system

Abstract

The system according to the embodiment aims to efficiently commercialize new business ideas and match them with appropriate members and investors. [Solution] A system according to an embodiment includes a reception unit, a generation unit, a prediction unit, a matching unit, and an investor matching unit. The reception unit registers ideas. The generation unit generates steps leading to commercialization based on the ideas registered by the reception unit. The prediction unit calculates profit forecasts based on the steps generated by the generation unit. The matching unit matches appropriate members based on the profit forecasts calculated by the prediction unit. The investor matching unit matches investors based on the members matched by the matching unit.

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, the process of commercializing new business ideas was complicated, and it was difficult to find the right people and investors.

[0005] The system according to the embodiment aims to efficiently commercialize new business ideas and match them with appropriate members and investors. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, a prediction unit, a matching unit, and an investor matching unit. The reception unit registers ideas. The generation unit generates steps leading to commercialization based on the ideas registered by the reception unit. The prediction unit calculates profit forecasts based on the steps generated by the generation unit. The matching unit matches appropriate members based on the profit forecasts calculated by the prediction unit. The investor matching unit matches investors based on the members matched by the matching unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently commercialize new business ideas and match them with appropriate members and investors. [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) The new business matching platform according to an embodiment of the present invention is a system in which, when an idea provider registers an idea, the system automatically generates steps to commercialization, calculates profit forecasts, and matches the idea with the most suitable business members and engineers from among the registered members. The system allows members to decide whether to participate based on a success fee amount based on the profit forecast. The process also includes matching with investors. All members are free to join, and the service is a success fee system. The Idea Booster service itself also charges a success fee. For example, an idea provider registers an idea on the platform. At this time, they enter details of the idea, its goals, and the necessary resources. The system then automatically generates steps to commercialization. These steps include market research, prototyping, and marketing strategy development. The system then calculates a profit forecast. The profit forecast is based on factors such as the market size, competitive situation, and cost structure of the idea. For example, it predicts the expected sales and costs of the idea. The system then matches the idea with the most suitable business members and engineers from among the registered members. Members can confirm the success fee amount based on the profit forecast and decide to participate. For example, if the profit forecast is high, the success fee will also be high, increasing members' motivation to participate. Furthermore, the process also includes matching with investors. The system automatically matches ideas with investors who want to invest. This allows idea creators to avoid the hassle of fundraising. All members are free to join and the system is based on a success fee system. This means that members only receive rewards if the project is successful. In addition, the Idea Booster service itself is based on a success fee system. This allows platform users to minimize risk. This system allows idea creators to efficiently commercialize their ideas and members can earn success fees. It also gives investors the opportunity to invest in promising ideas.For example, an idea provider can register an idea for a new app, and the system will automatically generate steps to commercialize it and match the optimal members with investors, which is expected to lead to efficient commercialization. This allows the new business matching platform to enable idea providers to efficiently commercialize their ideas, allowing members to earn success fees, and providing investors with the opportunity to invest in promising ideas.

[0029] A new business matching platform according to an embodiment includes a reception unit, a generation unit, a prediction unit, a matching unit, and an investor matching unit. The reception unit is a unit through which idea providers register their ideas. Idea providers can input, for example, details of their ideas, goals, and necessary resources. The reception unit, for example, stores the information input by the idea providers in a database and uses it for subsequent processing. The generation unit is a unit that generates steps leading to commercialization based on the ideas registered by the reception unit. The generation unit generates steps such as market research, prototype creation, and marketing strategy formulation. The generation unit can use AI to analyze the content of the idea and the market situation and automatically generate optimal commercialization steps. For example, the generation unit can propose specific market research methods taking into account the market size and competitive situation of the idea. The generation unit can also provide detailed information on the resources and procedures required to create a prototype. Furthermore, the generation unit can specifically propose target markets and promotion methods when formulating a marketing strategy. The prediction unit is a unit that calculates profit forecasts based on the steps generated by the generation unit. The forecasting department calculates profit forecasts, for example, by taking into account the market size, competitive situation, cost structure, etc. of an idea. The forecasting department can use AI to analyze past data and market trends to provide accurate profit forecasts. For example, the forecasting department can make detailed predictions about how much sales an idea can generate and how much it will cost. The matching department is a department that matches optimal members based on the profit forecasts calculated by the forecasting department. For example, the matching department presents a success fee based on the profit forecast and matches optimal business members and engineers. The matching department can use AI to analyze the skills and experience of registered members and select optimal members. For example, the matching department can select optimal members by taking into account the business members' past project experience and expertise. The matching department can also analyze the engineers' technical skills and development experience to select optimal engineers.The investor matching department is a department that matches investors based on members matched by the matching department. For example, the investor matching department automatically matches investors who wish to invest in an idea. The investor matching department can use AI to analyze the investors' investment portfolios and investment strategies and select the most suitable investors. For example, the investor matching department can select the most suitable investors by taking into consideration the investors' past investment performance and the fields of investment targets. As a result, the new business matching platform according to the embodiment allows idea providers to efficiently commercialize their ideas and members to earn success fees. In addition, investors can have the opportunity to invest in promising ideas.

[0030] The reception unit can input details of an idea, goals, and required resources. The reception unit, for example, provides an interface for an idea provider to input details of an idea, goals, and required resources. For example, the reception unit can provide fields for inputting the purpose, background, and technical details of the idea. The reception unit can also provide fields for inputting the idea's short-term and long-term goals and achievement criteria. The reception unit can also provide fields for inputting the type and amount of required resources. For example, the reception unit can provide fields for inputting resources such as human resources, funds, and equipment. By inputting detailed information about an idea, more specific steps for commercialization can be generated. Some or all of the above-mentioned processing in the reception unit can be performed using AI, or can be performed without AI. For example, the reception unit can input the information input by the idea provider into AI, which can then automatically analyze the information and store it in a database.

[0031] The generation unit can generate steps for market research, creating a prototype, and formulating a marketing strategy. The generation unit generates, for example, market research steps. For example, the generation unit can propose specific methods such as identifying a target market, conducting competitive analysis, and conducting consumer research. The generation unit can also generate steps for creating a prototype. For example, the generation unit can indicate specific procedures for designing, manufacturing, and testing a prototype. The generation unit can also generate steps for formulating a marketing strategy. For example, the generation unit can propose specific methods such as a promotion plan, pricing, and selecting sales channels. This specifically generates steps for commercialization, thereby increasing the feasibility of the idea. Some or all of the above-mentioned processing in the generation unit may be performed using AI, or may be performed without AI. For example, the generation unit can input the content of the idea and market conditions into AI, which can then automatically generate optimal commercialization steps.

[0032] The prediction unit can calculate a profit forecast by taking into account the market size, competitive situation, and cost structure of the idea. For example, the prediction unit can calculate a profit forecast by taking into account the market size of the idea. For example, the prediction unit can analyze the size, growth rate, revenue potential, etc. of the target market. The prediction unit can also calculate a profit forecast by taking into account the competitive situation. For example, the prediction unit can identify major competitors, compare competing products, and evaluate competitive advantages. Furthermore, the prediction unit can calculate a profit forecast by taking into account the cost structure. For example, the prediction unit can analyze manufacturing costs, operating costs, marketing costs, etc. By calculating a profit forecast that takes into account the market size and competitive situation, the likelihood of business success can be increased. Some or all of the above-described processing in the prediction unit may be performed using AI, or may be performed without AI. For example, the prediction unit can input past data and market trends into AI, which can then automatically calculate a profit forecast.

[0033] The matching unit can present a success fee amount based on profit forecasts and match appropriate business members and engineers. The matching unit presents, for example, a success fee amount based on profit forecasts. For example, the matching unit can present specific calculation methods such as a fixed percentage of profits, a fixed fee, or a performance-based fee. The matching unit can also match appropriate business members and engineers. For example, the matching unit can select the optimal member by taking into account the business member's experience, skills, expertise, etc. Furthermore, the matching unit can analyze the engineer's technical skills and development experience to select the optimal engineer. This can increase members' motivation to participate by presenting a success fee amount based on profit forecasts. Some or all of the above-mentioned processing in the matching unit may be performed using AI, or may be performed without AI. For example, the matching unit can input the skills and experience of registered members into AI, which can then automatically select the optimal member.

