System

Abstract

An object of the system according to the embodiment is to allow an entrepreneur to put an idea into a specific business plan without expert knowledge.SOLUTION: A system includes a reception part, an analysis part, a business planning part, and a proposal part. The reception part inputs the idea or vision of the entrepreneur. The analysis unit analyzes the information input by the reception unit and selects a necessary skill, procedure, and business domain. The business planning unit creates a business plan based on the information selected by the analysis unit. The proposal unit proposes a branding or marketing strategy based on the business plan created by the business planning unit.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] With conventional technology, the process for entrepreneurs to turn their ideas into concrete business plans was complicated and difficult to implement without specialized knowledge.

[0005] The system according to the embodiment aims to enable entrepreneurs to turn their ideas into concrete business plans even if they do not have specialized knowledge. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a business planning unit, and a proposal unit. The reception unit inputs the entrepreneur's idea or vision. The analysis unit analyzes the information input by the reception unit and selects the necessary skills, procedures, and business domain. The business planning unit creates a business plan based on the information selected by the analysis unit. The proposal unit proposes branding and marketing strategies based on the business plan created by the business planning unit. [Effects of the Invention]

[0007] The system according to the embodiment allows entrepreneurs to turn their ideas into concrete business plans even if they do not have specialized knowledge. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The entrepreneurial support system according to an embodiment of the present invention automatically analyzes an entrepreneur's idea and vision and provides comprehensive support, including the selection of necessary skills and procedures, business domain selection, business planning, branding, and marketing. In the entrepreneurial support system, entrepreneurs input their ideas and visions, and AI analyzes the information to select necessary skills, procedures, and business domains. The AI ​​then creates a business plan and proposes branding and marketing strategies. Finally, the AI ​​presents the optimal plan to the user, and the entrepreneur executes it based on the AI's guidance. For example, in the entrepreneurial support system, an entrepreneur inputs a specific idea, such as "I want to develop a new eco-friendly product." This information is then input into the AI. The AI ​​then analyzes the input information and selects the necessary skills, procedures, and business domains. For example, the AI ​​determines that developing an eco-friendly product requires knowledge of environmental science and product design. The AI ​​then creates a business plan, planning, for example, the product development schedule, budget, and required resources. The AI ​​then proposes branding and marketing strategies, such as an advertising campaign and social media strategy to promote the eco-friendly product. Finally, the AI ​​presents the optimal plan to the user, and the entrepreneur executes it based on the AI's guidance. This allows the entrepreneurial support system to promote the creation and growth of new businesses and contribute to the promotion of innovation. It can efficiently analyze entrepreneurs' ideas and visions and provide comprehensive support, from the necessary skills and procedures to selecting a business domain, business planning, branding, and marketing. For example, with AI support, entrepreneurs can run their businesses more efficiently, increasing their chances of success.

[0029] An entrepreneurial support system according to an embodiment includes a reception unit, an analysis unit, a business planning unit, and a proposal unit. The reception unit inputs an entrepreneur's idea or vision. The entrepreneur's idea or vision may be, for example, a business idea, a technical vision, or a creative vision, but is not limited to these examples. The reception unit may receive the idea or vision via, for example, text input, voice input, or image input. The analysis unit analyzes the information input by the reception unit and selects necessary skills, procedures, and a business domain. The analysis may be performed using, for example, text analysis, data mining, or a machine learning algorithm, but is not limited to these examples. For example, the analysis unit may use text analysis to extract keywords from the input idea or vision and identify related skills and procedures. The analysis unit may also use data mining to analyze past success stories and select an optimal business domain. The analysis unit may also use a machine learning algorithm to select skills and procedures based on the input information. The business planning unit creates a business plan based on the information selected by the analysis unit. The business plan may include, but is not limited to, a financial plan, a marketing plan, an operation plan, etc. For example, the business planning department may prepare a financial plan and propose a budget and fundraising methods. The business planning department may also prepare a marketing plan and propose a target market and a promotion strategy. The business planning department may also prepare an operation plan and plan the necessary resources and schedule. The proposal department may propose branding and marketing strategies based on the business plan prepared by the business planning department. Branding may include, but is not limited to, building a brand image, a brand strategy, and evaluating brand value. For example, the proposal department may build a brand image and send an effective message to the target market. The proposal department may also formulate a brand strategy and differentiate the company from competitors. The proposal department may also evaluate brand value and analyze the strengths and weaknesses of the brand. The marketing strategy may include, but is not limited to, selecting a target market, selecting a marketing channel, and developing a promotion strategy.For example, the proposal unit selects a target market and optimal marketing channels. The proposal unit can also formulate a promotion strategy and propose advertising campaigns and social media strategies. As a result, the business startup support system according to the embodiment can efficiently analyze the ideas and visions of entrepreneurs and provide comprehensive support, including the necessary skills and procedures, business domain selection, business planning, branding, and marketing.

[0030] The analysis unit includes a skill selection unit that identifies required skills. The skill selection unit identifies the required skills. The required skills include, but are not limited to, technical skills, business skills, and marketing skills. For example, the skill selection unit identifies programming, data analysis, machine learning, and the like as technical skills. The skill selection unit can also identify financial management, project management, leadership, and the like as business skills. The skill selection unit can also identify market research, advertising strategy, brand management, and the like as marketing skills. In this way, the analysis unit can identify the required skills and provide appropriate skills to entrepreneurs.

[0031] The analysis unit includes a procedure selection unit that identifies necessary procedures. The procedure selection unit identifies necessary procedures. The necessary procedures include, but are not limited to, legal procedures, business procedures, and technical procedures. For example, the procedure selection unit identifies legal procedures such as company formation, patent application, and contract creation. The procedure selection unit can also identify business procedures such as market research, business model development, and fundraising. The procedure selection unit can also identify technical procedures such as prototype development, technology evaluation, and quality control. In this way, the analysis unit can identify necessary procedures, thereby providing appropriate procedures to entrepreneurs.

