system

Abstract

The system according to the embodiment aims to determine in advance the appropriateness and feasibility of a crowdfunding idea and provide advice to users. [Solution] A system according to an embodiment includes an input unit, an analysis unit, a determination unit, and a prediction unit. The input unit inputs a crowdfunding idea. The analysis unit analyzes the idea input by the input unit and compares it with existing projects. The determination unit determines whether the set amount is appropriate based on the results of the analysis by the analysis unit. The prediction unit predicts the likelihood of the crowdfunding being successful based on the results of the determination unit and provides advice to the user.

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to determine in advance whether a crowdfunding idea overlapped with existing projects, whether the set amount was appropriate, and what the likelihood of success was.

[0005] The system according to the embodiment aims to determine in advance the appropriateness and feasibility of a crowdfunding idea and provide advice to users. [Means for solving the problem]

[0006] The system according to the embodiment includes an input unit, an analysis unit, a determination unit, and a prediction unit. The input unit inputs a crowdfunding idea. The analysis unit analyzes the idea input by the input unit and compares it with existing projects. The determination unit determines whether the set amount is appropriate based on the results of the analysis by the analysis unit. The prediction unit predicts the likelihood of the crowdfunding being successful based on the results of the determination by the determination unit and provides advice to the user. [Effects of the Invention]

[0007] The system according to the embodiment can determine in advance the appropriateness and feasibility of a crowdfunding idea and provide advice to users. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A crowdfunding support system according to an embodiment of the present invention provides advice to users before launching a crowdfunding campaign, such as whether there are any overlapping ideas, whether the set amount is appropriate, and the likelihood of success. In the crowdfunding support system, a user inputs a crowdfunding idea, and AI analyzes the idea and compares it with existing projects to determine whether the idea overlaps with other ideas. Furthermore, the AI ​​determines whether the set amount is appropriate based on past crowdfunding data, predicts the likelihood of success, and provides advice to the user. For example, in a crowdfunding support system, a user inputs a crowdfunding idea. At this time, information such as details of the idea, the target amount, and the project duration is input. For example, in the case of a project to develop a new gadget, the user inputs the gadget's features, target amount, project duration, etc. This information is input into the AI. Next, the crowdfunding support system uses AI to analyze the input idea. The AI ​​uses natural language processing technology to understand the content of the idea and compare it with existing crowdfunding projects. For example, it checks whether a similar gadget has been successfully crowdfunded in the past. This makes it possible to determine whether the idea overlaps with other ideas. Furthermore, the crowdfunding support system uses AI to determine whether the set amount is appropriate based on past crowdfunding data. The AI ​​analyzes data from past successful projects to determine whether the target amount is appropriate. For example, it refers to the amount of money that similar gadget projects have previously raised. This allows users to know whether the set amount is appropriate. Finally, the crowdfunding support system uses AI to predict the possibility of crowdfunding being successful and provide advice to users. The AI ​​predicts the probability of a project's success based on past data. For example, it calculates the probability of success by taking into account the project's duration, target amount, and the content of the idea. This allows users to know the possibility of a project being successful.This allows the crowdfunding support system to check in advance whether there are any overlapping ideas, whether the set amount is appropriate, and the likelihood of success before the crowdfunding campaign is launched, improving the success rate of crowdfunding and making it easier to realize projects.

[0029] A crowdfunding support system according to an embodiment includes an input unit, an analysis unit, a determination unit, and a prediction unit. The input unit inputs a crowdfunding idea. The crowdfunding idea may include, but is not limited to, details of the idea, a target amount, and a project duration. The input unit may input the idea using text input, for example. Alternatively, the input unit may input the idea using voice input. Alternatively, the input unit may input the idea using image input. For example, the input unit may allow a user to describe the idea using voice, convert the description into text, and input it. Alternatively, the input unit may allow a user to handwrite the idea and input it as an image. The analysis unit may use AI to analyze the idea input by the input unit. The analysis unit may use natural language processing technology to understand the content of the idea and compare it with existing crowdfunding projects. For example, the analysis unit may check whether a similar gadget has been successful in crowdfunding in the past. Alternatively, the analysis unit may use data mining technology to perform a detailed analysis of the content of the idea. Alternatively, the analysis unit may use AI to classify the content of the idea and compare it with existing projects. For example, the analysis unit uses natural language processing technology to extract keywords from the idea and compare it with existing projects based on those keywords. The judgment unit determines whether the set amount is appropriate based on the results of the analysis by the analysis unit. The judgment unit analyzes data from past successful projects and determines whether the target amount is appropriate. For example, the judgment unit determines whether the target amount is appropriate based on data from past successful projects. The judgment unit can also use statistical methods to determine whether the target amount is appropriate. The judgment unit can also use AI to determine whether the target amount is appropriate. For example, the judgment unit inputs data from past successful projects into AI and determines the appropriateness of the target amount. The prediction unit predicts the possibility of crowdfunding success based on the results determined by the judgment unit and provides advice to the user. The prediction unit predicts the probability of project success based on past data.For example, the prediction unit calculates the probability of success by taking into account the project's duration, target amount, idea content, etc. The prediction unit can also predict the project's probability of success using a machine learning algorithm. The prediction unit can also predict the project's probability of success using AI. For example, the prediction unit inputs data on past successful projects into AI to predict the success probability. This allows the crowdfunding support system according to the embodiment to consistently perform processes from inputting crowdfunding ideas to analysis, judgment, and prediction.

[0030] The input unit can input details of the crowdfunding idea, the target amount, and the project duration. The details include, but are not limited to, for example, the project objective, the target market, and a competitive analysis. The input unit can input, for example, the project objective. The input unit can also input the target market. The input unit can also input a competitive analysis. The target amount includes, but is not limited to, for example, a cost analysis and a revenue forecast. The input unit can also input a cost analysis. The input unit can also input a revenue forecast. The project duration includes, but is not limited to, for example, a project progress schedule and milestones. The input unit can also input, for example, the project progress schedule. The input unit can also input milestones. By inputting detailed crowdfunding information, more accurate analysis and determination are possible. Some or all of the above-described processing in the input unit can be performed using, for example, AI, or without AI. For example, the input unit inputs the information entered by the user into AI, which then analyzes it.

[0031] The analysis unit can use natural language processing technology to understand the content of the idea and compare it with existing crowdfunding projects. Natural language processing technology includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis. The analysis unit can understand the content of the idea using, for example, morphological analysis. The analysis unit can also understand the content of the idea using grammatical analysis. The analysis unit can also understand the content of the idea using semantic analysis. In this way, by using natural language processing technology, the content of the idea can be accurately understood and compared with existing projects. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit inputs the content of the idea into AI using natural language processing technology, and the AI ​​analyzes it.

[0032] The determination unit can analyze data on past successful projects to determine whether the target amount is appropriate. Examples of data on past successful projects include, but are not limited to, data collected from crowdfunding platforms and public databases. For example, the determination unit collects data from crowdfunding platforms. The determination unit can also collect data from public databases. The determination unit can also analyze data on past successful projects to determine whether the target amount is appropriate. This improves the success rate of crowdfunding by determining the appropriateness of the target amount based on past success data. Some or all of the above-described processing by the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit inputs data on past successful projects into AI, which then determines the appropriateness of the target amount.

