System

Abstract

An object of a system according to an embodiment is to efficiently evaluate ideas that are considered by people outside a company and to support realization of the ideas.SOLUTION: A system according to an embodiment includes a reception unit, an evaluation unit, and a support unit. The reception unit receives an application process. The evaluation unit evaluates the idea received by the reception unit. The support unit supports realization of the idea selected by the evaluation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology does not adequately establish a process for efficiently evaluating ideas from people outside the company and supporting their realization, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently evaluate ideas conceived by people outside the company and support their realization. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an evaluation unit, and a support unit. The reception unit receives an application process. The evaluation unit evaluates the ideas received by the reception unit. The support unit supports the realization of the ideas selected by the evaluation unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently evaluate ideas conceived by people outside the company and support their realization. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention hosts a contest to promote the resolution of social issues using generative AI and accepts submissions. This system allows external parties to come up with ideas for solving social issues using generative AI and submit them to the contest. The submitted ideas are then evaluated, and the best ideas are selected. Furthermore, the system provides support for the realization of the selected ideas. For example, applicants explain their ideas in detail and clearly state how they will utilize generative AI. For example, they propose solutions to specific social issues, such as solving environmental problems or applying it to the medical field. The submitted ideas are then evaluated. This evaluation is conducted through expert review and public voting. Evaluation criteria include the idea's originality, feasibility, social impact, sustainability, and economic impact. As a result of the evaluation, the best ideas are selected. Furthermore, the system provides support for the realization of the selected ideas. Specifically, the system provides technical support, funding, marketing assistance, legal advice, networking opportunities, and more. This allows applicants to obtain resources to realize their ideas. For example, applicants who propose solutions to environmental issues receive advice from environmental technology experts and funding. This allows participants to receive social recognition by having their ideas evaluated and realized. This satisfies participants' desire for recognition and encourages further creative activity. Furthermore, the know-how from this contest can be provided as a service to support other companies and organizations in hosting similar contests. This is expected to spread opportunities for solving social issues, encouraging more people to use generative AI to address them. The system can promote problem-solving throughout society by soliciting, evaluating, and supporting the realization of ideas for solving social issues using generative AI. For example, if an idea for a new diagnostic method using generative AI is submitted as an application in the medical field, the idea will be evaluated and support will be provided for its realization. This will lead to the realization of new diagnostic methods and improve the quality of medical care. Similarly, if a proposal for a recycling system using generative AI is submitted as a solution to environmental issues, the idea will be evaluated and support will be provided for its realization.This will create a recycling system and promote environmental protection.

[0029] A system for promoting solutions to social problems according to an embodiment includes a reception unit, an evaluation unit, and a support unit. The reception unit receives an application process. The application process includes, but is not limited to, application procedures, required documents, and submission methods. The reception unit, for example, receives applications via an online form. The reception unit can also accept applications by mail or in person. For example, the reception unit receives information entered by applicants into the online form and stores it in a database. The reception unit can also scan application documents sent by mail, convert them into digital data, and store it in a database. The evaluation unit evaluates the ideas received by the reception unit. Examples of the evaluation include, but are not limited to, evaluation items, evaluation scales, and evaluator qualifications. For example, the evaluation unit conducts an expert review. The evaluation unit can also conduct evaluations through public voting. For example, the evaluation unit has experts evaluate the ideas' originality, feasibility, social impact, sustainability, economic impact, etc. The evaluation unit can also collect social evaluations of the applicants' ideas through public voting. The support unit supports the realization of the ideas selected by the evaluation unit. Examples of support include, but are not limited to, funding, technical support, and mentoring. For example, the support unit provides funding for the selected ideas. The support unit can also provide technical support. For example, the support unit provides technical advice necessary for realizing the selected ideas. The support unit can also support the realization of applicants' ideas through mentoring. In this way, the system for promoting solutions to social problems according to the embodiment consistently accepts, evaluates, and supports the application process. Some or all of the above-described processing in the support unit may be performed using, or without, AI. For example, the support unit can optimize and provide resources necessary for realizing the selected ideas using AI.

[0030] The social problem solving promotion system includes a standard setting unit that sets evaluation criteria. The standard setting unit sets the evaluation criteria. The evaluation criteria include, for example, evaluation items, evaluation scales, and standard weightings, but are not limited to these examples. The standard setting unit, for example, sets criteria for evaluating the originality of an idea. The standard setting unit can also set criteria for evaluating feasibility. For example, the standard setting unit sets criteria for evaluating the technical feasibility of an idea. The standard setting unit can also set criteria for evaluating social influence. For example, the standard setting unit sets criteria for evaluating the magnitude of the impact of an idea on society. This makes it possible to set the evaluation criteria. Some or all of the above-described processing in the standard setting unit may be performed using, for example, AI, or may be performed without using AI. For example, the standard setting unit can analyze past evaluation data using AI to optimize the evaluation criteria.

[0031] The social problem solving promotion system includes a providing unit that provides guidelines or templates to applicants. The providing unit provides the guidelines or templates to applicants. The guidelines include, for example, application procedures, evaluation criteria, and submission formats, but are not limited to these examples. For example, the providing unit provides application document formats to applicants. The providing unit can also provide examples of how to fill out application documents to applicants. For example, the providing unit provides templates showing examples of how to fill out application documents. The providing unit can also provide guidelines that explain the evaluation criteria to applicants. For example, the providing unit provides guidelines that explain the details of the evaluation criteria. This makes it possible to provide guidelines or templates to applicants. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can analyze an applicant's past application history using AI and provide optimal guidelines or templates.

[0032] The social problem solving promotion system includes a disclosure unit that discloses the evaluation process. The disclosure unit discloses the evaluation process. The evaluation process includes, for example, the evaluation procedure, a method for selecting evaluators, and a method for disclosing the evaluation results, but is not limited to these examples. The disclosure unit discloses, for example, the evaluation procedure. The disclosure unit can also disclose the method for selecting evaluators. For example, the disclosure unit discloses the qualifications and selection criteria of the evaluators. The disclosure unit can also provide a method for disclosing the evaluation results. For example, the disclosure unit discloses the evaluation results on a website. This ensures the transparency of the evaluation process. Some or all of the above-mentioned processing in the disclosure unit may be performed using, for example, AI, or may be performed without using AI. For example, the disclosure unit can analyze data from the evaluation process using AI and provide an optimal disclosure method for ensuring transparency.

[0033] The social problem solving promotion system includes a know-how providing unit that provides know-how to other companies or organizations. The know-how providing unit provides contest know-how to other companies or organizations. Know-how includes, but is not limited to, success stories, failure stories, and implementation procedures. For example, the know-how providing unit provides success stories to other companies or organizations. The know-how providing unit can also share failure stories. For example, the know-how providing unit analyzes failure stories from past contests and provides lessons learned to other companies or organizations. The know-how providing unit can also provide procedures for implementing contests. For example, the know-how providing unit provides guidelines that detail the procedures from planning to implementing a contest. This makes it possible to provide know-how to other companies or organizations. Some or all of the above-described processing in the know-how providing unit may be performed using, for example, AI, or may be performed without AI. For example, the know-how providing unit can analyze past contest data using AI and provide optimal know-how.

[0034] The reception unit can analyze the applicant's past application history and select the optimal application method. For example, the reception unit prioritizes suggesting application methods that the applicant has used in the past. For example, the reception unit retrieves the applicant's past application history from a database and identifies the application method that the applicant used most frequently. The reception unit can also select the optimal application method by referring to the application method that the applicant used successfully in the past. For example, the reception unit analyzes the applicant's past application history and identifies the application method with a high success rate. The reception unit can also suggest the most efficient application method based on the applicant's past application history. For example, the reception unit selects the fastest and most efficient application method based on the applicant's past application history. This allows the optimal application method to be selected based on the past application history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the applicant's past application history data into a generation AI and have the generation AI select the optimal application method.

[0035] The reception unit can filter applications based on the applicant's current project or areas of interest when accepting applications during the application process. For example, the reception unit prioritizes application content related to the applicant's current project. For example, the reception unit retrieves the applicant's current project information from a database and identifies related application content. The reception unit can also filter related application content based on the applicant's areas of interest. For example, the reception unit retrieves the applicant's areas of interest from a database and filters the application content based on related keywords. The reception unit can also suggest optimal application content by referring to the applicant's past project history. For example, the reception unit analyzes the applicant's past project history and identifies highly relevant application content. This enables filtering based on the applicant's areas of interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the applicant's current project information into a generation AI and have the generation AI filter related application content.

[0036] The reception unit can select an appropriate reception means depending on the applicant's input method when accepting applications for the application process. For example, if an applicant desires voice input, the reception unit prioritizes receiving the voice input. For example, the reception unit retrieves the applicant's input method from a database and provides a voice input reception means for applicants who desire voice input. Furthermore, if an applicant desires text input, the reception unit can also prioritize receiving text input. For example, the reception unit retrieves the applicant's input method from a database and provides a text input reception means for applicants who desire text input. Furthermore, if an applicant desires image input, the reception unit can also prioritize receiving image input. For example, the reception unit retrieves the applicant's input method from a database and provides an image input reception means for applicants who desire image input. This allows the reception means to be selected depending on the applicant's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the applicant's input method data into a generation AI and have the generation AI select the optimal reception means.

[0037] The reception unit can prioritize receiving relevant ideas during the application process, taking into account the applicant's geographical location information. For example, if the applicant lives in a specific region, the reception unit can prioritize receiving ideas related to that region. For example, the reception unit can obtain the applicant's geographical location information from a database and identify ideas related to that region. Furthermore, if the applicant lives in a specific city, the reception unit can prioritize receiving ideas related to that city. For example, the reception unit can obtain the applicant's geographical location information from a database and identify ideas related to that city. Furthermore, if the applicant lives in a specific country, the reception unit can prioritize receiving ideas related to that country. For example, the reception unit can obtain the applicant's geographical location information from a database and identify ideas related to that country. This allows for prioritized reception of ideas that are highly relevant based on the geographical location information. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the applicant's geographical location information data into a generation AI and cause the generation AI to identify related ideas.

[0038] The reception unit can analyze the applicant's social media activity during the application process and receive relevant ideas. For example, the reception unit prioritizes receiving ideas shared by the applicant on social media. For example, the reception unit retrieves the applicant's social media activity from a database and identifies the shared ideas. The reception unit can also analyze the applicant's social media activity and receive relevant ideas. For example, the reception unit can analyze the applicant's social media activity and identify highly relevant ideas. The reception unit can also receive related ideas based on the activity of the applicant's friends on social media. For example, the reception unit can analyze the social media activity of the applicant's friends and identify highly relevant ideas. This makes it possible to receive related ideas based on social media activity. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the applicant's social media activity data into a generation AI and have the generation AI identify related ideas.

[0039] The reception unit can adjust the reception method by reflecting the applicant's past feedback when receiving the application. For example, the reception unit suggests the optimal reception method based on feedback provided by the applicant in the past. For example, the reception unit retrieves the applicant's past feedback from a database and identifies the optimal reception method. The reception unit can also analyze the applicant's past feedback to improve the reception method. For example, the reception unit analyzes the applicant's past feedback and identifies areas for improvement. The reception unit can also customize the reception method by referring to the applicant's past feedback. For example, the reception unit provides an individually customized reception method based on the applicant's past feedback. This allows the reception method to be customized based on the past feedback. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the applicant's past feedback data into a generation AI and have the generation AI identify the optimal reception method.

[0040] During evaluation, the evaluation unit can adjust the details of the evaluation based on the importance of the idea. For example, the evaluation unit performs a detailed evaluation for ideas with high importance. For example, the evaluation unit evaluates the importance based on the scope of impact and feasibility of the idea and provides detailed feedback. The evaluation unit can also perform a concise evaluation for ideas with low importance. For example, the evaluation unit evaluates the importance based on the innovativeness and economic impact of the idea and provides concise feedback. The evaluation unit can also gradually adjust the level of detail of the evaluation depending on the importance. For example, the evaluation unit sets detailed evaluation items for ideas with high importance and concise evaluation items for ideas with low importance. This makes it possible to adjust the level of detail of the evaluation depending on the importance of the idea. Some or all of the above-mentioned processing in the evaluation unit may be performed using, or without, AI. For example, the evaluation unit can input idea importance data to the generation AI and cause the generation AI to adjust the level of detail of the evaluation.

[0041] During evaluation, the evaluation unit can apply different evaluation algorithms depending on the category of the idea. For example, the evaluation unit applies an environmental evaluation algorithm to ideas related to environmental issues. For example, the evaluation unit sets evaluation criteria specialized for environmental issues and performs evaluation using an environmental evaluation algorithm. The evaluation unit can also apply a medical evaluation algorithm to ideas related to the medical field. For example, the evaluation unit sets evaluation criteria specialized for the medical field and performs evaluation using an medical evaluation algorithm. The evaluation unit can also apply an education evaluation algorithm to ideas related to the education field. For example, the evaluation unit sets evaluation criteria specialized for the education field and performs evaluation using an education evaluation algorithm. This allows the application of an evaluation algorithm depending on the category of the idea. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input idea category data into the generation AI and cause the generation AI to apply the evaluation algorithm.

