send their ideas to a designated email address, which the reception department receives and registers in its database. Ideas submitted through the dedicated app are stored on the app's server. The app has a user-friendly interface designed to allow applicants to easily input and submit their ideas. Furthermore, the reception department can save submitted ideas to a database. For example, ideas submitted via the online form can be automatically saved to the database. The database is a system for efficiently managing applicant information and submitted ideas, and includes search and filtering functions. This allows the reception department to quickly and accurately manage submitted ideas and provide them to the judging panel.

[0031] The judging department reviews and scores ideas submitted by the reception department based on evaluation criteria. The judging department uses, for example, generative AI to evaluate ideas. Generative AI learns evaluation criteria such as originality, profitability, social contribution, feasibility, and usefulness, and uses them for evaluation. Specifically, the generative AI learns the judging criteria and scoring results of past contests and improves the evaluation criteria. For example, the generative AI analyzes the characteristics of past winning ideas and highly-rated ideas, and evaluates new ideas based on those characteristics. The generative AI can also learn about AI applications in the world and update the evaluation criteria. For example, the generative AI learns the latest AI technologies and trends and reflects them in the evaluation criteria. This allows the judging department to always conduct highly accurate evaluations based on the latest information. Furthermore, the generative AI can use natural language processing technology to analyze the text of applicants' ideas and evaluate the consistency and logic of the content. This allows the judging department to evaluate the quality of ideas from multiple perspectives and conduct fair and objective evaluations.

[0032] The Feedback Department provides applicants with feedback on the evaluation results obtained by the Review Department. For example, the Feedback Department provides applicants with the reasons for the scoring along with the evaluation results. Specifically, the Feedback Department provides applicants with scoring results and comments based on each evaluation criterion. For example, if the originality evaluation is high, the Feedback Department will explain in detail the reasons and specific points. Also, if the profitability or feasibility evaluation is low, the Feedback Department will provide areas for improvement and specific advice. This allows applicants to understand the strengths and weaknesses of their ideas and improve them for future applications. The Feedback Department can also notify applicants of the evaluation results via email or a dedicated app. For example, the Feedback Department can send the evaluation results by email so that applicants can check the results. The email will include details of the evaluation results and feedback comments, which applicants can use to review their ideas. When notifying applicants of the evaluation results via a dedicated app, the app's notification function will be used to display the results. The app's notification function notifies applicants of the evaluation results in real time, allowing them to check the results quickly. This allows the Feedback Department to provide applicants with quick and detailed feedback and support their growth.

[0033] The Public Section will publish the review results, which have been fed back by the Feedback Section, on a dedicated website. For example, the Public Section will post the review results on the dedicated website, making them accessible to other applicants and the general public. Specifically, the Public Section will post the review results on a specific page of the dedicated website, allowing viewers to search for the results. The dedicated website will have a user-friendly interface, designed to allow viewers to easily search and view the review results. The Public Section can also collect opinions and questions regarding the review results to improve operations. For example, the Public Section will install a feedback form on the dedicated website to collect opinions and questions from applicants and viewers. The feedback form is designed to be easy to use, allowing applicants and viewers to freely enter their opinions and questions. This allows the Public Section to collect feedback on the review results and use it to improve the system and optimize operations. Furthermore, the Public Section will also consider privacy protection and data security when publishing the review results. For example, it will protect applicants' privacy by appropriately protecting their personal information and limiting the scope of publication of the review results. This allows the public disclosure department to publish the review results while ensuring transparency and reliability.

[0034] The judging department can learn evaluation criteria such as originality, profitability, social contribution, feasibility, and usefulness, and conduct evaluations. The judging department can learn evaluation criteria using, for example, generative AI. The generative AI learns evaluation criteria and scoring results from past contests and improves the evaluation criteria. For example, the generative AI analyzes evaluation data from past contests to improve the accuracy of the evaluation criteria. The generative AI can also learn about AI applications in the world and update the evaluation criteria. For example, the generative AI learns the latest AI technologies and trends and reflects them in the evaluation criteria. As a result, the accuracy of the evaluation is improved by learning the evaluation criteria. Some or all of the above processes in the judging department may be performed using, for example, generative AI, or without using generative AI. For example, the judging department can input evaluation data from past contests into the generative AI and have the generative AI improve the evaluation criteria.

[0035] The feedback unit can provide applicants with feedback on the evaluation results along with the reasons for their scoring. The feedback unit can, for example, provide specific comments and feedback based on the evaluation results. For example, the feedback unit can provide applicants with scoring results and comments based on each evaluation criterion. The feedback unit can also notify applicants of the evaluation results via email or a dedicated app. For example, the feedback unit can send the evaluation results by email so that applicants can check the results. When notifying applicants of the evaluation results via a dedicated app, the app's notification function is used to display the results. This allows applicants to understand the details of the evaluation by providing feedback on the reasons for their scoring along with the evaluation results. Some or all of the above processes in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input the evaluation results into AI and have the AI ​​generate specific comments and feedback.

[0036] The Public Department can collect opinions and questions regarding the review results and use them to improve operations. For example, the Public Department can set up a feedback form on a dedicated website to collect opinions and questions from applicants and viewers. For example, the Public Department can store the opinions and questions collected through the feedback form in a database and use them to improve operations. The Public Department can also analyze the collected opinions and questions and reflect the results in improving the evaluation criteria and review process. For example, the Public Department can analyze the content of the opinions and questions to identify areas for revision of the evaluation criteria and improvement of the review process. In this way, by collecting opinions and questions, it becomes possible to improve the operation of the system. Some or all of the above processes in the Public Department may be performed using AI, or not. For example, the Public Department can input the opinions and questions collected through the feedback form into an AI and have the AI ​​perform the analysis and identification of areas for improvement.

[0037] The judging department can improve its evaluation criteria by learning from past contest judging criteria and scoring results, as well as examples of AI applications in the real world. For example, the judging department can use generative AI to learn from past contest judging criteria and scoring results. The generative AI analyzes past judging data and improves the accuracy of the evaluation criteria. For example, the generative AI identifies areas for improvement in the evaluation criteria based on past contest judging data. The generative AI can also learn from examples of AI applications in the real world and update the evaluation criteria. For example, the generative AI learns the latest AI technologies and trends and reflects them in the evaluation criteria. As a result, the accuracy of the evaluation criteria improves by learning from past data. Some or all of the above processes in the judging department may be performed using, for example, generative AI, or without using generative AI. For example, the judging department can input past contest judging data into the generative AI and have the generative AI perform improvements to the evaluation criteria.

[0038] The Public Section can publish the judging results on a dedicated website. For example, the Public Section can post the judging results on a dedicated website so that other applicants and the general public can view them. For example, the Public Section can post the judging results on a specific page of the dedicated website so that viewers can search for the results. The Public Section can also organize the judging results by category so that viewers can easily find results in categories that interest them. For example, the Public Section can publish the judging results divided into categories such as technology, business, and social contribution. This improves transparency by making the judging results public. Some or all of the above processes in the Public Section may be performed using AI, for example, or not using AI. For example, the Public Section can input the judging results into AI and have the AI ​​perform the category organization and generate the publication pages.

[0039] The reception department can analyze past application history and select the most suitable application method. For example, the reception department can prioritize suggesting application methods that the user has frequently used in the past (online forms, email, etc.). For instance, the reception department can retrieve past application history from a database and identify the most frequently used application method. The reception department can also recommend specific time slots if ideas submitted during those times have received high ratings based on the user's past application history. For example, the reception department can analyze past application data and identify the time slots when ideas received high ratings were submitted. Furthermore, the reception department can customize the optimal application method based on the evaluation results of ideas previously submitted by the user. For example, the reception department can analyze the user's past evaluation data and suggest the most suitable application method. This allows the reception department to select the most suitable application method by analyzing past application history. Some or all of the above processes in the reception department may be performed using AI, or not. For example, the reception department can input past application history data into an AI and have the AI ​​select the most suitable application method.