[0033] The prediction unit can predict the probability of success of a project based on past data. Examples of past data include, but are not limited to, data collected from crowdfunding platforms and public databases. For example, the prediction unit collects data from crowdfunding platforms. The prediction unit can also collect data from public databases. The prediction unit can also predict the probability of success of a project based on past data. This makes it possible to provide useful advice to users by predicting the probability of success based on past data. Some or all of the above-described processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit inputs past data into AI, which then predicts the probability of success of the project.

[0034] The input unit can analyze the user's past project history and select an appropriate input method. Examples of past project history include, but are not limited to, the user's project history database and data collected from a crowdfunding platform. For example, the input unit can refer to the input method used by the user for successful projects in the past and suggest a similar input method. The input unit can also advise the user to avoid input methods used in unsuccessful projects in the past. The input unit can also select and suggest the most efficient input method based on the user's past project history. By analyzing the past project history, the optimal input method can be provided to the user. Some or all of the above-described processing in the input unit can be performed using, for example, AI, or without AI. For example, the input unit inputs the user's past project history data into AI, which then selects the optimal input method.

[0035] When inputting ideas, the input unit may filter ideas based on the user's current project or area of ​​interest. Examples of current projects include, but are not limited to, the user's project management tool and data collection from a crowdfunding platform. The input unit, for example, prioritizes input of ideas related to the user's ongoing project. The input unit may also filter and input highly relevant ideas based on the user's area of ​​interest. The input unit may also exclude less relevant ideas by referring to the user's past project history. In this way, by filtering based on the user's current project or area of ​​interest, highly relevant ideas can be preferentially input. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without AI. For example, the input unit inputs the user's current project data into AI, and the AI ​​performs filtering.

[0036] When inputting an idea, the input unit can select an appropriate input means depending on the user's input method. Input methods include, but are not limited to, voice input, text input, and image input. For example, if the user prefers voice input, the input unit can provide voice input preferentially. Also, if the user prefers text input, the input unit can provide text input preferentially. Also, if the user prefers image input, the input unit can provide image input preferentially. This improves user convenience by selecting the optimal input means depending on the user's input method. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit inputs the user's input method data to AI, and the AI ​​selects the optimal input means.

[0037] When inputting ideas, the input unit can prioritize inputting highly relevant ideas based on the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, location information obtained from an IP address, and the like. For example, when the user is in a specific area, the input unit can prioritize inputting ideas related to that area. Furthermore, when the user is traveling, the input unit can prioritize inputting ideas related to the travel destination. Furthermore, when the user is at home, the input unit can prioritize inputting ideas related to the user's home. In this way, highly relevant ideas can be prioritized by taking the user's geographical location information into consideration. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit inputs the user's geographical location information data to AI, which selects highly relevant ideas.

[0038] When an idea is input, the input unit can analyze the user's social media activity and input related ideas. Social media activity includes, but is not limited to, for example, social media post content and follower analysis. The input unit, for example, automatically inputs ideas shared by the user on social media. The input unit can also analyze the user's social media activity and suggest related ideas. The input unit can also input related ideas by referring to the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, related ideas can be input efficiently. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit inputs the user's social media data into AI, which selects related ideas.

[0039] When inputting an idea, the input unit can adjust the input method by reflecting the user's past feedback. Past feedback includes, but is not limited to, user reviews, comments, and ratings. For example, the input unit can preferentially provide input methods that the user has previously preferred. The input unit can also exclude input methods that the user has previously avoided. The input unit can also suggest an optimal input method based on the user's past feedback. In this way, the optimal input method can be provided by reflecting the user's past feedback. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit inputs the user's past feedback data into AI, which then selects the optimal input method.

[0040] During analysis, the analysis unit can set the level of detail of the analysis based on the importance of the idea. The importance of an idea includes, but is not limited to, the impact of the project and the size of the target market. For example, the analysis unit can perform a detailed analysis for important ideas. The analysis unit can also perform a standard analysis for general ideas. The analysis unit can also perform a simplified analysis for ideas with low importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the idea. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit inputs idea importance data into AI, which then sets the level of detail of the analysis.

[0041] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the idea. Idea categories include, but are not limited to, technical categories, business categories, and creative categories. For example, the analysis unit applies a technical analysis algorithm to technical ideas. The analysis unit can also apply a creative analysis algorithm to creative ideas. The analysis unit can also apply a business analysis algorithm to business ideas. This improves the accuracy of the analysis by applying an appropriate analysis algorithm depending on the category of the idea. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit inputs idea category data into AI, which selects an appropriate analysis algorithm.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's past analysis results. Past analysis results include, but are not limited to, the user's project history and data collected from crowdfunding platforms. The analysis unit can improve the accuracy of the analysis, for example, by referring to the user's past successful analysis results. The analysis unit can also improve the accuracy of the analysis by avoiding the user's past unsuccessful analysis results. The analysis unit can also select an optimal analysis method based on the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-described processing in the analysis unit can be performed, for example, using AI or without AI. For example, the analysis unit inputs the user's past analysis result data into AI, which then improves the accuracy of the analysis.

[0043] During analysis, the analysis unit can set analysis priorities based on the submission dates of the ideas. The submission dates of the ideas include, but are not limited to, records of the submission dates and timestamps. For example, the analysis unit prioritizes the analysis of recently submitted ideas. The analysis unit can also postpone older submitted ideas. The analysis unit can also adjust the order of analysis based on the submission dates. This enables efficient analysis by determining the analysis priorities based on the submission dates of the ideas. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit inputs data on the submission dates of ideas into AI, which then sets the analysis priorities.

[0044] During analysis, the analysis unit can adjust the order of analysis based on the relevance of ideas. Examples of the relevance of ideas include, but are not limited to, common keywords, similar themes, etc. The analysis unit, for example, prioritizes the analysis of highly related ideas. The analysis unit can also postpone less related ideas. The analysis unit can also adjust the order of analysis based on the relevance. In this way, by adjusting the order of analysis based on the relevance of ideas, highly related ideas can be analyzed preferentially. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit inputs idea relevance data into AI, and the AI ​​adjusts the order of analysis.

[0045] During analysis, the analysis unit can set the use of technical terms in the analysis according to the user's level of expertise. Examples of the user's level of expertise include, but are not limited to, the user's occupation, educational background, and past project experience. For example, the analysis unit uses a lot of technical terms if the user has technical expertise. The analysis unit can also avoid technical terms if the user does not have technical expertise. The analysis unit can also adjust the use of technical terms in the analysis according to the user's level of expertise. This allows for the provision of analysis results that are easy for the user to understand. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit inputs the user's level of expertise data into AI, which then adjusts the use of technical terms.

[0046] The determination unit can improve the determination algorithm by referring to data on past successful projects when making a determination. Data on past successful projects includes, but is not limited to, data collected from crowdfunding platforms and public databases. The determination unit, for example, optimizes the determination algorithm based on data on past successful projects. The determination unit can also optimize the determination algorithm by avoiding data on past unsuccessful projects. The determination unit can also select an optimal determination algorithm by referring to data on past successful projects. This allows the determination algorithm to be optimized and the accuracy of the determination to be improved by referring to data on past successful projects. Some or all of the above-described processing in the determination unit can be performed, for example, using AI or without AI. For example, the determination unit inputs data on past successful projects into AI, which then optimizes the determination algorithm.

[0047] The determination unit can update the determination criteria by reflecting user feedback when making a determination. User feedback includes, but is not limited to, user reviews, comments, and ratings. The determination unit updates the determination criteria, for example, based on user feedback. The determination unit can also optimize the determination criteria by referring to past user feedback. The determination unit can also flexibly adjust the determination criteria by reflecting user feedback. This makes it possible to flexibly adjust the determination criteria and provide a more appropriate determination result by reflecting user feedback. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit inputs user feedback data into AI, and the AI ​​updates the determination criteria.

