system

Abstract

The system according to this embodiment aims to use generative AI to evaluate only ideas and to discover and re-evaluate past contest entries. [Solution] The system according to the embodiment comprises an acquisition unit, an analysis unit, an evaluation unit, and a discovery unit. The acquisition unit acquires primary screening materials. The analysis unit analyzes the primary screening materials acquired by the acquisition unit and evaluates the ideas. The evaluation unit evaluates the ideas analyzed by the analysis unit. The discovery unit discovers past contest entries and re-evaluates them.

Description

Technical Field

[0004] ,

[0006] , , , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the review method for a generation AI utilization contest does not sufficiently address the evaluation of ideas, and there is room for improvement.

[0005] The system according to the embodiment aims to realize the evaluation of only ideas using a generation AI and to discover and re-evaluate past contest cases.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an acquisition unit, an analysis unit, an evaluation unit, and a discovery unit. The acquisition unit acquires primary screening materials. The analysis unit analyzes the primary screening materials acquired by the acquisition unit and evaluates the ideas. The evaluation unit evaluates the ideas analyzed by the analysis unit. The discovery unit discovers past contest entries and re-evaluates them. [Effects of the Invention]

[0007] The system according to this embodiment uses generative AI to evaluate only ideas and can discover and re-evaluate past contest entries. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The contest judging system according to an embodiment of the present invention is a system that improves the contest judging method by utilizing generative AI and realizes the evaluation of ideas alone. In this contest judging system, the generative AI analyzes the initial judging materials and evaluates the ideas. Next, the generative AI unearths past contest cases and re-evaluates them. Furthermore, the system aims to monetize the contest by turning it into a solution and selling it externally. For example, in the contest judging system, the generative AI analyzes the initial judging materials. In this process, the generative AI analyzes the submitted materials and evaluates the ideas. For example, it can evaluate the originality, feasibility, and social impact of the ideas. This makes it possible to evaluate ideas alone, without depending on presentation skills. Next, the contest judging system uses the generative AI to unearth and re-evaluate past contest cases. For example, it can re-evaluate ideas that did not advance to the final judging in past contests and unearth excellent ideas. This ensures that past ideas are also effectively utilized. Furthermore, the contest judging system aims to monetize the contest by turning it into a solution and selling it externally. Specifically, this involves providing services that undertake the holding and operation of contests, or offering the generative AI judging system as SaaS. Furthermore, the program also provides support for patent application procedures and new business development. This enables revenue generation through the external sale of contest results. This system allows for the evaluation of ideas alone, and past ideas are also effectively utilized. In addition, the ability to monetize contest results through external sale is expected to revitalize the industry and create collaborative ideas. As a result, the contest judging system can efficiently evaluate ideas.

[0029] The contest judging system according to this embodiment comprises an acquisition unit, an analysis unit, an evaluation unit, and a discovery unit. The acquisition unit acquires primary judging materials. Primary judging materials include, but are not limited to, documents, digital data, and images. The acquisition unit acquires primary judging materials from, for example, a judging file server. The acquisition unit can also acquire primary judging materials from cloud storage. Furthermore, the acquisition unit can acquire primary judging materials from a local device. For example, the acquisition unit accesses the judging file server and downloads the necessary primary judging materials. Acquisition from cloud storage is performed via the internet, allowing for quick and efficient acquisition of materials. Acquisition from a local device is a method of acquiring materials from a directly connected device and is available even in an offline environment. The analysis unit uses a generative AI to analyze the primary judging materials acquired by the acquisition unit and evaluate the ideas. Analysis is performed by, for example, text analysis, image analysis, and data mining, but is not limited to these methods. For example, the generative AI uses a text generation AI (e.g., LLM) to analyze the primary judging materials and evaluate the originality, feasibility, and social impact of the ideas. Furthermore, the analysis unit can analyze multiple modals, such as images and audio, in addition to text, using a multimodal generation AI. The analysis unit can also use generation AI and data mining techniques to extract and evaluate important information from the initial review materials. For example, the text generation AI has learned from a large amount of text data and possesses advanced natural language processing capabilities. The multimodal generation AI can handle multiple modals, such as images and audio, in addition to text. Data mining techniques are used to extract useful information from large amounts of data and play a crucial role in the analysis of the initial review materials. The evaluation unit evaluates the ideas analyzed by the analysis unit. Evaluation is carried out using methods such as scoring, ranking, and feedback, but is not limited to these examples. For example, the evaluation unit scores ideas based on the analysis results of the generation AI. The evaluation unit can also rank ideas based on the analysis results.The evaluation department can also provide feedback based on the analysis results. For example, the evaluation department evaluates the originality, feasibility, and social impact of ideas based on the analysis results of the generating AI, and assigns a score. Ranking is a method of ranking ideas based on the score, with superior ideas ranking higher. Feedback specifically indicates areas for improvement and strengths of the ideas based on the evaluation results, providing useful information to the submitter. The discovery department unearths and re-evaluates past contest entries. Discovery is carried out by methods such as database searches and reviews of past records, but is not limited to these examples. For example, the discovery department re-evaluates ideas that did not advance to the final round in past contests and discovers superior ideas. The discovery department can also search for past contest entries in a database and re-evaluate them. The discovery department can also review past records and re-evaluate them. For example, the discovery department re-evaluates excellent ideas that were overlooked in past contests and finds new value. Database searches are a method of searching for specific information from a large amount of data, and can efficiently unearth past entries. Reviewing past records is a method of re-examining and re-evaluating past judging results and evaluation comments, and is effective in uncovering overlooked ideas. As a result, the contest judging system according to this embodiment can efficiently acquire, analyze, evaluate, and discover initial judging materials.

[0030] The acquisition unit acquires primary review materials. Primary review materials include, but are not limited to, documents, digital data, and images. The acquisition unit can acquire primary review materials from, for example, a review file server. It can also acquire primary review materials from cloud storage. Furthermore, it can acquire primary review materials from a local device. For example, the acquisition unit accesses the review file server and downloads the necessary primary review materials. Acquisition from cloud storage is performed via the internet, allowing for quick and efficient acquisition of materials. Acquisition from a local device is a method of acquiring materials from a directly connected device and is available even in offline environments. By combining these diverse acquisition methods, the acquisition unit has the flexibility to handle any situation. For example, acquisition from a review file server utilizes an internal network, making it highly secure and suitable for acquiring confidential materials. Acquisition from cloud storage utilizes an external data center, allowing for the rapid acquisition of large amounts of data. Acquisition from a local device acquires data directly from a physically connected device, ensuring reliable acquisition of materials even in environments with unstable internet connections. Furthermore, the acquisition unit has a function to automatically classify and organize the acquired primary review materials. For example, the acquisition unit classifies documents by category, organizes digital data by format, and manages images by tagging them. This allows for efficient management of acquired materials and easy access for the analysis and evaluation units. The acquisition unit also has a function to automatically extract metadata from acquired materials and register it in a database. The metadata includes the creation date and time of the material, the creator, file size, and format, and materials can be searched based on this information. In this way, the acquisition unit contributes not only to the acquisition of primary review materials but also to the efficiency of management and searching.

[0031] The analysis unit uses generative AI to analyze the initial review materials acquired by the acquisition unit and evaluate the ideas. Analysis is performed using methods such as text analysis, image analysis, and data mining, but is not limited to these examples. For instance, the generative AI can use text generation AI (e.g., LLM) to analyze the initial review materials and evaluate the originality, feasibility, and social impact of the ideas. The analysis unit can also use multimodal generation AI to analyze multiple modals, including images and audio, in addition to text. Furthermore, the analysis unit can use generative AI and data mining techniques to extract and evaluate important information from the initial review materials. For example, text generation AI has learned from large amounts of text data and possesses advanced natural language processing capabilities. Multimodal generation AI can handle multiple modals, including images and audio, in addition to text. Data mining techniques are used to extract useful information from large amounts of data and play a crucial role in the analysis of initial review materials. By combining these techniques, the analysis unit conducts a multifaceted analysis of the initial review materials and evaluates the ideas. For example, the text generation AI analyzes the text data of the initial screening materials to evaluate the originality and feasibility of the ideas. Specifically, the text generation AI uses natural language processing technology to understand the content of the text data and evaluate its similarity to other ideas. In addition, the multimodal generation AI analyzes not only text data but also image and audio data to perform a multifaceted evaluation of the ideas. For example, it uses image analysis technology to evaluate the visual elements of the ideas and audio analysis technology to evaluate the quality of the idea's presentation. Furthermore, it uses data mining technology to extract important information from the initial screening materials and use it to evaluate the ideas. For example, it uses data mining technology to extract specific keywords and patterns from the initial screening materials to evaluate the originality and feasibility of the ideas. As a result, the analysis unit can perform a multifaceted analysis of the initial screening materials and evaluate the ideas.

[0032] The evaluation department evaluates the ideas analyzed by the analysis department. Evaluation is carried out using methods such as scoring, ranking, and feedback, but is not limited to these examples. For example, the evaluation department may score ideas based on the analysis results of the generative AI. The evaluation department can also rank ideas based on the analysis results. Furthermore, the evaluation department can provide feedback based on the analysis results. For example, the evaluation department may evaluate and score ideas based on their originality, feasibility, and social impact, using the analysis results of the generative AI. Ranking is a method of assigning ranks to ideas based on their scores, with superior ideas ranking higher. Feedback, based on the evaluation results, specifically identifies areas for improvement and strengths of the ideas, providing useful information to the submitter. The evaluation department combines these evaluation methods to conduct a comprehensive evaluation of the ideas. For example, scoring is a method of assigning points to each element of an idea (originality, feasibility, social impact, etc.), and by quantifying the evaluation of each element, a comprehensive evaluation of the idea is conducted. Ranking is a method of assigning ranks to ideas based on scoring results, improving the efficiency of the review process by ranking superior ideas higher. Feedback provides useful information to submitters by specifically indicating areas for improvement and strengths of ideas based on evaluation results. For example, feedback may include suggestions for enhancing the originality of the idea or advice for improving its feasibility. This allows the evaluation department to conduct a comprehensive evaluation of the ideas and provide useful feedback to submitters. Furthermore, the evaluation department provides feedback that specifically indicates areas for improvement and strengths of ideas based on evaluation results. For example, the evaluation department evaluates the originality, feasibility, and social impact of ideas based on the analysis results of the generative AI and assigns a score. Ranking is a method of assigning ranks to ideas based on scores, ranking superior ideas higher. Feedback provides useful information to submitters by specifically indicating areas for improvement and strengths of ideas based on evaluation results. This allows the evaluation department to conduct a comprehensive evaluation of the ideas and provide useful feedback to submitters.

