system

Abstract

The system according to this embodiment aims to enable the commercialization and establishment of new business ideas without requiring specialized knowledge. [Solution] The system according to the embodiment comprises a review unit, a cost calculation unit, a plan creation unit, an investor matching unit, and an administrative procedure support unit. The review unit reviews ideas using an interactive AI chatbot. The cost calculation unit calculates the necessary costs based on the ideas reviewed by the review unit. The plan creation unit creates a business plan based on the costs calculated by the cost calculation unit. The investor matching unit requests investment from investors using the business plan created by the plan creation unit. The administrative procedure support unit supports the administrative procedures and applications necessary for company establishment based on the investments obtained by the investor matching unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the process of commercializing and incorporating an idea for a new business is complex and difficult to execute without specialized knowledge.

[0005] The system according to the embodiment aims to commercialize and incorporate an idea for a new business even without specialized knowledge.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a review unit, a cost calculation unit, a plan creation unit, an investor matching unit, and an administrative procedure support unit. The review unit reviews ideas using an interactive AI chatbot. The cost calculation unit calculates the necessary costs based on the ideas reviewed by the review unit. The plan creation unit creates a business plan based on the costs calculated by the cost calculation unit. The investor matching unit uses the business plan created by the plan creation unit to request investment from investors. The administrative procedure support unit supports the administrative procedures and applications necessary for company establishment based on the investments obtained by the investor matching unit. [Effects of the Invention]

[0007] The system according to this embodiment allows new business ideas to be commercialized and incorporated into a company, even without specialized knowledge. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The new business support system according to an embodiment of the present invention is a system in which an AI agent provides total support to people with new business ideas, from idea consideration to company establishment. This new business support system consists of four steps: idea generation, business plan creation, investor matching, and company establishment. First, in the idea generation stage, an interactive AI chatbot is used to consider the idea. The AI ​​chatbot uses a relevant information provision function to provide information such as "comparison with current services" and "patent / paper research" to make the idea more concrete and novel. Next, in the business plan creation stage, the necessary costs are calculated based on the idea, and a business plan is created on the service. Furthermore, expenses are automatically calculated from the scale of the business, and a balance sheet is created on the service. The user refines the plan while interacting with the agent, clarifying "what is necessary" and "what can be cut." In the investor matching stage, it is possible to use the business plan created on the service to offer investment to those registered as investors in this service. Finally, in the company establishment stage, the AI ​​also supports the completion of necessary administrative procedures and applications for company establishment. This enables total support from "idea" to "company incorporation." For example, when a user inputs an idea into the new business support system, an AI chatbot provides relevant information to help concretize the idea. Next, when the user creates a business plan, the AI ​​calculates the necessary costs and automatically generates a balance sheet. Furthermore, during the investor matching stage, the AI ​​requests investment from potential investors, and during the company establishment stage, the AI ​​supports administrative procedures and applications. This makes it possible to create a world where anyone can start a "new business." In this way, the new business support system can provide total support for the process from considering a new business idea to establishing a company.

[0029] The new business support system according to this embodiment comprises a review unit, a cost calculation unit, a plan creation unit, an investor matching unit, and an administrative procedure support unit. The review unit reviews ideas using an interactive AI chatbot. For example, the review unit uses the AI ​​chatbot to provide relevant information in response to an idea entered by the user, thereby concretizing the idea. For example, the review unit uses the AI ​​chatbot to provide information such as "comparison with current services" and "patent / paper search." The review unit can also use the AI ​​chatbot to review ideas through dialogue with the user. For example, the AI ​​chatbot can ask the user questions to elicit details of the idea. The cost calculation unit calculates the necessary costs based on the ideas reviewed by the review unit. For example, the cost calculation unit automatically calculates the costs required to realize the idea. For example, the cost calculation unit can automatically calculate expenses according to the scale of the business. The cost calculation unit can also use AI to calculate costs. For example, the AI ​​predicts costs based on past data. The plan creation unit creates a business plan based on the costs calculated by the cost calculation unit. The business plan creation department can, for example, automatically generate business plans on the service. The business plan creation department can also, for example, automatically create balance sheets. Furthermore, the business plan creation department can use AI to create business plans. For example, the AI ​​can generate plans based on user input. The investor matching department uses the business plans created by the business plan creation department to solicit investments from investors. The investor matching department can, for example, present the business plans created on the service to investors. The investor matching department can also, for example, offer investment requests to investors. Furthermore, the investor matching department can use AI to match investors. For example, the AI ​​analyzes investor profiles and selects the most suitable investors. The administrative procedure support department supports the administrative procedures and applications necessary for company establishment based on the investments obtained by the investor matching department. For example, the administrative procedure support department assists in creating the documents necessary for company establishment. The administrative procedure support department can also, for example, manage the progress of administrative procedures. Furthermore, the administrative procedure support department can use AI to support administrative procedures.For example, the AI ​​automatically guides users through the necessary procedures. This allows the new business support system, according to this embodiment, to provide total support for the process from considering new business ideas to establishing a company.

[0030] The development team uses an interactive AI chatbot to explore ideas. Specifically, the AI ​​chatbot provides relevant information to help users refine their ideas. For example, if a user enters an idea for a new product, the AI ​​chatbot provides information on market trends and competing products related to that product. It can also search patent and research paper databases to investigate whether similar technologies or ideas already exist. Furthermore, the AI ​​chatbot deepens the idea through dialogue with the user. For example, it asks the user questions such as, "Who is the target user for this product?" or "What features are needed?" to elicit the user's thoughts. This allows the user to explore their idea in more concrete and detailed terms. The AI ​​chatbot uses natural language processing technology to understand user input and provide appropriate information. It also uses machine learning algorithms to learn from the user's past dialogue history and other users' ideas, enabling it to provide more accurate advice. In this way, the development team provides strong support to help users refine their new business ideas and increase their feasibility.

[0031] The cost calculation unit calculates the necessary costs based on the ideas considered by the review unit. Specifically, it automatically calculates the costs required to realize the idea. For example, it calculates in detail the material costs, manufacturing costs, and marketing costs required for developing a new product. It can also automatically calculate expenses according to the scale of the business. For example, it considers the different cost structures of a small startup and a large company and calculates appropriate costs for each. The cost calculation unit uses AI to calculate costs. Specifically, it uses a machine learning model to predict costs based on past data. For example, it learns from data of similar successful projects in the past and uses that to predict the cost of a new idea. It can also reflect market price and exchange rate fluctuations in real time, enabling cost calculations based on the latest information. As a result, the cost calculation unit provides users with important information to understand the specific costs required to realize a new business and to create a budget plan.

[0032] The planning department creates business plans based on the costs calculated by the cost calculation department. Specifically, it automatically generates business plans on the service. For example, it creates a detailed business plan including an overview of the business, goals, strategies, and financial plans based on information entered by the user and data from the cost calculation department. It can also automatically create balance sheets. For example, it generates a balance sheet that visually shows the future financial situation based on revenue forecasts and expenditure plans. The planning department uses AI to create business plans. Specifically, it uses natural language generation technology to generate text based on user input. For example, if a user enters business goals and strategies, the AI ​​will create a detailed explanation based on that. It can also learn from past success stories and industry best practices and propose the optimal plan based on that. In this way, the planning department provides support for users to quickly and easily create professional business plans.