[0042] During evaluation, the evaluation unit can improve the accuracy of the evaluation by referring to the applicant's past evaluation results. For example, the evaluation unit performs the evaluation by referring to the evaluation results of ideas previously submitted by the applicant. For example, the evaluation unit retrieves the applicant's past evaluation results from a database to ensure consistency in the evaluation. The evaluation unit can also analyze the applicant's past evaluation results and adjust the evaluation algorithm. For example, the evaluation unit optimizes the parameters of the evaluation algorithm based on the applicant's past evaluation results. The evaluation unit can also improve the accuracy of the evaluation based on the applicant's past evaluation results. For example, the evaluation unit adjusts the evaluation criteria by referring to the applicant's past evaluation results. This improves the accuracy of the evaluation based on the past evaluation results. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the applicant's past evaluation result data into the generation AI and cause the generation AI to improve the accuracy of the evaluation.

[0043] During evaluation, the evaluation unit can determine the order of evaluation based on the submission time of the ideas. The evaluation unit, for example, prioritizes evaluation of ideas submitted early. For example, the evaluation unit retrieves the submission time of ideas from a database and identifies ideas submitted early. The evaluation unit can also prioritize evaluation of ideas submitted just before the deadline. For example, the evaluation unit retrieves the submission time of ideas from a database and identifies ideas submitted just before the deadline. The evaluation unit can also adjust the priority of evaluation according to the submission time. For example, the evaluation unit applies an algorithm that determines the order of evaluation based on the submission time. This makes it possible to determine the priority of evaluation based on the submission time. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input data on the submission time of ideas to a generation AI and have the generation AI determine the order of evaluation.

[0044] During evaluation, the evaluation unit can adjust the order of evaluation based on the relevance of the ideas. The evaluation unit, for example, prioritizes evaluation of highly relevant ideas. For example, the evaluation unit obtains the relevance of ideas from a database and identifies highly relevant ideas. The evaluation unit can also postpone less relevant ideas. For example, the evaluation unit obtains the relevance of ideas from a database and identifies less relevant ideas. The evaluation unit can also adjust the order of evaluation according to the relevance of the ideas. For example, the evaluation unit applies an algorithm that prioritizes more relevant ideas. This makes it possible to adjust the order of evaluation based on the relevance of the ideas. Some or all of the above-described processing in the evaluation unit may be performed using, or without, AI. For example, the evaluation unit can input idea relevance data to a generation AI and cause the generation AI to adjust the order of evaluation.

[0045] During the evaluation, the evaluation unit can adjust the use of evaluation terminology according to the applicant's level of expertise. For example, if the applicant has specialized knowledge, the evaluation unit uses a lot of specialized terminology. For example, the evaluation unit retrieves the applicant's level of expertise from a database and performs an evaluation that uses a lot of specialized terminology. Alternatively, if the applicant does not have specialized knowledge, the evaluation unit can perform an evaluation in simple language. For example, the evaluation unit retrieves the applicant's level of expertise from a database and performs an evaluation in simple language. Alternatively, the evaluation unit can adjust the use of specialized terminology according to the applicant's level of expertise. For example, the evaluation unit adjusts the tone of the evaluation comments and the format of the feedback based on the applicant's level of expertise. This allows the use of specialized terminology in the evaluation to be adjusted according to the applicant's level of expertise. Some or all of the above-described processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the applicant's level of expertise data into the generation AI and have the generation AI adjust the terminology in the evaluation.

[0046] When providing support, the support department can analyze the applicant's past activity history and select an appropriate support method. For example, the support department selects a support method by referring to successful projects that the applicant has completed in the past. For example, the support department retrieves the applicant's past activity history from a database and identifies successful projects. The support department can also analyze the applicant's past activity history and propose an optimal support method. For example, the support department identifies an optimal support method based on the applicant's past activity history. The support department can also customize a support method based on the applicant's past activity history. For example, the support department analyzes the applicant's past activity history and provides an individually customized support method. This allows the optimal support method to be selected based on the past activity history. Some or all of the above-described processing in the support department may be performed using, for example, AI, or may be performed without using AI. For example, the support department can input the applicant's past activity history data into a generation AI and have the generation AI select an optimal support method.

[0047] When providing support, the support unit can customize the support means based on the applicant's current living situation. For example, if the applicant is busy, the support unit can suggest an efficient support method. For example, the support unit can obtain the applicant's current living situation from a database and identify an efficient support method. Furthermore, if the applicant has time, the support unit can also suggest a detailed support method. For example, the support unit can obtain the applicant's current living situation from a database and identify a detailed support method. Furthermore, the support unit can customize the support means according to the applicant's living situation. For example, the support unit can provide individually customized support means based on the applicant's living situation. This allows the support means to be customized based on the applicant's current living situation. Some or all of the above-mentioned processing in the support unit may be performed, for example, using AI or without AI. For example, the support unit can input the applicant's current living situation data into a generation AI and have the generation AI customize the support means.

[0048] The support unit can improve the support method by reflecting the applicant's feedback when providing support. The support unit improves the support method, for example, based on feedback provided by the applicant. For example, the support unit retrieves the applicant's feedback from a database and identifies areas for improvement. The support unit can also analyze the applicant's feedback and adjust the support method. For example, the support unit identifies areas for adjustment in the support method based on the applicant's feedback. The support unit can also customize the support method by referring to the applicant's feedback. For example, the support unit provides an individually customized support method based on the applicant's feedback. This allows the support method to be improved based on the feedback. Some or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can input the applicant's feedback data into a generation AI and have the generation AI improve the support method.

[0049] When providing support, the support unit can select an appropriate support method by taking into account the applicant's geographical location information. For example, if the applicant lives in a specific region, the support unit can suggest a support method related to that region. For example, the support unit can obtain the applicant's geographical location information from a database and identify a support method related to that region. Furthermore, if the applicant lives in a specific city, the support unit can suggest a support method related to that city. For example, the support unit can obtain the applicant's geographical location information from a database and identify a support method related to that city. Furthermore, if the applicant lives in a specific country, the support unit can suggest a support method related to that country. For example, the support unit can obtain the applicant's geographical location information from a database and identify a support method related to that country. This allows the optimal support method to be selected based on the geographical location information. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can input the applicant's geographical location information data into a generation AI and have the generation AI select the optimal support method.

[0050] When providing support, the support unit can analyze the applicant's social media activity and suggest support methods. The support unit, for example, suggests optimal support methods based on information shared by the applicant on social media. For example, the support unit retrieves the applicant's social media activity from a database and identifies the shared information. The support unit can also analyze the applicant's social media activity and suggest relevant support methods. For example, the support unit analyzes the applicant's social media activity and identifies relevant support methods. The support unit can also suggest relevant support methods based on the activity of the applicant's friends on social media. For example, the support unit analyzes the social media activity of the applicant's friends and identifies relevant support methods. This allows support methods to be suggested based on social media activity. Some or all of the above-mentioned processing in the support unit may be performed, for example, using AI, or may be performed without using AI. For example, the support unit can input the applicant's social media activity data into a generation AI and have the generation AI suggest optimal support methods.

[0051] When providing support, the support unit can adjust the support method by reflecting the applicant's past feedback. For example, the support unit proposes an optimal support method based on feedback provided by the applicant in the past. For example, the support unit retrieves the applicant's past feedback from a database and identifies the optimal support method. The support unit can also analyze the applicant's past feedback to improve the support method. For example, the support unit analyzes the applicant's past feedback and identifies areas for improvement. The support unit can also customize the support method by referring to the applicant's past feedback. For example, the support unit provides an individually customized support method based on the applicant's past feedback. This allows the support method to be customized based on the past feedback. Some or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can input the applicant's past feedback data into a generation AI and have the generation AI identify the optimal support method.

[0052] When setting the criteria, the criteria setting unit can adjust the evaluation criteria by referring to past evaluation data. The criteria setting unit, for example, analyzes past evaluation data and adjusts the evaluation criteria. For example, the criteria setting unit acquires past evaluation data from a database and identifies areas for improvement in the evaluation criteria. The criteria setting unit can also optimize the evaluation criteria based on the past evaluation data. For example, the criteria setting unit analyzes past evaluation data and optimizes parameters of the evaluation criteria. The criteria setting unit can also update the evaluation criteria by referring to the past evaluation data. For example, the criteria setting unit identifies areas for updating the evaluation criteria based on the past evaluation data. This allows the evaluation criteria to be optimized based on the past evaluation data. Some or all of the above-mentioned processing in the criteria setting unit may be performed using, for example, AI, or may be performed without using AI. For example, the criteria setting unit can input past evaluation data to a generation AI and cause the generation AI to adjust the evaluation criteria.

[0053] When setting the criteria, the criteria setting unit can update the evaluation criteria to reflect applicant feedback. The criteria setting unit updates the evaluation criteria, for example, based on feedback provided by the applicant. For example, the criteria setting unit retrieves applicant feedback from a database and identifies areas for improvement in the evaluation criteria. The criteria setting unit can also analyze the applicant feedback and adjust the evaluation criteria. For example, the criteria setting unit identifies areas for adjustment in the evaluation criteria based on the applicant feedback. The criteria setting unit can also improve the evaluation criteria by referring to the applicant feedback. For example, the criteria setting unit identifies areas for improvement in the evaluation criteria based on the applicant feedback. This allows the evaluation criteria to be updated based on the applicant feedback. Some or all of the above-described processing in the criteria setting unit may be performed using, for example, AI, or may be performed without using AI. For example, the criteria setting unit inputs applicant feedback data into a generation AI and causes the generation AI to update the evaluation criteria.

[0054] When setting the criteria, the criteria setting unit can weight the evaluation criteria based on the submission time of the idea. For example, the criteria setting unit weights the evaluation criteria to ideas submitted early. For example, the criteria setting unit obtains the submission time of the idea from a database and weights the idea submitted early. The criteria setting unit can also weight the evaluation criteria to ideas submitted just before the deadline. For example, the criteria setting unit obtains the submission time of the idea from a database and weights the idea submitted just before the deadline. The criteria setting unit can also adjust the weighting of the evaluation criteria depending on the submission time. For example, the criteria setting unit applies an algorithm that weights the evaluation criteria based on the submission time. This allows the evaluation criteria to be weighted based on the submission time. Some or all of the above-mentioned processing in the criteria setting unit may be performed using, for example, AI, or may be performed without using AI. For example, the criteria setting unit can input idea submission time data to the generation AI and cause the generation AI to weight the evaluation criteria.

[0055] When setting the criteria, the criteria setting unit can integrate information from different data sources to expand the evaluation criteria. For example, the criteria setting unit integrates information from different data sources to expand the evaluation criteria. For example, the criteria setting unit obtains and integrates internal data, external data, third-party data, etc. from a database. The criteria setting unit can also analyze information from different data sources to optimize the evaluation criteria. For example, the criteria setting unit optimizes parameters of the evaluation criteria based on information from different data sources. The criteria setting unit can also update the evaluation criteria by referring to information from different data sources. For example, the criteria setting unit identifies update points for the evaluation criteria based on information from different data sources. This allows the evaluation criteria to be expanded by integrating information from different data sources. Some or all of the above-described processing in the criteria setting unit may be performed using, for example, AI, or may be performed without using AI. For example, the criteria setting unit can input information from different data sources into the generation AI and cause the generation AI to expand the evaluation criteria.

[0056] When providing the guidelines or templates, the providing unit can refer to the applicant's past application history and provide an appropriate guideline or template. For example, the providing unit can prioritize providing guidelines that the applicant has used in the past. For example, the providing unit can retrieve the applicant's past application history from a database and identify the most frequently used guideline. The providing unit can also analyze the applicant's past application history and suggest the optimal guideline. For example, the providing unit can identify the optimal guideline based on the applicant's past application history. The providing unit can also provide the optimal template based on the applicant's past application history. For example, the providing unit can analyze the applicant's past application history and identify the most effective template. This makes it possible to provide the optimal guideline or template based on the past application history. Some or all of the above-described processing in the providing unit can be performed using, or without, AI. For example, the providing unit can input the applicant's past application history data into a generation AI and cause the generation AI to provide the optimal guideline or template.