[0033] The Discovery Department unearths and re-evaluates past contest entries. Discovery is carried out using methods such as database searches and reviews of past records, but is not limited to these examples. For instance, the Discovery Department re-evaluates ideas that did not advance to the final round of past contests to uncover excellent ideas. The Discovery Department can also search for and re-evaluate past contest entries from a database. Furthermore, the Discovery Department can review and re-evaluate past records. For example, the Discovery Department re-evaluates excellent ideas that were overlooked in past contests to discover new value. Database searches are a method of searching for specific information from a large amount of data, allowing for the efficient discovery of past entries. Reviewing past records involves re-examining past judging results and evaluation comments, and is effective in uncovering overlooked ideas. By combining these methods, the Discovery Department conducts a multifaceted discovery of past contest entries. For example, it uses database searches to search for and re-evaluate ideas that did not advance to the final round of past contests. Furthermore, the team reviews past records, re-examining past judging results and evaluation comments to uncover overlooked ideas. The team can also use generative AI to analyze past contest entries during the re-evaluation process. For example, they can use generative AI to analyze past judging materials and evaluate the originality, feasibility, and social impact of ideas. This allows the team to conduct a multifaceted search of past contest entries and discover new value. Moreover, based on the re-evaluation results, the team can improve past ideas and submit them to new contests. For instance, they can identify areas for improvement in past ideas, make those improvements, and resubmit them to contests, potentially achieving superior results. This allows the team to conduct a multifaceted search of past contest entries and discover new value.

[0034] The acquisition unit can acquire primary review materials from the review file server. For example, the acquisition unit can access the review file server and download the necessary primary review materials. The acquisition unit can analyze the folder structure of the review file server and efficiently acquire the desired materials. The acquisition unit can also check the access permissions of the review file server and acquire materials from users with appropriate permissions. For example, the acquisition unit can analyze the folder structure of the review file server and identify the folder where the desired materials are stored. Checking access permissions is a method of identifying users with the necessary permissions to acquire the materials and acquiring the materials from users with appropriate permissions. This allows for efficient acquisition of primary review materials from the review file server. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can have a generating AI perform the analysis of the folder structure of the review file server.

[0035] The analysis unit can analyze the initial review materials using generative AI and evaluate the originality, feasibility, and social impact of ideas. For example, the analysis unit can use generative AI to analyze the initial review materials and evaluate the originality of ideas. For example, the analysis unit can use generative AI to extract unique ideas from the initial review materials and evaluate their originality. The analysis unit can also use generative AI to evaluate the feasibility of the initial review materials. For example, the analysis unit can use generative AI to perform technical feasibility and cost evaluations. The analysis unit can also use generative AI to evaluate the social impact of the initial review materials. For example, the analysis unit can use generative AI to evaluate social acceptability and environmental impacts. As a result, using generative AI improves the accuracy of idea evaluation. Some or all of the above-described processes in the analysis unit may be performed using generative AI, or they may not. For example, the analysis unit can have generative AI perform the evaluation of the originality of the initial review materials.

[0036] The evaluation unit can evaluate ideas based on the analysis results of the generative AI. For example, the evaluation unit can score ideas based on the analysis results of the generative AI. For example, the evaluation unit can evaluate the originality, feasibility, and social impact of ideas based on the analysis results of the generative AI and assign scores. The evaluation unit can also rank ideas based on the analysis results of the generative AI. For example, the evaluation unit can rank ideas based on the analysis results of the generative AI, placing superior ideas at the top. The evaluation unit can also provide feedback based on the analysis results of the generative AI. For example, the evaluation unit can provide feedback that specifically indicates areas for improvement and strengths of ideas based on the analysis results of the generative AI. This allows for accurate evaluation of ideas based on the analysis results of the generative AI. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can have the generative AI perform scoring based on the analysis results of the generative AI.

[0037] The Discovery Department can unearth and re-evaluate past contest entries. For example, the Discovery Department can re-evaluate ideas that did not advance to the final round of past contests to uncover excellent ideas. For example, the Discovery Department can re-evaluate excellent ideas that were overlooked in past contests to find new value. The Discovery Department can also search for past contest entries in a database and re-evaluate them. For example, the Discovery Department can use database searches to efficiently unearth and re-evaluate past contest entries. The Discovery Department can also review past records and re-evaluate them. For example, the Discovery Department can re-examine past judging results and evaluation comments and re-evaluate them. In this way, excellent ideas can be unearthed by re-evaluating past contest entries. Some or all of the above processes in the Discovery Department may be performed using AI, for example, or not. For example, the Discovery Department can have a generating AI perform a database search of past contest entries.

[0038] The Discovery Department can re-evaluate ideas that did not advance to the final round of past contests and discover excellent ideas. For example, the Discovery Department can re-evaluate ideas that did not advance to the final round of past contests and find new value in them. The Discovery Department can also search for past contest entries in a database and re-evaluate them. For example, the Discovery Department can use database searches to efficiently discover and re-evaluate past contest entries. The Discovery Department can also review past records and re-evaluate them. For example, the Discovery Department can re-examine past judging results and evaluation comments and re-evaluate them. This allows for the re-evaluation of excellent ideas that were overlooked in past contests. Some or all of the above processes in the Discovery Department may be performed using AI, for example, or not. For example, the Discovery Department can have a generating AI perform a database search of past contest entries.

[0039] The evaluation unit can register evaluation results in a database. For example, the evaluation unit can register and manage evaluation results in a database. For example, the evaluation unit can register evaluation results in a database so that they can be referenced later. The evaluation unit can also register evaluation results in a database and use them as statistical data. For example, the evaluation unit can register evaluation results in a database, analyze them as statistical data, and understand evaluation trends. This makes it easier to manage evaluation results by registering them in a database. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can have a generating AI perform the registration of evaluation results in the database.

[0040] The retrieval unit can analyze the file metadata and select the optimal retrieval method when retrieving files from the review file server. For example, the retrieval unit can select the optimal retrieval method based on the file size and format. For example, the retrieval unit analyzes the file metadata and selects the optimal retrieval method based on the size and format. The retrieval unit can also prioritize retrieving the latest documents by considering the file creation date and modification date. For example, the retrieval unit analyzes the file metadata and prioritizes retrieving the latest documents based on the creation date and modification date. The retrieval unit can also check the file access permissions and retrieve files from users with appropriate permissions. For example, the retrieval unit analyzes the file metadata, checks the access permissions, and retrieves files from users with appropriate permissions. In this way, the optimal retrieval method can be selected by analyzing the file metadata. Some or all of the above processing in the retrieval unit may be performed using AI, for example, or without AI. For example, the retrieval unit can have a generating AI perform the analysis of the file metadata.

[0041] The acquisition unit can filter the initial review materials based on their content. For example, the acquisition unit can analyze the keywords in the materials and prioritize acquiring highly relevant materials. The acquisition unit can also determine the category of the materials and acquire materials belonging to a specific category. The acquisition unit can also analyze the content of the materials and exclude duplicate materials. By filtering based on the content of the materials, highly relevant materials can be prioritized. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can have a generating AI perform the analysis of the material content.

[0042] The acquisition unit can prioritize the acquisition of highly relevant materials when acquiring primary review materials, taking into account the attribute information of the material submitters. For example, the acquisition unit can prioritize the acquisition of highly relevant materials based on the submitter's field of expertise. For example, the acquisition unit can prioritize the acquisition of highly relevant materials based on the submitter's attribute information and field of expertise. The acquisition unit can also prioritize the acquisition of highly reliable materials by considering the submitter's past performance. For example, the acquisition unit can prioritize the acquisition of highly reliable materials by considering the submitter's attribute information and past performance. The acquisition unit can also prioritize the acquisition of highly relevant materials by considering the submitter's affiliated institution. For example, the acquisition unit can prioritize the acquisition of highly relevant materials by considering the submitter's attribute information and institution. In this way, by considering the submitter's attribute information, highly relevant materials can be prioritized. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can have a generating AI perform the analysis of the submitter's attribute information.

[0043] The acquisition unit can adjust the acquisition order based on the submission date of the materials when acquiring the initial screening materials. For example, the acquisition unit can prioritize acquiring materials with a more recent submission date. For example, the acquisition unit can analyze the submission dates of the materials and prioritize acquiring newer materials. The acquisition unit can also postpone acquiring older materials. For example, the acquisition unit can analyze the submission dates of the materials and postpone acquiring older materials. The acquisition unit can also optimize the acquisition order of materials based on the submission date. For example, the acquisition unit can analyze the submission dates of the materials and acquire them in the optimal order. This allows for the prioritization of acquiring the latest materials by adjusting the acquisition order based on the submission date. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can have a generating AI perform the analysis of the submission dates of the materials.

[0044] The analysis unit can apply different analysis algorithms depending on the category of the idea during analysis. For example, the analysis unit can apply a technical evaluation algorithm to a technical idea. For example, the analysis unit can determine the category of the idea and apply a technical evaluation algorithm to a technical idea. The analysis unit can also apply a business evaluation algorithm to a business idea. For example, the analysis unit can determine the category of the idea and apply a business evaluation algorithm to a business idea. The analysis unit can also apply a social impact evaluation algorithm to an idea that has social impact. For example, the analysis unit can determine the category of the idea and apply a social impact evaluation algorithm to an idea that has social impact. By applying an appropriate analysis algorithm according to the category of the idea, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can have a generative AI perform the determination of the idea category and the application of the analysis algorithm.