[0034] The investor matching unit can automatically match investors who wish to invest in an idea. For example, the investor matching unit automatically matches investors who wish to invest in an idea. For example, the investor matching unit can analyze the investors' investment portfolios and investment strategies and select the most suitable investors. For example, the investor matching unit can select the most suitable investors by taking into consideration the investors' past investment performance and the fields of investment targets. In this way, automatically matching investors can reduce the effort required for fundraising. Some or all of the above-mentioned processing in the investor matching unit may be performed using AI, or may be performed without using AI. For example, the investor matching unit can input the investors' investment portfolios and investment strategies into AI, which can then automatically select the most suitable investors.

[0035] When registering an idea, the reception unit can analyze the user's past idea submission history and provide input support. For example, the reception unit can collect data such as the number, content, and success rate of previously submitted ideas to analyze the user's past idea submission history. For example, the reception unit can automatically extract keywords from ideas previously submitted by the user and suggest related input fields. The reception unit can also prioritize suggestions based on input methods (such as voice or text) that the user has used in the past. Furthermore, the reception unit can automatically display input fields related to a specific field based on the user's past idea submission history. This allows the user to efficiently input ideas by analyzing the past idea submission history. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without AI. For example, the reception unit can input the user's past idea submission history into AI, which then automatically suggests related input fields.

[0036] When entering details of an idea, the reception unit can customize input fields based on the user's current project or areas of interest. The reception unit can collect data such as the user's project history and survey results on areas of interest to identify the user's current project or areas of interest. For example, the reception unit can prioritize displaying input fields related to the user's current project. The reception unit can also automatically suggest related input fields based on the user's areas of interest. Furthermore, the reception unit can also work with the user's project management tool to automatically obtain current project information and customize the input fields. This allows for customizing the input fields based on the user's current project or areas of interest, thereby improving the accuracy of input. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without AI. For example, the reception unit can input the user's project information into AI, which then automatically customizes the input fields.

[0037] When registering an idea, the reception unit can prioritize displaying highly relevant input items by taking into account the user's geographical location information. The reception unit can acquire the user's geographical location information using methods such as GPS data, IP address, or manual input by the user. For example, the reception unit can display region-specific market information as an input item based on the user's current location. The reception unit can also suggest regional competitive conditions as an input item based on the user's geographical location information. Furthermore, the reception unit can prioritize displaying input items related to regional laws and regulations by taking into account the user's location information. This allows the user to efficiently input region-specific information by taking into account the user's geographical location information. Some or all of the above-described processing by the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information into AI, which can then automatically suggest highly relevant input items.

[0038] The reception unit can analyze the user's social media activity and suggest related input items when registering an idea. The reception unit can, for example, collect data such as post content, number of followers, and engagement rate to analyze the user's social media activity. For example, the reception unit can analyze the user's social media post content and suggest related keywords as input items. The reception unit can also display related input items based on the interests of the user's followers and friends. Furthermore, the reception unit can analyze the user's social media activity history and customize input items based on the content of past posts. This allows related input items to be efficiently suggested by analyzing the user's social media activity. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without AI. For example, the reception unit can input the user's social media activity into AI, which can then automatically suggest related input items.

[0039] When generating commercialization steps, the generation unit can adjust the level of detail of the steps based on the importance of the idea. The generation unit can use criteria such as market potential, technological innovativeness, and social impact to evaluate the importance of an idea. For example, the generation unit can generate detailed commercialization steps for ideas with high importance. The generation unit can also generate simplified commercialization steps for ideas with low importance. Furthermore, the generation unit can adjust the number and content of steps according to the importance of the idea. By adjusting the level of detail of the steps based on the importance of the idea, more detailed steps can be provided for important ideas. Some or all of the above-described processing in the generation unit may be performed using AI, or may be performed without AI. For example, the generation unit can input the importance of the idea into AI, which can then automatically adjust the level of detail of the steps.

[0040] When generating commercialization steps, the generation unit can apply different generation algorithms depending on the category of the idea. For example, the generation unit can use criteria such as technology category, business category, or social category to categorize ideas. For example, the generation unit can generate commercialization steps specialized for technology development for technology-related ideas. For service-related ideas, the generation unit can generate commercialization steps specialized for marketing and customer service. For manufacturing-related ideas, the generation unit can generate commercialization steps specialized for prototyping and production planning. By applying a generation algorithm depending on the idea category, optimal commercialization steps can be provided for each category. Some or all of the above-described processing in the generation unit may be performed using AI, or may be performed without AI. For example, the generation unit can input the idea category into AI, and the AI ​​can automatically apply the optimal generation algorithm.

[0041] When generating commercialization steps, the generation unit can determine the priority of the steps based on the time of idea submission. The generation unit can use criteria such as the submission date, the time elapsed since submission, and seasonal factors to evaluate the time of idea submission. For example, the generation unit can prioritize generating commercialization steps for ideas submitted early. The generation unit can also postpone generating commercialization steps for ideas submitted late. Furthermore, the generation unit can dynamically adjust the priority of steps based on the time of submission. By determining the priority of steps based on the time of idea submission, appropriate steps can be provided according to the time of submission. Some or all of the above-described processing in the generation unit may be performed using AI, or may be performed without AI. For example, the generation unit can input the time of idea submission into AI, and the AI ​​can automatically determine the priority of steps.

[0042] When generating commercialization steps, the generation unit can adjust the order of the steps based on the relevance of the ideas. The generation unit can use criteria such as technical relevance, business relevance, and social relevance to evaluate the relevance of the ideas. For example, the generation unit can prioritize generating commercialization steps for highly relevant ideas. The generation unit can also postpone generating commercialization steps for less relevant ideas. Furthermore, the generation unit can dynamically adjust the order of the steps according to the relevance of the ideas. In this way, by adjusting the order of the steps based on the relevance of the ideas, commercialization steps can be prioritized for highly relevant ideas. Some or all of the above-described processing in the generation unit may be performed using AI, or may be performed without using AI. For example, the generation unit can input the relevance of the ideas into AI, and the AI ​​can automatically adjust the order of the steps.

[0043] When calculating a profit forecast, the prediction unit can adjust the level of detail of the forecast based on the market size of the idea. The prediction unit can use criteria such as the size of the target market, growth rate, and revenue potential to evaluate the market size of the idea. For example, the prediction unit can calculate a detailed profit forecast for an idea with a large market size. The prediction unit can also calculate a simplified profit forecast for an idea with a small market size. Furthermore, the prediction unit can dynamically adjust the level of detail of the forecast depending on the market size. This allows for an appropriate profit forecast to be provided by adjusting the level of detail of the forecast based on the market size. Some or all of the above-described processing in the prediction unit may be performed using AI, or may be performed without using AI. For example, the prediction unit can input the market size of the idea into AI, and the AI ​​can automatically adjust the level of detail of the forecast.

[0044] When calculating a profit forecast, the prediction unit can apply different prediction algorithms depending on the competitive situation of the idea. To evaluate the competitive situation of the idea, the prediction unit can use criteria such as identifying major competitors, comparing competing products, and evaluating competitive advantages. For example, the prediction unit can calculate a profit forecast that includes a competitive analysis for a highly competitive market. Alternatively, the prediction unit can calculate a simple profit forecast for a less competitive market. Furthermore, the prediction unit can dynamically adjust the prediction algorithm depending on the competitive situation. This allows for more accurate profit forecasts by applying a prediction algorithm appropriate to the competitive situation. Some or all of the above-described processing in the prediction unit may be performed using AI, or may be performed without AI. For example, the prediction unit can input the competitive situation of the idea into AI, which then automatically applies the optimal prediction algorithm.