[0057] The providing unit can customize the guidelines and templates according to the applicant's current project when providing them. The providing unit, for example, provides guidelines related to the project the applicant is currently working on. For example, the providing unit retrieves the applicant's current project information from a database and identifies related guidelines. The providing unit can also provide an optimal template according to the applicant's current project. For example, the providing unit identifies an optimal template based on the applicant's current project information. The providing unit can also analyze the applicant's current project and provide customized guidelines. For example, the providing unit provides individually customized guidelines based on the applicant's current project information. This allows the guidelines and templates to be customized according to the current project. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the applicant's current project information to a generation AI and cause the generation AI to customize the guidelines and templates.

[0058] The providing unit can improve the guidelines and templates by reflecting applicant feedback when providing them. The providing unit, for example, improves the guidelines based on feedback provided by the applicant. For example, the providing unit retrieves applicant feedback from a database and identifies areas for improvement. The providing unit can also analyze applicant feedback and adjust the templates. For example, the providing unit identifies areas for adjustment in the templates based on applicant feedback. The providing unit can also customize the guidelines and templates by referring to applicant feedback. For example, the providing unit provides individually customized guidelines and templates based on applicant feedback. This allows the guidelines and templates to be improved based on the feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input applicant feedback data into a generation AI and cause the generation AI to improve the guidelines and templates.

[0059] The providing unit can provide appropriate guidelines or templates by taking into account the applicant's geographic location information when providing the guidelines or templates. For example, if the applicant lives in a specific region, the providing unit can provide guidelines related to that region. For example, the providing unit can obtain the applicant's geographic location information from a database and identify guidelines related to that region. Furthermore, if the applicant lives in a specific city, the providing unit can provide templates related to that city. For example, the providing unit can obtain the applicant's geographic location information from a database and identify templates related to that city. Furthermore, if the applicant lives in a specific country, the providing unit can provide guidelines related to that country. For example, the providing unit can obtain the applicant's geographic location information from a database and identify guidelines related to that country. This allows the optimal guidelines or templates to be provided based on the geographic location information. Some or all of the above-described processing in the providing unit can be performed using, or without, AI. For example, the providing unit can input the applicant's geographic location information data into a generation AI and cause the generation AI to provide optimal guidelines or templates.

[0060] At the time of providing, the providing unit can analyze the applicant's social media activity and provide relevant guidelines and templates. The providing unit, for example, provides optimal guidelines based on information shared by the applicant on social media. For example, the providing unit retrieves the applicant's social media activity from a database and identifies the shared information. The providing unit can also analyze the applicant's social media activity and provide relevant templates. For example, the providing unit analyzes the applicant's social media activity and identifies highly relevant templates. The providing unit can also provide relevant guidelines based on the activity of the applicant's friends on social media. For example, the providing unit analyzes the social media activity of the applicant's friends and identifies highly relevant guidelines. This makes it possible to provide relevant guidelines and templates based on social media activity. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the applicant's social media activity data into a generation AI and cause the generation AI to provide optimal guidelines and templates.

[0061] The providing unit can adjust the guidelines and templates by reflecting the applicant's past feedback when providing them. The providing unit, for example, provides optimal guidelines based on feedback previously provided by the applicant. For example, the providing unit retrieves the applicant's past feedback from a database and identifies optimal guidelines. The providing unit can also analyze the applicant's past feedback and improve the templates. For example, the providing unit identifies areas for improvement in the templates based on the applicant's past feedback. The providing unit can also customize the guidelines and templates by referring to the applicant's past feedback. For example, the providing unit provides individually customized guidelines and templates based on the applicant's past feedback. This allows the guidelines and templates to be customized based on the past feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the applicant's past feedback data into a generation AI and cause the generation AI to adjust the guidelines and templates.

[0062] At the time of disclosure, the disclosure unit can select an appropriate disclosure method by referring to past evaluation processes. The disclosure unit, for example, analyzes past evaluation processes and selects the optimal disclosure method. For example, the disclosure unit retrieves past evaluation processes from a database and identifies the optimal disclosure method. The disclosure unit can also optimize the disclosure method based on the past evaluation processes. For example, the disclosure unit analyzes past evaluation processes and optimizes parameters of the disclosure method. The disclosure unit can also update the disclosure method by referring to the past evaluation processes. For example, the disclosure unit identifies updates to the disclosure method based on the past evaluation processes. This allows the optimal disclosure method to be selected based on the past evaluation processes. Some or all of the above-described processing in the disclosure unit may be performed using, for example, AI, or may be performed without using AI. For example, the disclosure unit can input past evaluation process data into a generation AI and have the generation AI select the optimal disclosure method.

[0063] The disclosure unit can improve the disclosure method of the evaluation process by reflecting the applicant's feedback at the time of disclosure. The disclosure unit improves the disclosure method of the evaluation process, for example, based on feedback provided by the applicant. For example, the disclosure unit retrieves the applicant's feedback from a database and identifies areas for improvement. The disclosure unit can also analyze the applicant's feedback and adjust the disclosure method. For example, the disclosure unit identifies areas for adjustment in the disclosure method based on the applicant's feedback. The disclosure unit can also customize the disclosure method by referring to the applicant's feedback. For example, the disclosure unit provides an individually customized disclosure method based on the applicant's feedback. This allows the disclosure method of the evaluation process to be improved based on the feedback. Some or all of the above-mentioned processing in the disclosure unit may be performed using, for example, AI, or may be performed without using AI. For example, the disclosure unit can input the applicant's feedback data into a generation AI and have the generation AI improve the disclosure method.

[0064] The disclosure unit can select the optimal disclosure method at the time of disclosure, taking into account the applicant's geographic location information. For example, if the applicant lives in a specific region, the disclosure unit selects a disclosure method related to that region. For example, the disclosure unit retrieves the applicant's geographic location information from a database and identifies a disclosure method related to that region. Furthermore, if the applicant lives in a specific city, the disclosure unit can select a disclosure method related to that city. For example, the disclosure unit retrieves the applicant's geographic location information from a database and identifies a disclosure method related to that city. Furthermore, if the applicant lives in a specific country, the disclosure unit can select a disclosure method related to that country. For example, the disclosure unit retrieves the applicant's geographic location information from a database and identifies a disclosure method related to that country. This allows the optimal disclosure method to be selected based on the geographic location information. Some or all of the above-described processing in the disclosure unit may be performed using, for example, AI, or may be performed without using AI. For example, the disclosure unit can input the applicant's geographic location information data into a generation AI and have the generation AI select the optimal disclosure method.

[0065] At the time of disclosure, the disclosure unit can adjust the disclosure method of the evaluation process by analyzing the applicant's social media activity. The disclosure unit, for example, selects the optimal disclosure method based on information shared by the applicant on social media. For example, the disclosure unit retrieves the applicant's social media activity from a database and identifies the shared information. The disclosure unit can also analyze the applicant's social media activity and select a relevant disclosure method. For example, the disclosure unit can analyze the applicant's social media activity and identify a relevant disclosure method. The disclosure unit can also select a relevant disclosure method based on the activity of the applicant's friends on social media. For example, the disclosure unit can analyze the social media activity of the applicant's friends and identify a relevant disclosure method. This makes it possible to adjust the disclosure method of the evaluation process based on social media activity. Some or all of the above-mentioned processing in the disclosure unit may be performed using, for example, AI, or may be performed without using AI. For example, the disclosure unit can input the applicant's social media activity data into a generation AI and have the generation AI select the optimal disclosure method.

[0066] When providing know-how, the know-how provision unit can select an appropriate provision method by referring to past know-how provision data. The know-how provision unit, for example, analyzes past know-how provision data and selects an optimal provision method. For example, the know-how provision unit retrieves past know-how provision data from a database and identifies an optimal provision method. The know-how provision unit can also optimize the provision method based on the past know-how provision data. For example, the know-how provision unit analyzes past know-how provision data and optimizes parameters of the provision method. The know-how provision unit can also update the provision method by referring to the past know-how provision data. For example, the know-how provision unit identifies points to update the provision method based on the past know-how provision data. This allows the optimal provision method to be selected based on the past know-how provision data. Some or all of the above-described processing in the know-how provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the know-how provision unit can input past know-how provision data to a generation AI and cause the generation AI to select an optimal provision method.

[0067] The know-how provision unit can improve the provision method by reflecting the applicant's feedback when providing know-how. The know-how provision unit improves the provision method, for example, based on the feedback provided by the applicant. For example, the know-how provision unit retrieves the applicant's feedback from a database and identifies areas for improvement. The know-how provision unit can also analyze the applicant's feedback and adjust the provision method. For example, the know-how provision unit identifies areas for adjustment in the provision method based on the applicant's feedback. The know-how provision unit can also customize the provision method by referring to the applicant's feedback. For example, the know-how provision unit provides an individually customized provision method based on the applicant's feedback. This allows the provision method to be improved based on the feedback. Some or all of the above-described processing in the know-how provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the know-how provision unit can input the applicant's feedback data into a generation AI and have the generation AI improve the provision method.

[0068] When providing know-how, the know-how providing unit can select the optimal provision method by taking into account the applicant's geographical location information. For example, if the applicant lives in a specific region, the know-how providing unit selects a provision method related to that region. For example, the know-how providing unit retrieves the applicant's geographical location information from a database and identifies a provision method related to that region. Furthermore, if the applicant lives in a specific city, the know-how providing unit can also select a provision method related to that city. For example, the know-how providing unit retrieves the applicant's geographical location information from a database and identifies a provision method related to that city. Furthermore, if the applicant lives in a specific country, the know-how providing unit can also select a provision method related to that country. For example, the know-how providing unit retrieves the applicant's geographical location information from a database and identifies a provision method related to that country. This allows the optimal provision method to be selected based on the geographical location information. Some or all of the above-described processing in the know-how providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the know-how providing unit can input the applicant's geographical location information data into a generation AI and have the generation AI select the optimal provision method.

[0069] When providing know-how, the know-how provision unit can analyze the applicant's social media activity and propose a method for providing know-how. The know-how provision unit can propose an optimal means for providing know-how based on, for example, information shared by the applicant on social media. For example, the know-how provision unit can retrieve the applicant's social media activity from a database and identify the shared information. The know-how provision unit can also analyze the applicant's social media activity and propose related means for providing know-how. For example, the know-how provision unit can analyze the applicant's social media activity and identify highly relevant means for providing know-how. The know-how provision unit can also propose related means for providing know-how based on the activity of the applicant's friends on social media. For example, the know-how provision unit can analyze the social media activity of the applicant's friends and identify highly relevant means for providing know-how. This makes it possible to propose means for providing know-how based on social media activity. Some or all of the above-described processing in the know-how provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the know-how provision unit can input the applicant's social media activity data into a generation AI and have the generation AI propose an optimal means for providing know-how.

[0070] When providing know-how, the know-how provision unit can customize the provision method by reflecting the applicant's past feedback. The know-how provision unit, for example, proposes an optimal know-how provision method based on feedback provided by the applicant in the past. For example, the know-how provision unit retrieves the applicant's past feedback from a database and identifies an optimal know-how provision method. The know-how provision unit can also analyze the applicant's past feedback and improve the provision method. For example, the know-how provision unit identifies areas for improvement in the provision method based on the applicant's past feedback. The know-how provision unit can also customize the know-how provision method by referring to the applicant's past feedback. For example, the know-how provision unit provides an individually customized know-how provision method based on the applicant's past feedback. This allows the provision method to be customized based on the past feedback. Some or all of the above-described processing in the know-how provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the know-how provision unit can input the applicant's past feedback data into a generation AI and have the generation AI execute a proposal for an optimal know-how provision method.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The reception unit can analyze the applicant's past application history and propose the optimal application process. For example, the reception unit can prioritize the proposal of application methods that the applicant has used in the past. The reception unit can also select the optimal application process by referring to application methods that the applicant has used successfully in the past. Furthermore, the reception unit can also propose the most efficient application process from the applicant's past application history. This makes it possible to select the optimal application process based on the applicant's past application history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the applicant's past application history data into a generation AI and have the generation AI select the optimal application process.

[0073] The support department can analyze the applicant's past activity history to select an appropriate support method. For example, the support method can be selected by referring to the applicant's past successful projects. The support department can also analyze the applicant's past activity history to propose the optimal support method. Furthermore, the support method can be customized based on the applicant's past activity history. This makes it possible to select the optimal support method based on the applicant's past activity history. Some or all of the above-mentioned processing in the support department may be performed using, for example, AI, or may be performed without using AI. For example, the support department can input the applicant's past activity history data into the generation AI and have the generation AI select the optimal support method.

[0074] The evaluation unit can adjust the details of the evaluation based on the importance of the idea during evaluation. For example, a detailed evaluation can be performed for ideas with high importance. A brief evaluation can also be performed for ideas with low importance. Furthermore, the level of detail of the evaluation can be adjusted in stages depending on the importance. This allows the level of detail of the evaluation to be adjusted depending on the importance of the idea. Some or all of the above-mentioned processing in the evaluation unit can be performed using, for example, AI, or can be performed without using AI. For example, the evaluation unit can input idea importance data to the generation AI and have the generation AI adjust the level of detail of the evaluation.