[0045] The analysis unit can improve the accuracy of its analysis by considering the past performance of the idea submitter. For example, the analysis unit can improve the accuracy of its analysis by referring to the submitter's past success stories. For example, the analysis unit can analyze the submitter's past performance and improve the accuracy of its analysis by referring to success stories. The analysis unit can also improve the accuracy of its analysis by considering the submitter's past failure stories. For example, the analysis unit can analyze the submitter's past performance and improve the accuracy of its analysis by considering failure stories. The analysis unit can also improve the accuracy of its analysis by comprehensively evaluating the submitter's past performance. For example, the analysis unit can comprehensively evaluate the submitter's past performance and improve the accuracy of its analysis. In this way, the accuracy of the analysis is improved by considering the submitter's past performance. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can have a generative AI perform the analysis of the submitter's past performance.

[0046] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on the idea during the analysis. For example, the analysis unit can improve the accuracy of its analysis by referring to patent documents related to the idea. For example, the analysis unit can search for relevant literature on the idea and improve the accuracy of its analysis by referring to patent documents. The analysis unit can also improve the accuracy of its analysis by referring to academic papers related to the idea. For example, the analysis unit can search for relevant literature on the idea and improve the accuracy of its analysis by referring to academic papers. The analysis unit can also improve the accuracy of its analysis by referring to market research reports related to the idea. For example, the analysis unit can search for relevant literature on the idea and improve the accuracy of its analysis by referring to market research reports. In this way, the accuracy of the analysis is improved by referring to relevant literature. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can have the generative AI perform the search and referencing of relevant literature.

[0047] The analysis unit can determine the priority of analysis based on when the ideas were submitted. For example, the analysis unit may prioritize the analysis of newer ideas. For example, the analysis unit may analyze the submission dates of ideas and prioritize the analysis of newer ideas. The analysis unit may also postpone the analysis of older ideas. For example, the analysis unit may analyze the submission dates of ideas and postpone the analysis of older ideas. The analysis unit may also optimize the priority of analysis based on the submission dates. For example, the analysis unit may analyze the submission dates of ideas and perform the analysis in the optimal order. This allows for prioritizing the analysis of the latest ideas by determining the priority of analysis based on the submission dates. Some or all of the above processes in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit may have a generative AI perform the analysis of the submission dates of ideas.

[0048] The evaluation unit can introduce methods for quantitatively evaluating the social impact of an idea during the evaluation process. For example, the evaluation unit can quantify and evaluate the social impact of an idea. For example, the evaluation unit can set indicators for quantitatively evaluating the social impact of an idea and quantify and evaluate them. The evaluation unit can also collect and analyze data for quantitatively evaluating the social impact of an idea. For example, the evaluation unit can collect and analyze data for evaluating the social impact of an idea. This improves the objectivity of the evaluation by quantitatively evaluating the social impact of an idea. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can have a generative AI perform the quantitative evaluation of social impact.

[0049] The evaluation unit can, during the evaluation process, assess the feasibility of an idea in detail from a technical standpoint. For example, the evaluation unit can set indicators for evaluating the technical feasibility of an idea and then perform the evaluation. The evaluation unit can also collect and analyze data for evaluating the technical feasibility of an idea. For example, the evaluation unit collects and analyzes data for evaluating the technical feasibility of an idea. Furthermore, the evaluation unit can gather expert opinions for evaluating the technical feasibility of an idea. For example, the evaluation unit gathers expert opinions to evaluate the technical feasibility of an idea and incorporates them into the evaluation. This allows for the selection of highly feasible ideas by evaluating their feasibility in detail from a technical standpoint. Some or all of the above processes in the evaluation unit may be performed using AI, or not. For example, the evaluation unit can have a generative AI perform the evaluation of technical feasibility.

[0050] The evaluation unit can determine the priority of evaluation by considering the marketability of ideas during the evaluation process. For example, the evaluation unit may prioritize evaluating ideas with high marketability. For example, the evaluation unit may evaluate the marketability of ideas and prioritize those with high marketability. The evaluation unit may also postpone evaluating ideas with low marketability. For example, the evaluation unit may evaluate the marketability of ideas and postpone those with low marketability. The evaluation unit may also optimize the evaluation priority based on marketability. For example, the evaluation unit may evaluate the marketability of ideas and evaluate them in the optimal order. This allows for prioritizing the evaluation of ideas that have a high probability of actually succeeding in the market by considering the marketability of ideas. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit may have a generative AI perform the marketability evaluation.

[0051] The evaluation unit can improve the accuracy of its evaluation by referring to relevant patent information of the idea during the evaluation process. For example, the evaluation unit can improve the accuracy of its evaluation by referring to patent information related to the idea. For example, the evaluation unit can search for relevant patent information of the idea and improve the accuracy of its evaluation by referring to the patent information. The evaluation unit can also collect and analyze patent information related to the idea. For example, the evaluation unit can collect and analyze relevant patent information of the idea. The evaluation unit can also improve the accuracy of its evaluation based on patent information related to the idea. For example, the evaluation unit can improve the accuracy of its evaluation based on relevant patent information of the idea. As a result, the accuracy of the evaluation is improved by referring to relevant patent information. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without using AI. For example, the evaluation unit can have a generating AI perform the search and referencing of relevant patent information.

[0052] The idea discovery department can analyze metadata from past contest entries to select the optimal discovery method. For example, the idea discovery department can analyze metadata from past contest entries and prioritize the discovery of highly relevant ideas. For example, the idea discovery department can analyze metadata from past contest entries and prioritize the discovery of highly relevant ideas. The idea discovery department can also analyze metadata from past contest entries and exclude duplicate ideas. For example, the idea discovery department can analyze metadata from past contest entries and exclude duplicate ideas. The idea discovery department can also analyze metadata from past contest entries and evaluate the originality and feasibility of ideas before discovery. For example, the idea discovery department can analyze metadata from past contest entries and evaluate the originality and feasibility of ideas before discovery. In this way, by analyzing metadata from past contest entries, the optimal discovery method can be selected. Some or all of the above processes in the idea discovery department may be performed using AI, for example, or without using AI. For example, the excavation department can have a generating AI analyze metadata from past contest entries.

[0053] The idea discovery department can improve the accuracy of its discovery process by considering the past performance of the idea submitter. For example, the department can improve the accuracy of its discovery by referring to the submitter's past success stories. For example, the department can analyze the submitter's past performance and improve the accuracy of its discovery by referring to success stories. The department can also improve the accuracy of its discovery by considering the submitter's past failure stories. For example, the department can analyze the submitter's past performance and improve the accuracy of its discovery by considering failure stories. The department can also improve the accuracy of its discovery by comprehensively evaluating the submitter's past performance. For example, the department can comprehensively evaluate the submitter's past performance and improve the accuracy of its discovery. In this way, the accuracy of the discovery process is improved by considering the submitter's past performance. Some or all of the above processes in the idea discovery department may be performed using AI, for example, or without AI. For example, the idea discovery department can have a generation AI perform the analysis of the submitter's past performance.

[0054] The discovery unit can improve the accuracy of its discovery process by referring to relevant literature from past contest cases. For example, the discovery unit can improve the accuracy of its discovery by referring to patent documents related to past contest cases. For example, the discovery unit can search for relevant literature from past contest cases and improve the accuracy of its discovery by referring to patent documents. The discovery unit can also improve the accuracy of its discovery by referring to academic papers related to past contest cases. For example, the discovery unit can search for relevant literature from past contest cases and improve the accuracy of its discovery by referring to academic papers. The discovery unit can also improve the accuracy of its discovery by referring to market research reports related to past contest cases. For example, the discovery unit can search for relevant literature from past contest cases and improve the accuracy of its discovery by referring to market research reports. In this way, the accuracy of the discovery process is improved by referring to relevant literature. Some or all of the above processing in the discovery unit may be performed using, for example, a generative AI, or without a generative AI. For example, the discovery unit can have a generative AI perform the search and referencing of relevant literature.

[0055] The discovery unit can determine the priority of discovery based on the submission dates of past contest entries. For example, the discovery unit can prioritize discovering newer entries. For example, the discovery unit can analyze the submission dates of past contest entries and prioritize discovering newer entries. The discovery unit can also postpone discovering older entries. For example, the discovery unit can analyze the submission dates of past contest entries and postpone discovering older entries. The discovery unit can also optimize the priority of discovery based on the submission dates. For example, the discovery unit can analyze the submission dates of past contest entries and discover them in the optimal order. This allows the discovery of the most recent entries to be prioritized by determining the priority of discovery based on the submission dates. Some or all of the above processes in the discovery unit may be performed using, for example, a generative AI, or not. For example, the discovery unit can have a generative AI perform the analysis of the submission dates of past contest entries.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The acquisition unit can prioritize the acquisition of highly relevant materials when acquiring primary review materials, taking into account the attribute information of the material submitters. For example, it can prioritize the acquisition of highly relevant materials based on the submitter's field of expertise. It can also prioritize the acquisition of highly reliable materials by considering the submitter's past achievements. Furthermore, it can prioritize the acquisition of highly relevant materials by considering the submitter's affiliated institution. In this way, by considering the submitter's attribute information, highly relevant materials can be prioritized. Some or all of the above processing in the acquisition unit may be performed using AI or not. For example, the acquisition unit can have a generating AI perform the analysis of the submitter's attribute information.

[0058] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on the idea during the analysis process. For example, it can improve the accuracy of its analysis by referring to patent documents related to the idea. It can also improve the accuracy of its analysis by referring to academic papers related to the idea. Furthermore, it can improve the accuracy of its analysis by referring to market research reports related to the idea. In this way, the accuracy of the analysis is improved by referring to relevant literature. Some or all of the above-described processes in the analysis unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the analysis unit can have a generation AI perform the search and referencing of relevant literature.

[0059] The evaluation unit can determine the priority of evaluations by considering the marketability of the ideas during the evaluation process. For example, it can prioritize the evaluation of ideas with high marketability, and postpone the evaluation of ideas with low marketability. Furthermore, it can optimize the evaluation priority based on marketability. This allows for the prioritization of ideas that are more likely to succeed in the market by considering their marketability. Some or all of the above processes in the evaluation unit may be performed using AI, or not. For example, the evaluation unit can have a generative AI perform the marketability evaluation.

[0060] The idea discovery unit can analyze metadata from past contest entries to select the optimal discovery method during the discovery process. For example, it can analyze metadata from past contest entries and prioritize the discovery of highly relevant ideas. It can also analyze metadata from past contest entries and exclude duplicate ideas during the discovery process. Furthermore, it can analyze metadata from past contest entries and evaluate the originality and feasibility of ideas during the discovery process. In this way, the optimal discovery method can be selected by analyzing metadata from past contest entries. Some or all of the above processes in the idea discovery unit may be performed using AI or not. For example, the idea discovery unit can have a generation AI perform the analysis of metadata from past contest entries.