[0040] The reception department can filter ideas based on the applicant's current projects and areas of interest when receiving them. For example, the reception department can prioritize ideas related to projects the applicant is currently working on. For example, the reception department can obtain the applicant's project data and identify highly relevant ideas. The reception department can also filter and accept highly relevant ideas based on the applicant's areas of interest. For example, the reception department can analyze the applicant's areas of interest data and extract highly relevant ideas. Furthermore, the reception department can refer to the applicant's past project history and prioritize accepting highly relevant ideas. For example, the reception department can identify highly relevant ideas based on the applicant's past project data. This allows the reception department to accept highly relevant ideas by filtering based on the applicant's projects and areas of interest. Some or all of the above processing in the reception department may be performed using AI, or not. For example, the reception department can input the applicant's project data and areas of interest data into an AI and have the AI ​​perform the filtering.

[0041] The reception department can prioritize accepting ideas that are highly relevant, taking into account the applicant's geographical location when receiving ideas. For example, if an applicant lives in a specific region, the reception department will prioritize accepting ideas related to that region. For example, the reception department will obtain the applicant's geographical location and identify highly relevant ideas. The reception department can also prioritize accepting ideas that address region-specific problems based on the applicant's geographical location. For example, the reception department will extract ideas related to region-specific problems and prioritize accepting them. Furthermore, the reception department can also prioritize accepting ideas that meet regional needs, taking into account the applicant's geographical location. For example, the reception department will identify highly relevant ideas based on regional needs. In this way, by considering geographical location, ideas that address region-specific problems can be prioritized. 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 applicant's geographical location into AI and have the AI ​​identify highly relevant ideas.

[0042] The reception department can analyze applicants' social media activity when receiving ideas and accept relevant ideas. For example, the reception department can prioritize ideas related to themes of interest based on the applicant's social media activity. For example, the reception department can acquire the applicant's social media data and identify themes of interest. The reception department can also analyze the applicant's statements and posts on social media and accept highly relevant ideas. For example, the reception department can analyze the content of the applicant's posts and extract highly relevant ideas. Furthermore, the reception department can adjust the acceptance of ideas considering the applicant's followers and influence on social media. For example, the reception department can determine the priority of ideas based on the applicant's number of followers and influence. This allows the reception department to accept highly relevant ideas by analyzing social media activity. Some or all of the above processes in the reception department may be performed using AI, or not. For example, the reception department can input the applicant's social media data into AI and have the AI ​​identify highly relevant ideas.

[0043] The review department can improve the accuracy of its review process by considering the interrelationships between ideas. For example, the review department can analyze the relationships between submitted ideas and give higher ratings to ideas that complement each other. For example, the review department can acquire idea relationship data and identify ideas that complement each other. The review department can also appropriately evaluate overlapping or similar ideas by considering their interrelationships. For example, the review department can analyze idea overlap data and identify overlapping or similar ideas. Furthermore, the review department can conduct a review that considers the overall balance based on the interrelationships of ideas. For example, the review department can conduct a review that considers the overall balance based on idea balance data. This improves the accuracy of the review by considering the interrelationships of ideas. Some or all of the above processes in the review department may be performed using AI, for example, or not. For example, the review department can input idea relationship data into AI and have the AI ​​perform the analysis of interrelationships and improve the accuracy of the review.

[0044] The review department can consider the attribute information of the idea submitter when conducting the review. For example, the review department can evaluate the feasibility of an idea by considering the submitter's field of expertise and experience. For example, the review department can acquire the submitter's attribute data and identify their field of expertise and experience. The review department can also evaluate the profitability of an idea based on the submitter's past performance. For example, the review department can analyze the submitter's past performance data to evaluate profitability. Furthermore, the review department can also evaluate the social contribution and usefulness by considering the submitter's attribute information. For example, the review department can evaluate the social contribution and usefulness based on the submitter's attribute data. This allows for a more appropriate review by considering the submitter's attribute information. Some or all of the above processes in the review department may be performed using AI, for example, or not. For example, the review department can input the submitter's attribute data into an AI and have the AI ​​perform the analysis and review of the attribute information.

[0045] The review department can consider the geographical distribution of ideas during the review process. For example, the review department can evaluate ideas from each region in a balanced manner to avoid geographical bias. For example, the review department can acquire geographical distribution data of ideas and consider the balance across regions. The review department can also give higher ratings to ideas that address region-specific problems. For example, the review department can identify ideas related to region-specific problems and give them high ratings. Furthermore, the review department can conduct reviews tailored to the needs of each region, taking geographical distribution into consideration. For example, the review department can conduct reviews tailored to the needs of each region based on regional needs data. This allows for the appropriate evaluation of ideas that address region-specific problems by considering geographical distribution. Some or all of the above processes in the review department may be performed using AI, or not. For example, the review department can input geographical distribution data of ideas into an AI and have the AI ​​perform geographical distribution analysis and review.

[0046] The review department can improve the accuracy of its review by referring to relevant literature on the idea during the review process. For example, the review department may evaluate the submitted idea by referring to academic papers and patent documents related to it. For example, the review department may acquire relevant literature data and use it to evaluate the idea. The review department may also refer to relevant literature to understand the technical background of the idea. For example, the review department may refer to relevant literature based on technical background data. Furthermore, the review department may also refer to relevant literature to evaluate the novelty of the idea. For example, the review department may refer to relevant literature based on novelty data. In this way, the accuracy of the review is improved by referring to relevant literature. Some or all of the above processes in the review department may be performed using AI, for example, or not using AI. For example, the review department may input relevant literature data into AI and have the AI ​​perform the literature referencing and review.

[0047] The feedback unit can adjust the level of detail in the feedback based on the importance of the review results. For example, the feedback unit can provide detailed feedback for important review results. For example, the feedback unit can acquire importance data for review results and provide detailed feedback for important results. The feedback unit can also provide concise feedback for general review results. For example, the feedback unit can provide concise feedback for general results based on importance data for review results. Furthermore, the feedback unit can provide focused feedback on particularly important points. For example, the feedback unit can analyze importance data for review results and provide focused feedback on particularly important points. This allows for more appropriate feedback to be provided by adjusting the level of detail in the feedback based on the importance of the review results. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input importance data for review results into AI and have the AI ​​adjust the level of detail in the feedback.

[0048] The feedback unit can apply different feedback algorithms depending on the category of the idea during the feedback process. For example, the feedback unit can provide technical feedback to technical ideas. For instance, it can acquire idea category data and provide technical feedback to technical ideas. The feedback unit can also provide profitability feedback to business ideas. For example, it can provide profitability feedback to business ideas based on idea category data. Furthermore, the feedback unit can provide social impact feedback to social contribution ideas. For example, it can analyze idea category data and provide social impact feedback to social contribution ideas. This allows for more appropriate feedback by providing feedback according to the idea category. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input idea category data into AI and have the AI ​​apply the feedback algorithm.

[0049] The feedback unit can determine the priority of feedback based on the submission timing of the review results. For example, the feedback unit can prioritize feedback for review results submitted early. For example, the feedback unit can acquire review result submission timing data and prioritize feedback for results submitted early. The feedback unit can also provide rapid feedback for review results submitted just before the deadline. For example, the feedback unit can provide rapid feedback for results submitted just before the deadline based on review result submission timing data. Furthermore, the feedback unit can adjust the priority of feedback according to the submission timing. For example, the feedback unit can analyze review result submission timing data and adjust the priority of feedback according to the submission timing. This allows for faster feedback by determining the priority of feedback based on the submission timing of the review results. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not. For example, the feedback unit can input review result submission timing data into AI and have the AI ​​determine the priority of feedback.

[0050] The feedback unit can adjust the order of feedback based on the relevance of the review results. For example, the feedback unit can prioritize providing feedback to particularly relevant review results. For example, the feedback unit can acquire relevance data of the review results and prioritize providing feedback to highly relevant results. The feedback unit can also provide feedback in the normal order for review results with general relevance. For example, the feedback unit can provide feedback in the normal order for results with general relevance based on the relevance data of the review results. Furthermore, the feedback unit can postpone providing feedback to review results with low relevance. For example, the feedback unit can analyze the relevance data of the review results and postpone providing feedback to results with low relevance. By adjusting the order of feedback based on the relevance of the review results, more appropriate feedback can be provided. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the relevance data of the review results into AI and have the AI ​​perform the adjustment of the feedback order.