[0075] When setting the criteria, the criteria setting unit can adjust the evaluation criteria by referring to past evaluation data. For example, the past evaluation data can be analyzed and the evaluation criteria can be adjusted. The evaluation criteria can also be optimized based on the past evaluation data. Furthermore, the evaluation criteria can be updated by referring to the past evaluation data. This allows the evaluation criteria to be optimized based on the past evaluation data. Some or all of the above-mentioned processing in the criteria setting unit may be performed using AI, for example, or may be performed without using AI. For example, the criteria setting unit can input past evaluation data into the generation AI and cause the generation AI to adjust the evaluation criteria.

[0076] When providing support, the support unit can customize the means of support based on the applicant's current living situation. For example, if the applicant is busy, it can suggest an efficient support method. Also, if the applicant has time, it can suggest a detailed support method. Furthermore, it can customize the means of support according to the applicant's living situation. This allows the means of support to be customized based on the applicant's current living situation. Some or all of the above-mentioned processing in the support unit may be performed using AI, for example, or may be performed without using AI. For example, the support unit can input the applicant's current living situation data into the generation AI and have the generation AI customize the means of support.

[0077] When providing the guidelines or templates, the providing unit can refer to the applicant's past application history and provide appropriate guidelines or templates. For example, the providing unit can provide guidelines that the applicant has used in the past with priority. The providing unit can also analyze the applicant's past application history and suggest optimal guidelines. Furthermore, the providing unit can provide optimal templates based on the applicant's past application history. This makes it possible to provide optimal guidelines or templates based on the past application history. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the applicant's past application history data into a generating AI and have the generating AI provide optimal guidelines or templates.

[0078] The processing flow of the first embodiment will be briefly explained below.

[0079] Step 1: The reception department accepts applications. The application process includes application procedures, required documents, and submission methods. The reception department can accept applications through an online form, as well as applications by mail or in person. For example, the reception department can receive information entered by applicants into an online form and store it in a database. It can also scan application documents sent by mail, convert them into digital data, and store it in a database. Step 2: The evaluation department evaluates the ideas received by the reception department. The evaluation includes evaluation criteria, evaluation scales, and evaluator qualifications. The evaluation department can conduct expert review or evaluation through public voting. For example, experts evaluate the ideas' originality, feasibility, social impact, sustainability, economic impact, etc. Public evaluation of the applicants' ideas can also be collected through public voting. Step 3: The Support Department supports the realization of the ideas selected by the Evaluation Department. Support can include funding, technical support, and mentoring. In addition to providing funding for the selected ideas, the Support Department also supports the realization of the ideas through technical support and mentoring. For example, the Support Department can provide the technical advice necessary to realize the selected ideas and optimize and provide the necessary resources.

[0080] (Example 2) A system according to an embodiment of the present invention hosts a contest to promote the resolution of social issues using generative AI and accepts submissions. This system allows external parties to come up with ideas for solving social issues using generative AI and submit them to the contest. The submitted ideas are then evaluated, and the best ideas are selected. Furthermore, the system provides support for the realization of the selected ideas. For example, applicants explain their ideas in detail and clearly state how they will utilize generative AI. For example, they propose solutions to specific social issues, such as solving environmental problems or applying it to the medical field. The submitted ideas are then evaluated. This evaluation is conducted through expert review and public voting. Evaluation criteria include the idea's originality, feasibility, social impact, sustainability, and economic impact. As a result of the evaluation, the best ideas are selected. Furthermore, the system provides support for the realization of the selected ideas. Specifically, the system provides technical support, funding, marketing assistance, legal advice, networking opportunities, and more. This allows applicants to obtain resources to realize their ideas. For example, applicants who propose solutions to environmental issues receive advice from environmental technology experts and funding. This allows participants to receive social recognition by having their ideas evaluated and realized. This satisfies participants' desire for recognition and encourages further creative activity. Furthermore, the know-how from this contest can be provided as a service to support other companies and organizations in hosting similar contests. This is expected to spread opportunities for solving social issues, encouraging more people to use generative AI to address them. The system can promote problem-solving throughout society by soliciting, evaluating, and supporting the realization of ideas for solving social issues using generative AI. For example, if an idea for a new diagnostic method using generative AI is submitted as an application in the medical field, the idea will be evaluated and support will be provided for its realization. This will lead to the realization of new diagnostic methods and improve the quality of medical care. Similarly, if a proposal for a recycling system using generative AI is submitted as a solution to environmental issues, the idea will be evaluated and support will be provided for its realization.This will create a recycling system and promote environmental protection.

[0081] A system for promoting solutions to social problems according to an embodiment includes a reception unit, an evaluation unit, and a support unit. The reception unit receives an application process. The application process includes, but is not limited to, application procedures, required documents, and submission methods. The reception unit, for example, receives applications via an online form. The reception unit can also accept applications by mail or in person. For example, the reception unit receives information entered by applicants into the online form and stores it in a database. The reception unit can also scan application documents sent by mail, convert them into digital data, and store it in a database. The evaluation unit evaluates the ideas received by the reception unit. Examples of the evaluation include, but are not limited to, evaluation items, evaluation scales, and evaluator qualifications. For example, the evaluation unit conducts an expert review. The evaluation unit can also conduct evaluations through public voting. For example, the evaluation unit has experts evaluate the ideas' originality, feasibility, social impact, sustainability, economic impact, etc. The evaluation unit can also collect social evaluations of the applicants' ideas through public voting. The support unit supports the realization of the ideas selected by the evaluation unit. Examples of support include, but are not limited to, funding, technical support, and mentoring. For example, the support unit provides funding for the selected ideas. The support unit can also provide technical support. For example, the support unit provides technical advice necessary for realizing the selected ideas. The support unit can also support the realization of applicants' ideas through mentoring. In this way, the system for promoting solutions to social problems according to the embodiment consistently accepts, evaluates, and supports the application process. Some or all of the above-described processing in the support unit may be performed using, or without, AI. For example, the support unit can optimize and provide resources necessary for realizing the selected ideas using AI.

[0082] The social problem solving promotion system includes a standard setting unit that sets evaluation criteria. The standard setting unit sets the evaluation criteria. The evaluation criteria include, for example, evaluation items, evaluation scales, and standard weightings, but are not limited to these examples. The standard setting unit, for example, sets criteria for evaluating the originality of an idea. The standard setting unit can also set criteria for evaluating feasibility. For example, the standard setting unit sets criteria for evaluating the technical feasibility of an idea. The standard setting unit can also set criteria for evaluating social influence. For example, the standard setting unit sets criteria for evaluating the magnitude of the impact of an idea on society. This makes it possible to set the evaluation criteria. Some or all of the above-described processing in the standard setting unit may be performed using, for example, AI, or may be performed without using AI. For example, the standard setting unit can analyze past evaluation data using AI to optimize the evaluation criteria.

[0083] The social problem solving promotion system includes a providing unit that provides guidelines or templates to applicants. The providing unit provides the guidelines or templates to applicants. The guidelines include, for example, application procedures, evaluation criteria, and submission formats, but are not limited to these examples. For example, the providing unit provides application document formats to applicants. The providing unit can also provide examples of how to fill out application documents to applicants. For example, the providing unit provides templates showing examples of how to fill out application documents. The providing unit can also provide guidelines that explain the evaluation criteria to applicants. For example, the providing unit provides guidelines that explain the details of the evaluation criteria. This makes it possible to provide guidelines or templates to applicants. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can analyze an applicant's past application history using AI and provide optimal guidelines or templates.

[0084] The social problem solving promotion system includes a disclosure unit that discloses the evaluation process. The disclosure unit discloses the evaluation process. The evaluation process includes, for example, the evaluation procedure, a method for selecting evaluators, and a method for disclosing the evaluation results, but is not limited to these examples. The disclosure unit discloses, for example, the evaluation procedure. The disclosure unit can also disclose the method for selecting evaluators. For example, the disclosure unit discloses the qualifications and selection criteria of the evaluators. The disclosure unit can also provide a method for disclosing the evaluation results. For example, the disclosure unit discloses the evaluation results on a website. This ensures the transparency of the evaluation process. Some or all of the above-mentioned processing in the disclosure unit may be performed using, for example, AI, or may be performed without using AI. For example, the disclosure unit can analyze data from the evaluation process using AI and provide an optimal disclosure method for ensuring transparency.

[0085] The social problem solving promotion system includes a know-how providing unit that provides know-how to other companies or organizations. The know-how providing unit provides contest know-how to other companies or organizations. Know-how includes, but is not limited to, success stories, failure stories, and implementation procedures. For example, the know-how providing unit provides success stories to other companies or organizations. The know-how providing unit can also share failure stories. For example, the know-how providing unit analyzes failure stories from past contests and provides lessons learned to other companies or organizations. The know-how providing unit can also provide procedures for implementing contests. For example, the know-how providing unit provides guidelines that detail the procedures from planning to implementing a contest. This makes it possible to provide know-how to other companies or organizations. Some or all of the above-described processing in the know-how providing unit may be performed using, for example, AI, or may be performed without AI. For example, the know-how providing unit can analyze past contest data using AI and provide optimal know-how.

[0086] The reception unit can estimate the applicant's emotions and adjust the timing of the application process based on the estimated emotions. For example, if the applicant is nervous, the reception unit can proceed slowly through the application process to allow the applicant to relax. For example, the reception unit can capture the applicant's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. Furthermore, if the applicant is excited, the reception unit can also proceed quickly through the application process. For example, the reception unit can record the applicant's voice and estimate their emotions using voice analysis technology. Furthermore, if the applicant is feeling anxious, the reception unit can proceed through the application process while displaying reassuring messages. For example, the reception unit can collect the applicant's biometric data (heart rate and electrodermal activity) using a sensor and estimate their emotions using an emotion estimation algorithm. This allows the application process to proceed at a timing appropriate to the applicant's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI can be, 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 reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input image data of an applicant taken with a camera into the generation AI and have the generation AI estimate the applicant's emotions.

[0087] The reception unit can analyze the applicant's past application history and select the optimal application method. For example, the reception unit prioritizes suggesting application methods that the applicant has used in the past. For example, the reception unit retrieves the applicant's past application history from a database and identifies the application method that the applicant used most frequently. The reception unit can also select the optimal application method by referring to the application method that the applicant used successfully in the past. For example, the reception unit analyzes the applicant's past application history and identifies the application method with a high success rate. The reception unit can also suggest the most efficient application method based on the applicant's past application history. For example, the reception unit selects the fastest and most efficient application method based on the applicant's past application history. This allows the optimal application method to be selected based on the past application history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the applicant's past application history data into a generation AI and have the generation AI select the optimal application method.

[0088] The reception unit can filter applications based on the applicant's current project or areas of interest when accepting applications during the application process. For example, the reception unit prioritizes application content related to the applicant's current project. For example, the reception unit retrieves the applicant's current project information from a database and identifies related application content. The reception unit can also filter related application content based on the applicant's areas of interest. For example, the reception unit retrieves the applicant's areas of interest from a database and filters the application content based on related keywords. The reception unit can also suggest optimal application content by referring to the applicant's past project history. For example, the reception unit analyzes the applicant's past project history and identifies highly relevant application content. This enables filtering based on the applicant's areas of interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the applicant's current project information into a generation AI and have the generation AI filter related application content.

[0089] The reception unit can select an appropriate reception means depending on the applicant's input method when accepting applications for the application process. For example, if an applicant desires voice input, the reception unit prioritizes receiving the voice input. For example, the reception unit retrieves the applicant's input method from a database and provides a voice input reception means for applicants who desire voice input. Furthermore, if an applicant desires text input, the reception unit can also prioritize receiving text input. For example, the reception unit retrieves the applicant's input method from a database and provides a text input reception means for applicants who desire text input. Furthermore, if an applicant desires image input, the reception unit can also prioritize receiving image input. For example, the reception unit retrieves the applicant's input method from a database and provides an image input reception means for applicants who desire image input. This allows the reception means to be selected depending on the applicant's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the applicant's input method data into a generation AI and have the generation AI select the optimal reception means.

[0090] The reception unit can estimate the applicant's emotions and determine the priority of the ideas to be accepted based on the estimated emotions of the applicant. For example, if the applicant is excited, the reception unit can prioritize accepting the applicant's idea. For example, the reception unit can capture the applicant's facial expression with a camera and estimate the applicant's emotions using an emotion estimation algorithm. Furthermore, if the applicant is relaxed, the reception unit can also accept the applicant's idea with normal priority. For example, the reception unit can record the applicant's voice and estimate the applicant's emotions using voice analysis technology. Furthermore, if the applicant is feeling anxious, the reception unit can carefully accept the applicant's idea. For example, the reception unit can collect the applicant's biometric data (heart rate and electrodermal activity) using a sensor and estimate the applicant's emotions using an emotion estimation algorithm. This allows the priority of ideas to be determined based on the applicant's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input image data of an applicant taken with a camera into the generation AI and have the generation AI estimate the applicant's emotions.