[0061] The evaluation unit can improve the accuracy of its evaluation by referring to relevant patent information for the idea during the evaluation process. For example, it can improve the accuracy of the evaluation by referring to patent information related to the idea. It can also collect and analyze patent information related to the idea. Furthermore, it can improve the accuracy of the evaluation based on the patent information related to the idea. Thus, the accuracy of the evaluation is improved by referring to relevant patent information. Some or all of the above processes in the evaluation unit may be performed using AI or not. For example, the evaluation unit can have a generating AI perform the search and referencing of relevant patent information.

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

[0063] Step 1: The acquisition unit acquires the primary review materials. These materials include documents, digital data, and images. The acquisition unit can acquire primary review materials from the review file server, cloud storage, and local devices. For example, it can access the review file server to download the necessary primary review materials, quickly and efficiently acquire materials from cloud storage via the internet, and acquire materials from a directly connected device. Step 2: The analysis unit uses a generation AI to analyze the initial review materials acquired by the acquisition unit and evaluate the ideas. The analysis is performed using methods such as text analysis, image analysis, and data mining. For example, a text generation AI (LLM) is used to analyze the initial review materials and evaluate the originality, feasibility, and social impact of the ideas. It is also possible to analyze multiple modals, such as images and audio, in addition to text, using a multimodal generation AI. It is also possible to extract important information from the initial review materials using advanced data mining techniques and perform evaluations. Step 3: The evaluation unit evaluates the ideas analyzed by the analysis unit. The evaluation is carried out using methods such as scoring, ranking, and feedback. For example, ideas are scored based on the analysis results of the generation AI, ideas are ranked based on the analysis results, and feedback is provided based on the analysis results. This evaluates the originality, feasibility, and social impact of the ideas, assigns scores, ranks them, and specifically identifies areas for improvement and strengths. Step 4: The Discovery Department unearths and re-evaluates past contest entries. Discovery is carried out using methods such as database searches and reviews of past records. For example, ideas that did not advance to the final round in past contests are re-evaluated to uncover excellent ideas. This involves searching the database, re-evaluating, reviewing past records, and re-evaluating. This allows for the re-evaluation of excellent ideas that were overlooked in past contests, and the discovery of new value.

[0064] (Example of form 2) The contest judging system according to an embodiment of the present invention is a system that improves the contest judging method by utilizing generative AI and realizes the evaluation of ideas alone. In this contest judging system, the generative AI analyzes the initial judging materials and evaluates the ideas. Next, the generative AI unearths past contest cases and re-evaluates them. Furthermore, the system aims to monetize the contest by turning it into a solution and selling it externally. For example, in the contest judging system, the generative AI analyzes the initial judging materials. In this process, the generative AI analyzes the submitted materials and evaluates the ideas. For example, it can evaluate the originality, feasibility, and social impact of the ideas. This makes it possible to evaluate ideas alone, without depending on presentation skills. Next, the contest judging system uses the generative AI to unearth and re-evaluate past contest cases. For example, it can re-evaluate ideas that did not advance to the final judging in past contests and unearth excellent ideas. This ensures that past ideas are also effectively utilized. Furthermore, the contest judging system aims to monetize the contest by turning it into a solution and selling it externally. Specifically, this involves providing services that undertake the holding and operation of contests, or offering the generative AI judging system as SaaS. Furthermore, the program also provides support for patent application procedures and new business development. This enables revenue generation through the external sale of contest results. This system allows for the evaluation of ideas alone, and past ideas are also effectively utilized. In addition, the ability to monetize contest results through external sale is expected to revitalize the industry and create collaborative ideas. As a result, the contest judging system can efficiently evaluate ideas.

[0065] The contest judging system according to this embodiment comprises an acquisition unit, an analysis unit, an evaluation unit, and a discovery unit. The acquisition unit acquires primary judging materials. Primary judging materials include, but are not limited to, documents, digital data, and images. The acquisition unit acquires primary judging materials from, for example, a judging file server. The acquisition unit can also acquire primary judging materials from cloud storage. Furthermore, the acquisition unit can acquire primary judging materials from a local device. For example, the acquisition unit accesses the judging file server and downloads the necessary primary judging materials. Acquisition from cloud storage is performed via the internet, allowing for quick and efficient acquisition of materials. Acquisition from a local device is a method of acquiring materials from a directly connected device and is available even in an offline environment. The analysis unit uses a generative AI to analyze the primary judging materials acquired by the acquisition unit and evaluate the ideas. Analysis is performed by, for example, text analysis, image analysis, and data mining, but is not limited to these methods. For example, the generative AI uses a text generation AI (e.g., LLM) to analyze the primary judging materials and evaluate the originality, feasibility, and social impact of the ideas. Furthermore, the analysis unit can analyze multiple modals, such as images and audio, in addition to text, using a multimodal generation AI. The analysis unit can also use generation AI and data mining techniques to extract and evaluate important information from the initial review materials. For example, the text generation AI has learned from a large amount of text data and possesses advanced natural language processing capabilities. The multimodal generation AI can handle multiple modals, such as images and audio, in addition to text. Data mining techniques are used to extract useful information from large amounts of data and play a crucial role in the analysis of the initial review materials. The evaluation unit evaluates the ideas analyzed by the analysis unit. Evaluation is carried out using methods such as scoring, ranking, and feedback, but is not limited to these examples. For example, the evaluation unit scores ideas based on the analysis results of the generation AI. The evaluation unit can also rank ideas based on the analysis results.The evaluation department can also provide feedback based on the analysis results. For example, the evaluation department evaluates the originality, feasibility, and social impact of ideas based on the analysis results of the generating AI, and assigns a score. Ranking is a method of ranking ideas based on the score, with superior ideas ranking higher. Feedback specifically indicates areas for improvement and strengths of the ideas based on the evaluation results, providing useful information to the submitter. The discovery department unearths and re-evaluates past contest entries. Discovery is carried out by methods such as database searches and reviews of past records, but is not limited to these examples. For example, the discovery department re-evaluates ideas that did not advance to the final round in past contests and discovers superior ideas. The discovery department can also search for past contest entries in a database and re-evaluate them. The discovery department can also review past records and re-evaluate them. For example, the discovery department re-evaluates excellent ideas that were overlooked in past contests and finds new value. Database searches are a method of searching for specific information from a large amount of data, and can efficiently unearth past entries. Reviewing past records is a method of re-examining and re-evaluating past judging results and evaluation comments, and is effective in uncovering overlooked ideas. As a result, the contest judging system according to this embodiment can efficiently acquire, analyze, evaluate, and discover initial judging materials.

[0066] The acquisition unit acquires primary review materials. Primary review materials include, but are not limited to, documents, digital data, and images. The acquisition unit can acquire primary review materials from, for example, a review file server. It can also acquire primary review materials from cloud storage. Furthermore, it can acquire primary review materials from a local device. For example, the acquisition unit accesses the review file server and downloads the necessary primary review materials. Acquisition from cloud storage is performed via the internet, allowing for quick and efficient acquisition of materials. Acquisition from a local device is a method of acquiring materials from a directly connected device and is available even in offline environments. By combining these diverse acquisition methods, the acquisition unit has the flexibility to handle any situation. For example, acquisition from a review file server utilizes an internal network, making it highly secure and suitable for acquiring confidential materials. Acquisition from cloud storage utilizes an external data center, allowing for the rapid acquisition of large amounts of data. Acquisition from a local device acquires data directly from a physically connected device, ensuring reliable acquisition of materials even in environments with unstable internet connections. Furthermore, the acquisition unit has a function to automatically classify and organize the acquired primary review materials. For example, the acquisition unit classifies documents by category, organizes digital data by format, and manages images by tagging them. This allows for efficient management of acquired materials and easy access for the analysis and evaluation units. The acquisition unit also has a function to automatically extract metadata from acquired materials and register it in a database. The metadata includes the creation date and time of the material, the creator, file size, and format, and materials can be searched based on this information. In this way, the acquisition unit contributes not only to the acquisition of primary review materials but also to the efficiency of management and searching.

[0067] The analysis unit uses generative AI to analyze the initial review materials acquired by the acquisition unit and evaluate the ideas. Analysis is performed using methods such as text analysis, image analysis, and data mining, but is not limited to these examples. For instance, the generative AI can use text generation AI (e.g., LLM) to analyze the initial review materials and evaluate the originality, feasibility, and social impact of the ideas. The analysis unit can also use multimodal generation AI to analyze multiple modals, including images and audio, in addition to text. Furthermore, the analysis unit can use generative AI and data mining techniques to extract and evaluate important information from the initial review materials. For example, text generation AI has learned from large amounts of text data and possesses advanced natural language processing capabilities. Multimodal generation AI can handle multiple modals, including images and audio, in addition to text. Data mining techniques are used to extract useful information from large amounts of data and play a crucial role in the analysis of initial review materials. By combining these techniques, the analysis unit conducts a multifaceted analysis of the initial review materials and evaluates the ideas. For example, the text generation AI analyzes the text data of the initial screening materials to evaluate the originality and feasibility of the ideas. Specifically, the text generation AI uses natural language processing technology to understand the content of the text data and evaluate its similarity to other ideas. In addition, the multimodal generation AI analyzes not only text data but also image and audio data to perform a multifaceted evaluation of the ideas. For example, it uses image analysis technology to evaluate the visual elements of the ideas and audio analysis technology to evaluate the quality of the idea's presentation. Furthermore, it uses data mining technology to extract important information from the initial screening materials and use it to evaluate the ideas. For example, it uses data mining technology to extract specific keywords and patterns from the initial screening materials to evaluate the originality and feasibility of the ideas. As a result, the analysis unit can perform a multifaceted analysis of the initial screening materials and evaluate the ideas.