[0051] The publishing unit can optimize the current publishing method by referring to past publishing data at the time of publication. For example, the publishing unit can analyze past publishing data and select the most effective display method. For example, the publishing unit can acquire past publishing data and identify effective display methods. The publishing unit can also prioritize display methods that received good user responses from past publishing data. For example, the publishing unit can identify display methods that received good user responses based on past publishing data. Furthermore, the publishing unit can customize the current publishing method based on past publishing data. For example, the publishing unit can analyze past publishing data and optimize the current publishing method. This allows the optimal publishing method to be selected by referring to past publishing data. Some or all of the above processes in the publishing unit may be performed using AI, for example, or not using AI. For example, the publishing unit can input past publishing data into AI and have the AI ​​perform the optimization of the publishing method.

[0052] The publishing department can apply different publishing methods to each category of idea at the time of publication. For example, for technical ideas, the publishing department may adopt a publishing method that includes technical details. For example, the publishing department may acquire idea category data and apply a publishing method that includes technical details to technical ideas. The publishing department may also adopt a publishing method that emphasizes profitability information for business ideas. For example, based on idea category data, the publishing department may apply a publishing method that emphasizes profitability information to business ideas. Furthermore, the publishing department may adopt a publishing method that emphasizes social impact information for social contribution ideas. For example, the publishing department may analyze idea category data and apply a publishing method that emphasizes social impact information to social contribution ideas. This allows for more appropriate information to be provided by applying a publishing method appropriate to the category of the idea. Some or all of the above processing in the publishing department may be performed using AI, for example, or not using AI. For example, the publishing department may input idea category data into AI and have the AI ​​perform the application of publishing methods.

[0053] The publishing department can determine the order of publication based on the submission date of ideas at the time of publication. For example, the publishing department can prioritize the publication of ideas submitted early. For example, the publishing department can acquire idea submission date data and prioritize the publication of ideas submitted early. The publishing department can also quickly publish ideas submitted just before the deadline. For example, the publishing department can quickly publish ideas submitted just before the deadline based on idea submission date data. Furthermore, the publishing department can adjust the order of publication according to the submission date. For example, the publishing department can analyze idea submission date data and adjust the order of publication according to the submission date. This allows for more appropriate information to be provided by determining the order of publication based on the submission date. Some or all of the above processes in the publishing department may be performed using AI, for example, or not using AI. For example, the publishing department can input idea submission date data into AI and have the AI ​​determine the order of publication.

[0054] The publishing unit can optimize the publishing method by referring to relevant market data for the idea at the time of publication. For example, the publishing unit can select the most effective publishing method based on relevant market data. For example, the publishing unit can acquire relevant market data and identify effective publishing methods. The publishing unit can also prioritize publishing methods that have received a good response from users based on relevant market data. For example, the publishing unit can identify publishing methods that have received a good response from users based on relevant market data. Furthermore, the publishing unit can customize the current publishing method based on relevant market data. For example, the publishing unit can analyze relevant market data and optimize the current publishing method. This allows the optimal publishing method to be selected by referring to relevant market data. Some or all of the above processes in the publishing unit may be performed using AI, for example, or not using AI. For example, the publishing unit can input relevant market data into AI and have the AI ​​perform the optimization of the publishing method.

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

[0056] The reception department can analyze past application history and select the most suitable application method. For example, it can prioritize suggesting application methods that users have frequently used in the past (online forms, email, etc.). It retrieves past application history from a database and identifies the most frequently used application method. Furthermore, if ideas submitted during a specific time slot have received high ratings based on the user's past application history, it can recommend that time slot. In addition, it can customize the optimal application method based on the evaluation results of ideas previously submitted by the user. In this way, the optimal application method can be selected by analyzing past application history.

[0057] The reception department can filter ideas based on the applicant's current projects and areas of interest. For example, it can prioritize ideas related to the applicant's current projects. It can retrieve the applicant's project data to identify highly relevant ideas. It can also filter and accept highly relevant ideas based on the applicant's areas of interest. Furthermore, it can refer to the applicant's past project history to prioritize highly relevant ideas. In this way, by filtering based on the applicant's projects and areas of interest, it is possible to accept highly relevant ideas.

[0058] The review department can improve the accuracy of its evaluation by considering the interrelationships between ideas during the review process. For example, it can analyze the relationships between submitted ideas and give higher ratings to ideas that complement each other. It can acquire data on the relationships between ideas and identify ideas that complement each other. It can also appropriately evaluate overlapping or similar ideas by considering their interrelationships. Furthermore, it can conduct evaluations that take the overall balance into account based on the interrelationships of ideas. In this way, considering the interrelationships of ideas improves the accuracy of the evaluation.

[0059] The review department can consider the attribute information of the idea submitter during the review process. For example, it can evaluate the feasibility of an idea by considering the submitter's field of expertise and experience. It can acquire the submitter's attribute data and identify their field of expertise and experience. It can also evaluate the profitability of an idea based on the submitter's past achievements. Furthermore, it can evaluate the degree of social contribution and usefulness by considering the submitter's attribute information. In this way, a more appropriate review can be conducted by considering the submitter's attribute information.

[0060] The review department can consider the geographical distribution of ideas during the review process. For example, it can evaluate ideas from each region in a balanced manner to avoid geographical bias. It can acquire geographical distribution data of ideas and consider the balance across regions. It can also give higher evaluations to ideas that address region-specific problems. Furthermore, it can conduct reviews tailored to the needs of each region, taking geographical distribution into consideration. In this way, by considering geographical distribution, ideas that address region-specific problems can be appropriately evaluated.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The reception desk receives ideas from contest applicants. The reception desk can accept ideas via online forms, email, and a dedicated app. The reception desk also stores the submitted ideas in a database. For example, ideas submitted via online forms are automatically saved to the database, ideas submitted via email are manually registered in the database, and ideas submitted via the dedicated app are saved to the app's server. Step 2: The judging department reviews and scores the ideas submitted by the reception department based on evaluation criteria. The judging department uses generative AI to evaluate the ideas. The generative AI learns evaluation criteria such as originality, profitability, social contribution, feasibility, and usefulness, and uses them for evaluation. For example, the generative AI learns the judging criteria and scoring results from past contests and improves the evaluation criteria. In addition, the generative AI learns the latest AI technologies and trends and reflects them in the evaluation criteria. Step 3: The Feedback Department provides applicants with feedback on the evaluation results obtained by the Review Department. The Feedback Department provides applicants with the reasons for the scoring along with the evaluation results. For example, it provides applicants with scoring results and comments based on each evaluation criterion. The Feedback Department also notifies applicants of the evaluation results via email or a dedicated app. For example, it sends the evaluation results via email so that applicants can check the results. When notifying applicants of the evaluation results via a dedicated app, it uses the app's notification function to display the results. Step 4: The Public Section publishes the review results, which have been fed back by the Feedback Section, on a dedicated website. The Public Section posts the review results on a dedicated website so that other applicants and the general public can view them. For example, the review results can be posted on a specific page of the dedicated website so that viewers can search for the results. The Public Section can also collect opinions and questions regarding the review results and use them to improve the process. For example, a feedback form can be set up on the dedicated website to collect opinions and questions from applicants and viewers.

[0063] (Example of form 2) The contest judging system according to an embodiment of the present invention is a system that uses generative AI to evaluate and score contest ideas. In this contest judging system, contest applicants submit ideas, and the generative AI evaluates and scores the submitted ideas based on evaluation criteria. The evaluation criteria include originality, profitability, social contribution, feasibility, and usefulness. The generative AI learns from past contest evaluation criteria and scoring results, as well as examples of AI applications in the world, and improves its evaluation criteria. The evaluation results are fed back to the applicants and published on a dedicated website. This allows applicants to know how their ideas were evaluated and to identify areas for improvement for future applications. In addition, opinions and questions regarding the evaluation results can be collected and used to improve the system's operation. Based on its use in generative AI contests, this contest judging system can be developed into various contest judging AIs, both internally and externally. For example, it is expected to be used in various fields, such as corporate idea contests and idea solicitations for regional revitalization projects. This contest judging system allows applicants to receive consistent evaluations, and the organizing committee can efficiently evaluate a large number of ideas. Furthermore, continuous learning and improvement are carried out through the publication of judging results and the collection of feedback, resulting in higher quality judging. For example, the contest judging system provides an online form for applicants to submit their ideas. Applicants can enter the necessary information into the form and submit their ideas. Next, the generative AI judges the submitted ideas based on evaluation criteria and scores them. For example, the generative AI evaluates the differences and novelty of ideas as a criterion for originality. It evaluates the projected revenue and cost performance as a criterion for profitability. It evaluates the social impact and environmental considerations as a criterion for social contribution. It evaluates the technical feasibility and resource requirements as a criterion for feasibility. It evaluates the satisfaction of user needs and actual use value as a criterion for usefulness. The generative AI judges and scores ideas based on these evaluation criteria. The judging results are fed back to the applicants. For example, the judging results include scores and comments based on each evaluation criterion.Applicants can learn how their ideas were evaluated through the judging results. The results are also published on a dedicated website, allowing other applicants and the general public to view them. Furthermore, opinions and questions regarding the judging results can be collected and used to improve the system. For example, feedback from applicants and judges can be gathered and used to improve evaluation criteria and the judging process. This allows the contest judging system to continuously learn and provide higher quality evaluations. Ultimately, the contest judging system can efficiently evaluate applicants' ideas and provide feedback and publish the results.