[0045] When calculating profit forecasts, the prediction unit can determine the priority of predictions based on the submission date of the ideas. The prediction unit can use criteria such as the submission date, the time elapsed since submission, and seasonal factors to evaluate the submission date of ideas. For example, the prediction unit can prioritize the calculation of profit forecasts for ideas submitted earlier. The prediction unit can also calculate profit forecasts for ideas submitted later, but at a later date. Furthermore, the prediction unit can dynamically adjust the priority of predictions based on the submission date. By determining the priority of predictions based on the submission date, it is possible to provide appropriate profit forecasts according to the submission date. Some or all of the above-described processing in the prediction unit may be performed using AI, or may be performed without AI. For example, the prediction unit can input the submission date of ideas into AI, and the AI ​​can automatically determine the priority of predictions.

[0046] The prediction unit can improve the accuracy of the prediction when calculating the profit forecast by referring to market data related to the idea. For example, the prediction unit can use information sources such as industry reports, competitive analysis data, and consumer survey data to collect relevant market data. For example, the prediction unit can calculate a detailed profit forecast based on the relevant market data. The prediction unit can also calculate a profit forecast including competitive analysis based on the relevant market data. Furthermore, the prediction unit can dynamically improve the accuracy of the forecast based on the relevant market data. As a result, by referring to the relevant market data, more accurate profit forecasts can be provided. Some or all of the above-mentioned processing in the prediction unit may be performed using AI or without AI. For example, the prediction unit can input relevant market data into AI, which can automatically improve the accuracy of the forecast.

[0047] The matching unit can improve the accuracy of matching by taking into account the interrelationships between ideas when matching members. For example, the matching unit can use criteria such as technical relevance, business relevance, and social relevance to evaluate the interrelationships between ideas. For example, the matching unit can analyze the interrelationships between ideas and prioritize matching with highly relevant members. The matching unit can also optimize the division of roles within a team based on the interrelationships between ideas. Furthermore, the matching unit can optimally combine the skill sets of members by taking into account the interrelationships between ideas. In this way, highly relevant members can be matched by taking into account the interrelationships between ideas. Some or all of the above-described processing in the matching unit may be performed using AI, or may be performed without using AI. For example, the matching unit can input the interrelationships between ideas into AI, which can automatically improve the accuracy of matching.

[0048] When matching members, the matching unit can take into account attribute information of the idea submitter. For example, the matching unit can use criteria such as age, gender, occupation, and expertise to evaluate the submitter's attribute information. For example, the matching unit matches relevant members based on the idea submitter's field of expertise. The matching unit can also select optimal members by taking into account the idea submitter's past project experience. Furthermore, the matching unit can ensure team diversity based on the attribute information of the idea submitter. In this way, by taking the submitter's attribute information into consideration, it is possible to match optimal members for the submitter. Some or all of the above-mentioned processing in the matching unit may be performed using AI, or may be performed without using AI. For example, the matching unit can input the submitter's attribute information into AI, which can then automatically select optimal members.

[0049] The matching unit can take into account the geographical distribution of ideas when matching members. For example, the matching unit can use criteria such as GPS data, IP addresses, and manual user input to evaluate geographical distribution. For example, the matching unit can prioritize matching members with region-specific knowledge based on the geographical distribution of ideas. The matching unit can also prioritize matching members who are geographically close to each other to improve communication efficiency. Furthermore, the matching unit can also form international teams taking geographical distribution into consideration. In this way, by taking geographical distribution into consideration, members with region-specific knowledge can be matched. Some or all of the above-mentioned processing in the matching unit can be performed using AI, or can be performed without using AI. For example, the matching unit can input geographical distribution into AI, which can then automatically select the optimal members.

[0050] The matching unit can improve the accuracy of matching by referring to literature related to an idea when matching members. For example, the matching unit can use information sources such as academic papers, patent documents, and industry reports to collect related literature. For example, the matching unit can prioritize matching members with specialized knowledge based on literature related to an idea. The matching unit can also refer to related literature to optimally combine the skill sets of members. Furthermore, the matching unit can ensure team diversity based on related literature. In this way, members with specialized knowledge can be matched by referring to related literature. Some or all of the above-mentioned processing in the matching unit may be performed using AI, or may be performed without using AI. For example, the matching unit can input related literature into AI, which can then automatically select the optimal members.

[0051] The investor matching unit can improve the accuracy of matching by taking into account the interrelationships between ideas when matching investors. For example, the investor matching unit can use criteria such as technical relevance, business relevance, and social relevance to evaluate the interrelationships between ideas. For example, the investor matching unit can analyze the interrelationships between ideas and prioritize matching with highly relevant investors. The investor matching unit can also optimize the investor's investment portfolio based on the interrelationships between ideas. Furthermore, the investor matching unit can optimally combine the investor's investment strategy by taking into account the interrelationships between ideas. In this way, highly relevant investors can be matched by taking into account the interrelationships between ideas. Some or all of the above-mentioned processing in the investor matching unit may be performed using AI, or may be performed without using AI. For example, the investor matching unit can input the interrelationships between ideas into AI, which can automatically improve the accuracy of matching.

[0052] When matching investors, the investor matching unit may take into account the attribute information of the idea submitter. For example, the investor matching unit may use criteria such as age, gender, occupation, and expertise to evaluate the attribute information of the submitter. For example, the investor matching unit may match relevant investors based on the idea submitter's field of expertise. The investor matching unit may also select the most suitable investor by taking into account the idea submitter's past project experience. Furthermore, the investor matching unit may diversify the investor's investment portfolio based on the attribute information of the idea submitter. In this way, by taking the attribute information of the submitter into consideration, the most suitable investor can be matched for the submitter. Some or all of the above-described processing in the investor matching unit may be performed using AI, or may be performed without using AI. For example, the investor matching unit may input the attribute information of the submitter into AI, which then automatically selects the most suitable investor.

[0053] The investor matching unit may take into account the geographic distribution of ideas when matching investors. The investor matching unit may use criteria such as GPS data, IP addresses, or manual user input to evaluate geographic distribution. For example, the investor matching unit may prioritize matching investors with regional knowledge based on the geographic distribution of ideas. The investor matching unit may also prioritize matching investors who are geographically close to the investor, improving communication efficiency. Furthermore, the investor matching unit may also match international investors with geographic distribution. By taking geographic distribution into consideration, investors with regional knowledge can be matched. Some or all of the above-described processes in the investor matching unit may be performed using AI, or may be performed without AI. For example, the investor matching unit may input the geographic distribution into AI, which then automatically selects the most suitable investor.

[0054] The investor matching unit can improve the accuracy of matching by referring to literature related to an idea when matching investors. For example, the investor matching unit can use information sources such as academic papers, patent documents, and industry reports to collect related literature. For example, the investor matching unit can preferentially match investors with specialized knowledge based on literature related to an idea. The investor matching unit can also optimally combine investors' investment portfolios by referring to related literature. Furthermore, the investor matching unit can diversify investors' investment strategies based on related literature. In this way, investors with specialized knowledge can be matched by referring to related literature. Some or all of the above-mentioned processing in the investor matching unit may be performed using AI, or may be performed without using AI. For example, the investor matching unit can input related literature into AI, which can then automatically select the most suitable investors.

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

[0056] When registering an idea, the reception unit can analyze the user's past idea submission history and provide input support. For example, to analyze the user's past idea submission history, data such as the number, content, and success rate of ideas submitted in the past can be collected. Keywords from ideas previously submitted by the user can be automatically extracted and related input fields can be suggested. It can also prioritize suggestions based on input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can automatically display input fields related to a specific field based on the user's past idea submission history. In this way, analyzing the user's past idea submission history can make input more efficient.

[0057] When generating commercialization steps, the generation unit can adjust the level of detail of the steps based on the importance of the idea. For example, criteria such as market potential, technological innovativeness, and social impact can be used to evaluate the importance of an idea. Detailed commercialization steps can be generated for ideas with high importance. Simplified commercialization steps can also be generated for ideas with low importance. Furthermore, the number and content of steps can be adjusted depending on the importance of the idea. In this way, by adjusting the level of detail of the steps based on the importance of the idea, more detailed steps can be provided for important ideas.