[0068] The evaluation department evaluates the ideas analyzed by the analysis department. Evaluation is carried out using methods such as scoring, ranking, and feedback, but is not limited to these examples. For example, the evaluation department may score ideas based on the analysis results of the generative AI. The evaluation department can also rank ideas based on the analysis results. Furthermore, the evaluation department can provide feedback based on the analysis results. For example, the evaluation department may evaluate and score ideas based on their originality, feasibility, and social impact, using the analysis results of the generative AI. Ranking is a method of assigning ranks to ideas based on their scores, with superior ideas ranking higher. Feedback, based on the evaluation results, specifically identifies areas for improvement and strengths of the ideas, providing useful information to the submitter. The evaluation department combines these evaluation methods to conduct a comprehensive evaluation of the ideas. For example, scoring is a method of assigning points to each element of an idea (originality, feasibility, social impact, etc.), and by quantifying the evaluation of each element, a comprehensive evaluation of the idea is conducted. Ranking is a method of assigning ranks to ideas based on scoring results, improving the efficiency of the review process by ranking superior ideas higher. Feedback provides useful information to submitters by specifically indicating areas for improvement and strengths of ideas based on evaluation results. For example, feedback may include suggestions for enhancing the originality of the idea or advice for improving its feasibility. This allows the evaluation department to conduct a comprehensive evaluation of the ideas and provide useful feedback to submitters. Furthermore, the evaluation department provides feedback that specifically indicates areas for improvement and strengths of ideas based on evaluation results. For example, the evaluation department evaluates the originality, feasibility, and social impact of ideas based on the analysis results of the generative AI and assigns a score. Ranking is a method of assigning ranks to ideas based on scores, ranking superior ideas higher. Feedback provides useful information to submitters by specifically indicating areas for improvement and strengths of ideas based on evaluation results. This allows the evaluation department to conduct a comprehensive evaluation of the ideas and provide useful feedback to submitters.

[0069] The Discovery Department unearths and re-evaluates past contest entries. Discovery is carried out using methods such as database searches and reviews of past records, but is not limited to these examples. For instance, the Discovery Department re-evaluates ideas that did not advance to the final round of past contests to uncover excellent ideas. The Discovery Department can also search for and re-evaluate past contest entries from a database. Furthermore, the Discovery Department can review and re-evaluate past records. For example, the Discovery Department re-evaluates excellent ideas that were overlooked in past contests to discover new value. Database searches are a method of searching for specific information from a large amount of data, allowing for the efficient discovery of past entries. Reviewing past records involves re-examining past judging results and evaluation comments, and is effective in uncovering overlooked ideas. By combining these methods, the Discovery Department conducts a multifaceted discovery of past contest entries. For example, it uses database searches to search for and re-evaluate ideas that did not advance to the final round of past contests. Furthermore, the team reviews past records, re-examining past judging results and evaluation comments to uncover overlooked ideas. The team can also use generative AI to analyze past contest entries during the re-evaluation process. For example, they can use generative AI to analyze past judging materials and evaluate the originality, feasibility, and social impact of ideas. This allows the team to conduct a multifaceted search of past contest entries and discover new value. Moreover, based on the re-evaluation results, the team can improve past ideas and submit them to new contests. For instance, they can identify areas for improvement in past ideas, make those improvements, and resubmit them to contests, potentially achieving superior results. This allows the team to conduct a multifaceted search of past contest entries and discover new value.

[0070] The acquisition unit can acquire primary review materials from the review file server. For example, the acquisition unit can access the review file server and download the necessary primary review materials. The acquisition unit can analyze the folder structure of the review file server and efficiently acquire the desired materials. The acquisition unit can also check the access permissions of the review file server and acquire materials from users with appropriate permissions. For example, the acquisition unit can analyze the folder structure of the review file server and identify the folder where the desired materials are stored. Checking access permissions is a method of identifying users with the necessary permissions to acquire the materials and acquiring the materials from users with appropriate permissions. This allows for efficient acquisition of primary review materials from the review file server. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can have a generating AI perform the analysis of the folder structure of the review file server.

[0071] The analysis unit can analyze the initial review materials using generative AI and evaluate the originality, feasibility, and social impact of ideas. For example, the analysis unit can use generative AI to analyze the initial review materials and evaluate the originality of ideas. For example, the analysis unit can use generative AI to extract unique ideas from the initial review materials and evaluate their originality. The analysis unit can also use generative AI to evaluate the feasibility of the initial review materials. For example, the analysis unit can use generative AI to perform technical feasibility and cost evaluations. The analysis unit can also use generative AI to evaluate the social impact of the initial review materials. For example, the analysis unit can use generative AI to evaluate social acceptability and environmental impacts. As a result, using generative AI improves the accuracy of idea evaluation. Some or all of the above-described processes in the analysis unit may be performed using generative AI, or they may not. For example, the analysis unit can have generative AI perform the evaluation of the originality of the initial review materials.

[0072] The evaluation unit can evaluate ideas based on the analysis results of the generative AI. For example, the evaluation unit can score ideas based on the analysis results of the generative AI. For example, the evaluation unit can evaluate the originality, feasibility, and social impact of ideas based on the analysis results of the generative AI and assign scores. The evaluation unit can also rank ideas based on the analysis results of the generative AI. For example, the evaluation unit can rank ideas based on the analysis results of the generative AI, placing superior ideas at the top. The evaluation unit can also provide feedback based on the analysis results of the generative AI. For example, the evaluation unit can provide feedback that specifically indicates areas for improvement and strengths of ideas based on the analysis results of the generative AI. This allows for accurate evaluation of ideas based on the analysis results of the generative AI. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can have the generative AI perform scoring based on the analysis results of the generative AI.

[0073] The Discovery Department can unearth and re-evaluate past contest entries. For example, the Discovery Department can re-evaluate ideas that did not advance to the final round of past contests to uncover excellent ideas. For example, the Discovery Department can re-evaluate excellent ideas that were overlooked in past contests to find new value. The Discovery Department can also search for past contest entries in a database and re-evaluate them. For example, the Discovery Department can use database searches to efficiently unearth and re-evaluate past contest entries. The Discovery Department can also review past records and re-evaluate them. For example, the Discovery Department can re-examine past judging results and evaluation comments and re-evaluate them. In this way, excellent ideas can be unearthed by re-evaluating past contest entries. Some or all of the above processes in the Discovery Department may be performed using AI, for example, or not. For example, the Discovery Department can have a generating AI perform a database search of past contest entries.

[0074] The Discovery Department can re-evaluate ideas that did not advance to the final round of past contests and discover excellent ideas. For example, the Discovery Department can re-evaluate ideas that did not advance to the final round of past contests and find new value in them. The Discovery Department can also search for past contest entries in a database and re-evaluate them. For example, the Discovery Department can use database searches to efficiently discover and re-evaluate past contest entries. The Discovery Department can also review past records and re-evaluate them. For example, the Discovery Department can re-examine past judging results and evaluation comments and re-evaluate them. This allows for the re-evaluation of excellent ideas that were overlooked in past contests. Some or all of the above processes in the Discovery Department may be performed using AI, for example, or not. For example, the Discovery Department can have a generating AI perform a database search of past contest entries.

[0075] The evaluation unit can register evaluation results in a database. For example, the evaluation unit can register and manage evaluation results in a database. For example, the evaluation unit can register evaluation results in a database so that they can be referenced later. The evaluation unit can also register evaluation results in a database and use them as statistical data. For example, the evaluation unit can register evaluation results in a database, analyze them as statistical data, and understand evaluation trends. This makes it easier to manage evaluation results by registering them in a database. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can have a generating AI perform the registration of evaluation results in the database.

[0076] The acquisition unit can estimate the user's emotions and adjust the timing of acquiring the initial review materials based on the estimated emotions. For example, if the user is feeling stressed, the acquisition unit can delay the acquisition timing to reduce the user's burden. For example, the acquisition unit estimates the user's emotions and adjusts the acquisition timing if the user is feeling stressed. The acquisition unit can also quickly acquire the initial review materials and start analysis if the user is relaxed. For example, the acquisition unit estimates the user's emotions and quickly acquires the materials if the user is relaxed. The acquisition unit can also immediately acquire the initial review materials and start analysis if the user is in a hurry. For example, the acquisition unit estimates the user's emotions and immediately acquires the materials if the user is in a hurry. In this way, the user's burden can be reduced by adjusting the acquisition timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the processing described above in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can have a generating AI perform the estimation of the user's emotions.

[0077] The retrieval unit can analyze the file metadata and select the optimal retrieval method when retrieving files from the review file server. For example, the retrieval unit can select the optimal retrieval method based on the file size and format. For example, the retrieval unit analyzes the file metadata and selects the optimal retrieval method based on the size and format. The retrieval unit can also prioritize retrieving the latest documents by considering the file creation date and modification date. For example, the retrieval unit analyzes the file metadata and prioritizes retrieving the latest documents based on the creation date and modification date. The retrieval unit can also check the file access permissions and retrieve files from users with appropriate permissions. For example, the retrieval unit analyzes the file metadata, checks the access permissions, and retrieves files from users with appropriate permissions. In this way, the optimal retrieval method can be selected by analyzing the file metadata. Some or all of the above processing in the retrieval unit may be performed using AI, for example, or without AI. For example, the retrieval unit can have a generating AI perform the analysis of the file metadata.

[0078] The acquisition unit can filter the initial review materials based on their content. For example, the acquisition unit can analyze the keywords in the materials and prioritize acquiring highly relevant materials. The acquisition unit can also determine the category of the materials and acquire materials belonging to a specific category. The acquisition unit can also analyze the content of the materials and exclude duplicate materials. By filtering based on the content of the materials, highly relevant materials can be prioritized. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can have a generating AI perform the analysis of the material content.

[0079] The retrieval unit can estimate the user's emotions and determine the priority of the primary review materials to retrieve based on the estimated emotions. For example, if the user is stressed, the retrieval unit will postpone retrieving materials of lower importance. For example, if the retrieval unit estimates the user's emotions and is stressed, it will postpone retrieving materials of lower importance. The retrieval unit can also prioritize retrieving high-importance materials if the user is relaxed. For example, if the retrieval unit estimates the user's emotions and is relaxed, it will prioritize retrieving high-importance materials. The retrieval unit can also immediately retrieve the most important materials if the user is in a hurry. For example, if the retrieval unit estimates the user's emotions and is in a hurry, it will immediately retrieve the most important materials. This reduces the user's burden by prioritizing materials according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can have a generating AI perform the estimation of the user's emotions.