[0048] The judgment unit can use different judgment methods for each idea category when making a judgment. Idea categories include, but are not limited to, technical categories, business categories, and creative categories, for example. The judgment unit can apply a technical judgment method to technical ideas, for example. The judgment unit can also apply a creative judgment method to creative ideas, and for business ideas, for example. This improves the accuracy of the judgment by applying an appropriate judgment method to each idea category. Some or all of the above-mentioned processing in the judgment unit can be performed using, for example, AI, or can be performed without using AI. For example, the judgment unit inputs idea category data into AI, which selects an appropriate judgment method.

[0049] The judgment unit can set a weighting for judgment based on the time of submission of the idea during judgment. The time of submission of the idea includes, for example, a record of the submission date and time, a timestamp, etc., but is not limited to such examples. For example, the judgment unit can assign a higher weighting to a recently submitted idea. The judgment unit can also assign a lower weighting to an older submitted idea. The judgment unit can also adjust the weighting for judgment based on the time of submission. In this way, efficient judgment is possible by weighting the judgment based on the time of submission of the idea. Some or all of the above-mentioned processing in the judgment unit may be performed using, for example, AI, or may be performed without using AI. For example, the judgment unit inputs data on the time of submission of the idea into AI, and the AI ​​sets the weighting for judgment.

[0050] The judgment unit can improve the accuracy of the judgment by referring to market data related to the idea when making a judgment. Examples of related market data include, but are not limited to, market research reports and industry databases. The judgment unit improves the accuracy of the judgment based on, for example, the related market data. The judgment unit can also improve the accuracy of the judgment by referring to trends in market data. The judgment unit can also analyze market data and select an optimal judgment method. Thus, by referring to the market data related to the idea, the accuracy of the judgment can be improved. Some or all of the above-described processing in the judgment unit may be performed using, for example, AI, or may be performed without using AI. For example, the judgment unit inputs the related market data into AI, which then improves the accuracy of the judgment.

[0051] The judgment unit can make a judgment based on the technical maturity of the idea when making the judgment. The technical maturity includes, for example, the development stage of the technology, whether or not it has a patent, etc., but is not limited to these examples. For example, the judgment unit makes a strict judgment for a technically mature idea. The judgment unit can also make a flexible judgment for a technically immature idea. The judgment unit can also adjust the strictness of the judgment based on the technical maturity. This makes it possible to provide a more appropriate judgment result by taking the technical maturity of the idea into consideration. Some or all of the above-mentioned processing in the judgment unit can be performed using, for example, AI, or can be performed without using AI. For example, the judgment unit inputs technical maturity data into AI, and the AI ​​makes the judgment.

[0052] The prediction unit can refer to past data when making predictions to improve the current prediction. Examples of past data include, but are not limited to, data collected from crowdfunding platforms and public databases. The prediction unit, for example, optimizes the current prediction based on data from past successful projects. The prediction unit can also optimize the current prediction by avoiding data from past unsuccessful projects. The prediction unit can also select an optimal prediction method by referring to past data. By doing so, the current prediction can be optimized by referring to past data, thereby improving the accuracy of the prediction. Some or all of the above-described processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit inputs past data into AI, which then improves the current prediction.

[0053] The prediction unit can apply different prediction algorithms to each idea category during prediction. Idea categories include, but are not limited to, technical categories, business categories, and creative categories. For example, the prediction unit can apply a technical prediction algorithm to technical ideas. The prediction unit can also apply a creative prediction algorithm to creative ideas. The prediction unit can also apply a business prediction algorithm to business ideas. This improves prediction accuracy by applying an appropriate prediction algorithm to each idea category. Some or all of the above-described processing in the prediction unit can be performed using, for example, AI, or without AI. For example, the prediction unit inputs idea category data into AI, which selects an appropriate prediction algorithm.

[0054] The prediction unit can make predictions taking into account the user's attribute information. User attribute information includes, but is not limited to, for example, age, gender, and occupation. The prediction unit makes predictions taking into account, for example, the user's age and gender. The prediction unit can also make predictions taking into account the user's occupation and interests. The prediction unit can also make predictions taking into account the user's past behavioral history. This makes it possible to provide more appropriate prediction results by taking into account the user's attribute information. Some or all of the above-described processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit inputs the user's attribute information data into AI, and the AI ​​makes the prediction.

[0055] The prediction unit can evaluate a change in the prediction based on the time of submission of the idea during prediction. The time of submission of the idea includes, for example, a record of the submission date and time, a timestamp, etc., but is not limited to such examples. For example, the prediction unit quickly analyzes the change in the prediction for a recently submitted idea. The prediction unit can also delay the change in the prediction for an older submitted idea. The prediction unit can also adjust the change in the prediction based on the time of submission. This enables efficient prediction by analyzing the change in the prediction based on the time of submission of the idea. Some or all of the above-described processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit inputs data on the time of submission of the idea into AI, and the AI ​​evaluates the change in the prediction.

[0056] When making a prediction, the prediction unit can analyze the prediction by referring to market data related to the idea. Examples of related market data include, but are not limited to, market research reports and industry databases. The prediction unit analyzes the prediction based on, for example, the related market data. The prediction unit can also analyze the prediction by referring to trends in market data. The prediction unit can also analyze market data and select an optimal prediction method. By doing so, the accuracy of the prediction can be improved by referring to the market data related to the idea. Some or all of the above-described processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit inputs the related market data into AI, which then analyzes the prediction.

[0057] The prediction unit can make predictions taking into account the technological maturity of the idea. Examples of technological maturity include, but are not limited to, the development stage of the technology and whether or not it has a patent. For example, the prediction unit makes strict predictions for technologically mature ideas. Furthermore, the prediction unit can also make flexible predictions for technologically immature ideas. Furthermore, the prediction unit can adjust the strictness of the prediction based on the technological maturity. This allows for more appropriate prediction results to be provided by taking the technological maturity of the idea into consideration. Some or all of the above-described processing in the prediction unit can be performed using, for example, AI, or can be performed without using AI. For example, the prediction unit inputs technological maturity data into AI, which then makes the prediction.

[0058] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0059] The crowdfunding support system may further include a reliability evaluation unit that analyzes the success rate of a user's past projects and evaluates the user's reliability. The reliability evaluation unit scores the user's reliability based on the success rate of the user's past projects and feedback. For example, the reliability score may be calculated taking into account the number of successful projects in the past and evaluations from backers. The reliability evaluation unit may also evaluate the user's project management skills and communication skills. This allows the user to objectively understand their own reliability and identify areas for improvement to increase the success rate of their projects.

[0060] The crowdfunding support system can further include a social media analysis unit that analyzes users' social media activities and provides advice on maximizing the effectiveness of project promotion. The social media analysis unit analyzes the content of users' posts and the reactions of their followers, and suggests the optimal timing for posting and effective hashtag usage. For example, it analyzes the characteristics of posts that have previously received high engagement from users and suggests the content of new posts based on that analysis. The social media analysis unit can also create messages that resonate with the target demographic, taking into account the interests of the user's followers. This allows users to effectively use social media and increase awareness of their projects.