[0032] The analysis unit includes a domain selection unit that selects a business domain. The domain selection unit selects the business domain. Examples of business domains include, but are not limited to, an IT domain, a healthcare domain, and a manufacturing domain. For example, the domain selection unit selects software development, cloud computing, cybersecurity, etc. as the IT domain. The domain selection unit can also select medical device development, biotechnology, digital health, etc. as the healthcare domain. The domain selection unit can also select automation technology, smart factories, supply chain management, etc. as the manufacturing domain. In this way, the analysis unit's selection of a business domain can provide an appropriate business domain to an entrepreneur.

[0033] The business planning unit includes a plan creation unit that creates a specific business plan. The plan creation unit creates the specific business plan. The specific business plan may include, but is not limited to, a financial plan, a marketing plan, and an operation plan. For example, the plan creation unit creates a financial plan and proposes a budget and fundraising methods. The plan creation unit may also create a marketing plan and propose a target market and a promotion strategy. The plan creation unit may also create an operation plan and plan the necessary resources and schedule. In this way, the business planning unit can create a specific business plan and provide it to the entrepreneur.

[0034] The proposal unit includes a market analysis unit that analyzes the target market. The market analysis unit analyzes the target market. The target market includes, but is not limited to, demographics, consumer behavior, and competitive analysis. For example, the market analysis unit analyzes demographics to identify the age group, gender, income, and so on of the target market. The market analysis unit can also analyze consumer behavior to identify the purchasing patterns and preferences of the target market. The market analysis unit can also perform competitive analysis to identify the strengths and weaknesses of competitors. As a result, the proposal unit can analyze the target market and provide the entrepreneur with an appropriate market analysis.

[0035] The proposal unit includes a competitor analysis unit that analyzes competitors. The competitor analysis unit analyzes competitors. The competitors include, but are not limited to, competitor market shares, competitor strengths and weaknesses, and competitor strategies. For example, the competitor analysis unit analyzes competitor market shares and identifies competitor market shares. The competitor analysis unit can also analyze competitor strengths and weaknesses and identify competitor advantages and challenges. The competitor analysis unit can also analyze competitor strategies and identify competitor marketing strategies and product strategies. This allows the proposal unit to analyze competitors and provide entrepreneurs with an appropriate competitor analysis.

[0036] The reception unit can analyze the user's past idea submission history and select an appropriate input method. For example, if the user has preferred text input in the past, the reception unit can preferentially suggest text input. Furthermore, if the user has frequently used voice input in the past, the reception unit can also recommend voice input. Furthermore, if the user has previously submitted ideas using images, the reception unit can also support image input. This allows the optimal input method to be selected based on the user's past history, providing an input method that is easy for the user to use. 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. For example, the reception unit can input the user's past idea submission history data into a generation AI and have the generation AI select an optimal input method.

[0037] When inputting ideas or visions, the reception unit can filter the ideas or visions based on the user's current project or field of interest. For example, the reception unit prioritizes inputting ideas related to the user's ongoing project. The reception unit can also filter and input related ideas based on the user's field of interest. The reception unit can also suggest ideas related to fields in which the user has shown interest in the past. In this way, by filtering based on the user's current project or field of interest, highly relevant ideas or visions can be input. 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. For example, the reception unit can input the user's project data and field of interest data to the generation AI and have the generation AI perform the filtering.

[0038] The reception unit can select the optimal input means according to the user's input method when inputting an idea or vision. For example, if the user selects voice input, the reception unit inputs the idea using voice recognition technology. Furthermore, if the user selects text input, the reception unit can also support keyboard input. Furthermore, if the user selects image input, the reception unit can also input the idea using image analysis technology. In this way, by selecting the optimal input means according to the user's input method, it is possible to provide an input means that is easy for the user to use. 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. For example, the reception unit can input the user's input method data to the generation AI and have the generation AI select the optimal input means.

[0039] When inputting ideas or visions, the reception unit can prioritize inputting highly relevant information by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize inputting ideas related to that area. Furthermore, if the user is traveling, the reception unit can prioritize inputting visions related to the travel destination. Furthermore, if the user is participating in a specific event, the reception unit can prioritize inputting ideas related to the event. In this way, highly relevant information can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information data to the generation AI and cause the generation AI to select highly relevant information.

[0040] The reception unit can analyze the user's social media activity and input related information when inputting an idea or vision. For example, the reception unit automatically inputs ideas shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and input related visions. The reception unit can also input related ideas by referring to the activity of the user's friends on social media. In this way, related information can be input by analyzing the user's social media activity. 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. For example, the reception unit can input the user's social media data to the generation AI and cause the generation AI to select related information.

[0041] The reception unit can customize the input method by reflecting the user's past feedback when inputting ideas or visions. For example, the reception unit preferentially suggests input methods that the user has provided feedback on in the past. The reception unit can also customize the input interface based on the user's past feedback. The reception unit can also select the optimal input means by referring to the user's past feedback. In this way, the optimal input method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's feedback data into a generation AI and have the generation AI customize the input method.

[0042] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the idea or vision. For example, the analysis unit performs a detailed analysis for important ideas. The analysis unit can also perform a concise analysis for general visions. The analysis unit can also perform a basic analysis for low-priority ideas. In this way, by adjusting the level of detail of the analysis based on the importance of the idea or vision, more important information can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input importance data of the idea or vision to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0043] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the idea or vision. For example, the analysis unit can apply a technical analysis algorithm to a technical idea. The analysis unit can also apply a business analysis algorithm to a business vision. The analysis unit can also apply a creative analysis algorithm to a creative idea. In this way, by applying different analysis algorithms depending on the category of the idea or vision, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input category data of the idea or vision into the generation AI and cause the generation AI to apply the analysis algorithm.

