System

Abstract

The system according to the embodiment is intended for entrepreneurs to efficiently create business plans and prepare for questions and answers with investors.SOLUTION: A system includes a reception part, a generation part, a confirmation part, a regeneration part, and a simulation part. The reception unit receives basic information of a business plan from an entrepreneur. The generation unit analyzes the information received by the reception unit and generates a draft of the business plan. A confirmation part allows the entrepreneur to confirm and correct the draft generated by the generation part. The regeneration unit analyzes the information corrected by the confirmation unit again and generates a new draft. The simulation part simulates questions and answers with the investor on the basis of the draft generated by the regeneration part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have made it difficult for entrepreneurs to efficiently create business plans and prepare for question and answer sessions with investors.

[0005] The system according to the embodiment aims to enable entrepreneurs to efficiently create business plans and prepare for question and answer sessions with investors. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a generation unit, a confirmation unit, a regeneration unit, and a simulation unit. The reception unit receives basic information about a business plan from an entrepreneur. The generation unit analyzes the information received by the reception unit and generates a draft of the business plan. The confirmation unit allows the entrepreneur to confirm and revise the draft generated by the generation unit. The regeneration unit reanalyzes the information revised by the confirmation unit and generates a new draft. The simulation unit simulates a question and answer session with investors based on the draft generated by the regeneration unit. [Effects of the Invention]

[0007] The system according to the embodiment allows entrepreneurs to efficiently create business plans and prepare for question and answer sessions with investors. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A business plan support system according to an embodiment of the present invention uses a generation AI to assist entrepreneurs in creating business plans and further supports Q&A sessions with investors through voice conversations. In the business plan support system, entrepreneurs input basic information about their business plans into the generation AI, which then analyzes the information to generate a draft business plan. The generated draft can be reviewed and revised by the entrepreneur, and the generation AI then analyzes it again to generate a new draft. Furthermore, the business plan support system simulates Q&A sessions with investors to help entrepreneurs respond effectively. For example, the business plan support system allows entrepreneurs to input information such as their business objectives, target market, competitive analysis, and revenue model. The business plan support system then analyzes the input information using the generation AI to automatically create each section of the business plan (e.g., business overview, market analysis, competitive analysis, marketing strategy, financial plan, etc.). The entrepreneur then reviews and revises the generated draft, and the generation AI then analyzes it again to generate a new draft. Furthermore, the business plan support system predicts frequently asked questions by investors and suggests appropriate answers. This allows entrepreneurs to effectively create business plans and prepare for Q&A sessions with investors. This allows the business plan support system to streamline the business plan creation process, enabling high-quality business plans to be created in a short amount of time. Furthermore, with the support of generative AI, entrepreneurs can respond with confidence during question and answer sessions with investors.

[0029] A business plan support system according to an embodiment includes a reception unit, a generation unit, a confirmation unit, a regeneration unit, and a simulation unit. The reception unit receives basic information about a business plan from an entrepreneur. The basic information includes, but is not limited to, the business objectives, target market, competitive analysis, and revenue model. The reception unit can receive the basic information by, for example, text input, voice input, or image input. The generation unit uses a generation AI to analyze the information received by the reception unit and generate a draft business plan. The generation unit automatically creates each section of the business plan (e.g., business overview, market analysis, competitive analysis, marketing strategy, financial plan, etc.) using, for example, a text generation AI (e.g., LLM). The generation unit can also analyze market data using publicly available market reports and statistical data. The confirmation unit allows the entrepreneur to review the draft generated by the generation unit and make revisions as necessary. The confirmation unit, for example, displays the contents of the draft and provides an interface for the entrepreneur to make revisions. The regeneration unit reanalyzes the information revised by the confirmation unit and generates a new draft. The regeneration unit uses a generation AI to generate a new draft that reflects the revisions. The simulation unit simulates a question-and-answer session with investors based on the draft generated by the regeneration unit. The simulation unit, for example, predicts questions that investors often ask and suggests appropriate answers to those questions. As a result, the business plan support system according to the embodiment allows entrepreneurs to effectively create business plans and prepare for question-and-answer sessions with investors.

[0030] The generation unit may analyze market data using publicly available market reports or statistical data. Market reports include, but are not limited to, reports issued by government agencies or industry associations. Statistical data include, but are not limited to, government statistics, industry statistics, etc. The generation unit may, for example, collect publicly available market reports and extract market data using text analysis technology. The generation unit may also collect statistical data and analyze the market data using data mining technology. For example, the generation unit may collect government statistical data and analyze the size and growth forecast of the target market. The generation unit may also collect industry statistical data and perform competitive analysis. This improves the accuracy of the market data analysis.

[0031] The simulation unit can predict the questions that investors often ask and propose appropriate answers to those questions. Examples of frequently asked questions include, but are not limited to, revenue models, competitive advantages, and marketing strategies. For example, the simulation unit can analyze past question history to predict the questions that investors often ask. The simulation unit can also use generative AI to propose appropriate answers to predicted questions. For example, the simulation unit can propose detailed answers about revenue structures and revenue sources in response to questions about revenue models. The simulation unit can also propose answers about comparisons with competitors and points of differentiation in response to questions about competitive advantages. This allows for effective preparation for Q&A sessions with investors.

[0032] The review unit allows entrepreneurs to review the draft created by the generation AI and make any necessary revisions. The review unit, for example, displays the contents of the draft and provides an interface for entrepreneurs to make revisions. Entrepreneurs can, for example, add specific market data or revise the content of the competitive analysis. The review unit saves the revisions and hands them over to the regeneration unit. The review unit also manages the revision history and can refer to past revisions. For example, the review unit highlights parts that have been revised in the past, allowing entrepreneurs to easily review the revisions. This improves the accuracy of the draft.

[0033] The regeneration unit can reanalyze the revised information and generate a new draft. The regeneration unit uses the generation AI to reanalyze the information revised by the verification unit and generate a new draft. For example, the regeneration unit automatically creates each section of a new business plan (e.g., business overview, market analysis, competitive analysis, marketing strategy, financial plan, etc.) that reflects the revisions. The regeneration unit can also review the overall structure of the business plan based on the revisions and adjust the order and content of the sections as necessary. For example, the regeneration unit can update the target market section based on the revised market data. The regeneration unit can also update the competitive advantage section based on the revised competitive analysis content. This generates a new draft that reflects the revisions.

[0034] The simulation unit can propose appropriate answers to questions about the revenue model. Examples of the revenue model include, but are not limited to, revenue structure, revenue sources, and revenue forecasts. For example, the simulation unit can propose detailed answers about the revenue structure and revenue sources in response to questions about the revenue model. The simulation unit can also propose answers about revenue growth forecasts and risk factors in response to questions about revenue forecasts. For example, the simulation unit can propose a graph showing the revenue ratio for each revenue source to explain the revenue structure. The simulation unit can also propose a chart showing revenue growth forecasts to explain the revenue forecasts. This allows for effective preparation for questions about the revenue model.

[0035] The reception unit can analyze the entrepreneur's past business plan submission history and select the optimal information acquisition method. The reception unit, for example, retrieves the past business plan submission history from a database and analyzes it using text analysis technology. For example, the reception unit may preferentially suggest text input to an entrepreneur who has preferred text input in the past. The reception unit may also preferentially suggest voice input to an entrepreneur who has used voice input in the past. For example, the reception unit may suggest a method for acquiring information during a specific time period based on the past submission history. This provides the optimal information acquisition method based on the past history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0036] When acquiring basic information for a business plan, the reception unit can select the optimal acquisition means depending on the entrepreneur's input method. The reception unit, for example, detects the entrepreneur's input method (voice, text, image, etc.) and selects the optimal acquisition means. For example, if the entrepreneur prefers voice input, the reception unit can prioritize voice input. Also, if the entrepreneur prefers text input, the reception unit can prioritize text input. For example, if the entrepreneur prefers image input, the reception unit can prioritize image input. This makes it possible to provide an input method that suits the entrepreneur's preferences. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI.