[0061] The crowdfunding support system may further include a feedback collection unit that collects feedback from backers about the user's project and proposes improvements to the project. The feedback collection unit analyzes comments and ratings from backers to identify the strengths and weaknesses of the project. For example, it may extract points of particular interest to backers or areas requiring improvement, and provide specific improvement proposals to the user. The feedback collection unit may also analyze backers' emotions and convey positive feedback to the user. This allows the user to improve the project by reflecting backers' opinions and increasing the success rate.

[0062] The crowdfunding support system may further include a market monitoring unit that monitors market response to a user's project in real time and adjusts the project's direction. The market monitoring unit collects data from crowdfunding platforms and social media and analyzes market response to the project. For example, the market monitoring unit evaluates the project's popularity based on backer comments, the number of shares, the number of likes, etc. The market monitoring unit may also analyze the trends of competing projects and provide users with advice on how to improve their competitiveness. This allows users to understand market trends and flexibly adjust the project's direction.

[0063] The crowdfunding support system may further include a supporter behavior prediction unit that predicts the behavior of supporters toward a user's project and adjusts the project strategy based on the supporter's behavior. The supporter behavior prediction unit analyzes past supporter behavior data and predicts when a supporter will support. For example, if a supporter tends to support during a specific time period, project update information can be provided based on that time period. Also, if a supporter tends to respond to a specific event, the project can be promoted based on that event. This allows the user to predict supporter behavior and implement an effective project strategy.

[0064] The processing flow of the first embodiment will be briefly explained below.

[0065] Step 1: The input unit inputs a crowdfunding idea. The crowdfunding idea may include, for example, details of the idea, the target amount, and the project period. The input unit can input the idea using text input, voice input, or image input. For example, the user can explain the idea using voice, which is converted into text and input. The user can also handwrite the idea and input it as an image. Step 2: The analysis unit uses AI to analyze the idea entered by the input unit. The analysis unit uses natural language processing technology to understand the content of the idea and compare it with existing crowdfunding projects. For example, it can check whether similar gadgets have been successful in crowdfunding in the past. It can also use data mining technology to analyze the content of the idea in detail. It also extracts keywords from the idea and uses them to compare it with existing projects. Step 3: The judgment unit determines whether the set amount is appropriate based on the results of the analysis by the analysis unit. The judgment unit analyzes data from past successful projects and determines whether the target amount is appropriate. For example, the appropriateness of the target amount can be determined using statistical methods or AI. Step 4: The prediction unit predicts the likelihood of the crowdfunding project being successful based on the results of the judgment by the judgment unit and provides advice to the user. The prediction unit predicts the probability of the project's success based on past data. For example, it calculates the probability of success by taking into account the project's duration, target amount, and content of the idea. It can also predict the probability of success using machine learning algorithms and AI.

[0066] (Example 2) A crowdfunding support system according to an embodiment of the present invention provides advice to users before launching a crowdfunding campaign, such as whether there are any overlapping ideas, whether the set amount is appropriate, and the likelihood of success. In the crowdfunding support system, a user inputs a crowdfunding idea, and AI analyzes the idea and compares it with existing projects to determine whether the idea overlaps with other ideas. Furthermore, the AI ​​determines whether the set amount is appropriate based on past crowdfunding data, predicts the likelihood of success, and provides advice to the user. For example, in a crowdfunding support system, a user inputs a crowdfunding idea. At this time, information such as details of the idea, the target amount, and the project duration is input. For example, in the case of a project to develop a new gadget, the user inputs the gadget's features, target amount, project duration, etc. This information is input into the AI. Next, the crowdfunding support system uses AI to analyze the input idea. The AI ​​uses natural language processing technology to understand the content of the idea and compare it with existing crowdfunding projects. For example, it checks whether a similar gadget has been successfully crowdfunded in the past. This makes it possible to determine whether the idea overlaps with other ideas. Furthermore, the crowdfunding support system uses AI to determine whether the set amount is appropriate based on past crowdfunding data. The AI ​​analyzes data from past successful projects to determine whether the target amount is appropriate. For example, it refers to the amount of money that similar gadget projects have previously raised. This allows users to know whether the set amount is appropriate. Finally, the crowdfunding support system uses AI to predict the possibility of crowdfunding being successful and provide advice to users. The AI ​​predicts the probability of a project's success based on past data. For example, it calculates the probability of success by taking into account the project's duration, target amount, and the content of the idea. This allows users to know the possibility of a project being successful.This allows the crowdfunding support system to check in advance whether there are any overlapping ideas, whether the set amount is appropriate, and the likelihood of success before the crowdfunding campaign is launched, improving the success rate of crowdfunding and making it easier to realize projects.

[0067] A crowdfunding support system according to an embodiment includes an input unit, an analysis unit, a determination unit, and a prediction unit. The input unit inputs a crowdfunding idea. The crowdfunding idea may include, but is not limited to, details of the idea, a target amount, and a project duration. The input unit may input the idea using text input, for example. Alternatively, the input unit may input the idea using voice input. Alternatively, the input unit may input the idea using image input. For example, the input unit may allow a user to describe the idea using voice, convert the description into text, and input it. Alternatively, the input unit may allow a user to handwrite the idea and input it as an image. The analysis unit may use AI to analyze the idea input by the input unit. The analysis unit may use natural language processing technology to understand the content of the idea and compare it with existing crowdfunding projects. For example, the analysis unit may check whether a similar gadget has been successful in crowdfunding in the past. Alternatively, the analysis unit may use data mining technology to perform a detailed analysis of the content of the idea. Alternatively, the analysis unit may use AI to classify the content of the idea and compare it with existing projects. For example, the analysis unit uses natural language processing technology to extract keywords from the idea and compare it with existing projects based on those keywords. The judgment unit determines whether the set amount is appropriate based on the results of the analysis by the analysis unit. The judgment unit analyzes data from past successful projects and determines whether the target amount is appropriate. For example, the judgment unit determines whether the target amount is appropriate based on data from past successful projects. The judgment unit can also use statistical methods to determine whether the target amount is appropriate. The judgment unit can also use AI to determine whether the target amount is appropriate. For example, the judgment unit inputs data from past successful projects into AI and determines the appropriateness of the target amount. The prediction unit predicts the possibility of crowdfunding success based on the results determined by the judgment unit and provides advice to the user. The prediction unit predicts the probability of project success based on past data.For example, the prediction unit calculates the probability of success by taking into account the project's duration, target amount, idea content, etc. The prediction unit can also predict the project's probability of success using a machine learning algorithm. The prediction unit can also predict the project's probability of success using AI. For example, the prediction unit inputs data on past successful projects into AI to predict the success probability. This allows the crowdfunding support system according to the embodiment to consistently perform processes from inputting crowdfunding ideas to analysis, judgment, and prediction.

[0068] The input unit can input details of the crowdfunding idea, the target amount, and the project duration. The details include, but are not limited to, for example, the project objective, the target market, and a competitive analysis. The input unit can input, for example, the project objective. The input unit can also input the target market. The input unit can also input a competitive analysis. The target amount includes, but is not limited to, for example, a cost analysis and a revenue forecast. The input unit can also input a cost analysis. The input unit can also input a revenue forecast. The project duration includes, but is not limited to, for example, a project progress schedule and milestones. The input unit can also input, for example, the project progress schedule. The input unit can also input milestones. By inputting detailed crowdfunding information, more accurate analysis and determination are possible. Some or all of the above-described processing in the input unit can be performed using, for example, AI, or without AI. For example, the input unit inputs the information entered by the user into AI, which then analyzes it.