[0044] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the current analysis based on the user's past analysis results. The analysis unit can also optimize the analysis algorithm by referring to the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by using the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0045] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of ideas and visions. For example, the analysis unit prioritizes analysis of recently submitted ideas. The analysis unit can also postpone analysis of ideas that were submitted earlier. The analysis unit can also adjust the order of analysis based on the time of submission. In this way, by determining the priority of analysis based on the time of submission of ideas and visions, more important information can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time of submission of ideas and visions to the generation AI and have the generation AI determine the priority of analysis.

[0046] During analysis, the analysis unit can adjust the order of analysis based on the relevance of ideas and visions. For example, the analysis unit prioritizes analysis of highly relevant ideas. The analysis unit can also postpone analysis of less relevant ideas. The analysis unit can also adjust the order of analysis based on the relevance of ideas and visions. In this way, by adjusting the order of analysis based on the relevance of ideas and visions, more relevant information can be analyzed preferentially. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of ideas and visions to the generation AI and cause the generation AI to adjust the order of analysis.

[0047] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has specialized knowledge, the analysis unit uses a lot of technical terminology. Furthermore, if the user is a beginner, the analysis unit can provide analysis results in simple language. Furthermore, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. By adjusting the use of technical terminology in the analysis according to the user's level of expertise, more understandable analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and have the generation AI execute the use of technical terminology.

[0048] When creating a business plan, the business planning department can adjust the level of detail of the plan based on the importance of the business. For example, the business planning department creates a detailed plan for an important business. The business planning department can also create a concise plan for a general business. The business planning department can also create a basic plan for a low-priority business. In this way, by adjusting the level of detail of the plan based on the importance of the business, a detailed plan can be provided for a more important business. Some or all of the above-mentioned processing in the business planning department may be performed using, for example, AI, or may be performed without using AI. For example, the business planning department can input business importance data into the generation AI and have the generation AI adjust the level of detail of the plan.

[0049] When creating a business plan, the business planning department can apply different planning algorithms depending on the business category. For example, the business planning department can apply a technical planning algorithm to a technical business. The business planning department can also apply a business planning algorithm to a business business. The business planning department can also apply a creative planning algorithm to a creative business. In this way, by applying different planning algorithms depending on the business category, a more appropriate business plan can be provided. Some or all of the above-mentioned processing in the business planning department can be performed using, for example, AI, or can be performed without using AI. For example, the business planning department can input business category data into a generation AI and have the generation AI apply a planning algorithm.

[0050] When creating a business plan, the business planning unit can improve the accuracy of the plan by referring to the user's past planning results. For example, the business planning unit adjusts the current plan based on the user's past planning results. The business planning unit can also optimize the planning algorithm by referring to the user's past planning results. The business planning unit can also improve the accuracy of the plan by using the user's past planning results. In this way, the accuracy of the plan can be improved by referring to the user's past planning results. Some or all of the above-mentioned processing in the business planning unit may be performed using, for example, AI, or may be performed without using AI. For example, the business planning unit can input the user's past planning result data into the generation AI and have the generation AI improve the accuracy of the plan.

[0051] When creating business plans, the business planning department can determine the priority of the plans based on the time of submission of the projects. For example, the business planning department can prioritize the creation of recently submitted business plans. The business planning department can also postpone business plans that were submitted earlier. The business planning department can also adjust the order of plans based on the time of submission. In this way, by determining the priority of plans based on the time of submission of the projects, more important projects can be planned with priority. Some or all of the above-mentioned processing in the business planning department may be performed using, for example, AI, or may be performed without using AI. For example, the business planning department can input project submission time data into the generation AI and have the generation AI determine the priority of the plans.

[0052] When creating business plans, the business planning department can adjust the order of the plans based on the relevance of the businesses. For example, the business planning department prioritizes creating business plans with high relevance. The business planning department can also postpone business plans with low relevance. The business planning department can also adjust the order of the plans based on the relevance of the businesses. In this way, by adjusting the order of the plans based on the relevance of the businesses, it is possible to plan more highly relevant businesses with priority. Some or all of the above-mentioned processing in the business planning department may be performed using, for example, AI, or may be performed without using AI. For example, the business planning department can input business relevance data into a generation AI and have the generation AI adjust the order of the plans.

[0053] When creating a business plan, the business planning unit can adjust the use of technical terminology in the plan according to the user's level of expertise. For example, if the user has technical expertise, the business planning unit uses a lot of technical terminology. Furthermore, if the user is a beginner, the business planning unit can provide a business plan in simple language. Furthermore, the business planning unit can adjust the use of technical terminology in the plan according to the user's level of expertise. This allows for the provision of a more understandable business plan by adjusting the use of technical terminology in the plan according to the user's level of expertise. Some or all of the above-described processing in the business planning unit may be performed using, for example, AI, or may be performed without using AI. For example, the business planning unit can input the user's level of expertise data into the generation AI and have the generation AI execute the use of technical terminology.

[0054] The suggestion unit can adjust the level of detail of the proposal based on the importance of the proposal content when making a proposal. For example, the suggestion unit provides a detailed proposal for an important proposal. The suggestion unit can also provide a concise proposal for a general proposal. The suggestion unit can also provide a basic proposal for a proposal with a low priority. In this way, by adjusting the level of detail of the proposal based on the importance of the proposal content, a detailed proposal can be provided for a more important proposal. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input importance data of the proposal content to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0055] When making a proposal, the suggestion unit can apply different suggestion algorithms depending on the category of the proposal content. For example, the suggestion unit can apply a technical suggestion algorithm to a technical proposal. The suggestion unit can also apply a business suggestion algorithm to a business proposal. The suggestion unit can also apply a creative suggestion algorithm to a creative proposal. In this way, by applying different suggestion algorithms depending on the category of the proposal content, more appropriate suggestions can be provided. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, AI, or can be performed without using AI. For example, the suggestion unit can input category data of the proposal content to the generation AI and cause the generation AI to apply the suggestion algorithm.