[0091] The reception unit can prioritize receiving relevant ideas during the application process, taking into account the applicant's geographical location information. For example, if the applicant lives in a specific region, the reception unit can prioritize receiving ideas related to that region. For example, the reception unit can obtain the applicant's geographical location information from a database and identify ideas related to that region. Furthermore, if the applicant lives in a specific city, the reception unit can prioritize receiving ideas related to that city. For example, the reception unit can obtain the applicant's geographical location information from a database and identify ideas related to that city. Furthermore, if the applicant lives in a specific country, the reception unit can prioritize receiving ideas related to that country. For example, the reception unit can obtain the applicant's geographical location information from a database and identify ideas related to that country. This allows for prioritized reception of ideas that are highly relevant based on the geographical location information. Some or all of the above-described processing in the reception unit may be performed using, or without, AI. For example, the reception unit can input the applicant's geographical location information data into a generation AI and cause the generation AI to identify related ideas.

[0092] The reception unit can analyze the applicant's social media activity during the application process and receive relevant ideas. For example, the reception unit prioritizes receiving ideas shared by the applicant on social media. For example, the reception unit retrieves the applicant's social media activity from a database and identifies the shared ideas. The reception unit can also analyze the applicant's social media activity and receive relevant ideas. For example, the reception unit can analyze the applicant's social media activity and identify highly relevant ideas. The reception unit can also receive related ideas based on the activity of the applicant's friends on social media. For example, the reception unit can analyze the social media activity of the applicant's friends and identify highly relevant ideas. This makes it possible to receive related ideas based on social media activity. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the applicant's social media activity data into a generation AI and have the generation AI identify related ideas.

[0093] The reception unit can adjust the reception method by reflecting the applicant's past feedback when receiving the application. For example, the reception unit suggests the optimal reception method based on feedback provided by the applicant in the past. For example, the reception unit retrieves the applicant's past feedback from a database and identifies the optimal reception method. The reception unit can also analyze the applicant's past feedback to improve the reception method. For example, the reception unit analyzes the applicant's past feedback and identifies areas for improvement. The reception unit can also customize the reception method by referring to the applicant's past feedback. For example, the reception unit provides an individually customized reception method based on the applicant's past feedback. This allows the reception method to be customized based on the past feedback. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the applicant's past feedback data into a generation AI and have the generation AI identify the optimal reception method.

[0094] The evaluation unit can estimate the applicant's emotions and adjust the way the evaluation is expressed based on the estimated emotions. For example, if the applicant is nervous, the evaluation unit can use gentle expressions to evaluate the applicant. For example, the evaluation unit can capture the applicant's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The evaluation unit can also provide a more detailed evaluation if the applicant is relaxed. For example, the evaluation unit can record the applicant's voice and estimate their emotions using voice analysis technology. The evaluation unit can also use positive expressions to evaluate the applicant if the applicant is excited. For example, the evaluation unit can collect the applicant's biometric data (heart rate and electrodermal activity) with a sensor and estimate their emotions using an emotion estimation algorithm. This allows the way the evaluation is expressed to be adjusted according to the applicant's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the evaluation unit can be performed using AI, for example, or without AI. For example, the evaluation unit can input image data of an applicant taken with a camera into the generation AI and have the generation AI estimate the applicant's emotions.

[0095] During evaluation, the evaluation unit can adjust the details of the evaluation based on the importance of the idea. For example, the evaluation unit performs a detailed evaluation for ideas with high importance. For example, the evaluation unit evaluates the importance based on the scope of impact and feasibility of the idea and provides detailed feedback. The evaluation unit can also perform a concise evaluation for ideas with low importance. For example, the evaluation unit evaluates the importance based on the innovativeness and economic impact of the idea and provides concise feedback. The evaluation unit can also gradually adjust the level of detail of the evaluation depending on the importance. For example, the evaluation unit sets detailed evaluation items for ideas with high importance and concise evaluation items for ideas with low importance. This makes it possible to adjust the level of detail of the evaluation depending on the importance of the idea. Some or all of the above-mentioned processing in the evaluation unit may be performed using, or without, AI. For example, the evaluation unit can input idea importance data to the generation AI and cause the generation AI to adjust the level of detail of the evaluation.

[0096] During evaluation, the evaluation unit can apply different evaluation algorithms depending on the category of the idea. For example, the evaluation unit applies an environmental evaluation algorithm to ideas related to environmental issues. For example, the evaluation unit sets evaluation criteria specialized for environmental issues and performs evaluation using an environmental evaluation algorithm. The evaluation unit can also apply a medical evaluation algorithm to ideas related to the medical field. For example, the evaluation unit sets evaluation criteria specialized for the medical field and performs evaluation using an medical evaluation algorithm. The evaluation unit can also apply an education evaluation algorithm to ideas related to the education field. For example, the evaluation unit sets evaluation criteria specialized for the education field and performs evaluation using an education evaluation algorithm. This allows the application of an evaluation algorithm depending on the category of the idea. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input idea category data into the generation AI and cause the generation AI to apply the evaluation algorithm.

[0097] During evaluation, the evaluation unit can improve the accuracy of the evaluation by referring to the applicant's past evaluation results. For example, the evaluation unit performs the evaluation by referring to the evaluation results of ideas previously submitted by the applicant. For example, the evaluation unit retrieves the applicant's past evaluation results from a database to ensure consistency in the evaluation. The evaluation unit can also analyze the applicant's past evaluation results and adjust the evaluation algorithm. For example, the evaluation unit optimizes the parameters of the evaluation algorithm based on the applicant's past evaluation results. The evaluation unit can also improve the accuracy of the evaluation based on the applicant's past evaluation results. For example, the evaluation unit adjusts the evaluation criteria by referring to the applicant's past evaluation results. This improves the accuracy of the evaluation based on the past evaluation results. Some or all of the above-described processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input the applicant's past evaluation result data into the generation AI and cause the generation AI to improve the accuracy of the evaluation.

[0098] The evaluation unit can estimate the applicant's emotions and adjust the length of the evaluation based on the estimated emotions. For example, if the applicant is nervous, the evaluation unit provides a short and concise evaluation. For example, the evaluation unit captures the applicant's facial expressions with a camera and estimates their emotions using an emotion estimation algorithm. The evaluation unit can also provide a detailed evaluation if the applicant is relaxed. For example, the evaluation unit can record the applicant's voice and estimate their emotions using voice analysis technology. The evaluation unit can also provide a more positive evaluation if the applicant is excited. For example, the evaluation unit can collect the applicant's biometric data (heart rate and electrodermal activity) with a sensor and estimate their emotions using an emotion estimation algorithm. This allows the length of the evaluation to be adjusted according to the applicant's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the evaluation unit can be performed using, for example, AI, or without AI. For example, the evaluation unit can input image data of an applicant taken with a camera into the generation AI and have the generation AI estimate the applicant's emotions.

[0099] During evaluation, the evaluation unit can determine the order of evaluation based on the submission time of the ideas. The evaluation unit, for example, prioritizes evaluation of ideas submitted early. For example, the evaluation unit retrieves the submission time of ideas from a database and identifies ideas submitted early. The evaluation unit can also prioritize evaluation of ideas submitted just before the deadline. For example, the evaluation unit retrieves the submission time of ideas from a database and identifies ideas submitted just before the deadline. The evaluation unit can also adjust the priority of evaluation according to the submission time. For example, the evaluation unit applies an algorithm that determines the order of evaluation based on the submission time. This makes it possible to determine the priority of evaluation based on the submission time. Some or all of the above-mentioned processing in the evaluation unit may be performed using, for example, AI, or may be performed without using AI. For example, the evaluation unit can input data on the submission time of ideas to a generation AI and have the generation AI determine the order of evaluation.

[0100] During evaluation, the evaluation unit can adjust the order of evaluation based on the relevance of the ideas. The evaluation unit, for example, prioritizes evaluation of highly relevant ideas. For example, the evaluation unit obtains the relevance of ideas from a database and identifies highly relevant ideas. The evaluation unit can also postpone less relevant ideas. For example, the evaluation unit obtains the relevance of ideas from a database and identifies less relevant ideas. The evaluation unit can also adjust the order of evaluation according to the relevance of the ideas. For example, the evaluation unit applies an algorithm that prioritizes more relevant ideas. This makes it possible to adjust the order of evaluation based on the relevance of the ideas. Some or all of the above-described processing in the evaluation unit may be performed using, or without, AI. For example, the evaluation unit can input idea relevance data to a generation AI and cause the generation AI to adjust the order of evaluation.

[0101] During the evaluation, the evaluation unit can adjust the use of evaluation terminology according to the applicant's level of expertise. For example, if the applicant has specialized knowledge, the evaluation unit uses a lot of specialized terminology. For example, the evaluation unit retrieves the applicant's level of expertise from a database and performs an evaluation that uses a lot of specialized terminology. Alternatively, if the applicant does not have specialized knowledge, the evaluation unit can perform an evaluation in simple language. For example, the evaluation unit retrieves the applicant's level of expertise from a database and performs an evaluation in simple language. Alternatively, the evaluation unit can adjust the use of specialized terminology according to the applicant's level of expertise. For example, the evaluation unit adjusts the tone of the evaluation comments and the format of the feedback based on the applicant's level of expertise. This allows the use of specialized terminology in the evaluation to be adjusted according to the applicant's level of expertise. Some or all of the above-described processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can input the applicant's level of expertise data into the generation AI and have the generation AI adjust the terminology in the evaluation.

[0102] The support unit can estimate the applicant's emotions and adjust the support method based on the estimated emotions. For example, if the applicant is nervous, the support unit adjusts the support method to help the applicant relax. For example, the support unit captures the applicant's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. The support unit can also provide quick support if the applicant is excited. For example, the support unit records the applicant's voice and estimates the emotion using voice analysis technology. If the applicant is feeling anxious, the support unit can provide support while displaying a reassuring message. For example, the support unit collects the applicant's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotion using an emotion estimation algorithm. This allows the support method to be adjusted according to the applicant's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit may input image data of an applicant taken with a camera into the generation AI and have the generation AI estimate the applicant's emotions.

[0103] When providing support, the support department can analyze the applicant's past activity history and select an appropriate support method. For example, the support department selects a support method by referring to successful projects that the applicant has completed in the past. For example, the support department retrieves the applicant's past activity history from a database and identifies successful projects. The support department can also analyze the applicant's past activity history and propose an optimal support method. For example, the support department identifies an optimal support method based on the applicant's past activity history. The support department can also customize a support method based on the applicant's past activity history. For example, the support department analyzes the applicant's past activity history and provides an individually customized support method. This allows the optimal support method to be selected based on the past activity history. Some or all of the above-described processing in the support department may be performed using, for example, AI, or may be performed without using AI. For example, the support department can input the applicant's past activity history data into a generation AI and have the generation AI select an optimal support method.

[0104] When providing support, the support unit can customize the support means based on the applicant's current living situation. For example, if the applicant is busy, the support unit can suggest an efficient support method. For example, the support unit can obtain the applicant's current living situation from a database and identify an efficient support method. Furthermore, if the applicant has time, the support unit can also suggest a detailed support method. For example, the support unit can obtain the applicant's current living situation from a database and identify a detailed support method. Furthermore, the support unit can customize the support means according to the applicant's living situation. For example, the support unit can provide individually customized support means based on the applicant's living situation. This allows the support means to be customized based on the applicant's current living situation. Some or all of the above-mentioned processing in the support unit may be performed, for example, using AI or without AI. For example, the support unit can input the applicant's current living situation data into a generation AI and have the generation AI customize the support means.

[0105] The support unit can improve the support method by reflecting the applicant's feedback when providing support. The support unit improves the support method, for example, based on feedback provided by the applicant. For example, the support unit retrieves the applicant's feedback from a database and identifies areas for improvement. The support unit can also analyze the applicant's feedback and adjust the support method. For example, the support unit identifies areas for adjustment in the support method based on the applicant's feedback. The support unit can also customize the support method by referring to the applicant's feedback. For example, the support unit provides an individually customized support method based on the applicant's feedback. This allows the support method to be improved based on the feedback. Some or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can input the applicant's feedback data into a generation AI and have the generation AI improve the support method.