[0033] The Investor Matching Department uses business plans created by the Business Plan Creation Department to solicit investment from investors. Specifically, it presents business plans created on the service to investors. For example, it can send business plans to multiple investors simultaneously via an online platform to offer investment. It can also offer investment requests to investors. For example, it can analyze investors' profiles and investment history to select the most suitable investors and approach them individually. The Investor Matching Department uses AI to match investors. Specifically, it uses machine learning algorithms to analyze investors' profiles and select the most suitable investors. For example, it learns investors' past investment history and areas of interest and recommends the most suitable investors for new business ideas based on that. It can also adjust details such as investment conditions and expected returns through dialogue with investors. In this way, the Investor Matching Department provides support to users in finding suitable investors and successfully raising funds.

[0034] The Administrative Procedures Support Department provides support for the administrative procedures and applications necessary for company establishment based on investments obtained through the Investor Matching Department. Specifically, it assists in the creation of documents necessary for company establishment. For example, it automatically generates and provides legal documents such as articles of incorporation and registration applications to users. It can also manage the progress of administrative procedures. For example, it tracks the submission status of applications and the progress of the review in real time and notifies users. The Administrative Procedures Support Department uses AI to support administrative procedures. Specifically, it provides a navigation system to automatically guide users through the necessary procedures. For example, when a user starts the company establishment process, the AI ​​guides them step by step through the necessary procedures and provides the necessary documents and information. It can also predict the success rate and time required for procedures based on past data and provide optimal advice to users. In this way, the Administrative Procedures Support Department provides support to users so that they can smoothly complete the company establishment process and start their new business.

[0035] The review department can provide information such as "comparison with current services" and "patent / paper searches" using its related information provision function. For example, the review department's AI chatbot can provide users with comparative information with current services. For instance, the AI ​​chatbot could explain "how this idea differs from current services." The review department can also have the AI ​​chatbot provide the results of patent and paper searches. For example, the AI ​​chatbot could present information such as "the following patents and papers are related to this idea." Furthermore, the review department can have the AI ​​chatbot provide related information through dialogue with the user. For example, the AI ​​chatbot could explain "what technologies are needed to realize this idea." This makes the idea more concrete and novel.

[0036] The cost calculation unit can automatically calculate expenses based on the scale of the business. For example, the cost calculation unit automatically calculates the necessary expenses according to the scale of the business. For example, the cost calculation unit calculates expenses based on the business's sales revenue and number of employees. The cost calculation unit can also automatically calculate expenses using AI. For example, the AI ​​predicts expenses based on past data. The cost calculation unit can also calculate expenses according to the nature of the business. For example, in the case of manufacturing, the cost calculation unit calculates raw material costs and manufacturing costs, and in the case of service industries, it calculates personnel costs and operating costs. This allows for the automatic calculation of expenses according to the scale of the business.

[0037] The business plan creation function allows users to create balance sheets within the service. For example, it can automatically generate balance sheets based on data entered by the user. For instance, it can create balance sheets based on data such as sales, expenses, and profits. Furthermore, the business plan creation function can use AI to create balance sheets. For example, the AI ​​can predict balance sheets based on historical data. The business plan creation function can also reflect user-entered data in real time. For example, if a user changes sales figures, the balance sheet is automatically updated. This allows for the inclusion of balance sheets in business plans.

[0038] The Investor Matching Department can use business plans created on the service to offer investment opportunities to investors. For example, the Investor Matching Department can present user-created business plans to investors. For example, the Investor Matching Department can send business plans to investors via email. The Investor Matching Department can also use AI to offer investment opportunities to investors. For example, the AI ​​can analyze investor profiles and select the most suitable investors. The Investor Matching Department can also automatically offer investment opportunities to investors. For example, the Investor Matching Department can make offers based on investors' interests and preferences. This allows for investment requests to be made to investors based on the created business plan.

[0039] The Administrative Procedures Support Department can assist with the administrative procedures and application forms required for company establishment. For example, it can help create the necessary documents for company establishment. For instance, it can automatically generate documents such as articles of incorporation and registration applications. Furthermore, the Administrative Procedures Support Department can use AI to support the completion of administrative procedures and applications. For example, the AI ​​can automatically guide users through the necessary procedures. The Administrative Procedures Support Department can also manage the progress of administrative procedures. For example, it can display the progress of procedures in real time. This allows for comprehensive support for the administrative procedures and applications required for company establishment.

[0040] The review team can analyze the user's past idea submission history and select the most suitable review method. For example, the review team can analyze trends in the user's past submitted ideas and prioritize the review of similar ideas. For instance, the review team may prioritize suggesting review methods the user has used in the past (such as dialogue or questionnaire formats). The review team can also prioritize the review of ideas related to specific fields based on the user's past submission history. For example, the review team can select the most suitable review method based on the user's past idea submission history. This allows the team to select the most suitable review method based on the user's past idea submission history.

[0041] The review unit can filter ideas based on the user's current projects and areas of interest. For example, the review unit prioritizes ideas related to the user's current projects. For example, the review unit filters and provides relevant ideas based on the user's areas of interest. Furthermore, if the user is interested in a particular field, the review unit can prioritize ideas related to that field. For example, the review unit filters ideas based on the user's current projects and areas of interest. This allows the system to filter relevant ideas based on the user's current projects and areas of interest.

[0042] The review unit can prioritize considering highly relevant ideas by taking into account the user's geographical location. For example, if the user is in a specific region, the review unit will prioritize considering ideas related to that region. For instance, the review unit will provide ideas that are suited to the characteristics of the region based on the user's geographical location. Furthermore, if the user is on the move, the review unit can also prioritize considering ideas related to their current location. For example, the review unit will prioritize considering highly relevant ideas by taking into account the user's geographical location. This allows the review unit to prioritize considering highly relevant ideas based on the user's geographical location.

[0043] The review unit can analyze users' social media activity and consider relevant ideas. For example, the review unit prioritizes ideas related to topics that users have shown interest in on social media. For instance, the review unit provides ideas related to areas of interest based on the user's social media activity. The review unit can also consider relevant ideas based on information about accounts that users follow on social media. For example, the review unit analyzes users' social media activity and considers relevant ideas. This allows the review unit to consider relevant ideas based on the user's social media activity.

[0044] The cost calculation unit can select the optimal calculation method by referring to past cost data. For example, the cost calculation unit can select the optimal calculation method based on cost data from similar past projects. For example, the cost calculation unit can prioritize suggesting calculation methods that the user has used in the past. The cost calculation unit can also analyze past cost data and select the most efficient calculation method. For example, the cost calculation unit can select the optimal calculation method based on past cost data. This allows the system to select the optimal calculation method based on past cost data.

[0045] The cost calculation unit can apply different calculation algorithms depending on the scale and nature of the project. For example, it can apply a simplified calculation algorithm to small-scale projects and a more detailed calculation algorithm to large-scale projects. For example, the cost calculation unit can prioritize the calculation of specific cost items depending on the nature of the project. Furthermore, the cost calculation unit can select the optimal calculation algorithm based on the scale and nature of the project. For example, the cost calculation unit can apply different calculation algorithms depending on the scale and nature of the project. This allows for the application of the most suitable calculation algorithm depending on the scale and nature of the project.

[0046] The cost calculation unit can prioritize calculating highly relevant cost data by considering the user's geographical location information. For example, if the user is in a specific region, the cost calculation unit will prioritize calculating cost data related to that region. For instance, the cost calculation unit will provide cost data that matches the characteristics of the region based on the user's geographical location information. Furthermore, if the user is on the move, the cost calculation unit can also prioritize calculating cost data related to the user's current location. For example, the cost calculation unit will prioritize calculating highly relevant cost data by considering the user's geographical location information. This allows the cost calculation unit to prioritize calculating highly relevant cost data based on the user's geographical location information.