[0080] The acquisition unit can prioritize the acquisition of highly relevant materials when acquiring primary review materials, taking into account the attribute information of the material submitters. For example, the acquisition unit can prioritize the acquisition of highly relevant materials based on the submitter's field of expertise. For example, the acquisition unit can prioritize the acquisition of highly relevant materials based on the submitter's attribute information and field of expertise. The acquisition unit can also prioritize the acquisition of highly reliable materials by considering the submitter's past performance. For example, the acquisition unit can prioritize the acquisition of highly reliable materials by considering the submitter's attribute information and past performance. The acquisition unit can also prioritize the acquisition of highly relevant materials by considering the submitter's affiliated institution. For example, the acquisition unit can prioritize the acquisition of highly relevant materials by considering the submitter's attribute information and institution. In this way, by considering the submitter's attribute information, highly relevant materials can be prioritized. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can have a generating AI perform the analysis of the submitter's attribute information.

[0081] The acquisition unit can adjust the acquisition order based on the submission date of the materials when acquiring the initial screening materials. For example, the acquisition unit can prioritize acquiring materials with a more recent submission date. For example, the acquisition unit can analyze the submission dates of the materials and prioritize acquiring newer materials. The acquisition unit can also postpone acquiring older materials. For example, the acquisition unit can analyze the submission dates of the materials and postpone acquiring older materials. The acquisition unit can also optimize the acquisition order of materials based on the submission date. For example, the acquisition unit can analyze the submission dates of the materials and acquire them in the optimal order. This allows for the prioritization of acquiring the latest materials by adjusting the acquisition order based on the submission date. Some or all of the above processing in the acquisition unit may be performed using AI, for example, or without AI. For example, the acquisition unit can have a generating AI perform the analysis of the submission dates of the materials.

[0082] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide a simple and easily understandable presentation. For example, if the user is nervous, the analysis unit can provide a simple and easily understandable presentation. The analysis unit can also provide a presentation that includes detailed information if the user is relaxed. For example, if the user is nervous, the analysis unit can provide a presentation that includes detailed information if the user is relaxed. The analysis unit can also provide a concise presentation if the user is in a hurry. For example, if the analysis unit is nervous, the analysis unit can provide a concise presentation. By adjusting the presentation of the analysis results according to the user's emotions, the analysis results can be provided that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have a generating AI perform the estimation of the user's emotions.

[0083] The analysis unit can apply different analysis algorithms depending on the category of the idea during analysis. For example, the analysis unit can apply a technical evaluation algorithm to a technical idea. For example, the analysis unit can determine the category of the idea and apply a technical evaluation algorithm to a technical idea. The analysis unit can also apply a business evaluation algorithm to a business idea. For example, the analysis unit can determine the category of the idea and apply a business evaluation algorithm to a business idea. The analysis unit can also apply a social impact evaluation algorithm to an idea that has social impact. For example, the analysis unit can determine the category of the idea and apply a social impact evaluation algorithm to an idea that has social impact. By applying an appropriate analysis algorithm according to the category of the idea, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can have a generative AI perform the determination of the idea category and the application of the analysis algorithm.

[0084] The analysis unit can improve the accuracy of its analysis by considering the past performance of the idea submitter. For example, the analysis unit can improve the accuracy of its analysis by referring to the submitter's past success stories. For example, the analysis unit can analyze the submitter's past performance and improve the accuracy of its analysis by referring to success stories. The analysis unit can also improve the accuracy of its analysis by considering the submitter's past failure stories. For example, the analysis unit can analyze the submitter's past performance and improve the accuracy of its analysis by considering failure stories. The analysis unit can also improve the accuracy of its analysis by comprehensively evaluating the submitter's past performance. For example, the analysis unit can comprehensively evaluate the submitter's past performance and improve the accuracy of its analysis. In this way, the accuracy of the analysis is improved by considering the submitter's past performance. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can have a generative AI perform the analysis of the submitter's past performance.

[0085] The analysis unit can estimate the user's emotions and adjust the level of detail in the analysis results based on the estimated emotions. For example, if the user is nervous, the analysis unit can provide simple and concise analysis results. For example, if the user is nervous, the analysis unit can provide simple and concise analysis results. The analysis unit can also provide detailed analysis results if the user is relaxed. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. The analysis unit can also provide concise and quick analysis results if the user is in a hurry. For example, if the analysis unit is nervous, the analysis unit can provide concise and quick analysis results. By adjusting the level of detail in the analysis results according to the user's emotions, the system can provide analysis results with an appropriate level of detail for the user. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can have a generating AI perform the estimation of the user's emotions.

[0086] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on the idea during the analysis. For example, the analysis unit can improve the accuracy of its analysis by referring to patent documents related to the idea. For example, the analysis unit can search for relevant literature on the idea and improve the accuracy of its analysis by referring to patent documents. The analysis unit can also improve the accuracy of its analysis by referring to academic papers related to the idea. For example, the analysis unit can search for relevant literature on the idea and improve the accuracy of its analysis by referring to academic papers. The analysis unit can also improve the accuracy of its analysis by referring to market research reports related to the idea. For example, the analysis unit can search for relevant literature on the idea and improve the accuracy of its analysis by referring to market research reports. In this way, the accuracy of the analysis is improved by referring to relevant literature. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can have the generative AI perform the search and referencing of relevant literature.

[0087] The analysis unit can determine the priority of analysis based on when the ideas were submitted. For example, the analysis unit may prioritize the analysis of newer ideas. For example, the analysis unit may analyze the submission dates of ideas and prioritize the analysis of newer ideas. The analysis unit may also postpone the analysis of older ideas. For example, the analysis unit may analyze the submission dates of ideas and postpone the analysis of older ideas. The analysis unit may also optimize the priority of analysis based on the submission dates. For example, the analysis unit may analyze the submission dates of ideas and perform the analysis in the optimal order. This allows for prioritizing the analysis of the latest ideas by determining the priority of analysis based on the submission dates. Some or all of the above processes in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit may have a generative AI perform the analysis of the submission dates of ideas.

[0088] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria based on the estimated emotions. For example, if the user is nervous, the evaluation unit can relax the evaluation criteria and perform the evaluation. For example, if the evaluation unit estimates the user's emotions and finds them nervous, it can relax the evaluation criteria and perform the evaluation. The evaluation unit can also apply strict evaluation criteria if the user is relaxed. For example, if the evaluation unit estimates the user's emotions and finds them relaxed, it can apply strict evaluation criteria. The evaluation unit can also set criteria for a quick evaluation if the user is in a hurry. For example, if the evaluation unit estimates the user's emotions and finds them in a hurry, it can set criteria for a quick evaluation. By adjusting the evaluation criteria according to the user's emotions, a more appropriate evaluation becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can have a generative AI perform the estimation of the user's emotions.

[0089] The evaluation unit can introduce methods for quantitatively evaluating the social impact of an idea during the evaluation process. For example, the evaluation unit can quantify and evaluate the social impact of an idea. For example, the evaluation unit can set indicators for quantitatively evaluating the social impact of an idea and quantify and evaluate them. The evaluation unit can also collect and analyze data for quantitatively evaluating the social impact of an idea. For example, the evaluation unit can collect and analyze data for evaluating the social impact of an idea. This improves the objectivity of the evaluation by quantitatively evaluating the social impact of an idea. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit can have a generative AI perform the quantitative evaluation of social impact.

[0090] The evaluation unit can, during the evaluation process, assess the feasibility of an idea in detail from a technical standpoint. For example, the evaluation unit can set indicators for evaluating the technical feasibility of an idea and then perform the evaluation. The evaluation unit can also collect and analyze data for evaluating the technical feasibility of an idea. For example, the evaluation unit collects and analyzes data for evaluating the technical feasibility of an idea. Furthermore, the evaluation unit can gather expert opinions for evaluating the technical feasibility of an idea. For example, the evaluation unit gathers expert opinions to evaluate the technical feasibility of an idea and incorporates them into the evaluation. This allows for the selection of highly feasible ideas by evaluating their feasibility in detail from a technical standpoint. Some or all of the above processes in the evaluation unit may be performed using AI, or not. For example, the evaluation unit can have a generative AI perform the evaluation of technical feasibility.

[0091] The evaluation unit can estimate the user's emotions and adjust the display method of the evaluation results based on the estimated user emotions. For example, if the user is nervous, the evaluation unit can provide a simple and highly visible display method. For example, if the evaluation unit estimates the user's emotions and is nervous, it can provide a simple and highly visible display method. The evaluation unit can also provide a display method that includes detailed information if the user is relaxed. For example, if the evaluation unit estimates the user's emotions and is relaxed, it can provide a display method that includes detailed information. The evaluation unit can also provide a concise display method if the user is in a hurry. For example, if the evaluation unit estimates the user's emotions and is in a hurry, it can provide a concise display method. By adjusting the display method of the evaluation results according to the user's emotions, the evaluation results can be provided that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processes in the evaluation unit may be performed using AI, for example, or without AI. For example, the evaluation unit may have a generative AI perform the estimation of the user's emotions.

[0092] The evaluation unit can determine the priority of evaluation by considering the marketability of ideas during the evaluation process. For example, the evaluation unit may prioritize evaluating ideas with high marketability. For example, the evaluation unit may evaluate the marketability of ideas and prioritize those with high marketability. The evaluation unit may also postpone evaluating ideas with low marketability. For example, the evaluation unit may evaluate the marketability of ideas and postpone those with low marketability. The evaluation unit may also optimize the evaluation priority based on marketability. For example, the evaluation unit may evaluate the marketability of ideas and evaluate them in the optimal order. This allows for prioritizing the evaluation of ideas that have a high probability of actually succeeding in the market by considering the marketability of ideas. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or not using AI. For example, the evaluation unit may have a generative AI perform the marketability evaluation.