[0056] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal results. The suggestion unit, for example, adjusts the current proposal based on the user's past proposal results. The suggestion unit can also optimize the proposal algorithm by referring to the user's past proposal results. The suggestion unit can also improve the accuracy of the proposal by using the user's past proposal results. In this way, the accuracy of the proposal can be improved by referring to the user's past proposal results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past proposal result data into the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0057] When making a proposal, the proposal unit can determine the priority of the proposal based on the submission time of the proposal content. For example, the proposal unit can prioritize the most recently submitted proposal. The proposal unit can also postpone proposals that were submitted earlier. The proposal unit can also adjust the order of the proposals based on the submission time. In this way, by determining the priority of the proposals based on the submission time of the proposal content, more important proposals can be given priority. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input submission time data of the proposal content to the generation AI and have the generation AI determine the priority of the proposals.

[0058] The suggestion unit can adjust the order of the suggestions based on the relevance of the suggestions when making suggestions. For example, the suggestion unit prioritizes highly relevant suggestions. The suggestion unit can also postpone less relevant suggestions. The suggestion unit can also adjust the order of the suggestions based on the relevance of the suggestions. In this way, by adjusting the order of the suggestions based on the relevance of the suggestions, more relevant suggestions can be prioritized. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input relevance data of the suggestions to the generation AI and cause the generation AI to adjust the order of the suggestions.

[0059] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. For example, if the user has technical expertise, the suggestion unit uses a lot of technical terminology. Furthermore, if the user is a beginner, the suggestion unit can provide a proposal in simple language. Furthermore, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. In this way, by adjusting the use of technical terminology in the proposal according to the user's level of expertise, it is possible to provide a proposal that is easier to understand. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the user's level of expertise data into a generation AI and cause the generation AI to execute the use of technical terminology.

[0060] When selecting skills, the skill selection unit can select optimal skills by referring to the user's past skill history. The skill selection unit, for example, selects current skills based on the user's past skill history. The skill selection unit can also optimize the skill selection algorithm by referring to the user's past skill history. The skill selection unit can also improve the accuracy of skill selection by using the user's past skill history. This allows more appropriate skills to be selected by referring to the user's past skill history. Some or all of the above-described processing in the skill selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the skill selection unit can input the user's past skill history data into the generation AI and cause the generation AI to select optimal skills.

[0061] When selecting skills, the skill selection unit can select skills based on the user's current project or area of ​​interest. For example, the skill selection unit preferentially selects skills related to the user's current project. The skill selection unit can also select related skills based on the user's area of ​​interest. The skill selection unit can also suggest skills related to areas in which the user has previously shown interest. This allows for the provision of more relevant skills by selecting skills based on the user's current project or area of ​​interest. Some or all of the above-described processing in the skill selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the skill selection unit can input the user's project data and area of ​​interest data into the generation AI and cause the generation AI to select skills.

[0062] The skill selection unit can customize the skill selection method by reflecting the user's past feedback when selecting a skill. For example, the skill selection unit preferentially suggests a skill selection method that the user has provided feedback on in the past. The skill selection unit can also customize the skill selection interface based on the user's past feedback. The skill selection unit can also select an optimal skill selection means by referring to the user's past feedback. This makes it possible to provide a more appropriate skill selection method by reflecting the user's past feedback. Some or all of the above-described processing in the skill selection unit can be performed using, for example, AI, or can be performed without using AI. For example, the skill selection unit can input the user's feedback data into the generation AI and cause the generation AI to customize the skill selection method.

[0063] When selecting skills, the skill selection unit can prioritize highly relevant skills by taking into account the user's geographical location information. For example, if the user is in a specific area, the skill selection unit can prioritize selecting skills related to that area. Furthermore, if the user is traveling, the skill selection unit can also prioritize selecting skills related to the travel destination. Furthermore, if the user is participating in a specific event, the skill selection unit can also prioritize selecting skills related to the event. In this way, by taking the user's geographical location information into account, more relevant skills can be provided. Some or all of the above-described processing in the skill selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the skill selection unit can input the user's geographical location information data into the generation AI and cause the generation AI to select highly relevant skills.

[0064] When selecting skills, the skill selection unit can analyze the user's social media activities and select relevant skills. For example, the skill selection unit automatically selects skills shared by the user on social media. The skill selection unit can also analyze the content of the user's social media posts and select relevant skills. The skill selection unit can also select relevant skills based on the activities of the user's friends on social media. In this way, by analyzing the user's social media activities, more relevant skills can be provided. Some or all of the above-described processing in the skill selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the skill selection unit can input the user's social media data into the generation AI and cause the generation AI to select relevant skills.

[0065] The skill selection unit can customize the skill selection method by reflecting the user's past feedback when selecting a skill. For example, the skill selection unit preferentially suggests a skill selection method that the user has provided feedback on in the past. The skill selection unit can also customize the skill selection interface based on the user's past feedback. The skill selection unit can also select an optimal skill selection means by referring to the user's past feedback. This makes it possible to provide a more appropriate skill selection method by reflecting the user's past feedback. Some or all of the above-described processing in the skill selection unit can be performed using, for example, AI, or can be performed without using AI. For example, the skill selection unit can input the user's feedback data into the generation AI and cause the generation AI to customize the skill selection method.