[0047] The cost calculation unit can analyze a user's social media activity and calculate relevant cost data. For example, the cost calculation unit prioritizes calculating cost data related to topics the user has shown interest in on social media. For instance, the cost calculation unit provides cost data related to areas of interest based on the user's social media activity. The cost calculation unit can also calculate relevant cost data based on information about accounts the user follows on social media. For example, the cost calculation unit analyzes the user's social media activity and calculates relevant cost data. This allows the cost calculation unit to calculate relevant cost data based on the user's social media activity.

[0048] The planning department can select the optimal planning method by referring to past planning data. For example, the planning department can select the optimal method based on planning data from similar past projects. For example, the planning department can prioritize suggesting planning methods that the user has used in the past. The planning department can also analyze past planning data and select the most efficient planning method. For example, the planning department can select the optimal method based on past planning data. This allows the department to select the optimal planning method based on past planning data.

[0049] The planning department can apply different creation algorithms depending on the business category. For example, it can apply a simplified creation algorithm to small-scale projects and a detailed creation algorithm to large-scale projects. For example, the planning department can prioritize the creation of specific planning items depending on the content of the project. The planning department can also select the optimal creation algorithm based on the business category. For example, it can apply different creation algorithms depending on the business category. This allows the department to apply the most suitable creation algorithm for each business category.

[0050] The planning unit can prioritize creating highly relevant planning data by considering the user's geographical location. For example, if the user is in a specific region, the planning unit will prioritize creating planning data related to that region. For instance, the planning unit will provide planning data that matches the characteristics of the region based on the user's geographical location. Furthermore, if the user is on the move, the planning unit can also prioritize creating planning data related to their current location. For example, the planning unit will prioritize creating highly relevant planning data by considering the user's geographical location. This allows the system to prioritize creating highly relevant planning data based on the user's geographical location.

[0051] The planning unit can analyze a user's social media activity and create relevant planning data. For example, the planning unit prioritizes creating planning data related to topics the user has shown interest in on social media. For instance, it provides planning data related to areas of interest based on the user's social media activity. The planning unit can also create relevant planning data based on information about accounts the user follows on social media. For example, it analyzes the user's social media activity and creates relevant planning data. This allows the creation of relevant planning data based on the user's social media activity.

[0052] The investor matching department can select the optimal matching method by referring to past investor data. For example, the investor matching department selects the optimal matching method based on investor data from similar past projects. For example, the investor matching department prioritizes suggesting matching methods that users have used in the past. The investor matching department can also analyze past investor data and select the most efficient matching method. For example, the investor matching department selects the optimal matching method based on past investor data. This allows the department to select the optimal matching method based on past investor data.

[0053] The investor matching department can apply different matching algorithms depending on the business category. For example, it can apply a simple matching algorithm to small-scale businesses and a detailed matching algorithm to large-scale businesses. For instance, the investor matching department can prioritize matching specific investors based on the nature of the business. Furthermore, the investor matching department can select the optimal matching algorithm based on the business category. For example, it can apply different matching algorithms depending on the business category, thereby ensuring that the most suitable matching algorithm is applied to each business category.

[0054] The investor matching unit can prioritize matching highly relevant investor data by considering the user's geographical location. For example, if the user is in a specific region, the investor matching unit will prioritize matching investor data related to that region. For instance, based on the user's geographical location, the investor matching unit will provide investor data that matches the characteristics of the region. Furthermore, if the user is on the move, the investor matching unit can also prioritize matching investor data related to the user's current location. For example, the investor matching unit will prioritize matching highly relevant investor data by considering the user's geographical location. This allows for the priority matching of highly relevant investor data based on the user's geographical location.

[0055] The investor matching department can analyze a user's social media activity and match them with relevant investor data. For example, the investor matching department prioritizes matching investor data related to topics the user has shown interest in on social media. For instance, it provides investor data related to areas of interest based on the user's social media activity. Furthermore, the investor matching department can also match relevant investor data based on information about accounts the user follows on social media. For example, the investor matching department analyzes the user's social media activity and matches them with relevant investor data. This allows for matching relevant investor data based on the user's social media activity.

[0056] The Administrative Procedure Support Department can select the optimal support method by referring to past procedure data. For example, the Administrative Procedure Support Department can select the optimal support method based on procedure data from similar past projects. For example, the Administrative Procedure Support Department can prioritize suggesting procedure methods that the user has used in the past. The Administrative Procedure Support Department can also analyze past procedure data and select the most efficient support method. For example, the Administrative Procedure Support Department can select the optimal support method based on past procedure data. This allows the department to select the optimal support method based on past procedure data.

[0057] The Administrative Procedure Support Department can apply different support algorithms depending on the business category. For example, it can apply a simplified support algorithm to small-scale businesses and a detailed support algorithm to large-scale businesses. For instance, the Administrative Procedure Support Department can prioritize support for specific procedural items depending on the nature of the business. Furthermore, the Administrative Procedure Support Department can select the optimal support algorithm based on the business category. For example, it can apply different support algorithms depending on the business category, thereby ensuring that the most suitable support algorithm is applied to each business category.

[0058] The Administrative Procedure Support Department can prioritize supporting highly relevant procedural data by considering the user's geographical location. For example, if the user is in a specific region, the Administrative Procedure Support Department will prioritize supporting procedural data related to that region. For instance, the Administrative Procedure Support Department will provide procedural data that matches the characteristics of the region based on the user's geographical location. Furthermore, if the user is on the move, the Administrative Procedure Support Department can also prioritize supporting procedural data related to their current location. For example, the Administrative Procedure Support Department will prioritize supporting highly relevant procedural data by considering the user's geographical location. This allows the department to prioritize supporting highly relevant procedural data based on the user's geographical location.

[0059] The Administrative Procedure Support Department can analyze users' social media activity and support relevant procedural data. For example, the Administrative Procedure Support Department prioritizes supporting procedural data related to topics the user has shown interest in on social media. For instance, it provides procedural data related to areas of interest based on the user's social media activity. Furthermore, the Administrative Procedure Support Department can also support relevant procedural data based on information about accounts the user follows on social media. For example, the Administrative Procedure Support Department analyzes the user's social media activity and supports relevant procedural data. This allows it to support relevant procedural data based on the user's social media activity.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The review unit can analyze the user's past idea submission history and select the most suitable review method. For example, it can analyze trends in ideas previously submitted by the user and prioritize the review of similar ideas. It can also prioritize suggesting review methods previously used by the user (such as dialogue or questionnaire formats). Furthermore, it can prioritize the review of ideas related to specific fields based on the user's past submission history. This allows the system to select the most suitable review method based on the user's past idea submission history.

[0062] The cost calculation unit can select the optimal calculation method by referring to past cost data. For example, it can select the optimal calculation method based on cost data from similar past projects. It can also prioritize suggesting calculation methods previously used by the user. Furthermore, it can analyze past cost data and select the most efficient calculation method. This allows for the selection of the optimal calculation method based on past cost data.

[0063] The project planning department can select the optimal planning method by referring to past project data. For example, it can select the optimal method based on project data from similar past projects. It can also prioritize suggesting planning methods previously used by the user. Furthermore, it can analyze past project data to select the most efficient method. This allows for the selection of the optimal planning method based on past project data.

[0064] The investor matching department can select the optimal matching method by referring to past investor data. For example, it can select the optimal matching method based on investor data from similar past projects. It can also prioritize suggesting matching methods that users have used in the past. Furthermore, it can analyze past investor data to select the most efficient matching method. This allows for the selection of the optimal matching method based on past investor data.