[0069] The analysis unit can use natural language processing technology to understand the content of the idea and compare it with existing crowdfunding projects. Natural language processing technology includes, but is not limited to, morphological analysis, grammatical analysis, and semantic analysis. The analysis unit can understand the content of the idea using, for example, morphological analysis. The analysis unit can also understand the content of the idea using grammatical analysis. The analysis unit can also understand the content of the idea using semantic analysis. In this way, by using natural language processing technology, the content of the idea can be accurately understood and compared with existing projects. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit inputs the content of the idea into AI using natural language processing technology, and the AI ​​analyzes it.

[0070] The determination unit can analyze data on past successful projects to determine whether the target amount is appropriate. Examples of data on past successful projects include, but are not limited to, data collected from crowdfunding platforms and public databases. For example, the determination unit collects data from crowdfunding platforms. The determination unit can also collect data from public databases. The determination unit can also analyze data on past successful projects to determine whether the target amount is appropriate. This improves the success rate of crowdfunding by determining the appropriateness of the target amount based on past success data. Some or all of the above-described processing by the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit inputs data on past successful projects into AI, which then determines the appropriateness of the target amount.

[0071] The prediction unit can predict the probability of success of a project based on past data. Examples of past data include, but are not limited to, data collected from crowdfunding platforms and public databases. For example, the prediction unit collects data from crowdfunding platforms. The prediction unit can also collect data from public databases. The prediction unit can also predict the probability of success of a project based on past data. This makes it possible to provide useful advice to users by predicting the probability of success based on past data. Some or all of the above-described processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit inputs past data into AI, which then predicts the probability of success of the project.

[0072] The input unit can estimate the user's emotions and adjust the timing of idea input based on the estimated user's emotions. Examples of user emotions include, but are not limited to, facial expression recognition, voice analysis, and text analysis. The input unit can estimate the user's emotions using, for example, facial expression recognition. The input unit can also estimate the user's emotions using voice analysis. The input unit can also estimate the user's emotions using text analysis. This allows for more efficient idea input by adjusting the input timing according to the user's emotions. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit inputs the user's emotion data into AI, and the AI ​​adjusts the input timing.

[0073] The input unit can analyze the user's past project history and select an appropriate input method. Examples of past project history include, but are not limited to, the user's project history database and data collected from a crowdfunding platform. For example, the input unit can refer to the input method used by the user for successful projects in the past and suggest a similar input method. The input unit can also advise the user to avoid input methods used in unsuccessful projects in the past. The input unit can also select and suggest the most efficient input method based on the user's past project history. By analyzing the past project history, the optimal input method can be provided to the user. Some or all of the above-described processing in the input unit can be performed using, for example, AI, or without AI. For example, the input unit inputs the user's past project history data into AI, which then selects the optimal input method.

[0074] When inputting ideas, the input unit may filter ideas based on the user's current project or area of ​​interest. Examples of current projects include, but are not limited to, the user's project management tool and data collection from a crowdfunding platform. The input unit, for example, prioritizes input of ideas related to the user's ongoing project. The input unit may also filter and input highly relevant ideas based on the user's area of ​​interest. The input unit may also exclude less relevant ideas by referring to the user's past project history. In this way, by filtering based on the user's current project or area of ​​interest, highly relevant ideas can be preferentially input. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without AI. For example, the input unit inputs the user's current project data into AI, and the AI ​​performs filtering.

[0075] When inputting an idea, the input unit can select an appropriate input means depending on the user's input method. Input methods include, but are not limited to, voice input, text input, and image input. For example, if the user prefers voice input, the input unit can provide voice input preferentially. Also, if the user prefers text input, the input unit can provide text input preferentially. Also, if the user prefers image input, the input unit can provide image input preferentially. This improves user convenience by selecting the optimal input means depending on the user's input method. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit inputs the user's input method data to AI, and the AI ​​selects the optimal input means.

[0076] The input unit can estimate the user's emotions and determine the priority of ideas to be input based on the estimated user's emotions. Examples of user emotions include, but are not limited to, facial expression recognition, voice analysis, and text analysis. The input unit can estimate the user's emotions using, for example, facial expression recognition. The input unit can also estimate the user's emotions using voice analysis. The input unit can also estimate the user's emotions using text analysis. This enables more effective idea input by determining the priority of ideas according to the user's emotions. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit inputs the user's emotion data into AI, which then determines the priority of ideas.

[0077] When inputting ideas, the input unit can prioritize inputting highly relevant ideas based on the user's geographical location information. Geographical location information includes, but is not limited to, GPS data, location information obtained from an IP address, and the like. For example, when the user is in a specific area, the input unit can prioritize inputting ideas related to that area. Furthermore, when the user is traveling, the input unit can prioritize inputting ideas related to the travel destination. Furthermore, when the user is at home, the input unit can prioritize inputting ideas related to the user's home. In this way, highly relevant ideas can be prioritized by taking the user's geographical location information into consideration. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit inputs the user's geographical location information data to AI, which selects highly relevant ideas.

[0078] When an idea is input, the input unit can analyze the user's social media activity and input related ideas. Social media activity includes, but is not limited to, for example, social media post content and follower analysis. The input unit, for example, automatically inputs ideas shared by the user on social media. The input unit can also analyze the user's social media activity and suggest related ideas. The input unit can also input related ideas by referring to the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, related ideas can be input efficiently. Some or all of the above-described processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit inputs the user's social media data into AI, which selects related ideas.

[0079] When inputting an idea, the input unit can adjust the input method by reflecting the user's past feedback. Past feedback includes, but is not limited to, user reviews, comments, and ratings. For example, the input unit can preferentially provide input methods that the user has previously preferred. The input unit can also exclude input methods that the user has previously avoided. The input unit can also suggest an optimal input method based on the user's past feedback. In this way, the optimal input method can be provided by reflecting the user's past feedback. Some or all of the above-mentioned processing in the input unit may be performed using, for example, AI, or may be performed without using AI. For example, the input unit inputs the user's past feedback data into AI, which then selects the optimal input method.

[0080] The analysis unit can estimate the user's emotion and adjust the way the analysis is presented based on the estimated user's emotion. Examples of the user's emotion include, but are not limited to, facial expression recognition, voice analysis, and text analysis. The analysis unit can estimate the user's emotion using, for example, facial expression recognition. The analysis unit can also estimate the user's emotion using voice analysis. The analysis unit can also estimate the user's emotion using text analysis. This allows the analysis result to be more easily understood by adjusting the way the analysis is presented according to the user's emotion. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit inputs the user's emotion data into AI, which then adjusts the way the analysis is presented.

[0081] During analysis, the analysis unit can set the level of detail of the analysis based on the importance of the idea. The importance of an idea includes, but is not limited to, the impact of the project and the size of the target market. For example, the analysis unit can perform a detailed analysis for important ideas. The analysis unit can also perform a standard analysis for general ideas. The analysis unit can also perform a simplified analysis for ideas with low importance. This allows for efficient analysis by adjusting the level of detail of the analysis based on the importance of the idea. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit inputs idea importance data into AI, which then sets the level of detail of the analysis.

[0082] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the idea. Idea categories include, but are not limited to, technical categories, business categories, and creative categories. For example, the analysis unit applies a technical analysis algorithm to technical ideas. The analysis unit can also apply a creative analysis algorithm to creative ideas. The analysis unit can also apply a business analysis algorithm to business ideas. This improves the accuracy of the analysis by applying an appropriate analysis algorithm depending on the category of the idea. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit inputs idea category data into AI, which selects an appropriate analysis algorithm.