[0066] When selecting a procedure, the procedure selection unit can select the optimal procedure by referring to the user's past procedure history. The procedure selection unit, for example, selects the current procedure based on the user's past procedure history. The procedure selection unit can also optimize the procedure selection algorithm by referring to the user's past procedure history. The procedure selection unit can also improve the accuracy of procedure selection by using the user's past procedure history. In this way, by referring to the user's past procedure history, a more appropriate procedure can be selected. Some or all of the above-described processing in the procedure selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the procedure selection unit can input the user's past procedure history data into a generation AI and have the generation AI select the optimal procedure.

[0067] When selecting a procedure, the procedure selection unit can select a procedure based on the user's current project or area of ​​interest. For example, the procedure selection unit preferentially selects procedures related to the user's current project. The procedure selection unit can also select related procedures based on the user's area of ​​interest. The procedure selection unit can also suggest procedures related to areas in which the user has previously shown interest. This makes it possible to provide more relevant procedures by selecting procedures based on the user's current project or area of ​​interest. Some or all of the above-described processing in the procedure selection unit may be performed using, or without, AI, for example. For example, the procedure selection unit can input the user's project data and area of ​​interest data into a generation AI and have the generation AI select a procedure.

[0068] When selecting a procedure, the procedure selection unit can customize the procedure selection method by reflecting the user's past feedback. For example, the procedure selection unit preferentially suggests procedure selection methods that the user has provided feedback on in the past. The procedure selection unit can also customize the procedure selection interface based on the user's past feedback. The procedure selection unit can also select the optimal procedure selection means by referring to the user's past feedback. In this way, a more appropriate procedure selection method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the procedure selection unit may be performed using, or without, AI, for example. For example, the procedure selection unit can input user feedback data into a generation AI and have the generation AI customize the procedure selection method.

[0069] When selecting a procedure, the procedure selection unit can prioritize selecting a highly relevant procedure by taking into account the user's geographical location information. For example, if the user is in a specific area, the procedure selection unit can prioritize selecting a procedure related to that area. Furthermore, if the user is traveling, the procedure selection unit can also prioritize selecting a procedure related to the travel destination. Furthermore, if the user is participating in a specific event, the procedure selection unit can also prioritize selecting a procedure related to the event. In this way, by taking the user's geographical location information into account, more relevant procedures can be provided. Some or all of the above-described processing in the procedure selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the procedure selection unit can input the user's geographical location information data into the generation AI and cause the generation AI to select a highly relevant procedure.

[0070] When selecting a procedure, the procedure selection unit can analyze the user's social media activity and select relevant procedures. For example, the procedure selection unit automatically selects procedures shared by the user on social media. The procedure selection unit can also analyze the content of the user's social media posts and select relevant procedures. The procedure selection unit can also select relevant procedures based on the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, more relevant procedures can be provided. Some or all of the above-described processing in the procedure selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the procedure selection unit can input the user's social media data into a generation AI and cause the generation AI to select relevant procedures.

[0071] When selecting a procedure, the procedure selection unit can customize the procedure selection method by reflecting the user's past feedback. For example, the procedure selection unit preferentially suggests procedure selection methods that the user has provided feedback on in the past. The procedure selection unit can also customize the procedure selection interface based on the user's past feedback. The procedure selection unit can also select the optimal procedure selection means by referring to the user's past feedback. In this way, a more appropriate procedure selection method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the procedure selection unit may be performed using, or without, AI, for example. For example, the procedure selection unit can input user feedback data into a generation AI and have the generation AI customize the procedure selection method.

[0072] When selecting a domain, the domain selection unit can select an optimal domain by referring to the user's past business history. The domain selection unit, for example, selects a current domain based on the user's past business history. The domain selection unit can also optimize the domain selection algorithm by referring to the user's past business history. The domain selection unit can also improve the accuracy of domain selection by using the user's past business history. This allows a more appropriate domain to be selected by referring to the user's past business history. Some or all of the above-described processing in the domain selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the domain selection unit can input the user's past business history data into a generation AI and have the generation AI select an optimal domain.

[0073] When selecting a domain, the domain selection unit can select a domain based on the user's current project or field of interest. For example, the domain selection unit preferentially selects a domain related to the user's current project. The domain selection unit can also select a related domain based on the user's field of interest. The domain selection unit can also suggest domains related to fields in which the user has previously shown interest. By selecting a domain based on the user's current project or field of interest, more relevant domains can be provided. Some or all of the above-described processing in the domain selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the domain selection unit can input the user's project data and field of interest data into a generation AI and have the generation AI select a domain.

[0074] When selecting a domain, the domain selection unit can customize the domain selection method by reflecting the user's past feedback. For example, the domain selection unit preferentially suggests domain selection methods that the user has previously provided feedback on. The domain selection unit can also customize the domain selection interface based on the user's past feedback. The domain selection unit can also select the optimal domain selection method by referring to the user's past feedback. In this way, a more appropriate domain selection method can be provided by reflecting the user's past feedback. Some or all of the above-mentioned processing in the domain selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the domain selection unit can input the user's feedback data into the generation AI and cause the generation AI to customize the domain selection method.

[0075] When selecting a domain, the domain selection unit can prioritize selecting highly relevant domains by taking into account the user's geographical location information. For example, if the user is in a specific area, the domain selection unit can prioritize selecting domains related to that area. Furthermore, if the user is traveling, the domain selection unit can prioritize selecting domains related to the travel destination. Furthermore, if the user is participating in a specific event, the domain selection unit can prioritize selecting domains related to the event. This makes it possible to provide more relevant domains by taking the user's geographical location information into account. Some or all of the above-described processing in the domain selection unit may be performed using, or without, AI. For example, the domain selection unit can input the user's geographical location information data into the generation AI and cause the generation AI to select highly relevant domains.