[0093] The evaluation unit can improve the accuracy of its evaluation by referring to relevant patent information of the idea during the evaluation process. For example, the evaluation unit can improve the accuracy of its evaluation by referring to patent information related to the idea. For example, the evaluation unit can search for relevant patent information of the idea and improve the accuracy of its evaluation by referring to the patent information. The evaluation unit can also collect and analyze patent information related to the idea. For example, the evaluation unit can collect and analyze relevant patent information of the idea. The evaluation unit can also improve the accuracy of its evaluation based on patent information related to the idea. For example, the evaluation unit can improve the accuracy of its evaluation based on relevant patent information of the idea. As a result, the accuracy of the evaluation is improved by referring to relevant patent information. Some or all of the above processes in the evaluation unit may be performed using AI, for example, or without using AI. For example, the evaluation unit can have a generating AI perform the search and referencing of relevant patent information.

[0094] The idea generation system can estimate the user's emotions and prioritize the ideas to be discovered based on those emotions. For example, if the user is stressed, the system will postpone less important ideas. It can also prioritize discovering high-importance ideas if the user is relaxed. Furthermore, if the user is in a hurry, the system can immediately discover the most important ideas. This reduces the user's burden by prioritizing ideas according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. The generative AI may be, but is not limited to, text-generating AI (e.g., LLM) or multimodal generative AI. Some or all of the above-described processes in the excavation unit may be performed using AI, for example, or not using AI. For example, the excavation unit may have the generative AI perform the estimation of the user's emotions.

[0095] The idea discovery department can analyze metadata from past contest entries to select the optimal discovery method. For example, the idea discovery department can analyze metadata from past contest entries and prioritize the discovery of highly relevant ideas. For example, the idea discovery department can analyze metadata from past contest entries and prioritize the discovery of highly relevant ideas. The idea discovery department can also analyze metadata from past contest entries and exclude duplicate ideas. For example, the idea discovery department can analyze metadata from past contest entries and exclude duplicate ideas. The idea discovery department can also analyze metadata from past contest entries and evaluate the originality and feasibility of ideas before discovery. For example, the idea discovery department can analyze metadata from past contest entries and evaluate the originality and feasibility of ideas before discovery. In this way, by analyzing metadata from past contest entries, the optimal discovery method can be selected. Some or all of the above processes in the idea discovery department may be performed using AI, for example, or without using AI. For example, the excavation department can have a generating AI analyze metadata from past contest entries.

[0096] The idea discovery department can improve the accuracy of its discovery process by considering the past performance of the idea submitter. For example, the department can improve the accuracy of its discovery by referring to the submitter's past success stories. For example, the department can analyze the submitter's past performance and improve the accuracy of its discovery by referring to success stories. The department can also improve the accuracy of its discovery by considering the submitter's past failure stories. For example, the department can analyze the submitter's past performance and improve the accuracy of its discovery by considering failure stories. The department can also improve the accuracy of its discovery by comprehensively evaluating the submitter's past performance. For example, the department can comprehensively evaluate the submitter's past performance and improve the accuracy of its discovery. In this way, the accuracy of the discovery process is improved by considering the submitter's past performance. Some or all of the above processes in the idea discovery department may be performed using AI, for example, or without AI. For example, the idea discovery department can have a generation AI perform the analysis of the submitter's past performance.

[0097] The excavation unit can estimate the user's emotions and adjust the display method of the excavation results based on the estimated emotions. For example, if the user is nervous, the excavation unit can provide a simple and easy-to-read display method. For example, if the excavation unit estimates the user's emotions and, if they are nervous, provides a simple and easy-to-read display method. The excavation unit can also provide a display method that includes detailed information if the user is relaxed. For example, if the excavation unit estimates the user's emotions and, if they are relaxed, provides a display method that includes detailed information. The excavation unit can also provide a concise display method if the user is in a hurry. For example, if the excavation unit estimates the user's emotions and, if they are in a hurry, provides a display method that includes the key points. In this way, by adjusting the display method of the excavation results according to the user's emotions, it is possible to provide excavation results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processes in the excavation unit may be performed using AI, for example, or without AI. For example, the excavation unit can have a generative AI perform the estimation of the user's emotions.

[0098] The discovery unit can improve the accuracy of its discovery process by referring to relevant literature from past contest cases. For example, the discovery unit can improve the accuracy of its discovery by referring to patent documents related to past contest cases. For example, the discovery unit can search for relevant literature from past contest cases and improve the accuracy of its discovery by referring to patent documents. The discovery unit can also improve the accuracy of its discovery by referring to academic papers related to past contest cases. For example, the discovery unit can search for relevant literature from past contest cases and improve the accuracy of its discovery by referring to academic papers. The discovery unit can also improve the accuracy of its discovery by referring to market research reports related to past contest cases. For example, the discovery unit can search for relevant literature from past contest cases and improve the accuracy of its discovery by referring to market research reports. In this way, the accuracy of the discovery process is improved by referring to relevant literature. Some or all of the above processing in the discovery unit may be performed using, for example, a generative AI, or without a generative AI. For example, the discovery unit can have a generative AI perform the search and referencing of relevant literature.

[0099] The discovery unit can determine the priority of discovery based on the submission dates of past contest entries. For example, the discovery unit can prioritize discovering newer entries. For example, the discovery unit can analyze the submission dates of past contest entries and prioritize discovering newer entries. The discovery unit can also postpone discovering older entries. For example, the discovery unit can analyze the submission dates of past contest entries and postpone discovering older entries. The discovery unit can also optimize the priority of discovery based on the submission dates. For example, the discovery unit can analyze the submission dates of past contest entries and discover them in the optimal order. This allows the discovery of the most recent entries to be prioritized by determining the priority of discovery based on the submission dates. Some or all of the above processes in the discovery unit may be performed using, for example, a generative AI, or not. For example, the discovery unit can have a generative AI perform the analysis of the submission dates of past contest entries.

[0100] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0101] The acquisition unit can estimate the emotions of the submitter when acquiring initial review materials and adjust the acquisition method based on the estimated emotions. For example, if the submitter is nervous, the acquisition unit can acquire the materials in stages to avoid burdening the submitter. If the submitter is relaxed, the unit can acquire the materials quickly and begin analysis. Furthermore, if the submitter is in a hurry, the unit can prioritize acquiring the most important materials. By adjusting the acquisition method according to the submitter's emotions, the burden on the submitter is reduced and efficient material acquisition becomes possible. Emotion estimation is achieved using an emotion engine or generative AI. Some or all of the above processing in the acquisition unit may be performed using AI or not. For example, the acquisition unit can have a generative AI perform the estimation of the submitter's emotions.

[0102] The analysis unit can estimate the emotions of the submitter when analyzing the initial review materials and adjust the analysis priority based on the estimated emotions. For example, if the submitter is nervous, the analysis unit can adjust the analysis priority to avoid burdening the submitter. If the submitter is relaxed, it can perform the analysis quickly and provide the results. Furthermore, if the submitter is in a hurry, it can prioritize the analysis of the most important materials. By adjusting the analysis priority according to the submitter's emotions, the burden on the submitter is reduced and efficient analysis becomes possible. Emotion estimation is achieved using an emotion engine or generative AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can have a generative AI perform the estimation of the submitter's emotions.

[0103] The evaluation unit can estimate the emotions of the submitter when evaluating the initial review materials and adjust the evaluation criteria based on the estimated emotions. For example, if the submitter is nervous, the evaluation unit can relax the evaluation criteria to avoid burdening the submitter. Conversely, if the submitter is relaxed, strict evaluation criteria can be applied. Furthermore, if the submitter is in a hurry, criteria can be set to allow for a quick evaluation. By adjusting the evaluation criteria according to the submitter's emotions, the burden on the submitter is reduced, and an efficient evaluation becomes possible. Emotion estimation is achieved using an emotion engine or generative AI. Some or all of the above processing in the evaluation unit may be performed using AI or not. For example, the evaluation unit can have a generative AI perform the estimation of the submitter's emotions.

[0104] The discovery department can estimate the emotions of the submitters when discovering past contest entries and adjust the discovery priority based on the estimated emotions. For example, if the submitter is nervous, the discovery department can adjust the discovery priority to avoid burdening the submitter. If the submitter is relaxed, the discovery department can also perform the discovery quickly and provide results. Furthermore, if the submitter is in a hurry, the discovery department can prioritize the discovery of the most important entries. By adjusting the discovery priority according to the submitter's emotions, the burden on the submitter is reduced and discovery becomes more efficient. Emotion estimation is achieved using an emotion engine or generative AI. Some or all of the above processing in the discovery department may be performed using AI or not. For example, the discovery department can have a generative AI perform the estimation of the submitter's emotions.

[0105] The evaluation unit can estimate the user's emotions when displaying evaluation results and adjust the display method based on the estimated emotions. For example, if the user is nervous, the evaluation unit can provide a simple and highly visible display method. If the user is relaxed, it can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, it can provide a display method that gets straight to the point. By adjusting the display method according to the user's emotions, the evaluation results can be provided in a way that is easy for the user to understand. Emotion estimation is achieved using an emotion engine or generative AI. Some or all of the above-described processes in the evaluation unit may be performed using AI or not. For example, the evaluation unit can have a generative AI perform the estimation of the user's emotions.

[0106] The acquisition unit can prioritize the acquisition of highly relevant materials when acquiring primary review materials, taking into account the attribute information of the material submitters. For example, it can prioritize the acquisition of highly relevant materials based on the submitter's field of expertise. It can also prioritize the acquisition of highly reliable materials by considering the submitter's past achievements. Furthermore, it can prioritize the acquisition of highly relevant materials by considering the submitter's affiliated institution. In this way, by considering the submitter's attribute information, highly relevant materials can be prioritized. Some or all of the above processing in the acquisition unit may be performed using AI or not. For example, the acquisition unit can have a generating AI perform the analysis of the submitter's attribute information.

[0107] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on the idea during the analysis process. For example, it can improve the accuracy of its analysis by referring to patent documents related to the idea. It can also improve the accuracy of its analysis by referring to academic papers related to the idea. Furthermore, it can improve the accuracy of its analysis by referring to market research reports related to the idea. In this way, the accuracy of the analysis is improved by referring to relevant literature. Some or all of the above-described processes in the analysis unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the analysis unit can have a generation AI perform the search and referencing of relevant literature.

[0108] The evaluation unit can determine the priority of evaluations by considering the marketability of the ideas during the evaluation process. For example, it can prioritize the evaluation of ideas with high marketability, and postpone the evaluation of ideas with low marketability. Furthermore, it can optimize the evaluation priority based on marketability. This allows for the prioritization of ideas that are more likely to succeed in the market by considering their marketability. Some or all of the above processes in the evaluation unit may be performed using AI, or not. For example, the evaluation unit can have a generative AI perform the marketability evaluation.