[0106] The support unit can estimate the applicant's emotions and determine support priorities based on the estimated emotions. For example, if an applicant is nervous, the support unit can prioritize support for that applicant. For example, the support unit can capture the applicant's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. Furthermore, if an applicant is relaxed, the support unit can provide support at a normal priority. For example, the support unit can record the applicant's voice and estimate their emotions using voice analysis technology. Furthermore, if an applicant is feeling anxious, the support unit can quickly provide support to that applicant. For example, the support unit can collect the applicant's biometric data (heart rate and electrodermal activity) using a sensor and estimate their emotions using an emotion estimation algorithm. This allows support priorities to be determined based on the applicant's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit may input image data of an applicant taken with a camera into the generation AI and have the generation AI estimate the applicant's emotions.

[0107] When providing support, the support unit can select an appropriate support method by taking into account the applicant's geographical location information. For example, if the applicant lives in a specific region, the support unit can suggest a support method related to that region. For example, the support unit can obtain the applicant's geographical location information from a database and identify a support method related to that region. Furthermore, if the applicant lives in a specific city, the support unit can suggest a support method related to that city. For example, the support unit can obtain the applicant's geographical location information from a database and identify a support method related to that city. Furthermore, if the applicant lives in a specific country, the support unit can suggest a support method related to that country. For example, the support unit can obtain the applicant's geographical location information from a database and identify a support method related to that country. This allows the optimal support method to be selected based on the geographical location information. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can input the applicant's geographical location information data into a generation AI and have the generation AI select the optimal support method.

[0108] When providing support, the support unit can analyze the applicant's social media activity and suggest support methods. The support unit, for example, suggests optimal support methods based on information shared by the applicant on social media. For example, the support unit retrieves the applicant's social media activity from a database and identifies the shared information. The support unit can also analyze the applicant's social media activity and suggest relevant support methods. For example, the support unit analyzes the applicant's social media activity and identifies relevant support methods. The support unit can also suggest relevant support methods based on the activity of the applicant's friends on social media. For example, the support unit analyzes the social media activity of the applicant's friends and identifies relevant support methods. This allows support methods to be suggested based on social media activity. Some or all of the above-mentioned processing in the support unit may be performed, for example, using AI, or may be performed without using AI. For example, the support unit can input the applicant's social media activity data into a generation AI and have the generation AI suggest optimal support methods.

[0109] When providing support, the support unit can adjust the support method by reflecting the applicant's past feedback. For example, the support unit proposes an optimal support method based on feedback provided by the applicant in the past. For example, the support unit retrieves the applicant's past feedback from a database and identifies the optimal support method. The support unit can also analyze the applicant's past feedback to improve the support method. For example, the support unit analyzes the applicant's past feedback and identifies areas for improvement. The support unit can also customize the support method by referring to the applicant's past feedback. For example, the support unit provides an individually customized support method based on the applicant's past feedback. This allows the support method to be customized based on the past feedback. Some or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can input the applicant's past feedback data into a generation AI and have the generation AI identify the optimal support method.

[0110] The standard setting unit can estimate the applicant's emotions and adjust the evaluation criteria based on the estimated emotions. For example, the standard setting unit relaxes the evaluation criteria when the applicant is nervous. For example, the standard setting unit captures the applicant's facial expressions with a camera and estimates their emotions using an emotion estimation algorithm. The standard setting unit can also apply normal evaluation criteria when the applicant is relaxed. For example, the standard setting unit records the applicant's voice and estimates their emotions using voice analysis technology. The standard setting unit can also tighten the evaluation criteria when the applicant is excited. For example, the standard setting unit collects the applicant's biometric data (heart rate and electrodermal activity) using a sensor and estimates their emotions using an emotion estimation algorithm. This allows the evaluation criteria to be adjusted based on the applicant's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the criteria setting unit may be performed using, for example, AI, or may be performed without using AI. For example, the criteria setting unit may input image data of an applicant captured by a camera into the generation AI and cause the generation AI to estimate the applicant's emotions.

[0111] When setting the criteria, the criteria setting unit can adjust the evaluation criteria by referring to past evaluation data. The criteria setting unit, for example, analyzes past evaluation data and adjusts the evaluation criteria. For example, the criteria setting unit acquires past evaluation data from a database and identifies areas for improvement in the evaluation criteria. The criteria setting unit can also optimize the evaluation criteria based on the past evaluation data. For example, the criteria setting unit analyzes past evaluation data and optimizes parameters of the evaluation criteria. The criteria setting unit can also update the evaluation criteria by referring to the past evaluation data. For example, the criteria setting unit identifies areas for updating the evaluation criteria based on the past evaluation data. This allows the evaluation criteria to be optimized based on the past evaluation data. Some or all of the above-mentioned processing in the criteria setting unit may be performed using, for example, AI, or may be performed without using AI. For example, the criteria setting unit can input past evaluation data to a generation AI and cause the generation AI to adjust the evaluation criteria.

[0112] When setting the criteria, the criteria setting unit can update the evaluation criteria to reflect applicant feedback. The criteria setting unit updates the evaluation criteria, for example, based on feedback provided by the applicant. For example, the criteria setting unit retrieves applicant feedback from a database and identifies areas for improvement in the evaluation criteria. The criteria setting unit can also analyze the applicant feedback and adjust the evaluation criteria. For example, the criteria setting unit identifies areas for adjustment in the evaluation criteria based on the applicant feedback. The criteria setting unit can also improve the evaluation criteria by referring to the applicant feedback. For example, the criteria setting unit identifies areas for improvement in the evaluation criteria based on the applicant feedback. This allows the evaluation criteria to be updated based on the applicant feedback. Some or all of the above-described processing in the criteria setting unit may be performed using, for example, AI, or may be performed without using AI. For example, the criteria setting unit inputs applicant feedback data into a generation AI and causes the generation AI to update the evaluation criteria.

[0113] The criteria setting unit can estimate the applicant's emotions and determine the order of evaluation criteria based on the estimated emotions of the applicant. For example, if the applicant is nervous, the criteria setting unit can prioritize the evaluation criteria for that applicant. For example, the criteria setting unit can capture the applicant's facial expressions with a camera and estimate the emotions using an emotion estimation algorithm. Furthermore, if the applicant is relaxed, the criteria setting unit can also set the evaluation criteria in normal priority order. For example, the criteria setting unit can record the applicant's voice and estimate the emotions using voice analysis technology. Furthermore, if the applicant is excited, the criteria setting unit can quickly set the evaluation criteria for that applicant. For example, the criteria setting unit can collect the applicant's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. This allows the order of evaluation criteria to be determined based on the applicant's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the criteria setting unit may be performed using, for example, AI, or may be performed without using AI. For example, the criteria setting unit may input image data of an applicant captured by a camera into the generation AI and cause the generation AI to estimate the applicant's emotions.

[0114] When setting the criteria, the criteria setting unit can weight the evaluation criteria based on the submission time of the idea. For example, the criteria setting unit weights the evaluation criteria to ideas submitted early. For example, the criteria setting unit obtains the submission time of the idea from a database and weights the idea submitted early. The criteria setting unit can also weight the evaluation criteria to ideas submitted just before the deadline. For example, the criteria setting unit obtains the submission time of the idea from a database and weights the idea submitted just before the deadline. The criteria setting unit can also adjust the weighting of the evaluation criteria depending on the submission time. For example, the criteria setting unit applies an algorithm that weights the evaluation criteria based on the submission time. This allows the evaluation criteria to be weighted based on the submission time. Some or all of the above-mentioned processing in the criteria setting unit may be performed using, for example, AI, or may be performed without using AI. For example, the criteria setting unit can input idea submission time data to the generation AI and cause the generation AI to weight the evaluation criteria.

[0115] When setting the criteria, the criteria setting unit can integrate information from different data sources to expand the evaluation criteria. For example, the criteria setting unit integrates information from different data sources to expand the evaluation criteria. For example, the criteria setting unit obtains and integrates internal data, external data, third-party data, etc. from a database. The criteria setting unit can also analyze information from different data sources to optimize the evaluation criteria. For example, the criteria setting unit optimizes parameters of the evaluation criteria based on information from different data sources. The criteria setting unit can also update the evaluation criteria by referring to information from different data sources. For example, the criteria setting unit identifies update points for the evaluation criteria based on information from different data sources. This allows the evaluation criteria to be expanded by integrating information from different data sources. Some or all of the above-described processing in the criteria setting unit may be performed using, for example, AI, or may be performed without using AI. For example, the criteria setting unit can input information from different data sources into the generation AI and cause the generation AI to expand the evaluation criteria.

[0116] The providing unit can estimate the applicant's emotions and adjust the method of providing guidelines and templates based on the estimated emotions. For example, if the applicant is nervous, the providing unit provides simple guidelines. For example, the providing unit captures the applicant's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. Furthermore, if the applicant is relaxed, the providing unit can provide detailed guidelines. For example, the providing unit records the applicant's voice and estimates the emotions using voice analysis technology. Furthermore, if the applicant is excited, the providing unit can provide a visually appealing template. For example, the providing unit collects the applicant's biometric data (heart rate and electrodermal activity) with a sensor and estimates the emotions using an emotion estimation algorithm. This allows the method of providing guidelines and templates to be adjusted based on the applicant's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or without AI. For example, the providing unit can input image data of an applicant taken with a camera into the generating AI and have the generating AI estimate the applicant's emotions.

[0117] When providing the guidelines or templates, the providing unit can refer to the applicant's past application history and provide an appropriate guideline or template. For example, the providing unit can prioritize providing guidelines that the applicant has used in the past. For example, the providing unit can retrieve the applicant's past application history from a database and identify the most frequently used guideline. The providing unit can also analyze the applicant's past application history and suggest the optimal guideline. For example, the providing unit can identify the optimal guideline based on the applicant's past application history. The providing unit can also provide the optimal template based on the applicant's past application history. For example, the providing unit can analyze the applicant's past application history and identify the most effective template. This makes it possible to provide the optimal guideline or template based on the past application history. Some or all of the above-described processing in the providing unit can be performed using, or without, AI. For example, the providing unit can input the applicant's past application history data into a generation AI and cause the generation AI to provide the optimal guideline or template.

[0118] The providing unit can customize the guidelines and templates according to the applicant's current project when providing them. The providing unit, for example, provides guidelines related to the project the applicant is currently working on. For example, the providing unit retrieves the applicant's current project information from a database and identifies related guidelines. The providing unit can also provide an optimal template according to the applicant's current project. For example, the providing unit identifies an optimal template based on the applicant's current project information. The providing unit can also analyze the applicant's current project and provide customized guidelines. For example, the providing unit provides individually customized guidelines based on the applicant's current project information. This allows the guidelines and templates to be customized according to the current project. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the applicant's current project information to a generation AI and cause the generation AI to customize the guidelines and templates.

[0119] The providing unit can improve the guidelines and templates by reflecting applicant feedback when providing them. The providing unit, for example, improves the guidelines based on feedback provided by the applicant. For example, the providing unit retrieves applicant feedback from a database and identifies areas for improvement. The providing unit can also analyze applicant feedback and adjust the templates. For example, the providing unit identifies areas for adjustment in the templates based on applicant feedback. The providing unit can also customize the guidelines and templates by referring to applicant feedback. For example, the providing unit provides individually customized guidelines and templates based on applicant feedback. This allows the guidelines and templates to be improved based on the feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input applicant feedback data into a generation AI and cause the generation AI to improve the guidelines and templates.

[0120] The providing unit can estimate the applicant's emotions and determine the priority of guidelines and templates based on the estimated emotions. For example, if an applicant is nervous, the providing unit can provide guidelines to that applicant with priority. For example, the providing unit can capture the applicant's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. Furthermore, if the applicant is relaxed, the providing unit can provide guidelines with normal priority. For example, the providing unit can record the applicant's voice and estimate their emotions using voice analysis technology. Furthermore, if the applicant is excited, the providing unit can quickly provide templates to that applicant. For example, the providing unit can collect the applicant's biometric data (heart rate and electrodermal activity) with a sensor and estimate their emotions using an emotion estimation algorithm. This allows the priority of guidelines and templates to be determined based on the applicant's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may input image data of an applicant taken by a camera into the generating AI and cause the generating AI to estimate the applicant's emotions.

[0121] The providing unit can provide appropriate guidelines or templates by taking into account the applicant's geographic location information when providing the guidelines or templates. For example, if the applicant lives in a specific region, the providing unit can provide guidelines related to that region. For example, the providing unit can obtain the applicant's geographic location information from a database and identify guidelines related to that region. Furthermore, if the applicant lives in a specific city, the providing unit can provide templates related to that city. For example, the providing unit can obtain the applicant's geographic location information from a database and identify templates related to that city. Furthermore, if the applicant lives in a specific country, the providing unit can provide guidelines related to that country. For example, the providing unit can obtain the applicant's geographic location information from a database and identify guidelines related to that country. This allows the optimal guidelines or templates to be provided based on the geographic location information. Some or all of the above-described processing in the providing unit can be performed using, or without, AI. For example, the providing unit can input the applicant's geographic location information data into a generation AI and cause the generation AI to provide optimal guidelines or templates.