[0076] When selecting a domain, the domain selection unit can analyze the user's social media activity and select a related domain. For example, the domain selection unit can automatically select a domain shared by the user on social media. The domain selection unit can also select a related domain by analyzing the content of the user's social media posts. The domain selection unit can also select a related domain by referring to the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, more relevant domains can be provided. Some or all of the above-described processing by the domain selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the domain selection unit can input the user's social media data into a generation AI and cause the generation AI to select a related domain.

[0077] When selecting a domain, the domain selection unit can customize the domain selection method by reflecting the user's past feedback. For example, the domain selection unit preferentially suggests domain selection methods that the user has previously provided feedback on. The domain selection unit can also customize the domain selection interface based on the user's past feedback. The domain selection unit can also select the optimal domain selection method by referring to the user's past feedback. In this way, a more appropriate domain selection method can be provided by reflecting the user's past feedback. Some or all of the above-mentioned processing in the domain selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the domain selection unit can input the user's feedback data into the generation AI and cause the generation AI to customize the domain selection method.

[0078] When creating a plan, the plan creation unit can create an optimal plan by referring to the user's past plan history. The plan creation unit, for example, creates a current plan based on the user's past plan history. The plan creation unit can also optimize the plan creation algorithm by referring to the user's past plan history. The plan creation unit can also improve the accuracy of plan creation by using the user's past plan history. In this way, a more appropriate plan can be created by referring to the user's past plan history. Some or all of the above-mentioned processing in the plan creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the plan creation unit can input the user's past plan history data into a generation AI and cause the generation AI to create an optimal plan.

[0079] When creating a plan, the plan creation unit can create a plan based on the user's current project or areas of interest. For example, the plan creation unit prioritizes creating plans related to the user's current project. The plan creation unit can also create related plans based on the user's areas of interest. The plan creation unit can also suggest plans related to areas in which the user has previously shown interest. This allows for creating a plan based on the user's current project or areas of interest, thereby providing a more relevant plan. Some or all of the above-described processing in the plan creation unit may be performed using, or without, AI, for example. For example, the plan creation unit can input the user's project data and area of ​​interest data into a generation AI and cause the generation AI to create a plan.

[0080] The plan creation unit can customize the plan creation method by reflecting the user's past feedback when creating a plan. For example, the plan creation unit preferentially suggests a plan creation method that the user has provided feedback on in the past. The plan creation unit can also customize the plan creation interface based on the user's past feedback. The plan creation unit can also select an optimal plan creation means by referring to the user's past feedback. In this way, a more appropriate plan creation method can be provided by reflecting the user's past feedback. Some or all of the above-mentioned processing in the plan creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the plan creation unit can input the user's feedback data into the generation AI and cause the generation AI to customize the plan creation method.

[0081] When creating a plan, the plan creation unit can prioritize creating highly relevant plans by taking into account the user's geographical location information. For example, if the user is in a specific area, the plan creation unit can prioritize creating plans related to that area. Furthermore, if the user is traveling, the plan creation unit can prioritize creating plans related to the travel destination. Furthermore, if the user is participating in a specific event, the plan creation unit can prioritize creating plans related to that event. In this way, by taking the user's geographical location information into account, a more relevant plan can be provided. Some or all of the above-described processing in the plan creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the plan creation unit can input the user's geographical location information data into the generation AI and cause the generation AI to create a highly relevant plan.

[0082] The plan creation unit can analyze the user's social media activity and create a related plan when creating a plan. For example, the plan creation unit automatically creates a plan shared by the user on social media. The plan creation unit can also analyze the content of the user's social media posts and create a related plan. The plan creation unit can also create a related plan by referring to the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, it is possible to provide a more relevant plan. Some or all of the above-mentioned processing in the plan creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the plan creation unit can input the user's social media data into a generation AI and cause the generation AI to create a related plan.

[0083] The plan creation unit can customize the plan creation method by reflecting the user's past feedback when creating a plan. For example, the plan creation unit preferentially suggests a plan creation method that the user has provided feedback on in the past. The plan creation unit can also customize the plan creation interface based on the user's past feedback. The plan creation unit can also select an optimal plan creation means by referring to the user's past feedback. In this way, a more appropriate plan creation method can be provided by reflecting the user's past feedback. Some or all of the above-mentioned processing in the plan creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the plan creation unit can input the user's feedback data into the generation AI and cause the generation AI to customize the plan creation method.

[0084] During market analysis, the market analysis unit can perform optimal analysis by referring to the user's past market analysis history. For example, the market analysis unit performs current market analysis based on the user's past market analysis history. The market analysis unit can also optimize the market analysis algorithm by referring to the user's past market analysis history. The market analysis unit can also improve the accuracy of the market analysis by using the user's past market analysis history. In this way, by referring to the user's past market analysis history, more appropriate market analysis can be provided. Some or all of the above-described processing in the market analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the market analysis unit can input the user's past market analysis history data into the generation AI and cause the generation AI to perform optimal market analysis.

[0085] During market analysis, the market analysis unit can analyze the market based on the user's current project and areas of interest. For example, the market analysis unit prioritizes analysis of markets related to the user's current project. The market analysis unit can also analyze related markets based on the user's areas of interest. The market analysis unit can also suggest markets related to areas in which the user has previously shown interest. This allows for more relevant market analysis by analyzing the market based on the user's current project and areas of interest. Some or all of the above-described processing in the market analysis unit may be performed using, or without, AI, for example. For example, the market analysis unit can input the user's project data and area of ​​interest data into the generation AI and have the generation AI perform market analysis.