[0037] When acquiring basic information for a business plan, the reception unit can prioritize acquiring highly relevant information based on the entrepreneur's geographical location information. The reception unit, for example, acquires the entrepreneur's geographical location information from GPS data or location information services, and prioritizes acquiring highly relevant information. For example, the reception unit prioritizes acquiring market data related to the entrepreneur's location. The reception unit can also prioritize acquiring competitive information related to the entrepreneur's location. For example, the reception unit prioritizes acquiring legal and regulatory information related to the entrepreneur's location. This makes it possible to provide highly relevant information based on the geographical location information. Some or all of the above-described processing in the reception unit may be performed, for example, using AI, or may be performed without using AI.

[0038] When acquiring basic information about the business plan, the reception unit can analyze the entrepreneur's social media activity and acquire related information. For example, the reception unit acquires the entrepreneur's social media activity from a database and analyzes it using text analysis technology. For example, the reception unit acquires market data mentioned by the entrepreneur on social media. The reception unit can also acquire competitive information mentioned by the entrepreneur on social media. For example, the reception unit acquires legal and regulatory information mentioned by the entrepreneur on social media. This makes it possible to provide related information based on social media activity. Some or all of the above-described processing in the reception unit may be performed, for example, using AI, or may be performed without using AI.

[0039] When acquiring basic information for a business plan, the reception unit can customize the acquisition method by reflecting the entrepreneur's past feedback. The reception unit, for example, acquires past feedback from a database and analyzes it using text analysis technology. For example, the reception unit customizes the acquisition method to voice input based on the past feedback. The reception unit can also customize the acquisition method to text input based on the past feedback. For example, the reception unit customizes the acquisition method to image input based on the past feedback. This makes it possible to optimize the acquisition method based on the past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0040] The generation unit can adjust the level of detail of the draft based on the importance of the business plan when generating the business plan. The generation unit, for example, evaluates the importance of the business plan and adjusts the level of detail of the draft based on the importance. For example, the generation unit generates a draft including detailed information for an important business plan. The generation unit can also generate a concise draft for a business plan with low importance. For example, the generation unit adjusts the level of detail of the information according to the importance. This makes it possible to provide a detailed draft according to the importance of the business plan. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or without using AI.

[0041] The generation unit can apply different generation algorithms depending on the category of the business plan during generation. For example, the generation unit classifies the category of the business plan and applies different generation algorithms depending on the category. For example, the generation unit applies an algorithm that analyzes specific market data in the market analysis category. The generation unit can also apply an algorithm that analyzes competitive information in the competitive analysis category. For example, the generation unit applies an algorithm that analyzes financial data in the financial plan category. This makes it possible to provide an optimal generation algorithm depending on the category. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI.

[0042] During generation, the generation unit can improve the accuracy of generation by referring to the entrepreneur's past draft results. The generation unit, for example, retrieves past draft results from a database and analyzes them using text analysis technology. For example, the generation unit adjusts the generation algorithm based on the past draft results. The generation unit can also learn frequently used expression methods from the past draft results. For example, the generation unit improves the accuracy of generation by referring to the past draft results. This makes it possible to improve the accuracy of generation by referring to the past draft results. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI.

[0043] The generation unit can determine the priority of the drafts based on the submission time of the business plans at the time of generation. The generation unit, for example, evaluates the submission time of the business plans and determines the priority of the drafts based on the submission time. For example, the generation unit generates business plans with an upcoming submission deadline with priority. The generation unit can also postpone business plans with a distant submission deadline. For example, the generation unit adjusts the priority of the drafts based on the submission time. This makes it possible to adjust the priority of the drafts based on the submission time. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or may be performed without using AI.

[0044] The generation unit can adjust the order of the drafts based on the relevance of the business plans when generating them. The generation unit, for example, evaluates the relevance of the business plans and adjusts the order of the drafts based on the relevance. For example, the generation unit generates business plans with high relevance preferentially. The generation unit can also postpone business plans with low relevance. For example, the generation unit adjusts the order of the drafts based on the relevance. In this way, the order of the drafts can be adjusted based on the relevance. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI.

[0045] During generation, the generation unit can adjust the use of technical terminology in the draft according to the entrepreneur's level of expertise. The generation unit, for example, evaluates the entrepreneur's level of expertise and adjusts the use of technical terminology in the draft according to the level of expertise. For example, the generation unit generates a draft that uses a lot of technical terminology for entrepreneurs with high levels of expertise. The generation unit can also generate a draft that uses easy-to-understand expressions for entrepreneurs with low levels of expertise. For example, the generation unit adjusts the use of technical terminology according to the level of expertise. This makes it possible to provide the use of technical terminology according to the level of expertise. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI.

[0046] During verification, the verification unit can select the optimal verification method by referring to the entrepreneur's past revision history. The verification unit, for example, retrieves the past revision history from a database and analyzes it using text analysis technology. For example, the verification unit prioritizes verification of frequently revised sections based on the past revision history. The verification unit can also postpone sections with fewer revisions based on the past revision history. For example, the verification unit adjusts the verification method based on the revision history. This makes it possible to provide the optimal verification method based on the past revision history. Some or all of the above-described processing in the verification unit may be performed, for example, using AI, or may be performed without using AI.

[0047] The confirmation unit can customize the confirmation content based on the entrepreneur's current project status when confirming. For example, the confirmation unit obtains the entrepreneur's current project status from a database and analyzes it using text analysis technology. For example, the confirmation unit prioritizes checking parts related to currently ongoing projects. The confirmation unit can also adjust the confirmation content based on the current project status. For example, the confirmation unit customizes the confirmation method depending on the progress of the project. This makes it possible to provide confirmation content based on the current project status. Some or all of the above-mentioned processing in the confirmation unit may be performed, for example, using AI or without using AI.

[0048] The verification unit can improve the verification method by reflecting the entrepreneur's feedback during verification. For example, the verification unit obtains the entrepreneur's feedback from a database and analyzes it using text analysis technology. For example, the verification unit simplifies the verification method based on the feedback. The verification unit can also make the verification method more detailed based on the feedback. For example, the verification unit improves the verification method by reflecting the feedback. This allows the verification method to be improved based on the feedback. Some or all of the above-mentioned processing in the verification unit may be performed using AI, for example, or may be performed without using AI.

[0049] The verification unit can select the optimal verification method during verification by taking into account the entrepreneur's geographic location information. The verification unit, for example, obtains the entrepreneur's geographic location information from GPS data or location information services and selects the optimal verification method. For example, the verification unit provides a detailed verification method when the entrepreneur is in the office. The verification unit can also provide a simple verification method when the entrepreneur is out. For example, the verification unit adjusts the verification method based on the entrepreneur's geographic location information. This makes it possible to provide the optimal verification method based on the geographic location information. Some or all of the above-described processing in the verification unit may be performed, for example, using AI or without using AI.

[0050] During verification, the verification unit can analyze the entrepreneur's social media activity and suggest verification content. For example, the verification unit retrieves the entrepreneur's social media activity from a database and analyzes it using text analysis technology. For example, the verification unit suggests verification content based on what the entrepreneur has mentioned on social media. The verification unit can also include related information from the entrepreneur's social media activity in the verification content. For example, the verification unit analyzes social media activity and suggests optimal verification content. This makes it possible to provide verification content based on social media activity. Some or all of the above-described processing in the verification unit may be performed, for example, using AI, or may be performed without using AI.

[0051] The verification unit can customize the verification method by reflecting the entrepreneur's past feedback during verification. For example, the verification unit retrieves past feedback from a database and analyzes it using text analysis technology. For example, the verification unit customizes the verification method to voice input based on the past feedback. The verification unit can also customize the verification method to text input based on the past feedback. For example, the verification unit customizes the verification method to image input based on the past feedback. This makes it possible to optimize the verification method based on the past feedback. Some or all of the above-described processing in the verification unit may be performed using AI, for example, or may be performed without using AI.

[0052] During regeneration, the regeneration unit can adjust the level of detail of the draft based on the importance of the corrected information. The regeneration unit, for example, evaluates the importance of the corrected information and adjusts the level of detail of the draft based on the importance. For example, the regeneration unit regenerates a draft including detailed information in the case of important correction information. The regeneration unit can also regenerate a concise draft in the case of low-importance correction information. For example, the regeneration unit adjusts the level of detail of the information according to the importance. This makes it possible to provide a detailed draft according to the importance of the correction information. Some or all of the above-described processing in the regeneration unit may be performed, for example, using AI or without using AI.