[0065] The administrative procedure support department can select the optimal support method by referring to past procedure data. For example, it can select the optimal support method based on procedure data from similar past projects. It can also prioritize suggesting procedure methods that the user has used in the past. Furthermore, it can analyze past procedure data to select the most efficient support method. This allows for the selection of the optimal support method based on past procedure data.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The review team uses an interactive AI chatbot to explore ideas. The AI ​​chatbot provides relevant information in response to the user's input, helping to concretize the idea. For example, the AI ​​chatbot provides information such as "comparison with current services" and "patent / paper research," drawing out details of the idea through dialogue with the user. Step 2: The cost calculation unit calculates the necessary costs based on the ideas considered by the review unit. It automatically calculates the costs required to realize the ideas and automatically calculates expenses according to the scale of the business. It can also predict costs based on past data using AI. Step 3: The planning unit creates a business plan based on the costs calculated by the cost calculation unit. The service automatically generates the business plan and creates a balance sheet. It is also possible to generate the plan based on user input using AI. Step 4: The Investor Matching Department uses the business plan created by the Business Plan Creation Department to solicit investment from investors. The business plan created on the service is presented to investors, and investment offers are made. AI can also be used to analyze investor profiles and select the most suitable investors. Step 5: The Administrative Procedures Support Department supports the administrative procedures and applications necessary for company establishment based on the investments received by the Investor Matching Department. It assists in creating the necessary documents for company establishment and manages the progress of administrative procedures. It can also use AI to automatically guide users through the necessary procedures.

[0068] (Example of form 2) The new business support system according to an embodiment of the present invention is a system in which an AI agent provides total support to people with new business ideas, from idea consideration to company establishment. This new business support system consists of four steps: idea generation, business plan creation, investor matching, and company establishment. First, in the idea generation stage, an interactive AI chatbot is used to consider the idea. The AI ​​chatbot uses a relevant information provision function to provide information such as "comparison with current services" and "patent / paper research" to make the idea more concrete and novel. Next, in the business plan creation stage, the necessary costs are calculated based on the idea, and a business plan is created on the service. Furthermore, expenses are automatically calculated from the scale of the business, and a balance sheet is created on the service. The user refines the plan while interacting with the agent, clarifying "what is necessary" and "what can be cut." In the investor matching stage, it is possible to use the business plan created on the service to offer investment to those registered as investors in this service. Finally, in the company establishment stage, the AI ​​also supports the completion of necessary administrative procedures and applications for company establishment. This enables total support from "idea" to "company incorporation." For example, when a user inputs an idea into the new business support system, an AI chatbot provides relevant information to help concretize the idea. Next, when the user creates a business plan, the AI ​​calculates the necessary costs and automatically generates a balance sheet. Furthermore, during the investor matching stage, the AI ​​requests investment from potential investors, and during the company establishment stage, the AI ​​supports administrative procedures and applications. This makes it possible to create a world where anyone can start a "new business." In this way, the new business support system can provide total support for the process from considering a new business idea to establishing a company.

[0069] The new business support system according to this embodiment comprises a review unit, a cost calculation unit, a plan creation unit, an investor matching unit, and an administrative procedure support unit. The review unit reviews ideas using an interactive AI chatbot. For example, the review unit uses the AI ​​chatbot to provide relevant information in response to an idea entered by the user, thereby concretizing the idea. For example, the review unit uses the AI ​​chatbot to provide information such as "comparison with current services" and "patent / paper search." The review unit can also use the AI ​​chatbot to review ideas through dialogue with the user. For example, the AI ​​chatbot can ask the user questions to elicit details of the idea. The cost calculation unit calculates the necessary costs based on the ideas reviewed by the review unit. For example, the cost calculation unit automatically calculates the costs required to realize the idea. For example, the cost calculation unit can automatically calculate expenses according to the scale of the business. The cost calculation unit can also use AI to calculate costs. For example, the AI ​​predicts costs based on past data. The plan creation unit creates a business plan based on the costs calculated by the cost calculation unit. The business plan creation department can, for example, automatically generate business plans on the service. The business plan creation department can also, for example, automatically create balance sheets. Furthermore, the business plan creation department can use AI to create business plans. For example, the AI ​​can generate plans based on user input. The investor matching department uses the business plans created by the business plan creation department to solicit investments from investors. The investor matching department can, for example, present the business plans created on the service to investors. The investor matching department can also, for example, offer investment requests to investors. Furthermore, the investor matching department can use AI to match investors. For example, the AI ​​analyzes investor profiles and selects the most suitable investors. The administrative procedure support department supports the administrative procedures and applications necessary for company establishment based on the investments obtained by the investor matching department. For example, the administrative procedure support department assists in creating the documents necessary for company establishment. The administrative procedure support department can also, for example, manage the progress of administrative procedures. Furthermore, the administrative procedure support department can use AI to support administrative procedures.For example, the AI ​​automatically guides users through the necessary procedures. This allows the new business support system, according to this embodiment, to provide total support for the process from considering new business ideas to establishing a company.

[0070] The development team uses an interactive AI chatbot to explore ideas. Specifically, the AI ​​chatbot provides relevant information to help users refine their ideas. For example, if a user enters an idea for a new product, the AI ​​chatbot provides information on market trends and competing products related to that product. It can also search patent and research paper databases to investigate whether similar technologies or ideas already exist. Furthermore, the AI ​​chatbot deepens the idea through dialogue with the user. For example, it asks the user questions such as, "Who is the target user for this product?" or "What features are needed?" to elicit the user's thoughts. This allows the user to explore their idea in more concrete and detailed terms. The AI ​​chatbot uses natural language processing technology to understand user input and provide appropriate information. It also uses machine learning algorithms to learn from the user's past dialogue history and other users' ideas, enabling it to provide more accurate advice. In this way, the development team provides strong support to help users refine their new business ideas and increase their feasibility.

[0071] The cost calculation unit calculates the necessary costs based on the ideas considered by the review unit. Specifically, it automatically calculates the costs required to realize the idea. For example, it calculates in detail the material costs, manufacturing costs, and marketing costs required for developing a new product. It can also automatically calculate expenses according to the scale of the business. For example, it considers the different cost structures of a small startup and a large company and calculates appropriate costs for each. The cost calculation unit uses AI to calculate costs. Specifically, it uses a machine learning model to predict costs based on past data. For example, it learns from data of similar successful projects in the past and uses that to predict the cost of a new idea. It can also reflect market price and exchange rate fluctuations in real time, enabling cost calculations based on the latest information. As a result, the cost calculation unit provides users with important information to understand the specific costs required to realize a new business and to create a budget plan.

[0072] The planning department creates business plans based on the costs calculated by the cost calculation department. Specifically, it automatically generates business plans on the service. For example, it creates a detailed business plan including an overview of the business, goals, strategies, and financial plans based on information entered by the user and data from the cost calculation department. It can also automatically create balance sheets. For example, it generates a balance sheet that visually shows the future financial situation based on revenue forecasts and expenditure plans. The planning department uses AI to create business plans. Specifically, it uses natural language generation technology to generate text based on user input. For example, if a user enters business goals and strategies, the AI ​​will create a detailed explanation based on that. It can also learn from past success stories and industry best practices and propose the optimal plan based on that. In this way, the planning department provides support for users to quickly and easily create professional business plans.

[0073] The Investor Matching Department uses business plans created by the Business Plan Creation Department to solicit investment from investors. Specifically, it presents business plans created on the service to investors. For example, it can send business plans to multiple investors simultaneously via an online platform to offer investment. It can also offer investment requests to investors. For example, it can analyze investors' profiles and investment history to select the most suitable investors and approach them individually. The Investor Matching Department uses AI to match investors. Specifically, it uses machine learning algorithms to analyze investors' profiles and select the most suitable investors. For example, it learns investors' past investment history and areas of interest and recommends the most suitable investors for new business ideas based on that. It can also adjust details such as investment conditions and expected returns through dialogue with investors. In this way, the Investor Matching Department provides support to users in finding suitable investors and successfully raising funds.