[0086] The market analysis unit can customize the market analysis method by reflecting the user's past feedback when analyzing the market. For example, the market analysis unit preferentially suggests market analysis methods that the user has provided feedback on in the past. The market analysis unit can also customize the market analysis interface based on the user's past feedback. The market analysis unit can also select the optimal market analysis means by referring to the user's past feedback. In this way, a more appropriate market analysis method can be provided by reflecting the user's past feedback. Some or all of the above-mentioned processing in the market analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the market analysis unit can input the user's feedback data into the generation AI and have the generation AI customize the market analysis method.

[0087] During market analysis, the market analysis unit can prioritize analysis of highly relevant markets by taking into account the user's geographical location information. For example, if the user is in a specific region, the market analysis unit can prioritize analysis of markets related to that region. Furthermore, if the user is traveling, the market analysis unit can prioritize analysis of markets related to the travel destination. Furthermore, if the user is participating in a specific event, the market analysis unit can prioritize analysis of markets related to the event. This makes it possible to provide more relevant markets by taking the user's geographical location information into account. Some or all of the above-described processing in the market analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the market analysis unit can input the user's geographical location information data into the generation AI and cause the generation AI to perform an analysis of highly relevant markets.

[0088] During market analysis, the market analysis unit can analyze the user's social media activity and analyze related markets. For example, the market analysis unit automatically analyzes markets shared by the user on social media. The market analysis unit can also analyze the content of the user's social media posts and analyze related markets. The market analysis unit can also analyze related markets by referring to the activities of the user's friends on social media. This makes it possible to provide more relevant markets by analyzing the user's social media activity. Some or all of the above-described processing in the market analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the market analysis unit can input the user's social media data into a generation AI and have the generation AI perform an analysis of the related markets.

[0089] The market analysis unit can customize the market analysis method by reflecting the user's past feedback when analyzing the market. For example, the market analysis unit preferentially suggests market analysis methods that the user has provided feedback on in the past. The market analysis unit can also customize the market analysis interface based on the user's past feedback. The market analysis unit can also select the optimal market analysis means by referring to the user's past feedback. In this way, a more appropriate market analysis method can be provided by reflecting the user's past feedback. Some or all of the above-mentioned processing in the market analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the market analysis unit can input the user's feedback data into the generation AI and have the generation AI customize the market analysis method.

[0090] During competitive analysis, the competitive analysis unit can perform optimal analysis by referring to the user's past competitive analysis history. For example, the competitive analysis unit performs a current competitive analysis based on the user's past competitive analysis history. The competitive analysis unit can also optimize the competitive analysis algorithm by referring to the user's past competitive analysis history. The competitive analysis unit can also improve the accuracy of the competitive analysis by using the user's past competitive analysis history. Thus, by referring to the user's past competitive analysis history, a more appropriate competitive analysis can be provided. Some or all of the above-described processing in the competitive analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the competitive analysis unit can input the user's past competitive analysis history data into the generation AI and cause the generation AI to perform an optimal competitive analysis.

[0091] During the competitive analysis, the competitive analysis unit can analyze competitive issues based on the user's current project or area of ​​interest. For example, the competitive analysis unit prioritizes analysis of competitive issues related to the user's current project. The competitive analysis unit can also analyze relevant competitive issues based on the user's area of ​​interest. The competitive analysis unit can also suggest competitive issues related to areas in which the user has previously shown interest. This allows for more relevant competitive analysis by analyzing competitive issues based on the user's current project or area of ​​interest. Some or all of the above-described processing in the competitive analysis unit may be performed using, or without, AI. For example, the competitive analysis unit can input the user's project data or area of ​​interest data into a generation AI and have the generation AI perform a competitive analysis.

[0092] During competitive analysis, the competitive analysis unit can customize the competitive analysis method by reflecting the user's past feedback. For example, the competitive analysis unit prioritizes the proposal of competitive analysis methods for which the user has provided feedback in the past. The competitive analysis unit can also customize the competitive analysis interface based on the user's past feedback. The competitive analysis unit can also select the optimal competitive analysis means by referring to the user's past feedback. This makes it possible to provide a more appropriate competitive analysis method by reflecting the user's past feedback. Some or all of the above-described processing in the competitive analysis unit may be performed using, or without, AI, for example. For example, the competitive analysis unit can input user feedback data into a generation AI and have the generation AI customize the competitive analysis method.

[0093] During the competitor analysis, the competitor analysis unit can prioritize analysis of highly relevant competitors by taking into account the user's geographical location information. For example, if the user is in a specific region, the competitor analysis unit can prioritize analysis of competitors related to that region. Furthermore, if the user is traveling, the competitor analysis unit can prioritize analysis of competitors related to the travel destination. Furthermore, if the user is participating in a specific event, the competitor analysis unit can prioritize analysis of competitions related to the event. In this way, by taking the user's geographical location information into account, more relevant competitions can be provided. Some or all of the above-described processing in the competitor analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the competitor analysis unit can input the user's geographical location information data into the generation AI and cause the generation AI to analyze highly relevant competitors.

[0094] During the competitor analysis, the competitor analysis unit can analyze the user's social media activity and analyze related competitors. For example, the competitor analysis unit automatically analyzes competitors shared by the user on social media. The competitor analysis unit can also analyze the content of the user's social media posts and analyze related competitors. The competitor analysis unit can also analyze related competitors by referring to the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, more relevant competitors can be provided. Some or all of the above-described processing in the competitor analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the competitor analysis unit can input the user's social media data into a generation AI and cause the generation AI to analyze related competitors.