[0122] At the time of providing, the providing unit can analyze the applicant's social media activity and provide relevant guidelines and templates. The providing unit, for example, provides optimal guidelines based on information shared by the applicant on social media. For example, the providing unit retrieves the applicant's social media activity from a database and identifies the shared information. The providing unit can also analyze the applicant's social media activity and provide relevant templates. For example, the providing unit analyzes the applicant's social media activity and identifies highly relevant templates. The providing unit can also provide relevant guidelines based on the activity of the applicant's friends on social media. For example, the providing unit analyzes the social media activity of the applicant's friends and identifies highly relevant guidelines. This makes it possible to provide relevant guidelines and templates based on social media activity. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the applicant's social media activity data into a generation AI and cause the generation AI to provide optimal guidelines and templates.

[0123] The providing unit can adjust the guidelines and templates by reflecting the applicant's past feedback when providing them. The providing unit, for example, provides optimal guidelines based on feedback previously provided by the applicant. For example, the providing unit retrieves the applicant's past feedback from a database and identifies optimal guidelines. The providing unit can also analyze the applicant's past feedback and improve the templates. For example, the providing unit identifies areas for improvement in the templates based on the applicant's past feedback. The providing unit can also customize the guidelines and templates by referring to the applicant's past feedback. For example, the providing unit provides individually customized guidelines and templates based on the applicant's past feedback. This allows the guidelines and templates to be customized based on the past feedback. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input the applicant's past feedback data into a generation AI and cause the generation AI to adjust the guidelines and templates.

[0124] The disclosure unit can estimate the applicant's emotions and adjust the disclosure method of the evaluation process based on the estimated emotions of the applicant. For example, if the applicant is nervous, the disclosure unit briefly discloses the evaluation process. For example, the disclosure unit can capture the applicant's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. Alternatively, if the applicant is relaxed, the disclosure unit can disclose a detailed evaluation process. For example, the disclosure unit can record the applicant's voice and estimate their emotions using voice analysis technology. Alternatively, if the applicant is excited, the disclosure unit can disclose the evaluation process in a visually appealing manner. For example, the disclosure unit can collect the applicant's biometric data (heart rate and electrodermal activity) with a sensor and estimate their emotions using an emotion estimation algorithm. This allows the disclosure method of the evaluation process to be adjusted based on the applicant's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the disclosure section may be performed using, for example, AI, or may be performed without using AI. For example, the disclosure section may input image data of an applicant taken with a camera into the generation AI, and have the generation AI estimate the applicant's emotions.

[0125] At the time of disclosure, the disclosure unit can select an appropriate disclosure method by referring to past evaluation processes. The disclosure unit, for example, analyzes past evaluation processes and selects the optimal disclosure method. For example, the disclosure unit retrieves past evaluation processes from a database and identifies the optimal disclosure method. The disclosure unit can also optimize the disclosure method based on the past evaluation processes. For example, the disclosure unit analyzes past evaluation processes and optimizes parameters of the disclosure method. The disclosure unit can also update the disclosure method by referring to the past evaluation processes. For example, the disclosure unit identifies updates to the disclosure method based on the past evaluation processes. This allows the optimal disclosure method to be selected based on the past evaluation processes. Some or all of the above-described processing in the disclosure unit may be performed using, for example, AI, or may be performed without using AI. For example, the disclosure unit can input past evaluation process data into a generation AI and have the generation AI select the optimal disclosure method.

[0126] The disclosure unit can improve the disclosure method of the evaluation process by reflecting the applicant's feedback at the time of disclosure. The disclosure unit improves the disclosure method of the evaluation process, for example, based on feedback provided by the applicant. For example, the disclosure unit retrieves the applicant's feedback from a database and identifies areas for improvement. The disclosure unit can also analyze the applicant's feedback and adjust the disclosure method. For example, the disclosure unit identifies areas for adjustment in the disclosure method based on the applicant's feedback. The disclosure unit can also customize the disclosure method by referring to the applicant's feedback. For example, the disclosure unit provides an individually customized disclosure method based on the applicant's feedback. This allows the disclosure method of the evaluation process to be improved based on the feedback. Some or all of the above-mentioned processing in the disclosure unit may be performed using, for example, AI, or may be performed without using AI. For example, the disclosure unit can input the applicant's feedback data into a generation AI and have the generation AI improve the disclosure method.

[0127] The disclosure unit can estimate the applicant's emotions and determine the order in which the evaluation process will be disclosed based on the estimated emotions. For example, if an applicant is nervous, the disclosure unit prioritizes disclosing the applicant's evaluation process. For example, the disclosure unit captures the applicant's facial expressions with a camera and estimates their emotions using an emotion estimation algorithm. Alternatively, if an applicant is relaxed, the disclosure unit can disclose the applicant's evaluation process with normal priority. For example, the disclosure unit records the applicant's voice and estimates their emotions using voice analysis technology. Alternatively, if an applicant is excited, the disclosure unit can quickly disclose the applicant's evaluation process. For example, the disclosure unit collects the applicant's biometric data (heart rate and electrodermal activity) with a sensor and estimates their emotions using an emotion estimation algorithm. This allows the order in which the evaluation process will be disclosed to be determined based on the applicant's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, 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 disclosure section may be performed using, for example, AI, or may be performed without using AI. For example, the disclosure section may input image data of an applicant taken with a camera into the generation AI, and have the generation AI estimate the applicant's emotions.

[0128] The disclosure unit can select the optimal disclosure method at the time of disclosure, taking into account the applicant's geographic location information. For example, if the applicant lives in a specific region, the disclosure unit selects a disclosure method related to that region. For example, the disclosure unit retrieves the applicant's geographic location information from a database and identifies a disclosure method related to that region. Furthermore, if the applicant lives in a specific city, the disclosure unit can select a disclosure method related to that city. For example, the disclosure unit retrieves the applicant's geographic location information from a database and identifies a disclosure method related to that city. Furthermore, if the applicant lives in a specific country, the disclosure unit can select a disclosure method related to that country. For example, the disclosure unit retrieves the applicant's geographic location information from a database and identifies a disclosure method related to that country. This allows the optimal disclosure method to be selected based on the geographic location information. Some or all of the above-described processing in the disclosure unit may be performed using, for example, AI, or may be performed without using AI. For example, the disclosure unit can input the applicant's geographic location information data into a generation AI and have the generation AI select the optimal disclosure method.

[0129] At the time of disclosure, the disclosure unit can adjust the disclosure method of the evaluation process by analyzing the applicant's social media activity. The disclosure unit, for example, selects the optimal disclosure method based on information shared by the applicant on social media. For example, the disclosure unit retrieves the applicant's social media activity from a database and identifies the shared information. The disclosure unit can also analyze the applicant's social media activity and select a relevant disclosure method. For example, the disclosure unit can analyze the applicant's social media activity and identify a relevant disclosure method. The disclosure unit can also select a relevant disclosure method based on the activity of the applicant's friends on social media. For example, the disclosure unit can analyze the social media activity of the applicant's friends and identify a relevant disclosure method. This makes it possible to adjust the disclosure method of the evaluation process based on social media activity. Some or all of the above-mentioned processing in the disclosure unit may be performed using, for example, AI, or may be performed without using AI. For example, the disclosure unit can input the applicant's social media activity data into a generation AI and have the generation AI select the optimal disclosure method.

[0130] The know-how providing unit can estimate the applicant's emotions and adjust the method of providing know-how based on the estimated emotions. For example, if the applicant is nervous, the know-how providing unit adjusts the method of providing know-how to help the applicant relax. For example, the know-how providing unit captures the applicant's facial expression with a camera and estimates the applicant's emotions using an emotion estimation algorithm. The know-how providing unit can also quickly provide know-how if the applicant is excited. For example, the know-how providing unit records the applicant's voice and estimates the applicant's emotions using voice analysis technology. The know-how providing unit can also provide know-how while displaying a reassuring message if the applicant is feeling anxious. For example, the know-how providing unit collects the applicant's biometric data (heart rate and electrodermal activity) using a sensor and estimates the applicant's emotions using an emotion estimation algorithm. This makes it possible to adjust the method of providing know-how based on the applicant's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the know-how providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the know-how providing unit may input image data of an applicant taken by a camera into the generating AI and cause the generating AI to estimate the applicant's emotions.

[0131] When providing know-how, the know-how provision unit can select an appropriate provision method by referring to past know-how provision data. The know-how provision unit, for example, analyzes past know-how provision data and selects an optimal provision method. For example, the know-how provision unit retrieves past know-how provision data from a database and identifies an optimal provision method. The know-how provision unit can also optimize the provision method based on the past know-how provision data. For example, the know-how provision unit analyzes past know-how provision data and optimizes parameters of the provision method. The know-how provision unit can also update the provision method by referring to the past know-how provision data. For example, the know-how provision unit identifies points to update the provision method based on the past know-how provision data. This allows the optimal provision method to be selected based on the past know-how provision data. Some or all of the above-described processing in the know-how provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the know-how provision unit can input past know-how provision data to a generation AI and cause the generation AI to select an optimal provision method.

[0132] The know-how provision unit can improve the provision method by reflecting the applicant's feedback when providing know-how. The know-how provision unit improves the provision method, for example, based on the feedback provided by the applicant. For example, the know-how provision unit retrieves the applicant's feedback from a database and identifies areas for improvement. The know-how provision unit can also analyze the applicant's feedback and adjust the provision method. For example, the know-how provision unit identifies areas for adjustment in the provision method based on the applicant's feedback. The know-how provision unit can also customize the provision method by referring to the applicant's feedback. For example, the know-how provision unit provides an individually customized provision method based on the applicant's feedback. This allows the provision method to be improved based on the feedback. Some or all of the above-described processing in the know-how provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the know-how provision unit can input the applicant's feedback data into a generation AI and have the generation AI improve the provision method.

[0133] The know-how providing unit can estimate the emotions of applicants and determine the order in which know-how is provided based on the estimated emotions of the applicants. For example, if an applicant is nervous, the know-how providing unit can provide know-how to that applicant with priority. For example, the know-how providing unit can capture the applicant's facial expression with a camera and estimate the emotion using an emotion estimation algorithm. Furthermore, if an applicant is relaxed, the know-how providing unit can provide know-how with normal priority. For example, the know-how providing unit can record the applicant's voice and estimate the emotion using voice analysis technology. Furthermore, if an applicant is excited, the know-how providing unit can quickly provide know-how to that applicant. For example, the know-how providing unit can collect the applicant's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. This allows the order in which know-how is provided to be determined based on the applicant's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the know-how providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the know-how providing unit may input image data of an applicant taken by a camera into the generating AI and cause the generating AI to estimate the applicant's emotions.

[0134] When providing know-how, the know-how providing unit can select the optimal provision method by taking into account the applicant's geographical location information. For example, if the applicant lives in a specific region, the know-how providing unit selects a provision method related to that region. For example, the know-how providing unit retrieves the applicant's geographical location information from a database and identifies a provision method related to that region. Furthermore, if the applicant lives in a specific city, the know-how providing unit can also select a provision method related to that city. For example, the know-how providing unit retrieves the applicant's geographical location information from a database and identifies a provision method related to that city. Furthermore, if the applicant lives in a specific country, the know-how providing unit can also select a provision method related to that country. For example, the know-how providing unit retrieves the applicant's geographical location information from a database and identifies a provision method related to that country. This allows the optimal provision method to be selected based on the geographical location information. Some or all of the above-described processing in the know-how providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the know-how providing unit can input the applicant's geographical location information data into a generation AI and have the generation AI select the optimal provision method.

[0135] When providing know-how, the know-how provision unit can analyze the applicant's social media activity and propose a method for providing know-how. The know-how provision unit can propose an optimal means for providing know-how based on, for example, information shared by the applicant on social media. For example, the know-how provision unit can retrieve the applicant's social media activity from a database and identify the shared information. The know-how provision unit can also analyze the applicant's social media activity and propose related means for providing know-how. For example, the know-how provision unit can analyze the applicant's social media activity and identify highly relevant means for providing know-how. The know-how provision unit can also propose related means for providing know-how based on the activity of the applicant's friends on social media. For example, the know-how provision unit can analyze the social media activity of the applicant's friends and identify highly relevant means for providing know-how. This makes it possible to propose means for providing know-how based on social media activity. Some or all of the above-described processing in the know-how provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the know-how provision unit can input the applicant's social media activity data into a generation AI and have the generation AI propose an optimal means for providing know-how.