[0058] When calculating a profit forecast, the forecasting unit can adjust the level of detail of the forecast based on the market size of the idea. For example, criteria such as the size of the target market, growth rate, and revenue potential can be used to evaluate the market size of an idea. A detailed profit forecast can be calculated for an idea with a large market size. A simplified profit forecast can also be calculated for an idea with a small market size. Furthermore, the level of detail of the forecast can be dynamically adjusted depending on the market size. This allows for an appropriate profit forecast to be provided by adjusting the level of detail of the forecast based on the market size.

[0059] The matching unit can improve the accuracy of matching by taking into account the interrelationships between ideas when matching members. For example, criteria such as technical relevance, business relevance, and social relevance can be used to evaluate the interrelationships between ideas. The interrelationships between ideas can be analyzed and highly relevant members can be matched preferentially. The division of roles within a team can also be optimized based on the interrelationships between ideas. Furthermore, the skill sets of members can be optimally combined by taking into account the interrelationships between ideas. In this way, highly relevant members can be matched by taking into account the interrelationships between ideas.

[0060] The investor matching unit can improve the accuracy of matching by referring to literature related to the idea when matching investors. For example, to collect related literature, information sources such as academic papers, patent documents, and industry reports can be used. Based on the literature related to the idea, investors with specialized knowledge are preferentially matched. In addition, by referring to the related literature, the investment portfolio of the investor can be optimally combined. Furthermore, the investment strategy of the investor can be diversified based on the related literature. In this way, by referring to the related literature, investors with specialized knowledge can be matched.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The reception section is where idea providers can register their ideas. Idea providers can enter details of their idea, goals, required resources, etc. The reception section stores the information entered by the idea providers in a database and uses it for subsequent processing. Step 2: The generation unit generates steps to commercialization based on the idea registered by the reception unit. The generation unit generates steps such as market research, prototype creation, and marketing strategy formulation. The generation unit uses AI to analyze the content of the idea and market conditions, and can automatically generate the optimal commercialization steps. Step 3: The prediction unit calculates a profit forecast based on the steps generated by the generation unit. The prediction unit calculates a profit forecast by taking into account the market size of the idea, the competitive situation, cost structure, etc. The prediction unit uses AI to analyze past data and market trends and can provide accurate profit forecasts. Step 4: The matching department matches the most suitable members based on the profit forecast calculated by the prediction department. The matching department presents a success fee amount based on the profit forecast and matches the most suitable business members and engineers. The matching department uses AI to analyze the skills and experience of registered members and select the most suitable members. Step 5: The investor matching department matches investors based on the members matched by the matching department. The investor matching department automatically matches investors who wish to invest in the idea. The investor matching department can use AI to analyze the investors' investment portfolios and investment strategies and select the most suitable investors.

[0063] (Example 2) The new business matching platform according to an embodiment of the present invention is a system in which, when an idea provider registers an idea, the system automatically generates steps to commercialization, calculates profit forecasts, and matches the idea with the most suitable business members and engineers from among the registered members. The system allows members to decide whether to participate based on a success fee amount based on the profit forecast. The process also includes matching with investors. All members are free to join, and the service is a success fee system. The Idea Booster service itself also charges a success fee. For example, an idea provider registers an idea on the platform. At this time, they enter details of the idea, its goals, and the necessary resources. The system then automatically generates steps to commercialization. These steps include market research, prototyping, and marketing strategy development. The system then calculates a profit forecast. The profit forecast is based on factors such as the market size, competitive situation, and cost structure of the idea. For example, it predicts the expected sales and costs of the idea. The system then matches the idea with the most suitable business members and engineers from among the registered members. Members can confirm the success fee amount based on the profit forecast and decide to participate. For example, if the profit forecast is high, the success fee will also be high, increasing members' motivation to participate. Furthermore, the process also includes matching with investors. The system automatically matches ideas with investors who want to invest. This allows idea creators to avoid the hassle of fundraising. All members are free to join and the system is based on a success fee system. This means that members only receive rewards if the project is successful. In addition, the Idea Booster service itself is based on a success fee system. This allows platform users to minimize risk. This system allows idea creators to efficiently commercialize their ideas and members can earn success fees. It also gives investors the opportunity to invest in promising ideas.For example, an idea provider can register an idea for a new app, and the system will automatically generate steps to commercialize it and match the optimal members with investors, which is expected to lead to efficient commercialization. This allows the new business matching platform to enable idea providers to efficiently commercialize their ideas, allowing members to earn success fees, and providing investors with the opportunity to invest in promising ideas.

[0064] A new business matching platform according to an embodiment includes a reception unit, a generation unit, a prediction unit, a matching unit, and an investor matching unit. The reception unit is a unit through which idea providers register their ideas. Idea providers can input, for example, details of their ideas, goals, and necessary resources. The reception unit, for example, stores the information input by the idea providers in a database and uses it for subsequent processing. The generation unit is a unit that generates steps leading to commercialization based on the ideas registered by the reception unit. The generation unit generates steps such as market research, prototype creation, and marketing strategy formulation. The generation unit can use AI to analyze the content of the idea and the market situation and automatically generate optimal commercialization steps. For example, the generation unit can propose specific market research methods taking into account the market size and competitive situation of the idea. The generation unit can also provide detailed information on the resources and procedures required to create a prototype. Furthermore, the generation unit can specifically propose target markets and promotion methods when formulating a marketing strategy. The prediction unit is a unit that calculates profit forecasts based on the steps generated by the generation unit. The forecasting department calculates profit forecasts, for example, by taking into account the market size, competitive situation, cost structure, etc. of an idea. The forecasting department can use AI to analyze past data and market trends to provide accurate profit forecasts. For example, the forecasting department can make detailed predictions about how much sales an idea can generate and how much it will cost. The matching department is a department that matches optimal members based on the profit forecasts calculated by the forecasting department. For example, the matching department presents a success fee based on the profit forecast and matches optimal business members and engineers. The matching department can use AI to analyze the skills and experience of registered members and select optimal members. For example, the matching department can select optimal members by taking into account the business members' past project experience and expertise. The matching department can also analyze the engineers' technical skills and development experience to select optimal engineers.The investor matching department is a department that matches investors based on members matched by the matching department. For example, the investor matching department automatically matches investors who wish to invest in an idea. The investor matching department can use AI to analyze the investors' investment portfolios and investment strategies and select the most suitable investors. For example, the investor matching department can select the most suitable investors by taking into consideration the investors' past investment performance and the fields of investment targets. As a result, the new business matching platform according to the embodiment allows idea providers to efficiently commercialize their ideas and members to earn success fees. In addition, investors can have the opportunity to invest in promising ideas.

[0065] The reception unit can input details of an idea, goals, and required resources. The reception unit, for example, provides an interface for an idea provider to input details of an idea, goals, and required resources. For example, the reception unit can provide fields for inputting the purpose, background, and technical details of the idea. The reception unit can also provide fields for inputting the idea's short-term and long-term goals and achievement criteria. The reception unit can also provide fields for inputting the type and amount of required resources. For example, the reception unit can provide fields for inputting resources such as human resources, funds, and equipment. By inputting detailed information about an idea, more specific steps for commercialization can be generated. Some or all of the above-mentioned processing in the reception unit can be performed using AI, or can be performed without AI. For example, the reception unit can input the information input by the idea provider into AI, which can then automatically analyze the information and store it in a database.

[0066] The generation unit can generate steps for market research, creating a prototype, and formulating a marketing strategy. The generation unit generates, for example, market research steps. For example, the generation unit can propose specific methods such as identifying a target market, conducting competitive analysis, and conducting consumer research. The generation unit can also generate steps for creating a prototype. For example, the generation unit can indicate specific procedures for designing, manufacturing, and testing a prototype. The generation unit can also generate steps for formulating a marketing strategy. For example, the generation unit can propose specific methods such as a promotion plan, pricing, and selecting sales channels. This specifically generates steps for commercialization, thereby increasing the feasibility of the idea. Some or all of the above-mentioned processing in the generation unit may be performed using AI, or may be performed without AI. For example, the generation unit can input the content of the idea and market conditions into AI, which can then automatically generate optimal commercialization steps.