[0053] During regeneration, the regeneration unit can apply different regeneration algorithms depending on the category of the corrected information. For example, the regeneration unit classifies the category of the corrected information and applies different regeneration algorithms depending on the category. For example, the regeneration unit applies an algorithm that analyzes specific market data to corrected information for market analysis. The regeneration unit can also apply an algorithm that analyzes competitive information to corrected information for competitive analysis. For example, the regeneration unit applies an algorithm that analyzes financial data to corrected information for financial planning. This makes it possible to provide an optimal regeneration algorithm depending on the category. Some or all of the above-mentioned processing in the regeneration unit may be performed using AI, for example, or may be performed without using AI.

[0054] During regeneration, the regeneration unit can improve the accuracy of the regeneration by referring to the entrepreneur's past regeneration results. The regeneration unit, for example, retrieves past regeneration results from a database and analyzes them using text analysis technology. For example, the regeneration unit adjusts the regeneration algorithm based on the past regeneration results. The regeneration unit can also learn frequently used expression methods from the past regeneration results. For example, the regeneration unit improves the accuracy of the regeneration by referring to the past regeneration results. This makes it possible to improve the accuracy of the regeneration by referring to the past regeneration results. Some or all of the above-mentioned processing in the regeneration unit may be performed, for example, using AI, or may be performed without using AI.

[0055] During regeneration, the regeneration unit can determine the priority of the draft based on the submission time of the revised information. The regeneration unit, for example, evaluates the submission time of the revised information and determines the priority of the draft based on the submission time. For example, the regeneration unit preferentially regenerates revised information whose submission deadline is approaching. The regeneration unit can also postpone revised information whose submission deadline is far away. For example, the regeneration unit adjusts the priority of the draft based on the submission time. This makes it possible to adjust the priority of the draft based on the submission time. Some or all of the above-mentioned processing in the regeneration unit may be performed, for example, using AI or without using AI.

[0056] The regeneration unit can adjust the order of the drafts based on the relevance of the modified information during regeneration. The regeneration unit, for example, evaluates the relevance of the modified information and adjusts the order of the drafts based on the relevance. For example, the regeneration unit preferentially regenerates modified information with high relevance. The regeneration unit can also postpone modified information with low relevance. For example, the regeneration unit adjusts the order of the drafts based on the relevance. This makes it possible to adjust the order of the drafts based on the relevance. Some or all of the above-described processing in the regeneration unit may be performed using AI, for example, or may be performed without using AI.

[0057] During regeneration, the regeneration unit can adjust the use of technical terminology in the draft according to the entrepreneur's level of expertise. The regeneration unit, for example, evaluates the entrepreneur's level of expertise and adjusts the use of technical terminology in the draft according to the level of expertise. For example, the regeneration unit regenerates a draft that uses a lot of technical terminology for entrepreneurs with high levels of expertise. The regeneration unit can also regenerate a draft that uses easy-to-understand expressions for entrepreneurs with low levels of expertise. For example, the regeneration unit adjusts the use of technical terminology according to the level of expertise. This makes it possible to provide the use of technical terminology according to the level of expertise. Some or all of the above-described processing in the regeneration unit may be performed, for example, using AI, or may be performed without using AI.

[0058] During simulation, the simulation unit can select the optimal simulation method by referring to the investor's past question history. The simulation unit, for example, retrieves the investor's past question history from a database and analyzes it using text analysis technology. For example, the simulation unit prioritizes simulation of frequently asked questions based on the investor's past question history. The simulation unit can also postpone questions about which fewer questions are asked based on the investor's past question history. For example, the simulation unit adjusts the simulation method based on the question history. This makes it possible to provide the optimal simulation method based on the past question history. Some or all of the above-mentioned processing in the simulation unit may be performed using, for example, AI, or may be performed without using AI.

[0059] The simulation unit can perform a Q&A simulation taking into account investor attribute information during the simulation. The simulation unit, for example, obtains investor attribute information from a database and analyzes it using text analysis technology. For example, the simulation unit simulates specialized questions based on the investor's industry experience. The simulation unit can also simulate related questions based on the investor's areas of interest. For example, the simulation unit customizes the Q&A simulation based on the investor's attribute information. This makes it possible to provide a Q&A simulation based on the investor's attribute information. Some or all of the above-described processing in the simulation unit may be performed using, for example, AI, or may be performed without using AI.

[0060] The simulation unit can improve the simulation method by reflecting investor feedback during the simulation. For example, the simulation unit obtains investor feedback from a database and analyzes it using text analysis technology. For example, the simulation unit simplifies the simulation method based on the feedback. The simulation unit can also refine the simulation method based on the feedback. For example, the simulation unit improves the simulation method by reflecting the feedback. This allows the simulation method to be improved based on the feedback. Some or all of the above-mentioned processing in the simulation unit may be performed using AI, for example, or may be performed without using AI.

[0061] During the simulation, the simulation unit can select the optimal simulation method taking into account the geographical location information of the investor. The simulation unit, for example, obtains the geographical location information of the investor from GPS data or a location information service and selects the optimal simulation method. For example, the simulation unit provides a detailed simulation method when the investor is in the office. The simulation unit can also provide a simple simulation method when the investor is out. For example, the simulation unit adjusts the simulation method based on the geographical location information of the investor. This makes it possible to provide the optimal simulation method based on the geographical location information. Some or all of the above-described processing in the simulation unit may be performed, for example, using AI or without using AI.

[0062] During the simulation, the simulation unit can analyze the social media activity of investors to simulate a question and answer session. The simulation unit, for example, retrieves the social media activity of investors from a database and analyzes it using text analysis technology. For example, the simulation unit simulates a question and answer session based on the content mentioned by the investors on social media. The simulation unit can also simulate related questions from the investors' social media activity. For example, the simulation unit analyzes the social media activity to simulate an optimal question and answer session. This makes it possible to provide a question and answer session simulation based on the social media activity. Some or all of the above-described processing in the simulation unit may be performed using, for example, AI, or may be performed without using AI.

[0063] During the simulation, the simulation unit can customize the Q&A simulation method by reflecting past investor feedback. The simulation unit, for example, obtains past investor feedback from a database and analyzes it using text analysis technology. For example, the simulation unit simplifies the Q&A simulation method based on the past feedback. The simulation unit can also detail the Q&A simulation method based on the past feedback. For example, the simulation unit customizes the Q&A simulation method by reflecting past feedback. This makes it possible to optimize the Q&A simulation method based on the past feedback. Some or all of the above-described processing in the simulation unit may be performed, for example, using AI or without using AI.

[0064] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0065] The reception unit can analyze the success rate of entrepreneurs' past business plans and prioritize the acquisition of information with a high success rate. For example, the reception unit can acquire data on the success rate of past business plans from a database and analyze it using text analysis technology. By prioritizing the acquisition of information on business plans with a high success rate, the reception unit can help entrepreneurs create effective business plans. The reception unit can also filter out information with a low success rate to prevent entrepreneurs from being misled by useless information. This improves the quality of business plans and increases the likelihood of success.

[0066] When generating a draft of a business plan, the generation unit can adjust the generation algorithm by reflecting the entrepreneur's past feedback. For example, the generation unit retrieves past feedback data from a database and analyzes it using text analysis technology. Based on the feedback, the generation unit adjusts the generation algorithm to generate a draft that suits the entrepreneur's preferences. The generation unit can also reflect the feedback to optimize the content and structure of the draft. This makes it possible to provide a high-quality draft that meets the entrepreneur's needs.

[0067] The regeneration unit can adjust the level of detail of the draft based on the importance of the corrected information. For example, the regeneration unit evaluates the importance of the corrected information and adjusts the level of detail of the draft based on the importance. In the case of important correction information, the regeneration unit can regenerate a draft including detailed information, and in the case of correction information with low importance, the regeneration unit can regenerate a concise draft. This makes it possible to provide a detailed draft according to the importance of the correction information.