[0083] During analysis, the analysis unit can improve the accuracy of the analysis based on the user's past analysis results. Past analysis results include, but are not limited to, the user's project history and data collected from crowdfunding platforms. The analysis unit can improve the accuracy of the analysis, for example, by referring to the user's past successful analysis results. The analysis unit can also improve the accuracy of the analysis by avoiding the user's past unsuccessful analysis results. The analysis unit can also select an optimal analysis method based on the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-described processing in the analysis unit can be performed, for example, using AI or without AI. For example, the analysis unit inputs the user's past analysis result data into AI, which then improves the accuracy of the analysis.

[0084] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user's emotion. The user's emotion can be estimated using, but not limited to, facial expression recognition, voice analysis, text analysis, etc. The analysis unit can estimate the user's emotion using, for example, facial expression recognition. The analysis unit can also estimate the user's emotion using voice analysis. The analysis unit can also estimate the user's emotion using text analysis. This allows the length of the analysis to be adjusted according to the user's emotion, thereby providing an optimal analysis result for the user. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit inputs the user's emotion data into AI, and the AI ​​adjusts the length of the analysis.

[0085] During analysis, the analysis unit can set analysis priorities based on the submission dates of the ideas. The submission dates of the ideas include, but are not limited to, records of the submission dates and timestamps. For example, the analysis unit prioritizes the analysis of recently submitted ideas. The analysis unit can also postpone older submitted ideas. The analysis unit can also adjust the order of analysis based on the submission dates. This enables efficient analysis by determining the analysis priorities based on the submission dates of the ideas. Some or all of the above-described processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit inputs data on the submission dates of ideas into AI, which then sets the analysis priorities.

[0086] During analysis, the analysis unit can adjust the order of analysis based on the relevance of ideas. Examples of the relevance of ideas include, but are not limited to, common keywords, similar themes, etc. The analysis unit, for example, prioritizes the analysis of highly related ideas. The analysis unit can also postpone less related ideas. The analysis unit can also adjust the order of analysis based on the relevance. In this way, by adjusting the order of analysis based on the relevance of ideas, highly related ideas can be analyzed preferentially. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit inputs idea relevance data into AI, and the AI ​​adjusts the order of analysis.

[0087] During analysis, the analysis unit can set the use of technical terms in the analysis according to the user's level of expertise. Examples of the user's level of expertise include, but are not limited to, the user's occupation, educational background, and past project experience. For example, the analysis unit uses a lot of technical terms if the user has technical expertise. The analysis unit can also avoid technical terms if the user does not have technical expertise. The analysis unit can also adjust the use of technical terms in the analysis according to the user's level of expertise. This allows for the provision of analysis results that are easy for the user to understand. Some or all of the above-described processing in the analysis unit can be performed using, for example, AI, or without AI. For example, the analysis unit inputs the user's level of expertise data into AI, which then adjusts the use of technical terms.

[0088] The determination unit can estimate the user's emotion and adjust the determination method based on the estimated user's emotion. The user's emotion can be determined by, but not limited to, facial expression recognition, voice analysis, text analysis, etc. The determination unit can estimate the user's emotion using, for example, facial expression recognition. The determination unit can also estimate the user's emotion using voice analysis. The determination unit can also estimate the user's emotion using text analysis. This allows the determination method to be adjusted according to the user's emotion, thereby providing an optimal determination result for the user. Some or all of the above-described processing in the determination unit can be performed using, for example, AI, or can be performed without using AI. For example, the determination unit inputs the user's emotion data into AI, which then adjusts the determination method.

[0089] The determination unit can improve the determination algorithm by referring to data on past successful projects when making a determination. Data on past successful projects includes, but is not limited to, data collected from crowdfunding platforms and public databases. The determination unit, for example, optimizes the determination algorithm based on data on past successful projects. The determination unit can also optimize the determination algorithm by avoiding data on past unsuccessful projects. The determination unit can also select an optimal determination algorithm by referring to data on past successful projects. This allows the determination algorithm to be optimized and the accuracy of the determination to be improved by referring to data on past successful projects. Some or all of the above-described processing in the determination unit can be performed, for example, using AI or without AI. For example, the determination unit inputs data on past successful projects into AI, which then optimizes the determination algorithm.

[0090] The determination unit can update the determination criteria by reflecting user feedback when making a determination. User feedback includes, but is not limited to, user reviews, comments, and ratings. The determination unit updates the determination criteria, for example, based on user feedback. The determination unit can also optimize the determination criteria by referring to past user feedback. The determination unit can also flexibly adjust the determination criteria by reflecting user feedback. This makes it possible to flexibly adjust the determination criteria and provide a more appropriate determination result by reflecting user feedback. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit inputs user feedback data into AI, and the AI ​​updates the determination criteria.

[0091] The judgment unit can use different judgment methods for each idea category when making a judgment. Idea categories include, but are not limited to, technical categories, business categories, and creative categories, for example. The judgment unit can apply a technical judgment method to technical ideas, for example. The judgment unit can also apply a creative judgment method to creative ideas, and for business ideas, for example. This improves the accuracy of the judgment by applying an appropriate judgment method to each idea category. Some or all of the above-mentioned processing in the judgment unit can be performed using, for example, AI, or can be performed without using AI. For example, the judgment unit inputs idea category data into AI, which selects an appropriate judgment method.

[0092] The determination unit can estimate the user's emotion and determine a priority order of determination based on the estimated user's emotion. The user's emotion can be determined by, but not limited to, facial expression recognition, voice analysis, text analysis, and the like. The determination unit can estimate the user's emotion using, for example, facial expression recognition. The determination unit can also estimate the user's emotion using voice analysis. The determination unit can also estimate the user's emotion using text analysis. This enables more effective determination by determining the priority order of determination according to the user's emotion. Some or all of the above-described processing in the determination unit may be performed using, for example, AI, or may be performed without using AI. For example, the determination unit inputs the user's emotion data into AI, which then determines the priority order of determination.

[0093] The judgment unit can set a weighting for judgment based on the time of submission of the idea during judgment. The time of submission of the idea includes, for example, a record of the submission date and time, a timestamp, etc., but is not limited to such examples. For example, the judgment unit can assign a higher weighting to a recently submitted idea. The judgment unit can also assign a lower weighting to an older submitted idea. The judgment unit can also adjust the weighting for judgment based on the time of submission. In this way, efficient judgment is possible by weighting the judgment based on the time of submission of the idea. Some or all of the above-mentioned processing in the judgment unit may be performed using, for example, AI, or may be performed without using AI. For example, the judgment unit inputs data on the time of submission of the idea into AI, and the AI ​​sets the weighting for judgment.

[0094] The judgment unit can improve the accuracy of the judgment by referring to market data related to the idea when making a judgment. Examples of related market data include, but are not limited to, market research reports and industry databases. The judgment unit improves the accuracy of the judgment based on, for example, the related market data. The judgment unit can also improve the accuracy of the judgment by referring to trends in market data. The judgment unit can also analyze market data and select an optimal judgment method. Thus, by referring to the market data related to the idea, the accuracy of the judgment can be improved. Some or all of the above-described processing in the judgment unit may be performed using, for example, AI, or may be performed without using AI. For example, the judgment unit inputs the related market data into AI, which then improves the accuracy of the judgment.