[0067] The prediction unit can calculate a profit forecast by taking into account the market size, competitive situation, and cost structure of the idea. For example, the prediction unit can calculate a profit forecast by taking into account the market size of the idea. For example, the prediction unit can analyze the size, growth rate, revenue potential, etc. of the target market. The prediction unit can also calculate a profit forecast by taking into account the competitive situation. For example, the prediction unit can identify major competitors, compare competing products, and evaluate competitive advantages. Furthermore, the prediction unit can calculate a profit forecast by taking into account the cost structure. For example, the prediction unit can analyze manufacturing costs, operating costs, marketing costs, etc. By calculating a profit forecast that takes into account the market size and competitive situation, the likelihood of business success can be increased. Some or all of the above-described processing in the prediction unit may be performed using AI, or may be performed without AI. For example, the prediction unit can input past data and market trends into AI, which can then automatically calculate a profit forecast.

[0068] The matching unit can present a success fee amount based on profit forecasts and match appropriate business members and engineers. The matching unit presents, for example, a success fee amount based on profit forecasts. For example, the matching unit can present specific calculation methods such as a fixed percentage of profits, a fixed fee, or a performance-based fee. The matching unit can also match appropriate business members and engineers. For example, the matching unit can select the optimal member by taking into account the business member's experience, skills, expertise, etc. Furthermore, the matching unit can analyze the engineer's technical skills and development experience to select the optimal engineer. This can increase members' motivation to participate by presenting a success fee amount based on profit forecasts. Some or all of the above-mentioned processing in the matching unit may be performed using AI, or may be performed without AI. For example, the matching unit can input the skills and experience of registered members into AI, which can then automatically select the optimal member.

[0069] The investor matching unit can automatically match investors who wish to invest in an idea. For example, the investor matching unit automatically matches investors who wish to invest in an idea. For example, the investor matching unit can analyze the investors' investment portfolios and investment strategies and select the most suitable investors. For example, the investor matching unit can select the most suitable investors by taking into consideration the investors' past investment performance and the fields of investment targets. In this way, automatically matching investors can reduce the effort required for fundraising. Some or all of the above-mentioned processing in the investor matching unit may be performed using AI, or may be performed without using AI. For example, the investor matching unit can input the investors' investment portfolios and investment strategies into AI, which can then automatically select the most suitable investors.

[0070] The reception unit can estimate the user's emotions and adjust the interface for entering idea details based on the estimated user emotions. The reception unit can use technologies such as facial expression recognition, voice analysis, and text analysis to estimate the user's emotions. For example, if the user is feeling stressed, the reception unit can provide a simple interface and minimize input steps. If the user is relaxed, the reception unit can provide detailed input options and suggest customizable input methods. Furthermore, if the user is in a hurry, the reception unit can prioritize voice input to allow the user to quickly enter idea details. This reduces the user's input burden by adjusting the interface according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using AI, or without AI. For example, the reception unit can input the user's facial expression data into a generation AI, which can automatically estimate the user's emotions and adjust the interface.

[0071] When registering an idea, the reception unit can analyze the user's past idea submission history and provide input support. For example, the reception unit can collect data such as the number, content, and success rate of previously submitted ideas to analyze the user's past idea submission history. For example, the reception unit can automatically extract keywords from ideas previously submitted by the user and suggest related input fields. The reception unit can also prioritize suggestions based on input methods (such as voice or text) that the user has used in the past. Furthermore, the reception unit can automatically display input fields related to a specific field based on the user's past idea submission history. This allows the user to efficiently input ideas by analyzing the past idea submission history. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without AI. For example, the reception unit can input the user's past idea submission history into AI, which then automatically suggests related input fields.

[0072] When entering details of an idea, the reception unit can customize input fields based on the user's current project or areas of interest. The reception unit can collect data such as the user's project history and survey results on areas of interest to identify the user's current project or areas of interest. For example, the reception unit can prioritize displaying input fields related to the user's current project. The reception unit can also automatically suggest related input fields based on the user's areas of interest. Furthermore, the reception unit can also work with the user's project management tool to automatically obtain current project information and customize the input fields. This allows for customizing the input fields based on the user's current project or areas of interest, thereby improving the accuracy of input. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without AI. For example, the reception unit can input the user's project information into AI, which then automatically customizes the input fields.

[0073] The reception unit can estimate the user's emotions and adjust the order of idea input based on the estimated user emotions. The reception unit can use technologies such as facial expression recognition, voice analysis, and text analysis to estimate the user's emotions. For example, if the user is nervous, the reception unit can display important input items first and prompt the user to input detailed items later. Alternatively, if the user is relaxed, the reception unit can display detailed input items first and prompt the user to input important items later. Furthermore, if the user is in a hurry, the reception unit can display the most important input items first to allow the user to complete input quickly. This improves input efficiency by adjusting the input order according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using AI or without AI. For example, the reception unit can input the user's facial expression data into the generation AI, which can then automatically infer the emotion and adjust the input order.

[0074] When registering an idea, the reception unit can prioritize displaying highly relevant input items by taking into account the user's geographical location information. The reception unit can acquire the user's geographical location information using methods such as GPS data, IP address, or manual input by the user. For example, the reception unit can display region-specific market information as an input item based on the user's current location. The reception unit can also suggest regional competitive conditions as an input item based on the user's geographical location information. Furthermore, the reception unit can prioritize displaying input items related to regional laws and regulations by taking into account the user's location information. This allows the user to efficiently input region-specific information by taking into account the user's geographical location information. Some or all of the above-described processing by the reception unit may be performed using AI, or may be performed without using AI. For example, the reception unit can input the user's geographical location information into AI, which can then automatically suggest highly relevant input items.

[0075] The reception unit can analyze the user's social media activity and suggest related input items when registering an idea. The reception unit can, for example, collect data such as post content, number of followers, and engagement rate to analyze the user's social media activity. For example, the reception unit can analyze the user's social media post content and suggest related keywords as input items. The reception unit can also display related input items based on the interests of the user's followers and friends. Furthermore, the reception unit can analyze the user's social media activity history and customize input items based on the content of past posts. This allows related input items to be efficiently suggested by analyzing the user's social media activity. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without AI. For example, the reception unit can input the user's social media activity into AI, which can then automatically suggest related input items.

[0076] The generation unit can estimate the user's emotions and adjust the generation method of the commercialization steps based on the estimated user emotions. The generation unit can use technologies such as facial expression recognition, voice analysis, and text analysis to estimate the user's emotions. For example, the generation unit can generate detailed commercialization steps when the user is relaxed. The generation unit can also generate simplified commercialization steps when the user is in a hurry. Furthermore, the generation unit can generate visually appealing commercialization steps when the user is excited. This allows steps that meet the user's needs to be provided by adjusting the generation method of the commercialization steps according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the generation unit can be performed using AI or without AI. For example, the generation unit can input the user's facial expression data into the generation AI, which can automatically estimate the user's emotions and adjust the generation method of the commercialization steps.

[0077] When generating commercialization steps, the generation unit can adjust the level of detail of the steps based on the importance of the idea. The generation unit can use criteria such as market potential, technological innovativeness, and social impact to evaluate the importance of an idea. For example, the generation unit can generate detailed commercialization steps for ideas with high importance. The generation unit can also generate simplified commercialization steps for ideas with low importance. Furthermore, the generation unit can adjust the number and content of steps according to the importance of the idea. By adjusting the level of detail of the steps based on the importance of the idea, more detailed steps can be provided for important ideas. Some or all of the above-described processing in the generation unit may be performed using AI, or may be performed without AI. For example, the generation unit can input the importance of the idea into AI, which can then automatically adjust the level of detail of the steps.

[0078] When generating commercialization steps, the generation unit can apply different generation algorithms depending on the category of the idea. For example, the generation unit can use criteria such as technology category, business category, or social category to categorize ideas. For example, the generation unit can generate commercialization steps specialized for technology development for technology-related ideas. For service-related ideas, the generation unit can generate commercialization steps specialized for marketing and customer service. For manufacturing-related ideas, the generation unit can generate commercialization steps specialized for prototyping and production planning. By applying a generation algorithm depending on the idea category, optimal commercialization steps can be provided for each category. Some or all of the above-described processing in the generation unit may be performed using AI, or may be performed without AI. For example, the generation unit can input the idea category into AI, and the AI ​​can automatically apply the optimal generation algorithm.