[0074] The Administrative Procedures Support Department provides support for the administrative procedures and applications necessary for company establishment based on investments obtained through the Investor Matching Department. Specifically, it assists in the creation of documents necessary for company establishment. For example, it automatically generates and provides legal documents such as articles of incorporation and registration applications to users. It can also manage the progress of administrative procedures. For example, it tracks the submission status of applications and the progress of the review in real time and notifies users. The Administrative Procedures Support Department uses AI to support administrative procedures. Specifically, it provides a navigation system to automatically guide users through the necessary procedures. For example, when a user starts the company establishment process, the AI ​​guides them step by step through the necessary procedures and provides the necessary documents and information. It can also predict the success rate and time required for procedures based on past data and provide optimal advice to users. In this way, the Administrative Procedures Support Department provides support to users so that they can smoothly complete the company establishment process and start their new business.

[0075] The review department can provide information such as "comparison with current services" and "patent / paper searches" using its related information provision function. For example, the review department's AI chatbot can provide users with comparative information with current services. For instance, the AI ​​chatbot could explain "how this idea differs from current services." The review department can also have the AI ​​chatbot provide the results of patent and paper searches. For example, the AI ​​chatbot could present information such as "the following patents and papers are related to this idea." Furthermore, the review department can have the AI ​​chatbot provide related information through dialogue with the user. For example, the AI ​​chatbot could explain "what technologies are needed to realize this idea." This makes the idea more concrete and novel.

[0076] The cost calculation unit can automatically calculate expenses based on the scale of the business. For example, the cost calculation unit automatically calculates the necessary expenses according to the scale of the business. For example, the cost calculation unit calculates expenses based on the business's sales revenue and number of employees. The cost calculation unit can also automatically calculate expenses using AI. For example, the AI ​​predicts expenses based on past data. The cost calculation unit can also calculate expenses according to the nature of the business. For example, in the case of manufacturing, the cost calculation unit calculates raw material costs and manufacturing costs, and in the case of service industries, it calculates personnel costs and operating costs. This allows for the automatic calculation of expenses according to the scale of the business.

[0077] The business plan creation function allows users to create balance sheets within the service. For example, it can automatically generate balance sheets based on data entered by the user. For instance, it can create balance sheets based on data such as sales, expenses, and profits. Furthermore, the business plan creation function can use AI to create balance sheets. For example, the AI ​​can predict balance sheets based on historical data. The business plan creation function can also reflect user-entered data in real time. For example, if a user changes sales figures, the balance sheet is automatically updated. This allows for the inclusion of balance sheets in business plans.

[0078] The Investor Matching Department can use business plans created on the service to offer investment opportunities to investors. For example, the Investor Matching Department can present user-created business plans to investors. For example, the Investor Matching Department can send business plans to investors via email. The Investor Matching Department can also use AI to offer investment opportunities to investors. For example, the AI ​​can analyze investor profiles and select the most suitable investors. The Investor Matching Department can also automatically offer investment opportunities to investors. For example, the Investor Matching Department can make offers based on investors' interests and preferences. This allows for investment requests to be made to investors based on the created business plan.

[0079] The Administrative Procedures Support Department can assist with the administrative procedures and application forms required for company establishment. For example, it can help create the necessary documents for company establishment. For instance, it can automatically generate documents such as articles of incorporation and registration applications. Furthermore, the Administrative Procedures Support Department can use AI to support the completion of administrative procedures and applications. For example, the AI ​​can automatically guide users through the necessary procedures. The Administrative Procedures Support Department can also manage the progress of administrative procedures. For example, it can display the progress of procedures in real time. This allows for comprehensive support for the administrative procedures and applications required for company establishment.

[0080] The review unit can estimate the user's emotions and adjust the idea review process based on those emotions. For example, if the user is stressed, the review unit can provide a simple interface and minimize the review steps. For example, if the user is relaxed, the review unit can provide detailed review options and suggest a customizable review method. The review unit can also enable quick idea review if the user is in a hurry. For example, the review unit adjusts the review method according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows the idea review process to be adjusted according to the user's emotions.

[0081] The review team can analyze the user's past idea submission history and select the most suitable review method. For example, the review team can analyze trends in the user's past submitted ideas and prioritize the review of similar ideas. For instance, the review team may prioritize suggesting review methods the user has used in the past (such as dialogue or questionnaire formats). The review team can also prioritize the review of ideas related to specific fields based on the user's past submission history. For example, the review team can select the most suitable review method based on the user's past idea submission history. This allows the team to select the most suitable review method based on the user's past idea submission history.

[0082] The review unit can filter ideas based on the user's current projects and areas of interest. For example, the review unit prioritizes ideas related to the user's current projects. For example, the review unit filters and provides relevant ideas based on the user's areas of interest. Furthermore, if the user is interested in a particular field, the review unit can prioritize ideas related to that field. For example, the review unit filters ideas based on the user's current projects and areas of interest. This allows the system to filter relevant ideas based on the user's current projects and areas of interest.

[0083] The review unit can estimate the user's emotions and determine the priority of ideas to consider based on those emotions. For example, if the user is excited, the review unit will prioritize challenging ideas. For example, if the user is relaxed, the review unit will prioritize ideas that can be considered in a relaxed state. Also, if the user is stressed, the review unit may prioritize simple and feasible ideas. In short, the review unit determines the priority of ideas according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows the system to determine the priority of ideas to consider according to the user's emotions.

[0084] The review unit can prioritize considering highly relevant ideas by taking into account the user's geographical location. For example, if the user is in a specific region, the review unit will prioritize considering ideas related to that region. For instance, the review unit will provide ideas that are suited to the characteristics of the region based on the user's geographical location. Furthermore, if the user is on the move, the review unit can also prioritize considering ideas related to their current location. For example, the review unit will prioritize considering highly relevant ideas by taking into account the user's geographical location. This allows the review unit to prioritize considering highly relevant ideas based on the user's geographical location.

[0085] The review unit can analyze users' social media activity and consider relevant ideas. For example, the review unit prioritizes ideas related to topics that users have shown interest in on social media. For instance, the review unit provides ideas related to areas of interest based on the user's social media activity. The review unit can also consider relevant ideas based on information about accounts that users follow on social media. For example, the review unit analyzes users' social media activity and considers relevant ideas. This allows the review unit to consider relevant ideas based on the user's social media activity.

[0086] The cost calculation unit can estimate the user's emotions and adjust the cost calculation method based on the estimated emotions. For example, if the user is stressed, the cost calculation unit can provide a simple interface and minimize the calculation steps. For example, if the user is relaxed, the cost calculation unit can provide detailed calculation options and suggest a customizable calculation method. The cost calculation unit can also calculate costs quickly if the user is in a hurry. For example, the cost calculation unit adjusts the cost calculation method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows the cost calculation method to be adjusted according to the user's emotions.

[0087] The cost calculation unit can select the optimal calculation method by referring to past cost data. For example, the cost calculation unit can select the optimal calculation method based on cost data from similar past projects. For example, the cost calculation unit can prioritize suggesting calculation methods that the user has used in the past. The cost calculation unit can also analyze past cost data and select the most efficient calculation method. For example, the cost calculation unit can select the optimal calculation method based on past cost data. This allows the system to select the optimal calculation method based on past cost data.

[0088] The cost calculation unit can apply different calculation algorithms depending on the scale and nature of the project. For example, it can apply a simplified calculation algorithm to small-scale projects and a more detailed calculation algorithm to large-scale projects. For example, the cost calculation unit can prioritize the calculation of specific cost items depending on the nature of the project. Furthermore, the cost calculation unit can select the optimal calculation algorithm based on the scale and nature of the project. For example, the cost calculation unit can apply different calculation algorithms depending on the scale and nature of the project. This allows for the application of the most suitable calculation algorithm depending on the scale and nature of the project.