[0095] The judgment unit can make a judgment based on the technical maturity of the idea when making the judgment. The technical maturity includes, for example, the development stage of the technology, whether or not it has a patent, etc., but is not limited to these examples. For example, the judgment unit makes a strict judgment for a technically mature idea. The judgment unit can also make a flexible judgment for a technically immature idea. The judgment unit can also adjust the strictness of the judgment based on the technical maturity. This makes it possible to provide a more appropriate judgment result by taking the technical maturity of the idea into consideration. Some or all of the above-mentioned processing in the judgment unit can be performed using, for example, AI, or can be performed without using AI. For example, the judgment unit inputs technical maturity data into AI, and the AI ​​makes the judgment.

[0096] The prediction unit can estimate the user's emotion and adjust the display method of the prediction based on the estimated user's emotion. The user's emotion can be estimated using, but not limited to, facial expression recognition, voice analysis, text analysis, etc. The prediction unit can estimate the user's emotion using, for example, facial expression recognition. The prediction unit can also estimate the user's emotion using voice analysis. The prediction unit can also estimate the user's emotion using text analysis. This allows the optimal prediction result to be provided by adjusting the display method of the prediction according to the user's emotion. Some or all of the above-described processing in the prediction unit can be performed using, for example, AI, or without AI. For example, the prediction unit inputs the user's emotion data into AI, which then adjusts the display method of the prediction.

[0097] The prediction unit can refer to past data when making predictions to improve the current prediction. Examples of past data include, but are not limited to, data collected from crowdfunding platforms and public databases. The prediction unit, for example, optimizes the current prediction based on data from past successful projects. The prediction unit can also optimize the current prediction by avoiding data from past unsuccessful projects. The prediction unit can also select an optimal prediction method by referring to past data. By doing so, the current prediction can be optimized by referring to past data, thereby improving the accuracy of the prediction. Some or all of the above-described processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit inputs past data into AI, which then improves the current prediction.

[0098] The prediction unit can apply different prediction algorithms to each idea category during prediction. Idea categories include, but are not limited to, technical categories, business categories, and creative categories. For example, the prediction unit can apply a technical prediction algorithm to technical ideas. The prediction unit can also apply a creative prediction algorithm to creative ideas. The prediction unit can also apply a business prediction algorithm to business ideas. This improves prediction accuracy by applying an appropriate prediction algorithm to each idea category. Some or all of the above-described processing in the prediction unit can be performed using, for example, AI, or without AI. For example, the prediction unit inputs idea category data into AI, which selects an appropriate prediction algorithm.

[0099] The prediction unit can make predictions taking into account the user's attribute information. User attribute information includes, but is not limited to, for example, age, gender, and occupation. The prediction unit makes predictions taking into account, for example, the user's age and gender. The prediction unit can also make predictions taking into account the user's occupation and interests. The prediction unit can also make predictions taking into account the user's past behavioral history. This makes it possible to provide more appropriate prediction results by taking into account the user's attribute information. Some or all of the above-described processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit inputs the user's attribute information data into AI, and the AI ​​makes the prediction.

[0100] The prediction unit can estimate the user's emotion and adjust the importance of the prediction based on the estimated user's emotion. The user's emotion can be estimated using, but not limited to, facial expression recognition, voice analysis, text analysis, etc. The prediction unit can estimate the user's emotion using, for example, facial expression recognition. The prediction unit can also estimate the user's emotion using voice analysis. The prediction unit can also estimate the user's emotion using text analysis. This allows the importance of the prediction to be adjusted according to the user's emotion, thereby providing an optimal prediction result for the user. Some or all of the above-described processing in the prediction unit can be performed using, for example, AI, or can be performed without using AI. For example, the prediction unit inputs the user's emotion data into AI, and the AI ​​adjusts the importance of the prediction.

[0101] The prediction unit can evaluate a change in the prediction based on the time of submission of the idea during prediction. The time of submission of the idea includes, for example, a record of the submission date and time, a timestamp, etc., but is not limited to such examples. For example, the prediction unit quickly analyzes the change in the prediction for a recently submitted idea. The prediction unit can also delay the change in the prediction for an older submitted idea. The prediction unit can also adjust the change in the prediction based on the time of submission. This enables efficient prediction by analyzing the change in the prediction based on the time of submission of the idea. Some or all of the above-described processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit inputs data on the time of submission of the idea into AI, and the AI ​​evaluates the change in the prediction.

[0102] When making a prediction, the prediction unit can analyze the prediction by referring to market data related to the idea. Examples of related market data include, but are not limited to, market research reports and industry databases. The prediction unit analyzes the prediction based on, for example, the related market data. The prediction unit can also analyze the prediction by referring to trends in market data. The prediction unit can also analyze market data and select an optimal prediction method. By doing so, the accuracy of the prediction can be improved by referring to the market data related to the idea. Some or all of the above-described processing in the prediction unit may be performed using, for example, AI, or may be performed without using AI. For example, the prediction unit inputs the related market data into AI, which then analyzes the prediction.

[0103] The prediction unit can make predictions taking into account the technological maturity of the idea. Examples of technological maturity include, but are not limited to, the development stage of the technology and whether or not it has a patent. For example, the prediction unit makes strict predictions for technologically mature ideas. Furthermore, the prediction unit can also make flexible predictions for technologically immature ideas. Furthermore, the prediction unit can adjust the strictness of the prediction based on the technological maturity. This allows for more appropriate prediction results to be provided by taking the technological maturity of the idea into consideration. Some or all of the above-described processing in the prediction unit can be performed using, for example, AI, or can be performed without using AI. For example, the prediction unit inputs technological maturity data into AI, which then makes the prediction. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned input unit, analysis unit, determination unit, and prediction unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the input unit can input a crowdfunding idea using the reception device 38 of the smart device 14. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input idea using AI. The determination unit is realized by the specific processing unit 290 of the data processing device 12 and determines the appropriateness of the target amount based on data on past successful projects. The prediction unit is realized by the specific processing unit 290 of the data processing device 12 and predicts the probability of success of the project and provides advice to the user. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned input unit, analysis unit, determination unit, and prediction unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the input unit can input a crowdfunding idea by voice using the microphone 238 of the smart glasses 214. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input idea using AI. The determination unit is realized by the specific processing unit 290 of the data processing device 12 and determines the appropriateness of the target amount based on data of past successful projects. The prediction unit is realized by the specific processing unit 290 of the data processing device 12 and predicts the probability of success of the project and provides advice to the user. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned input unit, analysis unit, determination unit, and prediction unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the input unit can input a crowdfunding idea by voice using the microphone 238 of the headset-type terminal 314. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input idea using AI. The determination unit is realized by the specific processing unit 290 of the data processing device 12 and determines the appropriateness of the target amount based on data of past successful projects. The prediction unit is realized by the specific processing unit 290 of the data processing device 12 and predicts the probability of success of the project and provides advice to the user. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned input unit, analysis unit, determination unit, and prediction unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the input unit can input a crowdfunding idea by voice using the microphone 238 of the robot 414. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input idea using AI. The determination unit is realized by the specific processing unit 290 of the data processing device 12 and determines the appropriateness of the target amount based on data of past successful projects. The prediction unit is realized by the specific processing unit 290 of the data processing device 12 and predicts the probability of success of the project and provides advice to the user.

[0104] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0105] The crowdfunding support system may further include a reliability evaluation unit that analyzes the success rate of a user's past projects and evaluates the user's reliability. The reliability evaluation unit scores the user's reliability based on the success rate of the user's past projects and feedback. For example, the reliability score may be calculated taking into account the number of successful projects in the past and evaluations from backers. The reliability evaluation unit may also evaluate the user's project management skills and communication skills. This allows the user to objectively understand their own reliability and identify areas for improvement to increase the success rate of their projects.