[0064] The contest judging system according to this embodiment comprises a reception unit, a judging unit, a feedback unit, and a publication unit. The reception unit receives ideas from contest applicants. The reception unit can receive ideas, for example, through an online form. The reception unit can also receive ideas via email or a dedicated application. Furthermore, the reception unit can save the ideas submitted by applicants to a database. For example, the reception unit automatically saves ideas submitted through an online form to the database. Ideas submitted via email can be manually registered in the database. Ideas submitted through a dedicated application are saved on the application's server. The judging unit evaluates the ideas received by the reception unit based on evaluation criteria and assigns scores. The judging unit evaluates ideas, for example, using a generative AI. The generative AI learns evaluation criteria such as originality, profitability, social contribution, feasibility, and usefulness, and performs evaluations. For example, the generative AI learns the evaluation criteria and scoring results of past contests and improves the evaluation criteria. The generative AI can also learn about AI application examples in the world and update the evaluation criteria. For example, the Generative AI learns the latest AI technologies and trends and reflects them in the evaluation criteria. The Feedback Department provides applicants with feedback on the evaluation results obtained by the Review Department. For example, the Feedback Department provides applicants with the reasons for the scoring along with the evaluation results. For example, the Feedback Department provides applicants with scoring results and comments based on each evaluation criterion. The Feedback Department can also notify applicants of the evaluation results via email or a dedicated app. For example, the Feedback Department sends the evaluation results via email so that applicants can check the results. When notifying applicants of the evaluation results via a dedicated app, the app's notification function is used to display the results. The Public Department publishes the evaluation results that have been fed back by the Feedback Department on a dedicated website. For example, the Public Department posts the evaluation results on a dedicated website so that other applicants and the general public can view them. For example, the Public Department posts the evaluation results on a specific page of the dedicated website so that viewers can search for the results. The Public Department can also collect opinions and questions regarding the evaluation results and use them to improve operations.For example, the public section can set up a feedback form on a dedicated website to collect opinions and questions from applicants and viewers. This allows the contest judging system according to the embodiment to efficiently review applicants' ideas and provide feedback and make them public.

[0065] The reception department accepts ideas from contest applicants. For example, ideas can be submitted via an online form. Specifically, the online form is designed as an easily accessible webpage where applicants can enter the necessary information and submit their ideas. The form includes fields for idea title, summary, detailed description, and the ability to upload related materials. The reception department can also accept ideas via email or a dedicated app. For email submissions, applicants send their ideas to a designated email address, which the reception department receives and registers in its database. Ideas submitted through the dedicated app are stored on the app's server. The app has a user-friendly interface designed to allow applicants to easily input and submit their ideas. Furthermore, the reception department can save submitted ideas to a database. For example, ideas submitted via the online form can be automatically saved to the database. The database is a system for efficiently managing applicant information and submitted ideas, and includes search and filtering functions. This allows the reception department to quickly and accurately manage submitted ideas and provide them to the judging panel.

[0066] The judging department reviews and scores ideas submitted by the reception department based on evaluation criteria. The judging department uses, for example, generative AI to evaluate ideas. Generative AI learns evaluation criteria such as originality, profitability, social contribution, feasibility, and usefulness, and uses them for evaluation. Specifically, the generative AI learns the judging criteria and scoring results of past contests and improves the evaluation criteria. For example, the generative AI analyzes the characteristics of past winning ideas and highly-rated ideas, and evaluates new ideas based on those characteristics. The generative AI can also learn about AI applications in the world and update the evaluation criteria. For example, the generative AI learns the latest AI technologies and trends and reflects them in the evaluation criteria. This allows the judging department to always conduct highly accurate evaluations based on the latest information. Furthermore, the generative AI can use natural language processing technology to analyze the text of applicants' ideas and evaluate the consistency and logic of the content. This allows the judging department to evaluate the quality of ideas from multiple perspectives and conduct fair and objective evaluations.

[0067] The Feedback Department provides applicants with feedback on the evaluation results obtained by the Review Department. For example, the Feedback Department provides applicants with the reasons for the scoring along with the evaluation results. Specifically, the Feedback Department provides applicants with scoring results and comments based on each evaluation criterion. For example, if the originality evaluation is high, the Feedback Department will explain in detail the reasons and specific points. Also, if the profitability or feasibility evaluation is low, the Feedback Department will provide areas for improvement and specific advice. This allows applicants to understand the strengths and weaknesses of their ideas and improve them for future applications. The Feedback Department can also notify applicants of the evaluation results via email or a dedicated app. For example, the Feedback Department can send the evaluation results by email so that applicants can check the results. The email will include details of the evaluation results and feedback comments, which applicants can use to review their ideas. When notifying applicants of the evaluation results via a dedicated app, the app's notification function will be used to display the results. The app's notification function notifies applicants of the evaluation results in real time, allowing them to check the results quickly. This allows the Feedback Department to provide applicants with quick and detailed feedback and support their growth.

[0068] The Public Section will publish the review results, which have been fed back by the Feedback Section, on a dedicated website. For example, the Public Section will post the review results on the dedicated website, making them accessible to other applicants and the general public. Specifically, the Public Section will post the review results on a specific page of the dedicated website, allowing viewers to search for the results. The dedicated website will have a user-friendly interface, designed to allow viewers to easily search and view the review results. The Public Section can also collect opinions and questions regarding the review results to improve operations. For example, the Public Section will install a feedback form on the dedicated website to collect opinions and questions from applicants and viewers. The feedback form is designed to be easy to use, allowing applicants and viewers to freely enter their opinions and questions. This allows the Public Section to collect feedback on the review results and use it to improve the system and optimize operations. Furthermore, the Public Section will also consider privacy protection and data security when publishing the review results. For example, it will protect applicants' privacy by appropriately protecting their personal information and limiting the scope of publication of the review results. This allows the public disclosure department to publish the review results while ensuring transparency and reliability.

[0069] The judging department can learn evaluation criteria such as originality, profitability, social contribution, feasibility, and usefulness, and conduct evaluations. The judging department can learn evaluation criteria using, for example, generative AI. The generative AI learns evaluation criteria and scoring results from past contests and improves the evaluation criteria. For example, the generative AI analyzes evaluation data from past contests to improve the accuracy of the evaluation criteria. The generative AI can also learn about AI applications in the world and update the evaluation criteria. For example, the generative AI learns the latest AI technologies and trends and reflects them in the evaluation criteria. As a result, the accuracy of the evaluation is improved by learning the evaluation criteria. Some or all of the above processes in the judging department may be performed using, for example, generative AI, or without using generative AI. For example, the judging department can input evaluation data from past contests into the generative AI and have the generative AI improve the evaluation criteria.

[0070] The feedback unit can provide applicants with feedback on the evaluation results along with the reasons for their scoring. The feedback unit can, for example, provide specific comments and feedback based on the evaluation results. For example, the feedback unit can provide applicants with scoring results and comments based on each evaluation criterion. The feedback unit can also notify applicants of the evaluation results via email or a dedicated app. For example, the feedback unit can send the evaluation results by email so that applicants can check the results. When notifying applicants of the evaluation results via a dedicated app, the app's notification function is used to display the results. This allows applicants to understand the details of the evaluation by providing feedback on the reasons for their scoring along with the evaluation results. Some or all of the above processes in the feedback unit may be performed using AI, for example, or not using AI. For example, the feedback unit can input the evaluation results into AI and have the AI ​​generate specific comments and feedback.