[0089] The cost calculation unit can estimate the user's emotions and determine the priority of cost calculations based on the estimated emotions. For example, if the user is excited, the cost calculation unit will prioritize calculating challenging cost items. For example, if the user is relaxed, the cost calculation unit will prioritize providing cost items that can be calculated in a relaxed state. Also, if the user is stressed, the cost calculation unit can prioritize calculating simple and feasible cost items. For example, the cost calculation unit determines the priority of cost calculations according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows the cost calculation priority to be determined according to the user's emotions.

[0090] The cost calculation unit can prioritize calculating highly relevant cost data by considering the user's geographical location information. For example, if the user is in a specific region, the cost calculation unit will prioritize calculating cost data related to that region. For instance, the cost calculation unit will provide cost data that matches the characteristics of the region based on the user's geographical location information. Furthermore, if the user is on the move, the cost calculation unit can also prioritize calculating cost data related to the user's current location. For example, the cost calculation unit will prioritize calculating highly relevant cost data by considering the user's geographical location information. This allows the cost calculation unit to prioritize calculating highly relevant cost data based on the user's geographical location information.

[0091] The cost calculation unit can analyze a user's social media activity and calculate relevant cost data. For example, the cost calculation unit prioritizes calculating cost data related to topics the user has shown interest in on social media. For instance, the cost calculation unit provides cost data related to areas of interest based on the user's social media activity. The cost calculation unit can also calculate relevant cost data based on information about accounts the user follows on social media. For example, the cost calculation unit analyzes the user's social media activity and calculates relevant cost data. This allows the cost calculation unit to calculate relevant cost data based on the user's social media activity.

[0092] The planning unit can estimate the user's emotions and adjust the planning process based on those emotions. For example, if the user is stressed, the planning unit can provide a simple interface and minimize the creation process. For example, if the user is relaxed, the planning unit can provide detailed creation options and suggest a customizable creation method. Furthermore, if the user is in a hurry, the planning unit can enable rapid planning. For example, the planning unit adjusts the planning process according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows the planning process to be adjusted according to the user's emotions.

[0093] The planning department can select the optimal planning method by referring to past planning data. For example, the planning department can select the optimal method based on planning data from similar past projects. For example, the planning department can prioritize suggesting planning methods that the user has used in the past. The planning department can also analyze past planning data and select the most efficient planning method. For example, the planning department can select the optimal method based on past planning data. This allows the department to select the optimal planning method based on past planning data.

[0094] The planning department can apply different creation algorithms depending on the business category. For example, it can apply a simplified creation algorithm to small-scale projects and a detailed creation algorithm to large-scale projects. For example, the planning department can prioritize the creation of specific planning items depending on the content of the project. The planning department can also select the optimal creation algorithm based on the business category. For example, it can apply different creation algorithms depending on the business category. This allows the department to apply the most suitable creation algorithm for each business category.

[0095] The planning unit can estimate the user's emotions and prioritize the plan based on those emotions. For example, if the user is excited, the planning unit will prioritize creating challenging plan items. For example, if the user is relaxed, the planning unit will prioritize providing plan items that can be created in a relaxed state. Also, if the user is stressed, the planning unit can prioritize creating simple and feasible plan items. In short, the planning unit prioritizes the plan according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows the planning unit to prioritize the plan according to the user's emotions.

[0096] The planning unit can prioritize creating highly relevant planning data by considering the user's geographical location. For example, if the user is in a specific region, the planning unit will prioritize creating planning data related to that region. For instance, the planning unit will provide planning data that matches the characteristics of the region based on the user's geographical location. Furthermore, if the user is on the move, the planning unit can also prioritize creating planning data related to their current location. For example, the planning unit will prioritize creating highly relevant planning data by considering the user's geographical location. This allows the system to prioritize creating highly relevant planning data based on the user's geographical location.

[0097] The planning unit can analyze a user's social media activity and create relevant planning data. For example, the planning unit prioritizes creating planning data related to topics the user has shown interest in on social media. For instance, it provides planning data related to areas of interest based on the user's social media activity. The planning unit can also create relevant planning data based on information about accounts the user follows on social media. For example, it analyzes the user's social media activity and creates relevant planning data. This allows the creation of relevant planning data based on the user's social media activity.

[0098] The investor matching unit can estimate the user's emotions and adjust the investor matching method based on the estimated emotions. For example, if the user is stressed, the investor matching unit can provide a simple interface and minimize the matching procedure. For example, if the user is relaxed, the investor matching unit can provide detailed matching options and suggest a customizable matching method. The investor matching unit can also enable quick investor matching if the user is in a hurry. For example, the investor matching unit adjusts the investor matching method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows the investor matching method to be adjusted according to the user's emotions.

[0099] The investor matching department can select the optimal matching method by referring to past investor data. For example, the investor matching department selects the optimal matching method based on investor data from similar past projects. For example, the investor matching department prioritizes suggesting matching methods that users have used in the past. The investor matching department can also analyze past investor data and select the most efficient matching method. For example, the investor matching department selects the optimal matching method based on past investor data. This allows the department to select the optimal matching method based on past investor data.

[0100] The investor matching department can apply different matching algorithms depending on the business category. For example, it can apply a simple matching algorithm to small-scale businesses and a detailed matching algorithm to large-scale businesses. For instance, the investor matching department can prioritize matching specific investors based on the nature of the business. Furthermore, the investor matching department can select the optimal matching algorithm based on the business category. For example, it can apply different matching algorithms depending on the business category, thereby ensuring that the most suitable matching algorithm is applied to each business category.

[0101] The investor matching unit can estimate the user's emotions and determine the priority of investor matching based on the estimated emotions. For example, if the user is excited, the investor matching unit will prioritize matching with challenging investors. For example, if the user is relaxed, the investor matching unit will prioritize providing investors who can match in a relaxed state. Also, if the user is stressed, the investor matching unit can prioritize matching with investors who are easy to implement. For example, the investor matching unit will determine the priority of investor matching according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This makes it possible to determine the priority of investor matching according to the user's emotions.

[0102] The investor matching unit can prioritize matching highly relevant investor data by considering the user's geographical location. For example, if the user is in a specific region, the investor matching unit will prioritize matching investor data related to that region. For instance, based on the user's geographical location, the investor matching unit will provide investor data that matches the characteristics of the region. Furthermore, if the user is on the move, the investor matching unit can also prioritize matching investor data related to the user's current location. For example, the investor matching unit will prioritize matching highly relevant investor data by considering the user's geographical location. This allows for the priority matching of highly relevant investor data based on the user's geographical location.

[0103] The investor matching department can analyze a user's social media activity and match them with relevant investor data. For example, the investor matching department prioritizes matching investor data related to topics the user has shown interest in on social media. For instance, it provides investor data related to areas of interest based on the user's social media activity. Furthermore, the investor matching department can also match relevant investor data based on information about accounts the user follows on social media. For example, the investor matching department analyzes the user's social media activity and matches them with relevant investor data. This allows for matching relevant investor data based on the user's social media activity.

[0104] The administrative procedure support unit can estimate the user's emotions and adjust the support method for administrative procedures based on the estimated emotions. For example, if the user is stressed, the administrative procedure support unit can provide a simple interface and minimize the procedure steps. For example, if the user is relaxed, the administrative procedure support unit can provide detailed procedure options and suggest customizable support methods. The administrative procedure support unit can also provide rapid support for procedures if the user is in a hurry. For example, the administrative procedure support unit adjusts the support method for administrative procedures according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This makes it possible to adjust the support method for administrative procedures according to the user's emotions.