[0079] The generation unit can estimate the user's emotions and adjust the order of the commercialization steps based on the estimated user emotions. The generation unit can use technologies such as facial expression recognition, voice analysis, and text analysis to estimate the user's emotions. For example, if the user is nervous, the generation unit can display important steps first and detailed steps later. Alternatively, if the user is relaxed, the generation unit can display detailed steps first and important steps later. Furthermore, if the user is in a hurry, the generation unit can display the most important steps first to quickly advance commercialization. This allows the order of commercialization steps to be adjusted according to the user's emotions, thereby providing steps that meet the user's needs. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the generation unit can be performed using AI or without AI. For example, the generation unit can input the user's facial expression data into the generation AI, which can then automatically infer the emotion and adjust the order of the commercialization steps.

[0080] When generating commercialization steps, the generation unit can determine the priority of the steps based on the time of idea submission. The generation unit can use criteria such as the submission date, the time elapsed since submission, and seasonal factors to evaluate the time of idea submission. For example, the generation unit can prioritize generating commercialization steps for ideas submitted early. The generation unit can also postpone generating commercialization steps for ideas submitted late. Furthermore, the generation unit can dynamically adjust the priority of steps based on the time of submission. By determining the priority of steps based on the time of idea submission, appropriate steps can be provided according to the time of submission. Some or all of the above-described processing in the generation unit may be performed using AI, or may be performed without AI. For example, the generation unit can input the time of idea submission into AI, and the AI ​​can automatically determine the priority of steps.

[0081] When generating commercialization steps, the generation unit can adjust the order of the steps based on the relevance of the ideas. The generation unit can use criteria such as technical relevance, business relevance, and social relevance to evaluate the relevance of the ideas. For example, the generation unit can prioritize generating commercialization steps for highly relevant ideas. The generation unit can also postpone generating commercialization steps for less relevant ideas. Furthermore, the generation unit can dynamically adjust the order of the steps according to the relevance of the ideas. In this way, by adjusting the order of the steps based on the relevance of the ideas, commercialization steps can be prioritized for highly relevant ideas. Some or all of the above-described processing in the generation unit may be performed using AI, or may be performed without using AI. For example, the generation unit can input the relevance of the ideas into AI, and the AI ​​can automatically adjust the order of the steps.

[0082] The prediction unit can estimate the user's emotions and adjust the profit prediction calculation method based on the estimated user emotions. The prediction unit can use technologies such as facial expression recognition, voice analysis, and text analysis to estimate the user's emotions. For example, the prediction unit can calculate a detailed profit prediction when the user is relaxed. The prediction unit can also calculate a simplified profit prediction when the user is in a hurry. Furthermore, the prediction unit can calculate a visually appealing profit prediction when the user is excited. This allows the profit prediction calculation method to be adjusted according to the user's emotions, thereby providing a profit prediction that meets the user's needs. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the prediction unit can be performed using AI or without AI. For example, the prediction unit can input the user's facial expression data into the generation AI, which can automatically estimate the emotion and adjust the profit prediction calculation method.

[0083] When calculating a profit forecast, the prediction unit can adjust the level of detail of the forecast based on the market size of the idea. The prediction unit can use criteria such as the size of the target market, growth rate, and revenue potential to evaluate the market size of the idea. For example, the prediction unit can calculate a detailed profit forecast for an idea with a large market size. The prediction unit can also calculate a simplified profit forecast for an idea with a small market size. Furthermore, the prediction unit can dynamically adjust the level of detail of the forecast depending on the market size. This allows for an appropriate profit forecast to be provided by adjusting the level of detail of the forecast based on the market size. Some or all of the above-described processing in the prediction unit may be performed using AI, or may be performed without using AI. For example, the prediction unit can input the market size of the idea into AI, and the AI ​​can automatically adjust the level of detail of the forecast.

[0084] When calculating a profit forecast, the prediction unit can apply different prediction algorithms depending on the competitive situation of the idea. To evaluate the competitive situation of the idea, the prediction unit can use criteria such as identifying major competitors, comparing competing products, and evaluating competitive advantages. For example, the prediction unit can calculate a profit forecast that includes a competitive analysis for a highly competitive market. Alternatively, the prediction unit can calculate a simple profit forecast for a less competitive market. Furthermore, the prediction unit can dynamically adjust the prediction algorithm depending on the competitive situation. This allows for more accurate profit forecasts by applying a prediction algorithm appropriate to the competitive situation. Some or all of the above-described processing in the prediction unit may be performed using AI, or may be performed without AI. For example, the prediction unit can input the competitive situation of the idea into AI, which then automatically applies the optimal prediction algorithm.

[0085] The prediction unit can estimate the user's emotions and adjust the display method of the profit forecast based on the estimated user emotions. The prediction unit can use technologies such as facial expression recognition, voice analysis, and text analysis to estimate the user's emotions. For example, if the user is nervous, the prediction unit can provide a simple, highly visible display method. If the user is relaxed, the prediction unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the prediction unit can provide a display method that focuses on the main points. This allows the profit forecast display method to be adjusted according to the user's emotions, making it easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the prediction unit can be performed using AI or without AI. For example, the prediction unit can input the user's facial expression data into the generation AI, which can automatically estimate the emotion and adjust the display method of the profit forecast.

[0086] When calculating profit forecasts, the prediction unit can determine the priority of predictions based on the submission date of the ideas. The prediction unit can use criteria such as the submission date, the time elapsed since submission, and seasonal factors to evaluate the submission date of ideas. For example, the prediction unit can prioritize the calculation of profit forecasts for ideas submitted earlier. The prediction unit can also calculate profit forecasts for ideas submitted later, but at a later date. Furthermore, the prediction unit can dynamically adjust the priority of predictions based on the submission date. By determining the priority of predictions based on the submission date, it is possible to provide appropriate profit forecasts according to the submission date. Some or all of the above-described processing in the prediction unit may be performed using AI, or may be performed without AI. For example, the prediction unit can input the submission date of ideas into AI, and the AI ​​can automatically determine the priority of predictions.

[0087] The prediction unit can improve the accuracy of the prediction when calculating the profit forecast by referring to market data related to the idea. For example, the prediction unit can use information sources such as industry reports, competitive analysis data, and consumer survey data to collect relevant market data. For example, the prediction unit can calculate a detailed profit forecast based on the relevant market data. The prediction unit can also calculate a profit forecast including competitive analysis based on the relevant market data. Furthermore, the prediction unit can dynamically improve the accuracy of the forecast based on the relevant market data. As a result, by referring to the relevant market data, more accurate profit forecasts can be provided. Some or all of the above-mentioned processing in the prediction unit may be performed using AI or without AI. For example, the prediction unit can input relevant market data into AI, which can automatically improve the accuracy of the forecast.

[0088] The matching unit can estimate the user's emotions and adjust the member matching criteria based on the estimated user emotions. The matching unit can use technologies such as facial expression recognition, voice analysis, and text analysis to estimate the user's emotions. For example, the matching unit can apply detailed matching criteria when the user is relaxed. The matching unit can also apply simplified matching criteria when the user is in a hurry. Furthermore, the matching unit can apply visually appealing matching criteria when the user is excited. This allows the matching criteria to be adjusted according to the user's emotions, thereby matching the optimal member for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the matching unit can be performed using AI or without AI. For example, the matching unit can input the user's facial expression data into the generation AI, which can automatically estimate the emotion and adjust the matching criteria.