[0071] The Public Department can collect opinions and questions regarding the review results and use them to improve operations. For example, the Public Department can set up a feedback form on a dedicated website to collect opinions and questions from applicants and viewers. For example, the Public Department can store the opinions and questions collected through the feedback form in a database and use them to improve operations. The Public Department can also analyze the collected opinions and questions and reflect the results in improving the evaluation criteria and review process. For example, the Public Department can analyze the content of the opinions and questions to identify areas for revision of the evaluation criteria and improvement of the review process. In this way, by collecting opinions and questions, it becomes possible to improve the operation of the system. Some or all of the above processes in the Public Department may be performed using AI, or not. For example, the Public Department can input the opinions and questions collected through the feedback form into an AI and have the AI ​​perform the analysis and identification of areas for improvement.

[0072] The judging department can improve its evaluation criteria by learning from past contest judging criteria and scoring results, as well as examples of AI applications in the real world. For example, the judging department can use generative AI to learn from past contest judging criteria and scoring results. The generative AI analyzes past judging data and improves the accuracy of the evaluation criteria. For example, the generative AI identifies areas for improvement in the evaluation criteria based on past contest judging data. The generative AI can also learn from examples of AI applications in the real world and update the evaluation criteria. For example, the generative AI learns the latest AI technologies and trends and reflects them in the evaluation criteria. As a result, the accuracy of the evaluation criteria improves by learning from past data. Some or all of the above processes in the judging department may be performed using, for example, generative AI, or without using generative AI. For example, the judging department can input past contest judging data into the generative AI and have the generative AI perform improvements to the evaluation criteria.

[0073] The Public Section can publish the judging results on a dedicated website. For example, the Public Section can post the judging results on a dedicated website so that other applicants and the general public can view them. For example, the Public Section can post the judging results on a specific page of the dedicated website so that viewers can search for the results. The Public Section can also organize the judging results by category so that viewers can easily find results in categories that interest them. For example, the Public Section can publish the judging results divided into categories such as technology, business, and social contribution. This improves transparency by making the judging results public. Some or all of the above processes in the Public Section may be performed using AI, for example, or not using AI. For example, the Public Section can input the judging results into AI and have the AI ​​perform the category organization and generate the publication pages.

[0074] The reception system can estimate the user's emotions and adjust the timing of idea submission based on the estimated emotions. For example, if the user is stressed, the reception system can delay submission to allow the user to submit ideas in a relaxed state. For example, the reception system can monitor the user's emotions in real time and temporarily hold off on submission if the stress level is high. Alternatively, if the user is excited, the reception system can immediately accept ideas to encourage enthusiastic submission. For example, the reception system can analyze the user's emotional data and prioritize submission if the user is excited. Furthermore, if the user is tired, the reception system can adjust the timing of submission to allow the user to submit ideas after resting. For example, based on the user's emotional data, the reception system can delay submission if the user is fatigued. This allows for idea submission at a more appropriate time by adjusting the timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the reception area may be performed using AI, for example, or without AI. For example, the reception area can input user emotion data into a generating AI and have the generating AI perform emotion estimation and adjustment of reception timing.

[0075] The reception department can analyze past application history and select the most suitable application method. For example, the reception department can prioritize suggesting application methods that the user has frequently used in the past (online forms, email, etc.). For instance, the reception department can retrieve past application history from a database and identify the most frequently used application method. The reception department can also recommend specific time slots if ideas submitted during those times have received high ratings based on the user's past application history. For example, the reception department can analyze past application data and identify the time slots when ideas received high ratings were submitted. Furthermore, the reception department can customize the optimal application method based on the evaluation results of ideas previously submitted by the user. For example, the reception department can analyze the user's past evaluation data and suggest the most suitable application method. This allows the reception department to select the most suitable application method by analyzing past application history. Some or all of the above processes in the reception department may be performed using AI, or not. For example, the reception department can input past application history data into an AI and have the AI ​​select the most suitable application method.

[0076] The reception department can filter ideas based on the applicant's current projects and areas of interest when receiving them. For example, the reception department can prioritize ideas related to projects the applicant is currently working on. For example, the reception department can obtain the applicant's project data and identify highly relevant ideas. The reception department can also filter and accept highly relevant ideas based on the applicant's areas of interest. For example, the reception department can analyze the applicant's areas of interest data and extract highly relevant ideas. Furthermore, the reception department can refer to the applicant's past project history and prioritize accepting highly relevant ideas. For example, the reception department can identify highly relevant ideas based on the applicant's past project data. This allows the reception department to accept highly relevant ideas by filtering based on the applicant's projects and areas of interest. Some or all of the above processing in the reception department may be performed using AI, or not. For example, the reception department can input the applicant's project data and areas of interest data into an AI and have the AI ​​perform the filtering.

[0077] The reception system can estimate the user's emotions and prioritize ideas based on those emotions. For example, if the user is excited, the reception system will prioritize accepting those ideas. For example, the reception system will analyze the user's emotional data and prioritize accepting ideas if the user is excited. The reception system can also accept ideas with normal priority if the user is relaxed. For example, based on the user's emotional data, the reception system will prioritize accepting ideas with normal priority if the user is relaxed. Furthermore, if the user is stressed, the reception system can temporarily hold off on accepting an idea and prioritize it later. For example, the reception system will analyze the user's emotional data and hold off on accepting an idea if the user is stressed. This allows for prioritizing more appropriate ideas based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the reception area may be performed using AI, for example, or without AI. For example, the reception area can input user emotion data into a generating AI and have the generating AI perform emotion estimation and priority determination.

[0078] The reception department can prioritize accepting ideas that are highly relevant, taking into account the applicant's geographical location when receiving ideas. For example, if an applicant lives in a specific region, the reception department will prioritize accepting ideas related to that region. For example, the reception department will obtain the applicant's geographical location and identify highly relevant ideas. The reception department can also prioritize accepting ideas that address region-specific problems based on the applicant's geographical location. For example, the reception department will extract ideas related to region-specific problems and prioritize accepting them. Furthermore, the reception department can also prioritize accepting ideas that meet regional needs, taking into account the applicant's geographical location. For example, the reception department will identify highly relevant ideas based on regional needs. In this way, by considering geographical location, ideas that address region-specific problems can be prioritized. 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 applicant's geographical location into AI and have the AI ​​identify highly relevant ideas.

[0079] The reception department can analyze applicants' social media activity when receiving ideas and accept relevant ideas. For example, the reception department can prioritize ideas related to themes of interest based on the applicant's social media activity. For example, the reception department can acquire the applicant's social media data and identify themes of interest. The reception department can also analyze the applicant's statements and posts on social media and accept highly relevant ideas. For example, the reception department can analyze the content of the applicant's posts and extract highly relevant ideas. Furthermore, the reception department can adjust the acceptance of ideas considering the applicant's followers and influence on social media. For example, the reception department can determine the priority of ideas based on the applicant's number of followers and influence. This allows the reception department to accept highly relevant ideas by analyzing social media activity. Some or all of the above processes in the reception department may be performed using AI, or not. For example, the reception department can input the applicant's social media data into AI and have the AI ​​identify highly relevant ideas.

[0080] The review department can estimate the user's emotions and adjust the review criteria based on the estimated emotions. For example, if the user is excited, the review department can apply review criteria that emphasize originality. For example, the review department can analyze the user's emotional data and emphasize originality if the user is excited. The review department can also apply review criteria that emphasize profitability and feasibility if the user is relaxed. For example, based on the user's emotional data, the review department can emphasize profitability and feasibility if the user is relaxed. Furthermore, if the user is stressed, the review department can apply review criteria that emphasize social contribution. For example, the review department can analyze the user's emotional data and emphasize social contribution if the user is stressed. By adjusting the review criteria based on the user's emotions, more appropriate reviews can be conducted. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) and multimodal generation AI. Some or all of the above-described processes in the review department may be performed using AI, for example, or without AI. For example, the review department may input user emotion data into a generating AI and have the generating AI perform emotion estimation and adjustment of review criteria.

[0081] The review department can improve the accuracy of its review process by considering the interrelationships between ideas. For example, the review department can analyze the relationships between submitted ideas and give higher ratings to ideas that complement each other. For example, the review department can acquire idea relationship data and identify ideas that complement each other. The review department can also appropriately evaluate overlapping or similar ideas by considering their interrelationships. For example, the review department can analyze idea overlap data and identify overlapping or similar ideas. Furthermore, the review department can conduct a review that considers the overall balance based on the interrelationships of ideas. For example, the review department can conduct a review that considers the overall balance based on idea balance data. This improves the accuracy of the review by considering the interrelationships of ideas. Some or all of the above processes in the review department may be performed using AI, for example, or not. For example, the review department can input idea relationship data into AI and have the AI ​​perform the analysis of interrelationships and improve the accuracy of the review.