[0105] The Administrative Procedure Support Department can select the optimal support method by referring to past procedure data. For example, the Administrative Procedure Support Department can select the optimal support method based on procedure data from similar past projects. For example, the Administrative Procedure Support Department can prioritize suggesting procedure methods that the user has used in the past. The Administrative Procedure Support Department can also analyze past procedure data and select the most efficient support method. For example, the Administrative Procedure Support Department can select the optimal support method based on past procedure data. This allows the department to select the optimal support method based on past procedure data.

[0106] The Administrative Procedure Support Department can apply different support algorithms depending on the business category. For example, it can apply a simplified support algorithm to small-scale businesses and a detailed support algorithm to large-scale businesses. For instance, the Administrative Procedure Support Department can prioritize support for specific procedural items depending on the nature of the business. Furthermore, the Administrative Procedure Support Department can select the optimal support algorithm based on the business category. For example, it can apply different support algorithms depending on the business category, thereby ensuring that the most suitable support algorithm is applied to each business category.

[0107] The administrative procedure support unit can estimate the user's emotions and prioritize administrative procedures based on those emotions. For example, if the user is excited, the administrative procedure support unit will prioritize supporting challenging procedures. For example, if the user is relaxed, the administrative procedure support unit will prioritize providing procedures that can be completed in a relaxed state. Furthermore, if the user is stressed, the administrative procedure support unit can also prioritize supporting simple and feasible procedures. In short, the administrative procedure support unit determines the priority of administrative procedures according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. This allows the system to prioritize administrative procedures according to the user's emotions.

[0108] The Administrative Procedure Support Department can prioritize supporting highly relevant procedural data by considering the user's geographical location. For example, if the user is in a specific region, the Administrative Procedure Support Department will prioritize supporting procedural data related to that region. For instance, the Administrative Procedure Support Department will provide procedural data that matches the characteristics of the region based on the user's geographical location. Furthermore, if the user is on the move, the Administrative Procedure Support Department can also prioritize supporting procedural data related to their current location. For example, the Administrative Procedure Support Department will prioritize supporting highly relevant procedural data by considering the user's geographical location. This allows the department to prioritize supporting highly relevant procedural data based on the user's geographical location.

[0109] The Administrative Procedure Support Department can analyze users' social media activity and support relevant procedural data. For example, the Administrative Procedure Support Department prioritizes supporting procedural data related to topics the user has shown interest in on social media. For instance, it provides procedural data related to areas of interest based on the user's social media activity. Furthermore, the Administrative Procedure Support Department can also support relevant procedural data based on information about accounts the user follows on social media. For example, the Administrative Procedure Support Department analyzes the user's social media activity and supports relevant procedural data. This allows it to support relevant procedural data based on the user's social media activity.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The review unit can estimate the user's emotions and adjust the idea review process based on those emotions. For example, if the user is stressed, it can provide a simple interface and minimize the review steps. If the user is relaxed, it can provide detailed review options and suggest a customizable review method. Furthermore, if the user is in a hurry, it can enable them to review ideas quickly. This allows the idea review process to be adjusted according to the user's emotions.

[0112] The cost calculation unit can estimate the user's emotions and adjust the cost calculation method based on those emotions. For example, if the user is stressed, it can provide a simple interface and minimize the calculation steps. If the user is relaxed, it can provide detailed calculation options and suggest a customizable calculation method. Furthermore, if the user is in a hurry, it can calculate the cost quickly. This allows the cost calculation method to be adjusted according to the user's emotions.

[0113] The planning function can estimate the user's emotions and adjust the planning process based on those emotions. For example, if the user is stressed, it can provide a simple interface and minimize the creation process. If the user is relaxed, it can offer detailed creation options and suggest a customizable method. Furthermore, if the user is in a hurry, it can enable them to create the plan quickly. This allows the planning process to be adjusted according to the user's emotions.

[0114] The investor matching unit can estimate the user's emotions and adjust the investor matching method based on those emotions. For example, if the user is stressed, it can provide a simple interface and minimize the matching process. If the user is relaxed, it can provide detailed matching options and suggest a customizable matching method. Furthermore, if the user is in a hurry, it can enable quick investor matching. This allows the investor matching method to be adjusted according to the user's emotions.

[0115] The administrative procedure support unit can estimate the user's emotions and adjust the support method for administrative procedures based on those emotions. For example, if the user is stressed, it can provide a simple interface and minimize the procedure steps. If the user is relaxed, it can provide detailed procedure options and suggest customizable support methods. Furthermore, if the user is in a hurry, it can provide rapid support for the procedure. In this way, the support method for administrative procedures can be adjusted according to the user's emotions.

[0116] The review unit can analyze the user's past idea submission history and select the most suitable review method. For example, it can analyze trends in ideas previously submitted by the user and prioritize the review of similar ideas. It can also prioritize suggesting review methods previously used by the user (such as dialogue or questionnaire formats). Furthermore, it can prioritize the review of ideas related to specific fields based on the user's past submission history. This allows the system to select the most suitable review method based on the user's past idea submission history.

[0117] The cost calculation unit can select the optimal calculation method by referring to past cost data. For example, it can select the optimal calculation method based on cost data from similar past projects. It can also prioritize suggesting calculation methods previously used by the user. Furthermore, it can analyze past cost data and select the most efficient calculation method. This allows for the selection of the optimal calculation method based on past cost data.

[0118] The project planning department can select the optimal planning method by referring to past project data. For example, it can select the optimal method based on project data from similar past projects. It can also prioritize suggesting planning methods previously used by the user. Furthermore, it can analyze past project data to select the most efficient method. This allows for the selection of the optimal planning method based on past project data.

[0119] The investor matching department can select the optimal matching method by referring to past investor data. For example, it can select the optimal matching method based on investor data from similar past projects. It can also prioritize suggesting matching methods that users have used in the past. Furthermore, it can analyze past investor data to select the most efficient matching method. This allows for the selection of the optimal matching method based on past investor data.

[0120] The administrative procedure support department can select the optimal support method by referring to past procedure data. For example, it can select the optimal support method based on procedure data from similar past projects. It can also prioritize suggesting procedure methods that the user has used in the past. Furthermore, it can analyze past procedure data to select the most efficient support method. This allows for the selection of the optimal support method based on past procedure data.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The review team uses an interactive AI chatbot to explore ideas. The AI ​​chatbot provides relevant information in response to the user's input, helping to concretize the idea. For example, the AI ​​chatbot provides information such as "comparison with current services" and "patent / paper research," drawing out details of the idea through dialogue with the user. Step 2: The cost calculation unit calculates the necessary costs based on the ideas considered by the review unit. It automatically calculates the costs required to realize the ideas and automatically calculates expenses according to the scale of the business. It can also predict costs based on past data using AI. Step 3: The planning unit creates a business plan based on the costs calculated by the cost calculation unit. The service automatically generates the business plan and creates a balance sheet. It is also possible to generate the plan based on user input using AI. Step 4: The Investor Matching Department uses the business plan created by the Business Plan Creation Department to solicit investment from investors. The business plan created on the service is presented to investors, and investment offers are made. AI can also be used to analyze investor profiles and select the most suitable investors. Step 5: The Administrative Procedures Support Department supports the administrative procedures and applications necessary for company establishment based on the investments received by the Investor Matching Department. It assists in creating the necessary documents for company establishment and manages the progress of administrative procedures. It can also use AI to automatically guide users through the necessary procedures.