[0068] The reception unit can prioritize obtaining highly relevant information based on the entrepreneur's geographic location information. For example, the reception unit obtains the entrepreneur's geographic location information from GPS data or location information services, and prioritizes obtaining highly relevant information. By prioritizing obtaining market data, competitive information, and legal and regulatory information related to the entrepreneur's location, it is possible to provide highly relevant information based on the geographic location information.

[0069] The generation unit can determine the priority of the drafts based on the submission time of the business plans at the time of generation. For example, the generation unit evaluates the submission time of the business plans and determines the priority of the drafts based on the submission time. Business plans with upcoming submission deadlines can be generated preferentially, and business plans with distant submission deadlines can be postponed. This makes it possible to adjust the priority of the drafts based on the submission time.

[0070] The simulation unit can select the optimal simulation method by referring to the investor's past question history. For example, the simulation unit retrieves the investor's past question history from a database and analyzes it using text analysis technology. Based on the investor's past question history, it can prioritize simulation of frequently asked questions and postpone simulation of less frequently asked questions. This makes it possible to provide the optimal simulation method based on the investor's past question history.

[0071] The verification unit can select the optimal verification method by referring to the entrepreneur's past revision history. For example, the verification unit retrieves the past revision history from a database and analyzes it using text analysis technology. Based on the past revision history, it can prioritize checking areas that are frequently revised and postpone checking areas that are rarely revised. This makes it possible to provide the optimal verification method based on the past revision history.

[0072] The processing flow of the first embodiment will be briefly explained below.

[0073] Step 1: The reception unit receives basic information about the business plan from the entrepreneur. The basic information includes the business objectives, target market, competitive analysis, revenue model, etc. The reception unit can receive the basic information by text input, voice input, image input, etc. Step 2: The generation unit uses a generation AI to analyze the information received by the reception unit and generate a draft business plan. The generation unit uses a text generation AI (e.g., LLM) to automatically create each section of the business plan (business overview, market analysis, competitive analysis, marketing strategy, financial plan, etc.). The generation unit can also analyze market data using publicly available market reports and statistical data. Step 3: The confirmation unit allows the entrepreneur to check the draft generated by the generation unit and make corrections as necessary. The confirmation unit displays the contents of the draft and provides an interface for the entrepreneur to make corrections. Step 4: The regeneration unit re-analyzes the information corrected by the verification unit and generates a new draft. The regeneration unit uses the generation AI to generate a new draft that reflects the corrections. Step 5: The simulation unit simulates a question and answer session with investors based on the draft generated by the regeneration unit. The simulation unit predicts the questions that investors will frequently ask and suggests appropriate answers to those questions.

[0074] (Example 2) A business plan support system according to an embodiment of the present invention uses a generation AI to assist entrepreneurs in creating business plans and further supports Q&A sessions with investors through voice conversations. In the business plan support system, entrepreneurs input basic information about their business plans into the generation AI, which then analyzes the information to generate a draft business plan. The generated draft can be reviewed and revised by the entrepreneur, and the generation AI then analyzes it again to generate a new draft. Furthermore, the business plan support system simulates Q&A sessions with investors to help entrepreneurs respond effectively. For example, the business plan support system allows entrepreneurs to input information such as their business objectives, target market, competitive analysis, and revenue model. The business plan support system then analyzes the input information using the generation AI to automatically create each section of the business plan (e.g., business overview, market analysis, competitive analysis, marketing strategy, financial plan, etc.). The entrepreneur then reviews and revises the generated draft, and the generation AI then analyzes it again to generate a new draft. Furthermore, the business plan support system predicts frequently asked questions by investors and suggests appropriate answers. This allows entrepreneurs to effectively create business plans and prepare for Q&A sessions with investors. This allows the business plan support system to streamline the business plan creation process, enabling high-quality business plans to be created in a short amount of time. Furthermore, with the support of generative AI, entrepreneurs can respond with confidence during question and answer sessions with investors.

[0075] A business plan support system according to an embodiment includes a reception unit, a generation unit, a confirmation unit, a regeneration unit, and a simulation unit. The reception unit receives basic information about a business plan from an entrepreneur. The basic information includes, but is not limited to, the business objectives, target market, competitive analysis, and revenue model. The reception unit can receive the basic information by, for example, text input, voice input, or image input. The generation unit uses a generation AI to analyze the information received by the reception unit and generate a draft business plan. The generation unit automatically creates each section of the business plan (e.g., business overview, market analysis, competitive analysis, marketing strategy, financial plan, etc.) using, for example, a text generation AI (e.g., LLM). The generation unit can also analyze market data using publicly available market reports and statistical data. The confirmation unit allows the entrepreneur to review the draft generated by the generation unit and make revisions as necessary. The confirmation unit, for example, displays the contents of the draft and provides an interface for the entrepreneur to make revisions. The regeneration unit reanalyzes the information revised by the confirmation unit and generates a new draft. The regeneration unit uses a generation AI to generate a new draft that reflects the revisions. The simulation unit simulates a question-and-answer session with investors based on the draft generated by the regeneration unit. The simulation unit, for example, predicts questions that investors often ask and suggests appropriate answers to those questions. As a result, the business plan support system according to the embodiment allows entrepreneurs to effectively create business plans and prepare for question-and-answer sessions with investors.

[0076] The generation unit may analyze market data using publicly available market reports or statistical data. Market reports include, but are not limited to, reports issued by government agencies or industry associations. Statistical data include, but are not limited to, government statistics, industry statistics, etc. The generation unit may, for example, collect publicly available market reports and extract market data using text analysis technology. The generation unit may also collect statistical data and analyze the market data using data mining technology. For example, the generation unit may collect government statistical data and analyze the size and growth forecast of the target market. The generation unit may also collect industry statistical data and perform competitive analysis. This improves the accuracy of the market data analysis.

[0077] The simulation unit can predict the questions that investors often ask and propose appropriate answers to those questions. Examples of frequently asked questions include, but are not limited to, revenue models, competitive advantages, and marketing strategies. For example, the simulation unit can analyze past question history to predict the questions that investors often ask. The simulation unit can also use generative AI to propose appropriate answers to predicted questions. For example, the simulation unit can propose detailed answers about revenue structures and revenue sources in response to questions about revenue models. The simulation unit can also propose answers about comparisons with competitors and points of differentiation in response to questions about competitive advantages. This allows for effective preparation for Q&A sessions with investors.

[0078] The review unit allows entrepreneurs to review the draft created by the generation AI and make any necessary revisions. The review unit, for example, displays the contents of the draft and provides an interface for entrepreneurs to make revisions. Entrepreneurs can, for example, add specific market data or revise the content of the competitive analysis. The review unit saves the revisions and hands them over to the regeneration unit. The review unit also manages the revision history and can refer to past revisions. For example, the review unit highlights parts that have been revised in the past, allowing entrepreneurs to easily review the revisions. This improves the accuracy of the draft.

[0079] The regeneration unit can reanalyze the revised information and generate a new draft. The regeneration unit uses the generation AI to reanalyze the information revised by the verification unit and generate a new draft. For example, the regeneration unit automatically creates each section of a new business plan (e.g., business overview, market analysis, competitive analysis, marketing strategy, financial plan, etc.) that reflects the revisions. The regeneration unit can also review the overall structure of the business plan based on the revisions and adjust the order and content of the sections as necessary. For example, the regeneration unit can update the target market section based on the revised market data. The regeneration unit can also update the competitive advantage section based on the revised competitive analysis content. This generates a new draft that reflects the revisions.

[0080] The simulation unit can propose appropriate answers to questions about the revenue model. Examples of the revenue model include, but are not limited to, revenue structure, revenue sources, and revenue forecasts. For example, the simulation unit can propose detailed answers about the revenue structure and revenue sources in response to questions about the revenue model. The simulation unit can also propose answers about revenue growth forecasts and risk factors in response to questions about revenue forecasts. For example, the simulation unit can propose a graph showing the revenue ratio for each revenue source to explain the revenue structure. The simulation unit can also propose a chart showing revenue growth forecasts to explain the revenue forecasts. This allows for effective preparation for questions about the revenue model.