[0106] The crowdfunding support system can further include a presentation adjustment unit that estimates the user's emotions and adjusts the project presentation method based on the estimated emotions. The presentation adjustment unit estimates the user's emotions using facial expression recognition and voice analysis, and provides more detailed information if the user is relaxed. Also, it can provide concise and easy-to-understand information if the user is nervous. This allows the user to make an optimal presentation based on their emotional state and effectively promote the project to backers.

[0107] The crowdfunding support system can further include a social media analysis unit that analyzes users' social media activities and provides advice on maximizing the effectiveness of project promotion. The social media analysis unit analyzes the content of users' posts and the reactions of their followers, and suggests the optimal timing for posting and effective hashtag usage. For example, it analyzes the characteristics of posts that have previously received high engagement from users and suggests the content of new posts based on that analysis. The social media analysis unit can also create messages that resonate with the target demographic, taking into account the interests of the user's followers. This allows users to effectively use social media and increase awareness of their projects.

[0108] The crowdfunding support system may further include a progress reporting unit that estimates the user's emotions and adjusts the method of reporting project progress based on the estimated emotions. The progress reporting unit estimates the user's emotions using facial expression recognition and voice analysis, and if the user is feeling stressed, it may report progress that emphasizes positive progress. On the other hand, if the user is relaxed, it may report detailed progress. This allows the user to receive optimal progress reports according to their emotional state, helping them maintain motivation for the project.

[0109] The crowdfunding support system may further include a feedback collection unit that collects feedback from backers about the user's project and proposes improvements to the project. The feedback collection unit analyzes comments and ratings from backers to identify the strengths and weaknesses of the project. For example, it may extract points of particular interest to backers or areas requiring improvement, and provide specific improvement proposals to the user. The feedback collection unit may also analyze backers' emotions and convey positive feedback to the user. This allows the user to improve the project by reflecting backers' opinions and increasing the success rate.

[0110] The crowdfunding support system can further include a risk assessment unit that estimates the user's emotions and performs a risk assessment of the project based on the estimated emotions. The risk assessment unit estimates the user's emotions using facial expression recognition and voice analysis, and if the user feels anxious, it explains the risks in detail and suggests risk mitigation measures. If the user feels confident, it can briefly explain the risks and emphasize the positive aspects of the project. This allows the user to receive a risk assessment that corresponds to their emotional state, enabling effective risk management of the project.

[0111] The crowdfunding support system may further include a market monitoring unit that monitors market response to a user's project in real time and adjusts the project's direction. The market monitoring unit collects data from crowdfunding platforms and social media and analyzes market response to the project. For example, the market monitoring unit evaluates the project's popularity based on backer comments, the number of shares, the number of likes, etc. The market monitoring unit may also analyze the trends of competing projects and provide users with advice on how to improve their competitiveness. This allows users to understand market trends and flexibly adjust the project's direction.

[0112] The crowdfunding support system may further include a marketing strategy adjustment unit that estimates the user's emotions and adjusts the project's marketing strategy based on the estimated emotions. The marketing strategy adjustment unit estimates the user's emotions using facial expression recognition and voice analysis, and if the user is excited, it can propose an aggressive marketing strategy. On the other hand, if the user is calm, it can propose a cautious marketing strategy. This allows the user to implement an optimal marketing strategy according to their emotional state, thereby improving the success rate of the project.

[0113] The crowdfunding support system can further include a backer emotion analysis unit that analyzes backers' emotions toward the user's project and adjusts the progress of the project based on the backers' emotions. The backer emotion analysis unit analyzes backers' comments and feedback, and accelerates the progress of the project if the backers have positive emotions. Also, if the backers have negative emotions, the unit can temporarily halt the progress of the project and take measures to improve it. This allows the user to optimally progress the project based on the backers' emotions, thereby increasing backer satisfaction.

[0114] The crowdfunding support system may further include a supporter behavior prediction unit that predicts the behavior of supporters toward a user's project and adjusts the project strategy based on the supporter's behavior. The supporter behavior prediction unit analyzes past supporter behavior data and predicts when a supporter will support. For example, if a supporter tends to support during a specific time period, project update information can be provided based on that time period. Also, if a supporter tends to respond to a specific event, the project can be promoted based on that event. This allows the user to predict supporter behavior and implement an effective project strategy.

[0115] The processing flow of the second embodiment will be briefly explained below.

[0116] Step 1: The input unit inputs a crowdfunding idea. The crowdfunding idea may include, for example, details of the idea, the target amount, and the project period. The input unit can input the idea using text input, voice input, or image input. For example, the user can explain the idea using voice, which is converted into text and input. The user can also handwrite the idea and input it as an image. Step 2: The analysis unit uses AI to analyze the idea entered by the input unit. The analysis unit uses natural language processing technology to understand the content of the idea and compare it with existing crowdfunding projects. For example, it can check whether similar gadgets have been successful in crowdfunding in the past. It can also use data mining technology to analyze the content of the idea in detail. It also extracts keywords from the idea and uses them to compare it with existing projects. Step 3: The judgment unit determines whether the set amount is appropriate based on the results of the analysis by the analysis unit. The judgment unit analyzes data from past successful projects and determines whether the target amount is appropriate. For example, the appropriateness of the target amount can be determined using statistical methods or AI. Step 4: The prediction unit predicts the likelihood of the crowdfunding project being successful based on the results of the judgment by the judgment unit and provides advice to the user. The prediction unit predicts the probability of the project's success based on past data. For example, it calculates the probability of success by taking into account the project's duration, target amount, and content of the idea. It can also predict the probability of success using machine learning algorithms and AI.

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0118] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0119] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0120] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0121] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0122] 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.

[0123] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0137] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0138] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0139] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0148] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0154] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0155] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0156] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0157] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0159] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0160] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0161] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0164] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

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

[0166] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

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

[0168] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0170] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0171] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0172] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0173] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0174] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0175] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0176] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0177] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0178] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0179] 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.

[0180] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0181] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0182] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0183] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0184] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0185] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0186] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0187] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0188] [Explanation of symbols]

[0189] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An input section for entering crowdfunding ideas; an analysis unit that analyzes the idea input by the input unit and compares it with existing projects; a determination unit that determines whether the set amount is appropriate based on the analysis result by the analysis unit; a prediction unit that predicts the possibility of success of crowdfunding based on the result of the judgment by the judgment unit and provides advice to the user. A system characterized by:

2. The input unit Enter details about your crowdfunding idea, your goal amount, and the duration of your project.

2. The system of claim 1.

3. The analysis unit Use natural language processing to understand the idea and compare it with existing crowdfunding projects 2. The system of claim 1.

4. The determination unit Analyze data from past successful projects to determine whether the target amount is appropriate 2. The system of claim 1.

5. The prediction unit Predict the probability of project success based on past data 2. The system of claim 1.

6. The input unit Estimates the user's emotions and adjusts the timing of idea input based on the estimated user emotions.

2. The system of claim 1.

7. The input unit Analyze the user's past project history and select the appropriate input method 2. The system of claim 1.

8. The input unit As you enter ideas, filter them based on your current projects and interests 2. The system of claim 1.

9. The input unit When inputting ideas, select the appropriate input method depending on the user's input method.

2. The system of claim 1.

10. The input unit Estimate user emotions and prioritize ideas based on the estimated user emotions 2. The system of claim 1.

Images