[0123] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0124] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0125] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0126] Each of the multiple elements described above, including the planning unit, cost calculation unit, project plan creation unit, investor matching unit, and administrative procedure support unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the planning unit is implemented by the AI ​​chatbot of the smart device 14, which provides relevant information to the idea entered by the user and helps to materialize the idea. The cost calculation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which automatically calculates the cost required to realize the idea. The project plan creation unit is implemented by, for example, the control unit 46A of the smart device 14, which automatically generates a business plan on the service. The investor matching unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which requests investment from investors. The administrative procedure support unit is implemented by, for example, the control unit 46A of the smart device 14, which supports the administrative procedures required for company establishment. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0129] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0130] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0133] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0134] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0135] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0136] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0137] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0138] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0139] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0140] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0141] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0142] Each of the multiple elements described above, including the analysis unit, cost calculation unit, plan creation unit, investor matching unit, and administrative procedure support unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by the AI ​​chatbot of the smart glasses 214, which provides relevant information to the idea entered by the user and helps to materialize the idea. The cost calculation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which automatically calculates the cost required to realize the idea. The plan creation unit is implemented by, for example, the control unit 46A of the smart glasses 214, which automatically generates a business plan on the service. The investor matching unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which requests investment from investors. The administrative procedure support unit is implemented by, for example, the control unit 46A of the smart glasses 214, which supports the administrative procedures required for company establishment. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0145] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0146] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0149] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0150] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0151] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0152] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0153] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0154] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0155] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0157] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] Each of the multiple elements described above, including the planning unit, cost calculation unit, project plan creation unit, investor matching unit, and administrative procedure support unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the planning unit is implemented by the AI ​​chatbot of the headset terminal 314, which provides relevant information to the idea entered by the user and helps to materialize the idea. The cost calculation unit is implemented by the specific processing unit 290 of the data processing unit 12, which automatically calculates the cost required to realize the idea. The project plan creation unit is implemented by the control unit 46A of the headset terminal 314, which automatically generates a business plan on the service. The investor matching unit is implemented by the specific processing unit 290 of the data processing unit 12, which requests investment from investors. The administrative procedure support unit is implemented by the control unit 46A of the headset terminal 314, which supports the administrative procedures required for company establishment. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be changed in various ways.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0161] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0162] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0163] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0165] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0166] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0167] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0168] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0169] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0170] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0171] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0172] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0173] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0174] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0175] Each of the multiple elements described above, including the analysis unit, cost calculation unit, plan creation unit, investor matching unit, and administrative procedure support unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the robot 414's AI chatbot, which provides relevant information to ideas entered by the user and helps to materialize those ideas. The cost calculation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which automatically calculates the costs required to realize the idea. The plan creation unit is implemented by, for example, the control unit 46A of the robot 414, which automatically generates a business plan on the service. The investor matching unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which requests investment from investors. The administrative procedure support unit is implemented by, for example, the control unit 46A of the robot 414, which supports the administrative procedures required for company establishment. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various changes are possible.

[0176] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0177] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0178] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0179] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0180] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0181] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0182] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0183] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0184] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0185] 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.

[0186] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0187] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0188] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0189] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0190] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0191] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0192] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0193] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0194] (Note 1) The planning department uses an interactive AI chatbot to consider ideas, A cost calculation unit calculates the necessary costs based on the ideas considered by the aforementioned study unit, A planning unit prepares a business plan based on the costs calculated by the aforementioned cost calculation unit, The Investor Matching Department uses the business plan prepared by the aforementioned Plan Preparation Department to solicit investments from investors, The system includes an administrative procedures support department that supports the administrative procedures and applications necessary for company establishment based on investments obtained by the aforementioned investor matching department. A system characterized by the following features. (Note 2) The aforementioned examination unit is, The related information provision function provides information such as "comparison with current services" and "patent / paper searches." The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned cost calculation unit, Automatically calculate expenses based on business scale. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned plan creation department, Create a balance sheet on the service. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned investor matching department, The service allows you to use a business plan created on the platform to offer investment opportunities to potential investors. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned Administrative Procedures Support Department, We provide support for the administrative procedures and application forms required when establishing a company. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned examination unit is, We estimate user emotions and adjust the idea evaluation process based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned examination unit is, Analyze the user's past idea submission history and select the most suitable evaluation method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned examination unit is, Filter based on the user's current projects and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned examination unit is, Estimate user emotions and prioritize ideas to consider based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned examination unit is, Prioritize considering highly relevant ideas by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned examination unit is, Analyze users' social media activity and consider related ideas. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned cost calculation unit, We estimate the user's emotions and adjust the cost calculation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned cost calculation unit, Select the optimal calculation method by referring to past cost data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned cost calculation unit, Different calculation algorithms are applied depending on the scale and nature of the business. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned cost calculation unit, The system estimates user sentiment and prioritizes cost calculations based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned cost calculation unit, The system prioritizes calculating cost data that is highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned cost calculation unit, Analyze users' social media activity and calculate relevant cost data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned plan creation department, We estimate the user's emotions and adjust the plan creation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned plan creation department, Refer to past project data to select the optimal creation method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned plan creation department, Apply different creation algorithms depending on the business category. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned plan creation department, The system estimates user emotions and prioritizes the plan based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned plan creation department, Prioritize the creation of highly relevant plan data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned plan creation department, Analyze users' social media activity and create relevant planning data. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned investor matching department, We estimate user sentiment and adjust the investor matching method based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned investor matching department, We select the optimal matching method by referring to past investor data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned investor matching department, Apply different matching algorithms depending on the business category. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned investor matching department, The system estimates user sentiment and determines the priority of investor matching based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned investor matching department, The system prioritizes matching users with highly relevant investor data, taking into account their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned investor matching department, Analyze users' social media activity and match it with relevant investor data. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned Administrative Procedures Support Department, The system estimates the user's emotions and adjusts how administrative procedures are supported based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned Administrative Procedures Support Department, We will select the most suitable support method by referring to past procedural data. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned Administrative Procedures Support Department, Apply different support algorithms depending on the business category. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned Administrative Procedures Support Department, The system estimates user sentiment and determines the priority of administrative procedures based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned Administrative Procedures Support Department, Prioritize supporting highly relevant procedural data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned Administrative Procedures Support Department, Analyze users' social media activity and support relevant procedural data. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0195] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The planning department uses an interactive AI chatbot to consider ideas, A cost calculation unit calculates the necessary costs based on the ideas considered by the aforementioned study unit, A planning unit prepares a business plan based on the costs calculated by the aforementioned cost calculation unit, The Investor Matching Department uses the business plan prepared by the aforementioned Plan Preparation Department to solicit investments from investors, The system includes an administrative procedures support department that supports the administrative procedures and applications necessary for company establishment based on investments obtained by the aforementioned investor matching department. A system characterized by the following features.

2. The aforementioned examination unit is, Using the related information provision function, we provide information such as comparisons with current services and patent / paper searches. The system according to feature 1.

3. The aforementioned cost calculation unit, Automatically calculate expenses based on business scale. The system according to feature 1.

4. The aforementioned plan creation department, Create a balance sheet on the service. The system according to feature 1.

5. The aforementioned investor matching department, The service allows you to use a business plan created on the platform to offer investment opportunities to potential investors. The system according to feature 1.

6. The aforementioned Administrative Procedures Support Department, We provide support for the administrative procedures and application forms required when establishing a company. The system according to feature 1.

7. The aforementioned examination unit is, We estimate user emotions and adjust the idea evaluation process based on those estimated emotions. The system according to feature 1.

8. The aforementioned examination unit is, Analyze the user's past idea submission history and select the most suitable evaluation method. The system according to feature 1.

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