[0081] The reception unit can estimate the entrepreneur's emotions and adjust the timing of inputting basic information about the business plan based on the estimated emotions. For example, the reception unit captures the entrepreneur's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on changes in facial expressions. The reception unit can also record the entrepreneur's voice and estimate the emotions using voice analysis technology. For example, the reception unit analyzes the tone and speed of the voice and calculates an emotion score. The reception unit can also collect the entrepreneur's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on heart rate fluctuations. This reduces stress by adjusting the input timing according to the entrepreneur's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reception unit can be performed using, for example, AI, or without AI.

[0082] The reception unit can analyze the entrepreneur's past business plan submission history and select the optimal information acquisition method. The reception unit, for example, retrieves the past business plan submission history from a database and analyzes it using text analysis technology. For example, the reception unit may preferentially suggest text input to an entrepreneur who has preferred text input in the past. The reception unit may also preferentially suggest voice input to an entrepreneur who has used voice input in the past. For example, the reception unit may suggest a method for acquiring information during a specific time period based on the past submission history. This provides the optimal information acquisition method based on the past history. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0083] When acquiring basic information for a business plan, the reception unit can select the optimal acquisition means depending on the entrepreneur's input method. The reception unit, for example, detects the entrepreneur's input method (voice, text, image, etc.) and selects the optimal acquisition means. For example, if the entrepreneur prefers voice input, the reception unit can prioritize voice input. Also, if the entrepreneur prefers text input, the reception unit can prioritize text input. For example, if the entrepreneur prefers image input, the reception unit can prioritize image input. This makes it possible to provide an input method that suits the entrepreneur's preferences. Some or all of the above-mentioned processing in the reception unit may be performed, for example, using AI, or may be performed without using AI.

[0084] The reception unit can estimate the entrepreneur's emotions and determine the priority of information to be acquired based on the estimated emotions of the entrepreneur. For example, the reception unit captures the entrepreneur's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on changes in facial expressions. The reception unit can also record the entrepreneur's voice and estimate the emotions using voice analysis technology. For example, the reception unit analyzes the tone and speed of the voice and calculates an emotion score. The reception unit can also collect the entrepreneur's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on heart rate fluctuations. This makes it possible to adjust the priority of information according to the entrepreneur's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reception unit can be performed using, for example, AI, or without AI.

[0085] When acquiring basic information for a business plan, the reception unit can prioritize acquiring highly relevant information based on the entrepreneur's geographical location information. The reception unit, for example, acquires the entrepreneur's geographical location information from GPS data or location information services, and prioritizes acquiring highly relevant information. For example, the reception unit prioritizes acquiring market data related to the entrepreneur's location. The reception unit can also prioritize acquiring competitive information related to the entrepreneur's location. For example, the reception unit prioritizes acquiring legal and regulatory information related to the entrepreneur's location. This makes it possible to provide highly relevant information based on the geographical location information. Some or all of the above-described processing in the reception unit may be performed, for example, using AI, or may be performed without using AI.

[0086] When acquiring basic information about the business plan, the reception unit can analyze the entrepreneur's social media activity and acquire related information. For example, the reception unit acquires the entrepreneur's social media activity from a database and analyzes it using text analysis technology. For example, the reception unit acquires market data mentioned by the entrepreneur on social media. The reception unit can also acquire competitive information mentioned by the entrepreneur on social media. For example, the reception unit acquires legal and regulatory information mentioned by the entrepreneur on social media. This makes it possible to provide related information based on social media activity. Some or all of the above-described processing in the reception unit may be performed, for example, using AI, or may be performed without using AI.

[0087] When acquiring basic information for a business plan, the reception unit can customize the acquisition method by reflecting the entrepreneur's past feedback. The reception unit, for example, acquires past feedback from a database and analyzes it using text analysis technology. For example, the reception unit customizes the acquisition method to voice input based on the past feedback. The reception unit can also customize the acquisition method to text input based on the past feedback. For example, the reception unit customizes the acquisition method to image input based on the past feedback. This makes it possible to optimize the acquisition method based on the past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI.

[0088] The generation unit can estimate the entrepreneur's emotions and adjust the expression method of the business plan draft based on the estimated emotions. For example, the generation unit captures the entrepreneur's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on changes in facial expressions. The generation unit can also record the entrepreneur's voice and estimate the emotions using voice analysis technology. For example, the generation unit analyzes the tone and speed of the voice and calculates an emotion score. The generation unit can also collect the entrepreneur's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on heart rate fluctuations. This makes it possible to provide an expression method that corresponds to the entrepreneur's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or without AI.

[0089] The generation unit can adjust the level of detail of the draft based on the importance of the business plan when generating the business plan. The generation unit, for example, evaluates the importance of the business plan and adjusts the level of detail of the draft based on the importance. For example, the generation unit generates a draft including detailed information for an important business plan. The generation unit can also generate a concise draft for a business plan with low importance. For example, the generation unit adjusts the level of detail of the information according to the importance. This makes it possible to provide a detailed draft according to the importance of the business plan. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or without using AI.

[0090] The generation unit can apply different generation algorithms depending on the category of the business plan during generation. For example, the generation unit classifies the category of the business plan and applies different generation algorithms depending on the category. For example, the generation unit applies an algorithm that analyzes specific market data in the market analysis category. The generation unit can also apply an algorithm that analyzes competitive information in the competitive analysis category. For example, the generation unit applies an algorithm that analyzes financial data in the financial plan category. This makes it possible to provide an optimal generation algorithm depending on the category. Some or all of the above-mentioned processing in the generation unit may be performed using AI, for example, or may be performed without using AI.

[0091] During generation, the generation unit can improve the accuracy of generation by referring to the entrepreneur's past draft results. The generation unit, for example, retrieves past draft results from a database and analyzes them using text analysis technology. For example, the generation unit adjusts the generation algorithm based on the past draft results. The generation unit can also learn frequently used expression methods from the past draft results. For example, the generation unit improves the accuracy of generation by referring to the past draft results. This makes it possible to improve the accuracy of generation by referring to the past draft results. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI.

[0092] The generation unit can estimate the entrepreneur's emotions and adjust the length of the draft based on the estimated emotions. For example, the generation unit captures the entrepreneur's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on changes in facial expressions. The generation unit can also record the entrepreneur's voice and estimate the emotions using voice analysis technology. For example, the generation unit analyzes the tone and speed of the voice and calculates an emotion score. The generation unit can also collect the entrepreneur's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on heart rate fluctuations. This makes it possible to provide the length of the draft according to the entrepreneur's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or without AI.

[0093] The generation unit can estimate the entrepreneur's emotions and adjust the length of the draft based on the estimated emotions. For example, the generation unit captures the entrepreneur's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on changes in facial expressions. The generation unit can also record the entrepreneur's voice and estimate the emotions using voice analysis technology. For example, the generation unit analyzes the tone and speed of the voice and calculates an emotion score. The generation unit can also collect the entrepreneur's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on heart rate fluctuations. This makes it possible to provide the length of the draft according to the entrepreneur's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the generation unit can be performed using, for example, AI, or without AI.

[0094] The generation unit can determine the priority of the drafts based on the submission time of the business plans at the time of generation. The generation unit, for example, evaluates the submission time of the business plans and determines the priority of the drafts based on the submission time. For example, the generation unit generates business plans with an upcoming submission deadline with priority. The generation unit can also postpone business plans with a distant submission deadline. For example, the generation unit adjusts the priority of the drafts based on the submission time. This makes it possible to adjust the priority of the drafts based on the submission time. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI or may be performed without using AI.

[0095] The generation unit can adjust the order of the drafts based on the relevance of the business plans when generating them. The generation unit, for example, evaluates the relevance of the business plans and adjusts the order of the drafts based on the relevance. For example, the generation unit generates business plans with high relevance preferentially. The generation unit can also postpone business plans with low relevance. For example, the generation unit adjusts the order of the drafts based on the relevance. In this way, the order of the drafts can be adjusted based on the relevance. Some or all of the above-described processing in the generation unit may be performed using AI, for example, or may be performed without using AI.