[0109] The idea discovery unit can analyze metadata from past contest entries to select the optimal discovery method during the discovery process. For example, it can analyze metadata from past contest entries and prioritize the discovery of highly relevant ideas. It can also analyze metadata from past contest entries and exclude duplicate ideas during the discovery process. Furthermore, it can analyze metadata from past contest entries and evaluate the originality and feasibility of ideas during the discovery process. In this way, the optimal discovery method can be selected by analyzing metadata from past contest entries. Some or all of the above processes in the idea discovery unit may be performed using AI or not. For example, the idea discovery unit can have a generation AI perform the analysis of metadata from past contest entries.

[0110] The evaluation unit can improve the accuracy of its evaluation by referring to relevant patent information for the idea during the evaluation process. For example, it can improve the accuracy of the evaluation by referring to patent information related to the idea. It can also collect and analyze patent information related to the idea. Furthermore, it can improve the accuracy of the evaluation based on the patent information related to the idea. Thus, the accuracy of the evaluation is improved by referring to relevant patent information. Some or all of the above processes in the evaluation unit may be performed using AI or not. For example, the evaluation unit can have a generating AI perform the search and referencing of relevant patent information.

[0111] The following briefly describes the processing flow for example form 2.

[0112] Step 1: The acquisition unit acquires the primary review materials. These materials include documents, digital data, and images. The acquisition unit can acquire primary review materials from the review file server, cloud storage, and local devices. For example, it can access the review file server to download the necessary primary review materials, quickly and efficiently acquire materials from cloud storage via the internet, and acquire materials from a directly connected device. Step 2: The analysis unit uses a generation AI to analyze the initial review materials acquired by the acquisition unit and evaluate the ideas. The analysis is performed using methods such as text analysis, image analysis, and data mining. For example, a text generation AI (LLM) is used to analyze the initial review materials and evaluate the originality, feasibility, and social impact of the ideas. It is also possible to analyze multiple modals, such as images and audio, in addition to text, using a multimodal generation AI. It is also possible to extract important information from the initial review materials using advanced data mining techniques and perform evaluations. Step 3: The evaluation unit evaluates the ideas analyzed by the analysis unit. The evaluation is carried out using methods such as scoring, ranking, and feedback. For example, ideas are scored based on the analysis results of the generation AI, ideas are ranked based on the analysis results, and feedback is provided based on the analysis results. This evaluates the originality, feasibility, and social impact of the ideas, assigns scores, ranks them, and specifically identifies areas for improvement and strengths. Step 4: The Discovery Department unearths and re-evaluates past contest entries. Discovery is carried out using methods such as database searches and reviews of past records. For example, ideas that did not advance to the final round in past contests are re-evaluated to uncover excellent ideas. This involves searching the database, re-evaluating, reviewing past records, and re-evaluating. This allows for the re-evaluation of excellent ideas that were overlooked in past contests, and the discovery of new value.

[0113] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0115] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0116] Each of the multiple elements described above, including the acquisition unit, analysis unit, evaluation unit, and discovery unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the acquisition unit is implemented by the control unit 46A of the smart device 14 and acquires primary review materials from the review file server or cloud storage. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the primary review materials using a generation AI. The evaluation unit is implemented by the specific processing unit 290 of the data processing device 12 and evaluates ideas based on the analysis results. The discovery unit is implemented by the specific processing unit 290 of the data processing device 12 and discovers and re-evaluates past contest entries. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0117] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0118] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0119] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0120] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0121] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0123] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0124] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0125] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0126] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0127] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0128] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0129] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0130] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0131] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0132] Each of the multiple elements described above, including the acquisition unit, analysis unit, evaluation unit, and discovery unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the acquisition unit is implemented by the control unit 46A of the smart glasses 214 and acquires primary review materials from a review file server or cloud storage. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the primary review materials using a generation AI. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12 and evaluates ideas based on the analysis results. The discovery unit is implemented by the specific processing unit 290 of the data processing unit 12 and discovers and re-evaluates past contest entries. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0133] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0134] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0135] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0136] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0137] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0138] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0139] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0140] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0141] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0142] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0143] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0144] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0145] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0146] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0147] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0148] Each of the multiple elements described above, including the acquisition unit, analysis unit, evaluation unit, and discovery unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the acquisition unit is implemented by the control unit 46A of the headset terminal 314 and acquires primary review materials from the review file server or cloud storage. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the primary review materials using a generation AI. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12 and evaluates ideas based on the analysis results. The discovery unit is implemented by the specific processing unit 290 of the data processing unit 12 and discovers and re-evaluates past contest entries. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0149] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0150] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0151] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0152] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0153] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0154] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0155] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0156] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0157] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0158] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0159] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0160] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0161] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0162] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0163] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0164] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0165] Each of the multiple elements described above, including the acquisition unit, analysis unit, evaluation unit, and discovery unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the acquisition unit is implemented by the control unit 46A of the robot 414 and acquires primary review materials from a review file server or cloud storage. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the primary review materials using generated AI. The evaluation unit is implemented by the specific processing unit 290 of the data processing unit 12 and evaluates ideas based on the analysis results. The discovery unit is implemented by the specific processing unit 290 of the data processing unit 12 and discovers and re-evaluates past contest entries. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0166] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0167] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0168] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0169] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0170] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0171] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0172] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0173] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0174] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0175] 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.

[0176] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0177] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0178] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0179] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0180] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0181] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0182] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0183] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0184] (Note 1) The acquisition department obtains the initial screening materials, An analysis unit analyzes the primary screening materials obtained by the acquisition unit and evaluates the ideas, An evaluation unit that evaluates the ideas analyzed by the aforementioned analysis unit, It includes a discovery department that unearths and re-evaluates past contest entries. A system characterized by the following features. (Note 2) The acquisition unit is, Retrieve primary review materials from the review file server. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The initial screening materials are analyzed using a generation AI to evaluate the originality, feasibility, and social impact of the ideas. The system described in Appendix 1, characterized by the features described herein. (Note 4) The evaluation unit, Evaluate ideas based on the analysis results of the generative AI. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned excavation section is, We will unearth and re-evaluate past contest entries. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned excavation section is, We will re-evaluate ideas that did not make it to the final round in past contests and discover outstanding ideas. The system described in Appendix 1, characterized by the features described herein. (Note 7) The evaluation unit, Register the evaluation results in the database. The system described in Appendix 1, characterized by the features described herein. (Note 8) The acquisition unit is, The system estimates the user's emotions and adjusts the timing of acquiring the initial screening materials based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The acquisition unit is, When retrieving files from the review file server, the system analyzes the file metadata to select the optimal retrieval method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The acquisition unit is, When obtaining the initial screening materials, filtering will be performed based on the content of the materials. The system described in Appendix 1, characterized by the features described herein. (Note 11) The acquisition unit is, The system estimates the user's emotions and determines the priority of primary review materials to acquire based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The acquisition unit is, When acquiring initial screening materials, priority will be given to acquiring highly relevant materials, taking into account the attribute information of the material submitters. The system described in Appendix 1, characterized by the features described herein. (Note 13) The acquisition unit is, When obtaining the initial screening materials, the order of acquisition will be adjusted based on the submission dates of the materials. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, It estimates the user's emotions and adjusts the way the analysis results are presented based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the idea. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During the analysis, the accuracy of the analysis is improved by considering the past achievements of the idea submitter. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, It estimates the user's emotions and adjusts the level of detail in the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, we refer to relevant literature related to the idea to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, During the analysis, the priority of the analysis will be determined based on when the ideas were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 20) The evaluation unit, It estimates the user's emotions and adjusts the evaluation criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The evaluation unit, During the evaluation process, we will introduce methods to quantitatively assess the social impact of the idea. The system described in Appendix 1, characterized by the features described herein. (Note 22) The evaluation unit, During the evaluation, the feasibility of the idea will be assessed in detail from a technical standpoint. The system described in Appendix 1, characterized by the features described herein. (Note 23) The evaluation unit, The system estimates the user's emotions and adjusts how the evaluation results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The evaluation unit, When evaluating ideas, the marketability of each idea is taken into consideration to determine the priority of the evaluation. The system described in Appendix 1, characterized by the features described herein. (Note 25) The evaluation unit, During evaluation, we improve the accuracy of the evaluation by referring to relevant patent information for the idea. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned excavation section is, We estimate user emotions and prioritize ideas to explore based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned excavation section is, During the discovery process, metadata from past contest entries is analyzed to select the most suitable discovery method. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned excavation section is, During the discovery process, we improve the accuracy of the discovery by considering the past achievements of the idea submitters. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned excavation section is, The system estimates the user's emotions and adjusts how the discovery results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned excavation section is, During excavation, we improve the accuracy of the excavation by referring to relevant literature from past contest cases. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned excavation section is, During the discovery process, priority will be determined based on the submission dates of past contest entries. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0185] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The acquisition department obtains the initial screening materials, An analysis unit analyzes the primary screening materials obtained by the acquisition unit and evaluates the ideas, An evaluation unit that evaluates the ideas analyzed by the aforementioned analysis unit, It includes a discovery department that unearths and re-evaluates past contest entries. A system characterized by the following features.

2. The acquisition unit is, Retrieve primary review materials from the review file server. The system according to feature 1.

3. The aforementioned analysis unit, The initial screening materials are analyzed using a generation AI to evaluate the originality, feasibility, and social impact of the ideas. The system according to feature 1.

4. The evaluation unit, Evaluate ideas based on the analysis results of the generative AI. The system according to feature 1.

5. The aforementioned excavation section is, We will unearth and re-evaluate past contest entries. The system according to feature 1.

6. The aforementioned excavation section is, We will re-evaluate ideas that did not make it to the final round in past contests and discover outstanding ideas. The system according to feature 1.

7. The evaluation unit, Register the evaluation results in the database. The system according to feature 1.

8. The acquisition unit is, The system estimates the user's emotions and adjusts the timing of acquiring the initial screening materials based on those estimated emotions. The system according to feature 1.

Images