[0136] When providing know-how, the know-how provision unit can customize the provision method by reflecting the applicant's past feedback. The know-how provision unit, for example, proposes an optimal know-how provision method based on feedback provided by the applicant in the past. For example, the know-how provision unit retrieves the applicant's past feedback from a database and identifies an optimal know-how provision method. The know-how provision unit can also analyze the applicant's past feedback and improve the provision method. For example, the know-how provision unit identifies areas for improvement in the provision method based on the applicant's past feedback. The know-how provision unit can also customize the know-how provision method by referring to the applicant's past feedback. For example, the know-how provision unit provides an individually customized know-how provision method based on the applicant's past feedback. This allows the provision method to be customized based on the past feedback. Some or all of the above-described processing in the know-how provision unit may be performed using, for example, AI, or may be performed without using AI. For example, the know-how provision unit can input the applicant's past feedback data into a generation AI and have the generation AI execute a proposal for an optimal know-how provision method. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, evaluation unit, support unit, standard setting unit, provision unit, publication unit, know-how provision unit, and emotion estimation function, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and accepts applications through an online form. The evaluation unit is implemented by the specific processing unit 290 of the data processing device 12 and conducts expert review. The support unit is implemented by the specific processing unit 290 of the data processing device 12 and provides technical support and funding. The standard setting unit is implemented by the specific processing unit 290 of the data processing device 12 and sets evaluation standards. The provision unit is implemented by the control unit 46A of the smart device 14 and provides guidelines and templates to applicants. The publication unit is implemented by the specific processing unit 290 of the data processing device 12 and publishes the evaluation process. The know-how provision unit is implemented by the specific processing unit 290 of the data processing device 12 and provides know-how to other companies and organizations. The emotion estimation function estimates the emotion of the applicant using the camera 42 and microphone 38B of the smart device 14, and adjusts the timing of the application process by the control unit 46A of the reception unit. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, evaluation unit, support unit, standard setting unit, provision unit, publication unit, know-how provision unit, and emotion estimation function, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and accepts applications through an online form. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12 and conducts expert review. The support unit is realized by the specific processing unit 290 of the data processing device 12 and provides technical support and funding. The standard setting unit is realized by the specific processing unit 290 of the data processing device 12 and sets evaluation standards. The provision unit is realized by the control unit 46A of the smart glasses 214 and provides guidelines and templates to applicants. The publication unit is realized by the specific processing unit 290 of the data processing device 12 and publishes the evaluation process. The know-how provision unit is realized by the specific processing unit 290 of the data processing device 12 and provides know-how to other companies and organizations. The emotion estimation function estimates the emotion of the applicant using the camera 42 and microphone 238 of the smart glasses 214, and the reception unit's control unit 46A adjusts the timing of the application process. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, evaluation unit, support unit, standard setting unit, provision unit, publication unit, know-how provision unit, and emotion estimation function, is implemented, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is implemented by the control unit 46A of the headset-type terminal 314 and accepts applications through an online form. The evaluation unit is implemented by the specific processing unit 290 of the data processing device 12 and conducts expert review. The support unit is implemented by the specific processing unit 290 of the data processing device 12 and provides technical support and funding. The standard setting unit is implemented by the specific processing unit 290 of the data processing device 12 and sets evaluation standards. The provision unit is implemented by the control unit 46A of the headset-type terminal 314 and provides guidelines and templates to applicants. The publication unit is implemented by the specific processing unit 290 of the data processing device 12 and publishes the evaluation process. The know-how provision unit is implemented by the specific processing unit 290 of the data processing device 12 and provides know-how to other companies and organizations. The emotion estimation function estimates the emotion of the applicant using the camera 42 and microphone 238 of the headset terminal 314, and the timing of the application process is adjusted by the control unit 46A of the reception unit. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned reception unit, evaluation unit, support unit, standard setting unit, provision unit, publication unit, know-how provision unit, and emotion estimation function, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and accepts applications through an online form. The evaluation unit is realized by the specific processing unit 290 of the data processing device 12 and conducts expert review. The support unit is realized by the specific processing unit 290 of the data processing device 12 and provides technical support and funding. The standard setting unit is realized by the specific processing unit 290 of the data processing device 12 and sets evaluation standards. The provision unit is realized by the control unit 46A of the robot 414 and provides guidelines and templates to applicants. The publication unit is realized by the specific processing unit 290 of the data processing device 12 and publishes the evaluation process. The know-how provision unit is realized by the specific processing unit 290 of the data processing device 12 and provides know-how to other companies and organizations. The emotion estimation function estimates the emotions of the applicant using the camera 42 and microphone 238 of the robot 414, and the timing of the application process is adjusted by the control unit 46A of the reception unit.

[0137] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0138] The reception unit can analyze the applicant's past application history and propose the optimal application process. For example, the reception unit can prioritize the proposal of application methods that the applicant has used in the past. The reception unit can also select the optimal application process by referring to application methods that the applicant has used successfully in the past. Furthermore, the reception unit can also propose the most efficient application process from the applicant's past application history. This makes it possible to select the optimal application process based on the applicant's past application history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the applicant's past application history data into a generation AI and have the generation AI select the optimal application process.

[0139] The evaluation unit can estimate the applicant's emotions and adjust the way the evaluation is expressed based on the estimated emotions. For example, if the applicant is nervous, the evaluation can be expressed in a gentler way. If the applicant is relaxed, the evaluation can be expressed in a more detailed way. Furthermore, if the applicant is excited, the evaluation can be expressed in a more positive way. This allows the way the evaluation is expressed to be adjusted according to the applicant's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the evaluation unit can be performed using, for example, AI, or without AI. For example, the evaluation unit can input image data of the applicant taken with a camera into the generation AI and have the generation AI estimate the applicant's emotions.

[0140] The support department can analyze the applicant's past activity history to select an appropriate support method. For example, the support method can be selected by referring to the applicant's past successful projects. The support department can also analyze the applicant's past activity history to propose the optimal support method. Furthermore, the support method can be customized based on the applicant's past activity history. This makes it possible to select the optimal support method based on the applicant's past activity history. Some or all of the above-mentioned processing in the support department may be performed using, for example, AI, or may be performed without using AI. For example, the support department can input the applicant's past activity history data into the generation AI and have the generation AI select the optimal support method.

[0141] The providing unit can estimate the applicant's emotions and adjust the method of providing guidelines and templates based on the estimated applicant's emotions. For example, if the applicant is nervous, simple guidelines can be provided. If the applicant is relaxed, detailed guidelines can be provided. Furthermore, if the applicant is excited, a visually appealing template can be provided. This allows the method of providing guidelines and templates to be adjusted based on the applicant's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit can be performed using AI, for example, or without AI. For example, the providing unit can input image data of the applicant captured by a camera into the generation AI and have the generation AI estimate the applicant's emotions.

[0142] The disclosure unit can estimate the applicant's emotions and adjust the disclosure method of the evaluation process based on the estimated emotions of the applicant. For example, if the applicant is nervous, the evaluation process can be disclosed briefly. If the applicant is relaxed, the evaluation process can be disclosed in detail. Furthermore, if the applicant is excited, the evaluation process can be disclosed in a visually appealing manner. This allows the disclosure method of the evaluation process to be adjusted based on the applicant's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the disclosure unit can be performed using AI, for example, or without AI. For example, the disclosure unit can input image data of the applicant taken with a camera into the generation AI and have the generation AI estimate the applicant's emotions.

[0143] The know-how providing unit can estimate the applicant's emotions and adjust the method of providing know-how based on the estimated applicant's emotions. For example, if the applicant is nervous, the know-how providing unit can adjust the method of providing know-how to help the applicant relax. Furthermore, if the applicant is excited, the know-how providing unit can quickly provide know-how. Furthermore, if the applicant is feeling anxious, the know-how providing unit can provide know-how while displaying a message that gives the applicant a sense of security. This allows the method of providing know-how to be adjusted based on the applicant's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the know-how providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the know-how providing unit can input image data of the applicant taken with a camera into the generation AI and have the generation AI estimate the applicant's emotions.

[0144] The evaluation unit can adjust the details of the evaluation based on the importance of the idea during evaluation. For example, a detailed evaluation can be performed for ideas with high importance. A brief evaluation can also be performed for ideas with low importance. Furthermore, the level of detail of the evaluation can be adjusted in stages depending on the importance. This allows the level of detail of the evaluation to be adjusted depending on the importance of the idea. Some or all of the above-mentioned processing in the evaluation unit can be performed using, for example, AI, or can be performed without using AI. For example, the evaluation unit can input idea importance data to the generation AI and have the generation AI adjust the level of detail of the evaluation.

[0145] When setting the criteria, the criteria setting unit can adjust the evaluation criteria by referring to past evaluation data. For example, the past evaluation data can be analyzed and the evaluation criteria can be adjusted. The evaluation criteria can also be optimized based on the past evaluation data. Furthermore, the evaluation criteria can be updated by referring to the past evaluation data. This allows the evaluation criteria to be optimized based on the past evaluation data. Some or all of the above-mentioned processing in the criteria setting unit may be performed using AI, for example, or may be performed without using AI. For example, the criteria setting unit can input past evaluation data into the generation AI and cause the generation AI to adjust the evaluation criteria.

[0146] When providing support, the support unit can customize the means of support based on the applicant's current living situation. For example, if the applicant is busy, it can suggest an efficient support method. Also, if the applicant has time, it can suggest a detailed support method. Furthermore, it can customize the means of support according to the applicant's living situation. This allows the means of support to be customized based on the applicant's current living situation. Some or all of the above-mentioned processing in the support unit may be performed using AI, for example, or may be performed without using AI. For example, the support unit can input the applicant's current living situation data into the generation AI and have the generation AI customize the means of support.

[0147] When providing the guidelines or templates, the providing unit can refer to the applicant's past application history and provide appropriate guidelines or templates. For example, the providing unit can provide guidelines that the applicant has used in the past with priority. The providing unit can also analyze the applicant's past application history and suggest optimal guidelines. Furthermore, the providing unit can provide optimal templates based on the applicant's past application history. This makes it possible to provide optimal guidelines or templates based on the past application history. Some or all of the above-described processing by the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the applicant's past application history data into a generating AI and have the generating AI provide optimal guidelines or templates.

[0148] The processing flow of the second embodiment will be briefly explained below.

[0149] Step 1: The reception department accepts applications. The application process includes application procedures, required documents, and submission methods. The reception department can accept applications through an online form, as well as applications by mail or in person. For example, the reception department can receive information entered by applicants into an online form and store it in a database. It can also scan application documents sent by mail, convert them into digital data, and store it in a database. Step 2: The evaluation department evaluates the ideas received by the reception department. The evaluation includes evaluation criteria, evaluation scales, and evaluator qualifications. The evaluation department can conduct expert review or evaluation through public voting. For example, experts evaluate the ideas' originality, feasibility, social impact, sustainability, economic impact, etc. Public evaluation of the applicants' ideas can also be collected through public voting. Step 3: The Support Department supports the realization of the ideas selected by the Evaluation Department. Support can include funding, technical support, and mentoring. In addition to providing funding for the selected ideas, the Support Department also supports the realization of the ideas through technical support and mentoring. For example, the Support Department can provide the technical advice necessary to realize the selected ideas and optimize and provide the necessary resources.

[0150] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0151] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0153] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0154] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0155] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0156] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0157] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0158] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0159] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0160] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0161] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0162] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0163] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0164] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0165] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0166] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0167] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0168] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0169] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0170] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0171] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0172] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0173] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0174] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0175] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0176] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0177] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0178] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0179] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0180] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0181] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0182] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0183] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0184] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0185] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0186] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0187] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0188] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0189] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0190] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0191] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0192] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0193] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0194] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0195] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0196] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0197] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0198] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0199] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0200] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0201] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0202] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0203] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0204] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0205] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0206] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0207] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0208] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0209] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0210] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0211] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0212] 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.

[0213] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0214] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0215] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0216] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0217] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0218] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0219] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0220] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0221] [Explanation of symbols]

[0222] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception department that handles the application process; an evaluation unit that evaluates the ideas received by the reception unit; a support unit that supports the realization of the idea selected by the evaluation unit. A system characterized by:

2. Equipped with a standard setting unit that sets evaluation standards 2. The system of claim 1.

3. Have a submission department that provides guidelines or templates to applicants 2. The system of claim 1.

4. Have a public section that makes the evaluation process public 2. The system of claim 1.

5. Has a know-how provision department that provides know-how to other companies or organizations 2. The system of claim 1.

6. The reception unit Estimating applicant sentiment and adjusting the timing of the application process based on the estimated sentiment 2. The system of claim 1.

7. The reception unit Analyze applicants' past application history and select the appropriate reception method 2. The system of claim 1.

8. The reception unit Filtering applications based on current projects and areas of interest during the application process 2. The system of claim 1.

9. The reception unit When accepting applications, select the appropriate method of acceptance depending on the applicant's input method.

2. The system of claim 1.

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