[0096] During generation, the generation unit can adjust the use of technical terminology in the draft according to the entrepreneur's level of expertise. The generation unit, for example, evaluates the entrepreneur's level of expertise and adjusts the use of technical terminology in the draft according to the level of expertise. For example, the generation unit generates a draft that uses a lot of technical terminology for entrepreneurs with high levels of expertise. The generation unit can also generate a draft that uses easy-to-understand expressions for entrepreneurs with low levels of expertise. For example, the generation unit adjusts the use of technical terminology according to the level of expertise. This makes it possible to provide the use of technical terminology according to the level of expertise. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using AI, or may be performed without using AI.

[0097] The verification unit can estimate the entrepreneur's emotions and adjust the draft verification method based on the estimated entrepreneur's emotions. For example, the verification unit captures the entrepreneur's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the verification unit calculates an emotion score based on changes in facial expressions. The verification unit can also record the entrepreneur's voice and estimate the emotions using voice analysis technology. For example, the verification unit analyzes the tone and speed of the voice and calculates an emotion score. The verification unit can also collect the entrepreneur's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the verification unit calculates an emotion score based on heart rate fluctuations. This makes it possible to provide a verification method that corresponds to the entrepreneur's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the verification unit can be performed using, for example, AI, or without AI.

[0098] During verification, the verification unit can select the optimal verification method by referring to the entrepreneur's past revision history. The verification unit, for example, retrieves the past revision history from a database and analyzes it using text analysis technology. For example, the verification unit prioritizes verification of frequently revised sections based on the past revision history. The verification unit can also postpone sections with fewer revisions based on the past revision history. For example, the verification unit adjusts the verification method based on the revision history. This makes it possible to provide the optimal verification method based on the past revision history. Some or all of the above-described processing in the verification unit may be performed, for example, using AI, or may be performed without using AI.

[0099] The confirmation unit can customize the confirmation content based on the entrepreneur's current project status when confirming. For example, the confirmation unit obtains the entrepreneur's current project status from a database and analyzes it using text analysis technology. For example, the confirmation unit prioritizes checking parts related to currently ongoing projects. The confirmation unit can also adjust the confirmation content based on the current project status. For example, the confirmation unit customizes the confirmation method depending on the progress of the project. This makes it possible to provide confirmation content based on the current project status. Some or all of the above-mentioned processing in the confirmation unit may be performed, for example, using AI or without using AI.

[0100] The verification unit can improve the verification method by reflecting the entrepreneur's feedback during verification. For example, the verification unit obtains the entrepreneur's feedback from a database and analyzes it using text analysis technology. For example, the verification unit simplifies the verification method based on the feedback. The verification unit can also make the verification method more detailed based on the feedback. For example, the verification unit improves the verification method by reflecting the feedback. This allows the verification method to be improved based on the feedback. Some or all of the above-mentioned processing in the verification unit may be performed using AI, for example, or may be performed without using AI.

[0101] The confirmation unit can estimate the entrepreneur's emotions and determine the priority of drafts to be confirmed based on the estimated emotions of the entrepreneur. For example, the confirmation unit captures the entrepreneur's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the confirmation unit calculates an emotion score based on changes in facial expressions. The confirmation unit can also record the entrepreneur's voice and estimate the emotions using voice analysis technology. For example, the confirmation unit analyzes the tone and speed of the voice and calculates an emotion score. The confirmation unit can also collect the entrepreneur's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the confirmation unit calculates an emotion score based on heart rate fluctuations. This makes it possible to provide draft priorities according to the entrepreneur's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the confirmation unit can be performed, for example, using AI or without AI.

[0102] The verification unit can select the optimal verification method during verification by taking into account the entrepreneur's geographic location information. The verification unit, for example, obtains the entrepreneur's geographic location information from GPS data or location information services and selects the optimal verification method. For example, the verification unit provides a detailed verification method when the entrepreneur is in the office. The verification unit can also provide a simple verification method when the entrepreneur is out. For example, the verification unit adjusts the verification method based on the entrepreneur's geographic location information. This makes it possible to provide the optimal verification method based on the geographic location information. Some or all of the above-described processing in the verification unit may be performed, for example, using AI or without using AI.

[0103] During verification, the verification unit can analyze the entrepreneur's social media activity and suggest verification content. For example, the verification unit retrieves the entrepreneur's social media activity from a database and analyzes it using text analysis technology. For example, the verification unit suggests verification content based on what the entrepreneur has mentioned on social media. The verification unit can also include related information from the entrepreneur's social media activity in the verification content. For example, the verification unit analyzes social media activity and suggests optimal verification content. This makes it possible to provide verification content based on social media activity. Some or all of the above-described processing in the verification unit may be performed, for example, using AI, or may be performed without using AI.

[0104] The verification unit can customize the verification method by reflecting the entrepreneur's past feedback during verification. For example, the verification unit retrieves past feedback from a database and analyzes it using text analysis technology. For example, the verification unit customizes the verification method to voice input based on the past feedback. The verification unit can also customize the verification method to text input based on the past feedback. For example, the verification unit customizes the verification method to image input based on the past feedback. This makes it possible to optimize the verification method based on the past feedback. Some or all of the above-described processing in the verification unit may be performed using AI, for example, or may be performed without using AI.

[0105] The regeneration unit can estimate the entrepreneur's emotions and adjust the expression method of the regenerated draft based on the estimated emotions of the entrepreneur. For example, the regeneration unit captures the entrepreneur's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the regeneration unit calculates an emotion score based on changes in facial expressions. The regeneration unit can also record the entrepreneur's voice and estimate the emotions using voice analysis technology. For example, the regeneration unit analyzes the tone and speed of the voice and calculates an emotion score. The regeneration unit can also collect the entrepreneur's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the regeneration unit calculates an emotion score based on heart rate fluctuations. This makes it possible to provide an expression method that corresponds to the entrepreneur's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the regeneration unit may be performed using AI, for example, or may be performed without using AI.

[0106] During regeneration, the regeneration unit can adjust the level of detail of the draft based on the importance of the corrected information. The regeneration unit, for example, evaluates the importance of the corrected information and adjusts the level of detail of the draft based on the importance. For example, the regeneration unit regenerates a draft including detailed information in the case of important correction information. The regeneration unit can also regenerate a concise draft in the case of low-importance correction information. For example, the regeneration unit adjusts the level of detail of the information according to the importance. This makes it possible to provide a detailed draft according to the importance of the correction information. Some or all of the above-described processing in the regeneration unit may be performed, for example, using AI or without using AI.

[0107] During regeneration, the regeneration unit can apply different regeneration algorithms depending on the category of the corrected information. For example, the regeneration unit classifies the category of the corrected information and applies different regeneration algorithms depending on the category. For example, the regeneration unit applies an algorithm that analyzes specific market data to corrected information for market analysis. The regeneration unit can also apply an algorithm that analyzes competitive information to corrected information for competitive analysis. For example, the regeneration unit applies an algorithm that analyzes financial data to corrected information for financial planning. This makes it possible to provide an optimal regeneration algorithm depending on the category. Some or all of the above-mentioned processing in the regeneration unit may be performed using AI, for example, or may be performed without using AI.

[0108] During regeneration, the regeneration unit can improve the accuracy of the regeneration by referring to the entrepreneur's past regeneration results. The regeneration unit, for example, retrieves past regeneration results from a database and analyzes them using text analysis technology. For example, the regeneration unit adjusts the regeneration algorithm based on the past regeneration results. The regeneration unit can also learn frequently used expression methods from the past regeneration results. For example, the regeneration unit improves the accuracy of the regeneration by referring to the past regeneration results. This makes it possible to improve the accuracy of the regeneration by referring to the past regeneration results. Some or all of the above-mentioned processing in the regeneration unit may be performed, for example, using AI, or may be performed without using AI.

[0109] The regeneration unit can estimate the entrepreneur's emotions and adjust the length of the regenerated draft based on the estimated emotions of the entrepreneur. For example, the regeneration unit captures the entrepreneur's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the regeneration unit calculates an emotion score based on changes in facial expressions. The regeneration unit can also record the entrepreneur's voice and estimate the emotions using voice analysis technology. For example, the regeneration unit analyzes the tone and speed of the voice and calculates an emotion score. The regeneration unit can also collect the entrepreneur's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the regeneration unit calculates an emotion score based on heart rate fluctuations. This makes it possible to provide the length of the draft according to the entrepreneur's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the regeneration unit may be performed using AI, for example, or may be performed without using AI.