[0082] The review department can consider the attribute information of the idea submitter when conducting the review. For example, the review department can evaluate the feasibility of an idea by considering the submitter's field of expertise and experience. For example, the review department can acquire the submitter's attribute data and identify their field of expertise and experience. The review department can also evaluate the profitability of an idea based on the submitter's past performance. For example, the review department can analyze the submitter's past performance data to evaluate profitability. Furthermore, the review department can also evaluate the social contribution and usefulness by considering the submitter's attribute information. For example, the review department can evaluate the social contribution and usefulness based on the submitter's attribute data. This allows for a more appropriate review by considering the submitter's attribute information. Some or all of the above processes in the review department may be performed using AI, for example, or not. For example, the review department can input the submitter's attribute data into an AI and have the AI ​​perform the analysis and review of the attribute information.

[0083] The review unit can estimate the user's emotions and adjust the display order of the review results based on the estimated emotions. For example, if the user is excited, the review unit will prioritize displaying highly original ideas. For example, the review unit will analyze the user's emotional data and prioritize displaying highly original ideas if the user is excited. The review unit can also prioritize displaying highly profitable and feasible ideas if the user is relaxed. For example, based on the user's emotional data, the review unit will prioritize displaying highly profitable and feasible ideas if the user is relaxed. Furthermore, if the user is stressed, the review unit can prioritize displaying highly socially beneficial ideas. For example, the review unit will analyze the user's emotional data and prioritize displaying highly socially beneficial ideas if the user is stressed. By adjusting the display order based on the user's emotions, more appropriate review results can be displayed. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the review department may be performed using AI, for example, or without AI. For example, the review department may input user emotion data into a generating AI and have the generating AI perform emotion estimation and adjustment of the display order.

[0084] The review department can consider the geographical distribution of ideas during the review process. For example, the review department can evaluate ideas from each region in a balanced manner to avoid geographical bias. For example, the review department can acquire geographical distribution data of ideas and consider the balance across regions. The review department can also give higher ratings to ideas that address region-specific problems. For example, the review department can identify ideas related to region-specific problems and give them high ratings. Furthermore, the review department can conduct reviews tailored to the needs of each region, taking geographical distribution into consideration. For example, the review department can conduct reviews tailored to the needs of each region based on regional needs data. This allows for the appropriate evaluation of ideas that address region-specific problems by considering geographical distribution. Some or all of the above processes in the review department may be performed using AI, or not. For example, the review department can input geographical distribution data of ideas into an AI and have the AI ​​perform geographical distribution analysis and review.

[0085] The review department can improve the accuracy of its review by referring to relevant literature on the idea during the review process. For example, the review department may evaluate the submitted idea by referring to academic papers and patent documents related to it. For example, the review department may acquire relevant literature data and use it to evaluate the idea. The review department may also refer to relevant literature to understand the technical background of the idea. For example, the review department may refer to relevant literature based on technical background data. Furthermore, the review department may also refer to relevant literature to evaluate the novelty of the idea. For example, the review department may refer to relevant literature based on novelty data. In this way, the accuracy of the review is improved by referring to relevant literature. Some or all of the above processes in the review department may be performed using AI, for example, or not using AI. For example, the review department may input relevant literature data into AI and have the AI ​​perform the literature referencing and review.

[0086] The feedback unit can estimate the user's emotions and adjust the way it expresses the feedback based on the estimated emotions. For example, if the user is nervous, the feedback unit can provide feedback in gentle language. For example, the feedback unit can analyze the user's emotional data and use gentle language if the user is nervous. The feedback unit can also provide detailed feedback if the user is relaxed. For example, based on the user's emotional data, the feedback unit can provide detailed feedback if the user is relaxed. Furthermore, if the user is in a hurry, the feedback unit can provide concise feedback that gets straight to the point. For example, the feedback unit can analyze the user's emotional data and provide concise feedback if the user is in a hurry. In this way, by adjusting the way the feedback is expressed according to the user's emotions, more appropriate feedback can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without using AI. For example, the feedback unit can input user emotion data into a generating AI, which can then perform emotion estimation and adjust the way the feedback is expressed.

[0087] The feedback unit can adjust the level of detail in the feedback based on the importance of the review results. For example, the feedback unit can provide detailed feedback for important review results. For example, the feedback unit can acquire importance data for review results and provide detailed feedback for important results. The feedback unit can also provide concise feedback for general review results. For example, the feedback unit can provide concise feedback for general results based on importance data for review results. Furthermore, the feedback unit can provide focused feedback on particularly important points. For example, the feedback unit can analyze importance data for review results and provide focused feedback on particularly important points. This allows for more appropriate feedback to be provided by adjusting the level of detail in the feedback based on the importance of the review results. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input importance data for review results into AI and have the AI ​​adjust the level of detail in the feedback.

[0088] The feedback unit can apply different feedback algorithms depending on the category of the idea during the feedback process. For example, the feedback unit can provide technical feedback to technical ideas. For instance, it can acquire idea category data and provide technical feedback to technical ideas. The feedback unit can also provide profitability feedback to business ideas. For example, it can provide profitability feedback to business ideas based on idea category data. Furthermore, the feedback unit can provide social impact feedback to social contribution ideas. For example, it can analyze idea category data and provide social impact feedback to social contribution ideas. This allows for more appropriate feedback by providing feedback according to the idea category. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input idea category data into AI and have the AI ​​apply the feedback algorithm.

[0089] The feedback unit can estimate the user's emotions and adjust the length of the feedback based on the estimated emotions. For example, if the user is nervous, the feedback unit can provide short, concise feedback. For example, the feedback unit can analyze the user's emotional data and provide short, concise feedback if the user is nervous. The feedback unit can also provide detailed feedback if the user is relaxed. For example, based on the user's emotional data, the feedback unit can provide detailed feedback if the user is relaxed. Furthermore, the feedback unit can provide concise feedback if the user is in a hurry. For example, the feedback unit can analyze the user's emotional data and provide concise feedback if the user is in a hurry. This allows for more appropriate feedback to be provided by adjusting the length of the feedback according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input user emotion data into a generating AI, which can then perform emotion estimation and adjust the length of the feedback.

[0090] The feedback unit can determine the priority of feedback based on the submission timing of the review results. For example, the feedback unit can prioritize feedback for review results submitted early. For example, the feedback unit can acquire review result submission timing data and prioritize feedback for results submitted early. The feedback unit can also provide rapid feedback for review results submitted just before the deadline. For example, the feedback unit can provide rapid feedback for results submitted just before the deadline based on review result submission timing data. Furthermore, the feedback unit can adjust the priority of feedback according to the submission timing. For example, the feedback unit can analyze review result submission timing data and adjust the priority of feedback according to the submission timing. This allows for faster feedback by determining the priority of feedback based on the submission timing of the review results. Some or all of the above processing in the feedback unit may be performed using AI, for example, or not. For example, the feedback unit can input review result submission timing data into AI and have the AI ​​determine the priority of feedback.

[0091] The feedback unit can adjust the order of feedback based on the relevance of the review results. For example, the feedback unit can prioritize providing feedback to particularly relevant review results. For example, the feedback unit can acquire relevance data of the review results and prioritize providing feedback to highly relevant results. The feedback unit can also provide feedback in the normal order for review results with general relevance. For example, the feedback unit can provide feedback in the normal order for results with general relevance based on the relevance data of the review results. Furthermore, the feedback unit can postpone providing feedback to review results with low relevance. For example, the feedback unit can analyze the relevance data of the review results and postpone providing feedback to results with low relevance. By adjusting the order of feedback based on the relevance of the review results, more appropriate feedback can be provided. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the relevance data of the review results into AI and have the AI ​​perform the adjustment of the feedback order.