[0089] The matching unit can improve the accuracy of matching by taking into account the interrelationships between ideas when matching members. For example, the matching unit can use criteria such as technical relevance, business relevance, and social relevance to evaluate the interrelationships between ideas. For example, the matching unit can analyze the interrelationships between ideas and prioritize matching with highly relevant members. The matching unit can also optimize the division of roles within a team based on the interrelationships between ideas. Furthermore, the matching unit can optimally combine the skill sets of members by taking into account the interrelationships between ideas. In this way, highly relevant members can be matched by taking into account the interrelationships between ideas. Some or all of the above-described processing in the matching unit may be performed using AI, or may be performed without using AI. For example, the matching unit can input the interrelationships between ideas into AI, which can automatically improve the accuracy of matching.

[0090] When matching members, the matching unit can take into account attribute information of the idea submitter. For example, the matching unit can use criteria such as age, gender, occupation, and expertise to evaluate the submitter's attribute information. For example, the matching unit matches relevant members based on the idea submitter's field of expertise. The matching unit can also select optimal members by taking into account the idea submitter's past project experience. Furthermore, the matching unit can ensure team diversity based on the attribute information of the idea submitter. In this way, by taking the submitter's attribute information into consideration, it is possible to match optimal members for the submitter. Some or all of the above-mentioned processing in the matching unit may be performed using AI, or may be performed without using AI. For example, the matching unit can input the submitter's attribute information into AI, which can then automatically select optimal members.

[0091] The matching unit can estimate the user's emotions and adjust the display order of the matching results based on the estimated user emotions. The matching unit can use technologies such as facial expression recognition, voice analysis, and text analysis to estimate the user's emotions. For example, if the user is nervous, the matching unit can display the most relevant members first. If the user is relaxed, the matching unit can also display members with detailed information first. If the user is in a hurry, the matching unit can also display members that highlight the key points first. This allows the display order to be adjusted according to the user's emotions, providing matching results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the matching unit can be performed using AI, or without AI. For example, the matching unit can input the user's facial expression data into a generation AI, which can automatically estimate the emotions and adjust the display order of the matching results.

[0092] The matching unit can take into account the geographical distribution of ideas when matching members. For example, the matching unit can use criteria such as GPS data, IP addresses, and manual user input to evaluate geographical distribution. For example, the matching unit can prioritize matching members with region-specific knowledge based on the geographical distribution of ideas. The matching unit can also prioritize matching members who are geographically close to each other to improve communication efficiency. Furthermore, the matching unit can also form international teams taking geographical distribution into consideration. In this way, by taking geographical distribution into consideration, members with region-specific knowledge can be matched. Some or all of the above-mentioned processing in the matching unit can be performed using AI, or can be performed without using AI. For example, the matching unit can input geographical distribution into AI, which can then automatically select the optimal members.

[0093] The matching unit can improve the accuracy of matching by referring to literature related to an idea when matching members. For example, the matching unit can use information sources such as academic papers, patent documents, and industry reports to collect related literature. For example, the matching unit can prioritize matching members with specialized knowledge based on literature related to an idea. The matching unit can also refer to related literature to optimally combine the skill sets of members. Furthermore, the matching unit can ensure team diversity based on related literature. In this way, members with specialized knowledge can be matched by referring to related literature. Some or all of the above-mentioned processing in the matching unit may be performed using AI, or may be performed without using AI. For example, the matching unit can input related literature into AI, which can then automatically select the optimal members.

[0094] The investor matching unit can estimate a user's emotions and adjust the investor matching criteria based on the estimated user emotions. The investor matching unit can use technologies such as facial expression recognition, voice analysis, and text analysis to estimate a user's emotions. For example, the investor matching unit can apply detailed matching criteria when the user is relaxed. The investor matching unit can also apply simplified matching criteria when the user is in a hurry. Furthermore, the investor matching unit can apply visually appealing matching criteria when the user is excited. This allows the optimal investor to be matched by adjusting the investor matching criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the investor matching unit can be performed using AI, or without AI. For example, the investor matching unit can input the user's facial expression data into the generation AI, which can automatically estimate the user's emotions and adjust the investor matching criteria.

[0095] The investor matching unit can improve the accuracy of matching by taking into account the interrelationships between ideas when matching investors. For example, the investor matching unit can use criteria such as technical relevance, business relevance, and social relevance to evaluate the interrelationships between ideas. For example, the investor matching unit can analyze the interrelationships between ideas and prioritize matching with highly relevant investors. The investor matching unit can also optimize the investor's investment portfolio based on the interrelationships between ideas. Furthermore, the investor matching unit can optimally combine the investor's investment strategy by taking into account the interrelationships between ideas. In this way, highly relevant investors can be matched by taking into account the interrelationships between ideas. Some or all of the above-mentioned processing in the investor matching unit may be performed using AI, or may be performed without using AI. For example, the investor matching unit can input the interrelationships between ideas into AI, which can automatically improve the accuracy of matching.

[0096] When matching investors, the investor matching unit may take into account the attribute information of the idea submitter. For example, the investor matching unit may use criteria such as age, gender, occupation, and expertise to evaluate the attribute information of the submitter. For example, the investor matching unit may match relevant investors based on the idea submitter's field of expertise. The investor matching unit may also select the most suitable investor by taking into account the idea submitter's past project experience. Furthermore, the investor matching unit may diversify the investor's investment portfolio based on the attribute information of the idea submitter. In this way, by taking the attribute information of the submitter into consideration, the most suitable investor can be matched for the submitter. Some or all of the above-described processing in the investor matching unit may be performed using AI, or may be performed without using AI. For example, the investor matching unit may input the attribute information of the submitter into AI, which then automatically selects the most suitable investor.

[0097] The investor matching unit can estimate a user's emotions and adjust the display order of investor matching results based on the estimated user emotions. The investor matching unit can use technologies such as facial expression recognition, voice analysis, and text analysis to estimate a user's emotions. For example, if a user is nervous, the investor matching unit can display the most relevant investors first. If a user is relaxed, the investor matching unit can also display investors with detailed information first. Furthermore, if a user is in a hurry, the investor matching unit can also display investors with the most important points first. This allows for adjusting the display order according to the user's emotions to provide investor matching results that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the investor matching unit may be performed using AI or without AI. For example, the investor matching unit can input a user's facial expression data into a generation AI, which can automatically estimate the user's emotions and adjust the display order of the investor matching results.

[0098] The investor matching unit may take into account the geographic distribution of ideas when matching investors. The investor matching unit may use criteria such as GPS data, IP addresses, or manual user input to evaluate geographic distribution. For example, the investor matching unit may prioritize matching investors with regional knowledge based on the geographic distribution of ideas. The investor matching unit may also prioritize matching investors who are geographically close to the investor, improving communication efficiency. Furthermore, the investor matching unit may also match international investors with geographic distribution. By taking geographic distribution into consideration, investors with regional knowledge can be matched. Some or all of the above-described processes in the investor matching unit may be performed using AI, or may be performed without AI. For example, the investor matching unit may input the geographic distribution into AI, which then automatically selects the most suitable investor.

[0099] The investor matching unit can improve the accuracy of matching by referring to literature related to an idea when matching investors. For example, the investor matching unit can use information sources such as academic papers, patent documents, and industry reports to collect related literature. For example, the investor matching unit can preferentially match investors with specialized knowledge based on literature related to an idea. The investor matching unit can also optimally combine investors' investment portfolios by referring to related literature. Furthermore, the investor matching unit can diversify investors' investment strategies based on related literature. In this way, investors with specialized knowledge can be matched by referring to related literature. Some or all of the above-mentioned processing in the investor matching unit may be performed using AI, or may be performed without using AI. For example, the investor matching unit can input related literature into AI, which can then automatically select the most suitable investors. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, prediction unit, matching unit, and investor matching unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and stores information entered by the idea provider in the database 24. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically generates steps leading to commercialization. The prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and calculates profit forecasts. The matching unit is realized, for example, by the control unit 46A of the smart device 14 and selects optimal members. The investor matching unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically matches investors. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, prediction unit, matching unit, and investor matching unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and stores information entered by the idea provider in the database 24. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically generates steps leading up to commercialization. The prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and calculates profit forecasts. The matching unit is realized, for example, by the control unit 46A of the smart glasses 214 and selects optimal members. The investor matching unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically matches investors. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, generation unit, prediction unit, matching unit, and investor matching unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset-type terminal 314 and stores information entered by the idea provider in the database 24. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically generates steps leading up to commercialization. The prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and calculates profit forecasts. The matching unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and selects optimal members. The investor matching unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically matches investors. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, generation unit, prediction unit, matching unit, and investor matching unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and stores information input by the idea provider in the database 24. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically generates steps leading up to commercialization. The prediction unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and calculates profit forecasts. The matching unit is realized, for example, by the control unit 46A of the robot 414 and selects optimal members. The investor matching unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically matches investors.