[0110] During regeneration, the regeneration unit can determine the priority of the draft based on the submission time of the revised information. The regeneration unit, for example, evaluates the submission time of the revised information and determines the priority of the draft based on the submission time. For example, the regeneration unit preferentially regenerates revised information whose submission deadline is approaching. The regeneration unit can also postpone revised information whose submission deadline is far away. For example, the regeneration unit adjusts the priority of the draft based on the submission time. This makes it possible to adjust the priority of the draft based on the submission time. Some or all of the above-mentioned processing in the regeneration unit may be performed, for example, using AI or without using AI.

[0111] The regeneration unit can adjust the order of the drafts based on the relevance of the modified information during regeneration. The regeneration unit, for example, evaluates the relevance of the modified information and adjusts the order of the drafts based on the relevance. For example, the regeneration unit preferentially regenerates modified information with high relevance. The regeneration unit can also postpone modified information with low relevance. For example, the regeneration unit adjusts the order of the drafts based on the relevance. This makes it possible to adjust the order of the drafts based on the relevance. Some or all of the above-described processing in the regeneration unit may be performed using AI, for example, or may be performed without using AI.

[0112] During regeneration, the regeneration unit can adjust the use of technical terminology in the draft according to the entrepreneur's level of expertise. The regeneration unit, for example, evaluates the entrepreneur's level of expertise and adjusts the use of technical terminology in the draft according to the level of expertise. For example, the regeneration unit regenerates a draft that uses a lot of technical terminology for entrepreneurs with high levels of expertise. The regeneration unit can also regenerate a draft that uses easy-to-understand expressions for entrepreneurs with low levels of expertise. For example, the regeneration unit adjusts the use of technical terminology according to the level of expertise. This makes it possible to provide the use of technical terminology according to the level of expertise. Some or all of the above-described processing in the regeneration unit may be performed, for example, using AI, or may be performed without using AI.

[0113] The simulation unit can estimate the entrepreneur's emotions and adjust the question and answer simulation method based on the estimated entrepreneur's emotions. For example, the simulation unit captures the entrepreneur's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the simulation unit calculates an emotion score based on changes in facial expressions. The simulation unit can also record the entrepreneur's voice and estimate the emotions using voice analysis technology. For example, the simulation unit analyzes the tone and speed of the voice and calculates an emotion score. The simulation unit can also collect the entrepreneur's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the simulation unit calculates an emotion score based on heart rate fluctuations. This makes it possible to provide a question and answer simulation method that corresponds to the entrepreneur's emotions. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the simulation unit can be performed using, for example, AI, or without AI.

[0114] During simulation, the simulation unit can select the optimal simulation method by referring to the investor's past question history. The simulation unit, for example, retrieves the investor's past question history from a database and analyzes it using text analysis technology. For example, the simulation unit prioritizes simulation of frequently asked questions based on the investor's past question history. The simulation unit can also postpone questions about which fewer questions are asked based on the investor's past question history. For example, the simulation unit adjusts the simulation method based on the question history. This makes it possible to provide the optimal simulation method based on the past question history. Some or all of the above-mentioned processing in the simulation unit may be performed using, for example, AI, or may be performed without using AI.

[0115] The simulation unit can perform a Q&A simulation taking into account investor attribute information during the simulation. The simulation unit, for example, obtains investor attribute information from a database and analyzes it using text analysis technology. For example, the simulation unit simulates specialized questions based on the investor's industry experience. The simulation unit can also simulate related questions based on the investor's areas of interest. For example, the simulation unit customizes the Q&A simulation based on the investor's attribute information. This makes it possible to provide a Q&A simulation based on the investor's attribute information. Some or all of the above-described processing in the simulation unit may be performed using, for example, AI, or may be performed without using AI.

[0116] The simulation unit can improve the simulation method by reflecting investor feedback during the simulation. For example, the simulation unit obtains investor feedback from a database and analyzes it using text analysis technology. For example, the simulation unit simplifies the simulation method based on the feedback. The simulation unit can also refine the simulation method based on the feedback. For example, the simulation unit improves the simulation method by reflecting the feedback. This allows the simulation method to be improved based on the feedback. Some or all of the above-mentioned processing in the simulation unit may be performed using AI, for example, or may be performed without using AI.

[0117] The simulation unit can estimate the entrepreneur's emotions and determine the priority of Q&A sessions based on the estimated emotions. For example, the simulation unit captures the entrepreneur's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the simulation unit calculates an emotion score based on changes in facial expressions. The simulation unit can also record the entrepreneur's voice and estimate the emotions using voice analysis technology. For example, the simulation unit analyzes the tone and speed of the voice and calculates an emotion score. The simulation unit can also collect the entrepreneur's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the simulation unit calculates an emotion score based on heart rate fluctuations. This makes it possible to provide a priority of Q&A sessions based on the entrepreneur's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-mentioned processing in the simulation unit can be performed using, for example, AI, or without AI.

[0118] During the simulation, the simulation unit can select the optimal simulation method taking into account the geographical location information of the investor. The simulation unit, for example, obtains the geographical location information of the investor from GPS data or a location information service and selects the optimal simulation method. For example, the simulation unit provides a detailed simulation method when the investor is in the office. The simulation unit can also provide a simple simulation method when the investor is out. For example, the simulation unit adjusts the simulation method based on the geographical location information of the investor. This makes it possible to provide the optimal simulation method based on the geographical location information. Some or all of the above-described processing in the simulation unit may be performed, for example, using AI or without using AI.

[0119] During the simulation, the simulation unit can analyze the social media activity of investors to simulate a question and answer session. The simulation unit, for example, retrieves the social media activity of investors from a database and analyzes it using text analysis technology. For example, the simulation unit simulates a question and answer session based on the content mentioned by the investors on social media. The simulation unit can also simulate related questions from the investors' social media activity. For example, the simulation unit analyzes the social media activity to simulate an optimal question and answer session. This makes it possible to provide a question and answer session simulation based on the social media activity. Some or all of the above-described processing in the simulation unit may be performed using, for example, AI, or may be performed without using AI.

[0120] During the simulation, the simulation unit can customize the Q&A simulation method by reflecting past investor feedback. The simulation unit, for example, obtains past investor feedback from a database and analyzes it using text analysis technology. For example, the simulation unit simplifies the Q&A simulation method based on the past feedback. The simulation unit can also detail the Q&A simulation method based on the past feedback. For example, the simulation unit customizes the Q&A simulation method by reflecting past feedback. This makes it possible to optimize the Q&A simulation method based on the past feedback. Some or all of the above-described processing in the simulation unit may be performed, for example, using AI or without using AI. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, confirmation unit, regeneration unit, and simulation unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit can receive basic information about a business plan from an entrepreneur using the reception device 38 of the smart device 14. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a draft of the business plan using a generation AI. The confirmation unit displays the draft using the display 40A of the smart device 14 and provides an interface for the entrepreneur to make revisions. The regeneration unit is realized by the specific processing unit 290 of the data processing device 12 and generates a new draft that reflects the revisions. The simulation unit is realized by the specific processing unit 290 of the data processing device 12 and simulates a question-and-answer session with investors and proposes appropriate answers. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, confirmation unit, regeneration unit, and simulation unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit can receive basic information about the business plan from the entrepreneur using the microphone 238 of the smart glasses 214. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a draft of the business plan using a generation AI. The confirmation unit displays the draft using the display of the smart glasses 214 and provides an interface for the entrepreneur to make revisions. The regeneration unit is realized by the specific processing unit 290 of the data processing device 12 and generates a new draft that reflects the revisions. The simulation unit is realized by the specific processing unit 290 of the data processing device 12 and simulates a question-and-answer session with investors and proposes appropriate answers. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, confirmation unit, regeneration unit, and simulation unit, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit can receive basic information about the business plan from the entrepreneur using the microphone 238 of the headset-type terminal 314. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a draft of the business plan using a generation AI. The confirmation unit displays the draft using the display 343 of the headset-type terminal 314 and provides an interface for the entrepreneur to make revisions. The regeneration unit is realized by the specific processing unit 290 of the data processing device 12 and generates a new draft that reflects the revisions. The simulation unit is realized by the specific processing unit 290 of the data processing device 12 and simulates a question-and-answer session with investors and proposes appropriate answers. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, confirmation unit, regeneration unit, and simulation unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit can receive basic information about the business plan from the entrepreneur using the microphone 238 of the robot 414. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates a draft of the business plan using a generation AI. The confirmation unit displays the draft using the display of the robot 414 and provides an interface for the entrepreneur to make revisions. The regeneration unit is realized by the specific processing unit 290 of the data processing device 12 and generates a new draft that reflects the revisions. The simulation unit is realized by the specific processing unit 290 of the data processing device 12 and simulates a question-and-answer session with investors and proposes appropriate answers.