[0092] The public section can estimate the user's emotions and adjust the display method based on the estimated emotions. For example, if the user is tense, the public section can provide a simple and highly visible display method. For example, the public section can analyze the user's emotional data and provide a simple and highly visible display method if the user is tense. The public section can also provide a display method that includes detailed information if the user is relaxed. For example, based on the user's emotional data, the public section can provide a display method that includes detailed information if the user is relaxed. Furthermore, the public section can provide a concise display method if the user is in a hurry. For example, the public section can analyze the user's emotional data and provide a concise display method if the user is in a hurry. This allows for more appropriate information to be provided by adjusting the display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the public access section may be performed using AI, for example, or without AI. For example, the public access section may input user emotion data into a generating AI and have the generating AI perform emotion estimation and adjustment of the display method.

[0093] The publishing unit can optimize the current publishing method by referring to past publishing data at the time of publication. For example, the publishing unit can analyze past publishing data and select the most effective display method. For example, the publishing unit can acquire past publishing data and identify effective display methods. The publishing unit can also prioritize display methods that received good user responses from past publishing data. For example, the publishing unit can identify display methods that received good user responses based on past publishing data. Furthermore, the publishing unit can customize the current publishing method based on past publishing data. For example, the publishing unit can analyze past publishing data and optimize the current publishing method. This allows the optimal publishing method to be selected by referring to past publishing data. Some or all of the above processes in the publishing unit may be performed using AI, for example, or not using AI. For example, the publishing unit can input past publishing data into AI and have the AI ​​perform the optimization of the publishing method.

[0094] The publishing department can apply different publishing methods to each category of idea at the time of publication. For example, for technical ideas, the publishing department may adopt a publishing method that includes technical details. For example, the publishing department may acquire idea category data and apply a publishing method that includes technical details to technical ideas. The publishing department may also adopt a publishing method that emphasizes profitability information for business ideas. For example, based on idea category data, the publishing department may apply a publishing method that emphasizes profitability information to business ideas. Furthermore, the publishing department may adopt a publishing method that emphasizes social impact information for social contribution ideas. For example, the publishing department may analyze idea category data and apply a publishing method that emphasizes social impact information to social contribution ideas. This allows for more appropriate information to be provided by applying a publishing method appropriate to the category of the idea. Some or all of the above processing in the publishing department may be performed using AI, for example, or not using AI. For example, the publishing department may input idea category data into AI and have the AI ​​perform the application of publishing methods.

[0095] The public access system can estimate the user's emotions and adjust the importance of the information it provides based on those emotions. For example, if the user is excited, the public access system may prioritize displaying important information. For example, the public access system may analyze the user's emotional data and prioritize displaying important information if the user is excited. The public access system may also provide a display method that includes detailed information if the user is relaxed. For example, based on the user's emotional data, the public access system may provide a display method that includes detailed information if the user is relaxed. Furthermore, the public access system may provide a display method that focuses on the key points if the user is stressed. For example, the public access system may analyze the user's emotional data and provide a display method that focuses on the key points if the user is stressed. This allows for more appropriate information to be provided by adjusting the importance of the information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the public access section may be performed using AI, for example, or without AI. For example, the public access section may input user sentiment data into a generating AI and have the generating AI perform sentiment estimation and importance adjustment.

[0096] The publishing department can determine the order of publication based on the submission date of ideas at the time of publication. For example, the publishing department can prioritize the publication of ideas submitted early. For example, the publishing department can acquire idea submission date data and prioritize the publication of ideas submitted early. The publishing department can also quickly publish ideas submitted just before the deadline. For example, the publishing department can quickly publish ideas submitted just before the deadline based on idea submission date data. Furthermore, the publishing department can adjust the order of publication according to the submission date. For example, the publishing department can analyze idea submission date data and adjust the order of publication according to the submission date. This allows for more appropriate information to be provided by determining the order of publication based on the submission date. Some or all of the above processes in the publishing department may be performed using AI, for example, or not using AI. For example, the publishing department can input idea submission date data into AI and have the AI ​​determine the order of publication.

[0097] The publishing unit can optimize the publishing method by referring to relevant market data for the idea at the time of publication. For example, the publishing unit can select the most effective publishing method based on relevant market data. For example, the publishing unit can acquire relevant market data and identify effective publishing methods. The publishing unit can also prioritize publishing methods that have received a good response from users based on relevant market data. For example, the publishing unit can identify publishing methods that have received a good response from users based on relevant market data. Furthermore, the publishing unit can customize the current publishing method based on relevant market data. For example, the publishing unit can analyze relevant market data and optimize the current publishing method. This allows the optimal publishing method to be selected by referring to relevant market data. Some or all of the above processes in the publishing unit may be performed using AI, for example, or not using AI. For example, the publishing unit can input relevant market data into AI and have the AI ​​perform the optimization of the publishing method.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The reception system can estimate the user's emotions and adjust the timing of idea submission based on that estimation. For example, if a user is stressed, the submission timing can be delayed to allow them to submit their ideas in a relaxed state. The system can also monitor the user's emotions in real time and temporarily suspend submission if the stress level is high. Furthermore, if a user is excited, ideas can be accepted immediately to encourage enthusiastic submission. Additionally, if a user is tired, the submission timing can be adjusted to allow them to submit their ideas after resting. By adjusting the submission timing according to the user's emotions, ideas can be received at a more appropriate time.

[0100] The review department can estimate the user's emotions and adjust the review criteria based on those estimates. For example, if the user is excited, they can apply review criteria that emphasize originality. By analyzing the user's emotional data, they can prioritize originality if the user is in an excited state. If the user is relaxed, they can also apply review criteria that emphasize profitability and feasibility. Furthermore, if the user is stressed, they can apply review criteria that emphasize social contribution. By adjusting the review criteria based on the user's emotions, more appropriate reviews can be conducted.

[0101] The feedback unit can estimate the user's emotions and adjust the way feedback is presented based on those emotions. For example, if the user is nervous, it can provide feedback using gentle language. It analyzes the user's emotional data and uses gentle language if the user is in a state of tension. It can also provide detailed feedback if the user is relaxed. Furthermore, if the user is in a hurry, it can provide concise feedback that gets straight to the point. In this way, by adjusting the way feedback is presented according to the user's emotions, more appropriate feedback can be provided.

[0102] The public information section can estimate the user's emotions and adjust the display method based on those emotions. For example, if the user is nervous, it can provide a simple and highly visible display method. By analyzing the user's emotional data, it can provide a simple and highly visible display method when the user is nervous. It can also provide a display method that includes detailed information when the user is relaxed. Furthermore, if the user is in a hurry, it can provide a display method that gets straight to the point. In this way, by adjusting the display method of public information according to the user's emotions, more appropriate information can be provided.

[0103] The public access system can estimate the user's emotions and adjust the importance of the information provided based on those emotions. For example, if the user is excited, important information will be prioritized. By analyzing the user's emotional data, important information will be prioritized when the user is in an excited state. Furthermore, if the user is relaxed, a more detailed presentation method may be provided. Additionally, if the user is stressed, a concise presentation method may be provided. This allows for more appropriate information delivery by adjusting the importance of information according to the user's emotions.

[0104] The reception department can analyze past application history and select the most suitable application method. For example, it can prioritize suggesting application methods that users have frequently used in the past (online forms, email, etc.). It retrieves past application history from a database and identifies the most frequently used application method. Furthermore, if ideas submitted during a specific time slot have received high ratings based on the user's past application history, it can recommend that time slot. In addition, it can customize the optimal application method based on the evaluation results of ideas previously submitted by the user. In this way, the optimal application method can be selected by analyzing past application history.

[0105] The reception department can filter ideas based on the applicant's current projects and areas of interest. For example, it can prioritize ideas related to the applicant's current projects. It can retrieve the applicant's project data to identify highly relevant ideas. It can also filter and accept highly relevant ideas based on the applicant's areas of interest. Furthermore, it can refer to the applicant's past project history to prioritize highly relevant ideas. In this way, by filtering based on the applicant's projects and areas of interest, it is possible to accept highly relevant ideas.

[0106] The review department can improve the accuracy of its evaluation by considering the interrelationships between ideas during the review process. For example, it can analyze the relationships between submitted ideas and give higher ratings to ideas that complement each other. It can acquire data on the relationships between ideas and identify ideas that complement each other. It can also appropriately evaluate overlapping or similar ideas by considering their interrelationships. Furthermore, it can conduct evaluations that take the overall balance into account based on the interrelationships of ideas. In this way, considering the interrelationships of ideas improves the accuracy of the evaluation.