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

[0101] The receiving unit can estimate the user's emotions and adjust the interface for inputting idea details based on the estimated user emotions. For example, if the user is feeling stressed, a simple interface can be provided to minimize the input steps. Alternatively, if the user is relaxed, detailed input options can be provided and a customizable input method can be suggested. Furthermore, if the user is in a hurry, voice input can be prioritized to allow the user to quickly input idea details. In this way, adjusting the interface according to the user's emotions can reduce the input burden on the user.

[0102] The generation unit can estimate the user's emotions and adjust the method for generating commercialization steps based on the estimated user emotions. For example, if the user is relaxed, detailed commercialization steps can be generated. If the user is in a hurry, simplified commercialization steps can be generated. Furthermore, if the user is excited, visually appealing commercialization steps can be generated. In this way, by adjusting the method for generating commercialization steps according to the user's emotions, steps that meet the user's needs can be provided.

[0103] The prediction unit can estimate the user's emotions and adjust the profit prediction calculation method based on the estimated user's emotions. For example, if the user is relaxed, a detailed profit prediction can be calculated. If the user is in a hurry, a simplified profit prediction can be calculated. Furthermore, if the user is excited, a visually appealing profit prediction can be calculated. In this way, by adjusting the profit prediction calculation method according to the user's emotions, it is possible to provide a profit prediction that meets the user's needs.

[0104] The matching unit can estimate the user's emotions and adjust the member matching criteria based on the estimated user's emotions. For example, if the user is relaxed, detailed matching criteria can be applied. If the user is in a hurry, simplified matching criteria can be applied. Furthermore, if the user is excited, visually appealing matching criteria can be applied. In this way, by adjusting the matching criteria according to the user's emotions, the optimal member can be matched for the user.

[0105] The investor matching unit can estimate the user's emotions and adjust the investor matching criteria based on the estimated user's emotions. For example, if the user is relaxed, detailed matching criteria can be applied. If the user is in a hurry, simplified matching criteria can be applied. Furthermore, if the user is excited, visually appealing matching criteria can be applied. In this way, by adjusting the investor matching criteria according to the user's emotions, the optimal investor can be matched for the user.

[0106] When registering an idea, the reception unit can analyze the user's past idea submission history and provide input support. For example, to analyze the user's past idea submission history, data such as the number, content, and success rate of ideas submitted in the past can be collected. Keywords from ideas previously submitted by the user can be automatically extracted and related input fields can be suggested. It can also prioritize suggestions based on input methods (voice, text, etc.) that the user has used in the past. Furthermore, it can automatically display input fields related to a specific field based on the user's past idea submission history. In this way, analyzing the user's past idea submission history can make input more efficient.

[0107] When generating commercialization steps, the generation unit can adjust the level of detail of the steps based on the importance of the idea. For example, criteria such as market potential, technological innovativeness, and social impact can be used to evaluate the importance of an idea. Detailed commercialization steps can be generated for ideas with high importance. Simplified commercialization steps can also be generated for ideas with low importance. Furthermore, the number and content of steps can be adjusted depending on the importance of the idea. In this way, by adjusting the level of detail of the steps based on the importance of the idea, more detailed steps can be provided for important ideas.

[0108] When calculating a profit forecast, the forecasting unit can adjust the level of detail of the forecast based on the market size of the idea. For example, criteria such as the size of the target market, growth rate, and revenue potential can be used to evaluate the market size of an idea. A detailed profit forecast can be calculated for an idea with a large market size. A simplified profit forecast can also be calculated for an idea with a small market size. Furthermore, the level of detail of the forecast can be dynamically adjusted depending on the market size. This allows for an appropriate profit forecast to be provided by adjusting the level of detail of the forecast based on the market size.

[0109] The matching unit can improve the accuracy of matching by taking into account the interrelationships between ideas when matching members. For example, criteria such as technical relevance, business relevance, and social relevance can be used to evaluate the interrelationships between ideas. The interrelationships between ideas can be analyzed and highly relevant members can be matched preferentially. The division of roles within a team can also be optimized based on the interrelationships between ideas. Furthermore, the skill sets of members can be optimally combined by taking into account the interrelationships between ideas. In this way, highly relevant members can be matched by taking into account the interrelationships between ideas.

[0110] The investor matching unit can improve the accuracy of matching by referring to literature related to the idea when matching investors. For example, to collect related literature, information sources such as academic papers, patent documents, and industry reports can be used. Based on the literature related to the idea, investors with specialized knowledge are preferentially matched. In addition, by referring to the related literature, the investment portfolio of the investor can be optimally combined. Furthermore, the investment strategy of the investor can be diversified based on the related literature. In this way, by referring to the related literature, investors with specialized knowledge can be matched.

[0111] The processing flow of the second embodiment will be briefly explained below.

[0112] Step 1: The reception section is where idea providers can register their ideas. Idea providers can enter details of their idea, goals, required resources, etc. The reception section stores the information entered by the idea providers in a database and uses it for subsequent processing. Step 2: The generation unit generates steps to commercialization based on the idea registered by the reception unit. The generation unit generates steps such as market research, prototype creation, and marketing strategy formulation. The generation unit uses AI to analyze the content of the idea and market conditions, and can automatically generate the optimal commercialization steps. Step 3: The prediction unit calculates a profit forecast based on the steps generated by the generation unit. The prediction unit calculates a profit forecast by taking into account the market size of the idea, the competitive situation, cost structure, etc. The prediction unit uses AI to analyze past data and market trends and can provide accurate profit forecasts. Step 4: The matching department matches the most suitable members based on the profit forecast calculated by the prediction department. The matching department presents a success fee amount based on the profit forecast and matches the most suitable business members and engineers. The matching department uses AI to analyze the skills and experience of registered members and select the most suitable members. Step 5: The investor matching department matches investors based on the members matched by the matching department. The investor matching department automatically matches investors who wish to invest in the idea. The investor matching department can use AI to analyze the investors' investment portfolios and investment strategies and select the most suitable investors.

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

[0114] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0115] 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.

[0116] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

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

[0119] The data processing device 12 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

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

[0130] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0131] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is 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.

[0132] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0147] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is 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.

[0148] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

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

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the 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.

[0163] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0164] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is 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.

[0165] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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).

[0170] 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.

[0171] 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."

[0172] 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.

[0173] 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.

[0174] 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.

[0175] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] [Explanation of symbols]

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

Claims

1. A reception desk for registering ideas; a generation unit that generates steps to commercialization based on the idea registered by the reception unit; a prediction unit that calculates a profit prediction based on the steps generated by the generation unit; a matching unit that matches appropriate members based on the profit prediction calculated by the prediction unit; an investor matching unit that matches investors based on the members matched by the matching unit; A system characterized by:

2. The reception unit Enter details of your idea, goals, and required resources The system of claim 1 .

3. The generation unit Generate steps for market research, prototyping, and developing a marketing strategy The system of claim 1 .

4. The prediction unit Calculate profit forecasts based on the market size, competitive situation, and cost structure of your idea The system of claim 1 .

5. The matching unit We offer success fees based on profit projections and match suitable business members and engineers. The system of claim 1 .

6. The investor matching department Automatically match ideas with investors willing to invest The system of claim 1 .

7. The reception unit Estimates user's emotions and adjusts the interface for inputting details of ideas based on the estimated user's emotions. The system of claim 1 .

8. The reception unit When registering an idea, the system analyzes the user's past idea submission history and provides input assistance. The system of claim 1 .

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