[0121] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0122] The reception unit can analyze the success rate of entrepreneurs' past business plans and prioritize the acquisition of information with a high success rate. For example, the reception unit can acquire data on the success rate of past business plans from a database and analyze it using text analysis technology. By prioritizing the acquisition of information on business plans with a high success rate, the reception unit can help entrepreneurs create effective business plans. The reception unit can also filter out information with a low success rate to prevent entrepreneurs from being misled by useless information. This improves the quality of business plans and increases the likelihood of success.

[0123] When generating a draft of a business plan, the generation unit can adjust the generation algorithm by reflecting the entrepreneur's past feedback. For example, the generation unit retrieves past feedback data from a database and analyzes it using text analysis technology. Based on the feedback, the generation unit adjusts the generation algorithm to generate a draft that suits the entrepreneur's preferences. The generation unit can also reflect the feedback to optimize the content and structure of the draft. This makes it possible to provide a high-quality draft that meets the entrepreneur's needs.

[0124] The simulation unit can estimate the investor's emotions and adjust the Q&A simulation based on the estimated investor's emotions. For example, the simulation unit captures the investor's facial expressions with a camera and estimates their emotions using an emotion estimation algorithm. If the investor shows interest, the simulation unit suggests detailed answers, and if the investor shows no interest, the simulation unit suggests concise answers. The simulation unit can also record the investor's voice and estimate their emotions using voice analysis technology. This makes it possible to provide a Q&A simulation that matches the investor's emotions.

[0125] The verification unit can estimate the entrepreneur's emotions and adjust the draft verification method based on the estimated emotions. For example, the verification unit can capture the entrepreneur's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. If the entrepreneur is feeling stressed, the verification work can be divided into parts. It can also record the entrepreneur's voice and estimate their emotions using voice analysis technology. This makes it possible to provide a verification method that suits the entrepreneur's emotions.

[0126] The regeneration unit can adjust the level of detail of the draft based on the importance of the corrected information. For example, the regeneration unit evaluates the importance of the corrected information and adjusts the level of detail of the draft based on the importance. In the case of important correction information, the regeneration unit can regenerate a draft including detailed information, and in the case of correction information with low importance, the regeneration unit can regenerate a concise draft. This makes it possible to provide a detailed draft according to the importance of the correction information.

[0127] The reception unit can prioritize obtaining highly relevant information based on the entrepreneur's geographic location information. For example, the reception unit obtains the entrepreneur's geographic location information from GPS data or location information services, and prioritizes obtaining highly relevant information. By prioritizing obtaining market data, competitive information, and legal and regulatory information related to the entrepreneur's location, it is possible to provide highly relevant information based on the geographic location information.

[0128] The generation unit can determine the priority of the drafts based on the submission time of the business plans at the time of generation. For example, the generation unit evaluates the submission time of the business plans and determines the priority of the drafts based on the submission time. Business plans with upcoming submission deadlines can be generated preferentially, and business plans with distant submission deadlines can be postponed. This makes it possible to adjust the priority of the drafts based on the submission time.

[0129] The simulation unit can select the optimal simulation method by referring to the investor's past question history. For example, the simulation unit retrieves the investor's past question history from a database and analyzes it using text analysis technology. Based on the investor's past question history, it can prioritize simulation of frequently asked questions and postpone simulation of less frequently asked questions. This makes it possible to provide the optimal simulation method based on the investor's past question history.

[0130] The verification unit can select the optimal verification method by referring to the entrepreneur's past revision history. For example, the verification unit retrieves the past revision history from a database and analyzes it using text analysis technology. Based on the past revision history, it can prioritize checking areas that are frequently revised and postpone checking areas that are rarely revised. This makes it possible to provide the optimal verification method based on the past revision history.

[0131] The regeneration unit can estimate the entrepreneur's emotions and adjust the expression method of the regenerated draft based on the estimated emotions. For example, the regeneration unit captures the entrepreneur's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. If the entrepreneur is stressed, concise expressions are used, and if the entrepreneur is relaxed, detailed expressions are used. The regeneration unit can also record the entrepreneur's voice and estimate the emotions using voice analysis technology. This makes it possible to provide an expression method that matches the entrepreneur's emotions.

[0132] The processing flow of the second embodiment will be briefly explained below.

[0133] Step 1: The reception unit receives basic information about the business plan from the entrepreneur. The basic information includes the business objectives, target market, competitive analysis, revenue model, etc. The reception unit can receive the basic information by text input, voice input, image input, etc. Step 2: The generation unit uses a generation AI to analyze the information received by the reception unit and generate a draft business plan. The generation unit uses a text generation AI (e.g., LLM) to automatically create each section of the business plan (business overview, market analysis, competitive analysis, marketing strategy, financial plan, etc.). The generation unit can also analyze market data using publicly available market reports and statistical data. Step 3: The confirmation unit allows the entrepreneur to check the draft generated by the generation unit and make corrections as necessary. The confirmation unit displays the contents of the draft and provides an interface for the entrepreneur to make corrections. Step 4: The regeneration unit re-analyzes the information corrected by the verification unit and generates a new draft. The regeneration unit uses the generation AI to generate a new draft that reflects the corrections. Step 5: The simulation unit simulates a question and answer session with investors based on the draft generated by the regeneration unit. The simulation unit predicts the questions that investors will frequently ask and suggests appropriate answers to those questions.

[0134] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0137] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0138] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0139] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0140] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0141] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0145] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0148] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0149] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0152] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0154] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0155] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

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

[0157] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

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

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

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

[0161] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0164] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

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

[0166] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0168] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

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

[0170] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0171] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

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

[0173] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

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

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

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

[0177] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0178] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0179] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0180] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0181] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0182] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0183] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0184] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0185] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0186] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0187] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0188] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0189] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0190] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0191] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0192] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0193] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0194] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0195] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0196] 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.

[0197] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0198] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0199] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0200] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0201] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0202] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0203] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0204] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0205] [Explanation of symbols]

[0206] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception department that accepts basic information about business plans from entrepreneurs; a generation unit that analyzes the information received by the reception unit and generates a draft of the business plan; A confirmation unit in which the entrepreneur confirms and corrects the draft generated by the generation unit; a regeneration unit that reanalyzes the information corrected by the verification unit and generates a new draft; a simulation unit that simulates a question and answer session with investors based on the draft generated by the regeneration unit. A system characterized by:

2. The generation unit Analyzing market data using publicly available market reports or statistics 2. The system of claim 1.

3. The simulation unit Anticipate common investor questions and provide appropriate answers 2. The system of claim 1.

4. The confirmation unit The entrepreneur reviews the draft created by the AI ​​and makes any necessary corrections.

2. The system of claim 1.

5. The regeneration unit Re-analyze the revised information and generate a new draft 2. The system of claim 1.

6. The simulation unit Suggest appropriate answers to questions about revenue models 2. The system of claim 1.

7. The reception unit To estimate the sentiment of an entrepreneur and adjust the timing of inputting basic information of a business plan based on the estimated sentiment of the entrepreneur.

2. The system of claim 1.

8. The reception unit Analyze the entrepreneur's past business plan submission history and select the optimal method of obtaining information 2. The system of claim 1.

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