[0107] The review department can consider the attribute information of the idea submitter during the review process. For example, it can evaluate the feasibility of an idea by considering the submitter's field of expertise and experience. It can acquire the submitter's attribute data and identify their field of expertise and experience. It can also evaluate the profitability of an idea based on the submitter's past achievements. Furthermore, it can evaluate the degree of social contribution and usefulness by considering the submitter's attribute information. In this way, a more appropriate review can be conducted by considering the submitter's attribute information.

[0108] The review department can consider the geographical distribution of ideas during the review process. For example, it can evaluate ideas from each region in a balanced manner to avoid geographical bias. It can acquire geographical distribution data of ideas and consider the balance across regions. It can also give higher evaluations to ideas that address region-specific problems. Furthermore, it can conduct reviews tailored to the needs of each region, taking geographical distribution into consideration. In this way, by considering geographical distribution, ideas that address region-specific problems can be appropriately evaluated.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The reception desk receives ideas from contest applicants. The reception desk can accept ideas via online forms, email, and a dedicated app. The reception desk also stores the submitted ideas in a database. For example, ideas submitted via online forms are automatically saved to the database, ideas submitted via email are manually registered in the database, and ideas submitted via the dedicated app are saved to the app's server. Step 2: The judging department reviews and scores the ideas submitted by the reception department based on evaluation criteria. The judging department uses generative AI to evaluate the ideas. The generative AI learns evaluation criteria such as originality, profitability, social contribution, feasibility, and usefulness, and uses them for evaluation. For example, the generative AI learns the judging criteria and scoring results from past contests and improves the evaluation criteria. In addition, the generative AI learns the latest AI technologies and trends and reflects them in the evaluation criteria. Step 3: The Feedback Department provides applicants with feedback on the evaluation results obtained by the Review Department. The Feedback Department provides applicants with the reasons for the scoring along with the evaluation results. For example, it provides applicants with scoring results and comments based on each evaluation criterion. The Feedback Department also notifies applicants of the evaluation results via email or a dedicated app. For example, it sends the evaluation results via email so that applicants can check the results. When notifying applicants of the evaluation results via a dedicated app, it uses the app's notification function to display the results. Step 4: The Public Section publishes the review results, which have been fed back by the Feedback Section, on a dedicated website. The Public Section posts the review results on a dedicated website so that other applicants and the general public can view them. For example, the review results can be posted on a specific page of the dedicated website so that viewers can search for the results. The Public Section can also collect opinions and questions regarding the review results and use them to improve the process. For example, a feedback form can be set up on the dedicated website to collect opinions and questions from applicants and viewers.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] Each of the multiple elements described above, including the reception unit, review unit, feedback unit, and publication unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit can receive ideas through an online form on the smart device 14. The review unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which reviews and scores ideas using generating AI. The feedback unit provides feedback on the review results to the applicant by, for example, the control unit 46A of the smart device 14. The publication unit publishes the review results on a dedicated website by, for example, the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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).

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] Each of the multiple elements described above, including the reception unit, review unit, feedback unit, and publication unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit can receive ideas through an online form on the smart glasses 214. The review unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, which reviews and scores ideas using generating AI. The feedback unit provides feedback on the review results to the applicant, for example, by the control unit 46A of the smart glasses 214. The publication unit publishes the review results on a dedicated website, for example, by the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] Each of the multiple elements described above, including the reception unit, review unit, feedback unit, and publication unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit can receive ideas through an online form on the headset terminal 314. The review unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which reviews and scores ideas using generating AI. The feedback unit provides feedback on the review results to the applicant by, for example, the control unit 46A of the headset terminal 314. The publication unit publishes the review results on a dedicated website by, for example, the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

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

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

[0158] In 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.

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

[0160] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0162] The data processing system 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.

[0163] Each of the multiple elements described above, including the reception unit, review unit, feedback unit, and publication unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit can receive ideas through an online form on the robot 414. The review unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which reviews and scores ideas using a generating AI. The feedback unit provides feedback on the review results to the applicant by, for example, the control unit 46A of the robot 414. The publication unit publishes the review results on a dedicated website by, for example, the specific processing unit 290 of the data processing unit 12. The correspondence between each unit and the devices and control units is not limited to the example described above, and various modifications are possible.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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."

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] (Note 1) The reception desk accepts ideas from contest applicants, The review department reviews the ideas received by the aforementioned reception department based on evaluation criteria and assigns scores, A feedback unit that provides feedback to applicants on the results of the review obtained by the aforementioned review unit, The system includes a publication unit that publishes the review results, which have been fed back by the aforementioned feedback unit, on a dedicated website. A system characterized by the following features. (Note 2) The aforementioned review department, Evaluation criteria such as originality, profitability, social contribution, feasibility, and usefulness are learned and used for evaluation. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned feedback unit is The judges will provide applicants with feedback on the scoring criteria along with the evaluation results. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned public section is, We will collect opinions and questions regarding the review results and use them to improve our operations. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned review department, We learn from past contest judging criteria and scoring results, as well as real-world examples of AI applications, to improve our evaluation criteria. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned public section is, The results of the review will be published on a dedicated website. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is We estimate the user's emotions and adjust the timing of idea submission based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is We analyze past application history and select the most suitable application method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving ideas, we filter applicants based on their current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is It estimates the user's emotions and prioritizes the ideas to accept based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When accepting ideas, we will prioritize accepting ideas that are highly relevant, taking into account the applicant's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When accepting ideas, we analyze the applicants' social media activity and accept ideas that are relevant to that activity. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned review department, We estimate the user's emotions and adjust the review criteria based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned review department, During the review process, we will improve the accuracy of the review by considering the interrelationships between ideas. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned review department, During the review process, the applicant's attribute information will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned review department, The system estimates the user's emotions and adjusts the display order of the review results based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned review department, During the review process, the geographical distribution of ideas will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned review department, During the review process, we refer to relevant literature related to the idea to improve the accuracy of the review. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned feedback unit is It estimates the user's emotions and adjusts how feedback is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned feedback unit is When providing feedback, adjust the level of detail in the feedback based on the importance of the evaluation results. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned feedback unit is When providing feedback, different feedback algorithms are applied depending on the category of the idea. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned feedback unit is It estimates the user's emotions and adjusts the length of the feedback based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned feedback unit is When providing feedback, we will prioritize the feedback based on when the review results were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned feedback unit is When providing feedback, the order of feedback will be adjusted based on the relevance of the review results. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned public section is, It estimates user sentiment and adjusts how content is displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned public section is, When publishing, we optimize the current publishing method by referring to past publishing data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned public section is, When publishing, different publishing methods will be applied depending on the category of the idea. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned public section is, We estimate user sentiment and adjust the importance of publication based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned public section is, When publishing, the order of publication will be determined based on when the ideas were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned public section is, When publishing, we optimize the publishing method by referring to relevant market data for the idea. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0183] 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. The reception desk accepts ideas from contest applicants, The review department reviews the ideas received by the aforementioned reception department based on evaluation criteria and assigns scores, A feedback unit that provides feedback to applicants on the results of the review obtained by the aforementioned review unit, The system includes a publication unit that publishes the review results, which have been fed back by the aforementioned feedback unit, on a dedicated website. A system characterized by the following features.

2. The aforementioned review department, Evaluation criteria such as originality, profitability, social contribution, feasibility, and usefulness are learned and used for evaluation. The system according to feature 1.

3. The aforementioned feedback unit is The judges will provide applicants with feedback on the scoring criteria along with the evaluation results. The system according to feature 1.

4. The aforementioned public section is, We will collect opinions and questions regarding the review results and use them to improve our operations. The system according to feature 1.

5. The aforementioned review department, We will learn from past contest judging criteria and scoring results, as well as real-world examples of AI applications, to improve our evaluation criteria. The system according to feature 1.

6. The aforementioned public section is, The results of the review will be published on a dedicated website. The system according to feature 1.

7. The aforementioned reception unit is We estimate the user's emotions and adjust the timing of idea submission based on those estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is We analyze past application history and select the most suitable application method. The system according to feature 1.

9. The aforementioned reception unit is When receiving ideas, we filter applicants based on their current projects and areas of interest. The system according to feature 1.

10. The aforementioned reception unit is It estimates the user's emotions and prioritizes the ideas to accept based on those estimated emotions. The system according to feature 1.

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