[0095] During competitive analysis, the competitive analysis unit can customize the competitive analysis method by reflecting the user's past feedback. For example, the competitive analysis unit prioritizes the proposal of competitive analysis methods for which the user has provided feedback in the past. The competitive analysis unit can also customize the competitive analysis interface based on the user's past feedback. The competitive analysis unit can also select the optimal competitive analysis means by referring to the user's past feedback. This makes it possible to provide a more appropriate competitive analysis method by reflecting the user's past feedback. Some or all of the above-described processing in the competitive analysis unit may be performed using, or without, AI, for example. For example, the competitive analysis unit can input user feedback data into a generation AI and have the generation AI customize the competitive analysis method.

[0096] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0097] The reception unit can analyze the user's past idea submission history and select an appropriate input method. For example, if the user has preferred text input in the past, the reception unit can preferentially suggest text input. Furthermore, if the user has frequently used voice input in the past, the reception unit can also recommend voice input. Furthermore, if the user has previously submitted ideas using images, the reception unit can also support image input. This allows the optimal input method to be selected based on the user's past history, providing an input method that is easy for the user to use. 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. For example, the reception unit can input the user's past idea submission history data into the generation AI and have the generation AI select the optimal input method.

[0098] When creating a business plan, the business planning department can adjust the level of detail of the plan based on the importance of the business. For example, a detailed plan can be created for an important business. The business planning department can also create a concise plan for a general business. The business planning department can also create a basic plan for a low-priority business. In this way, by adjusting the level of detail of the plan based on the importance of the business, a detailed plan can be provided for a more important business. Some or all of the above-mentioned processing in the business planning department may be performed using, for example, AI, or may be performed without using AI. For example, the business planning department can input business importance data into the generation AI and have the generation AI adjust the level of detail of the plan.

[0099] When selecting skills, the skill selection unit can select optimal skills by referring to the user's past skill history. For example, the current skill selection is performed based on the user's past skill history. The skill selection unit can also optimize the skill selection algorithm by referring to the user's past skill history. The skill selection unit can also improve the accuracy of skill selection by using the user's past skill history. This allows more appropriate skills to be selected by referring to the user's past skill history. Some or all of the above-described processing in the skill selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the skill selection unit can input the user's past skill history data into the generation AI and cause the generation AI to select optimal skills.

[0100] When selecting a domain, the domain selection unit can select the optimal domain by referring to the user's past business history. For example, the current domain selection unit can select a domain based on the user's past business history. The domain selection unit can also optimize the domain selection algorithm by referring to the user's past business history. The domain selection unit can also improve the accuracy of domain selection by using the user's past business history. This allows a more appropriate domain to be selected by referring to the user's past business history. Some or all of the above-mentioned processing in the domain selection unit can be performed using, for example, AI, or can be performed without using AI. For example, the domain selection unit can input the user's past business history data into a generation AI and have the generation AI select the optimal domain.

[0101] During market analysis, the market analysis unit can perform optimal analysis by referring to the user's past market analysis history. For example, the current market analysis is performed based on the user's past market analysis history. The market analysis unit can also optimize the market analysis algorithm by referring to the user's past market analysis history. The market analysis unit can also improve the accuracy of the market analysis by using the user's past market analysis history. In this way, by referring to the user's past market analysis history, more appropriate market analysis can be provided. Some or all of the above-described processing in the market analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the market analysis unit can input the user's past market analysis history data into the generation AI and cause the generation AI to perform optimal market analysis.

[0102] The processing flow of the first embodiment will be briefly explained below.

[0103] Step 1: The reception unit inputs the entrepreneur's idea or vision. The entrepreneur's idea or vision can include a business idea, a technical vision, a creative vision, etc. The reception unit can accept the idea or vision by text input, voice input, image input, etc. Step 2: The analysis unit analyzes the information entered by the reception unit and selects the necessary skills, procedures, and business domains. The analysis is performed using methods such as text analysis, data mining, and machine learning algorithms. For example, text analysis can be used to extract keywords and identify related skills and procedures. Data mining can also be used to analyze past success stories and select the optimal business domain. Skills and procedures can also be selected using machine learning algorithms. Step 3: The business planning department creates a business plan based on the information selected by the analysis department. The business plan includes a financial plan, marketing plan, and operation plan. For example, a financial plan may be created to propose a budget and fundraising methods. A marketing plan may be created to propose a target market and promotion strategy. An operation plan may be created to plan the necessary resources and schedule. Step 4: The proposal department proposes branding and marketing strategies based on the business plan prepared by the business planning department. Branding includes building a brand image, brand strategy, and brand value evaluation. For example, building a brand image and sending an effective message to the target market. A brand strategy can be formulated to differentiate the brand from competitors. Brand value can also be evaluated and the brand's strengths and weaknesses analyzed. Marketing strategies include selecting a target market, selecting marketing channels, and a promotion strategy. For example, selecting a target market and selecting the optimal marketing channels. A promotion strategy can be formulated and advertising campaigns and social media strategies can be proposed.

[0104] (Example 2) The entrepreneurial support system according to an embodiment of the present invention automatically analyzes an entrepreneur's idea and vision and provides comprehensive support, including the selection of necessary skills and procedures, business domain selection, business planning, branding, and marketing. In the entrepreneurial support system, entrepreneurs input their ideas and visions, and AI analyzes the information to select necessary skills, procedures, and business domains. The AI ​​then creates a business plan and proposes branding and marketing strategies. Finally, the AI ​​presents the optimal plan to the user, and the entrepreneur executes it based on the AI's guidance. For example, in the entrepreneurial support system, an entrepreneur inputs a specific idea, such as "I want to develop a new eco-friendly product." This information is then input into the AI. The AI ​​then analyzes the input information and selects the necessary skills, procedures, and business domains. For example, the AI ​​determines that developing an eco-friendly product requires knowledge of environmental science and product design. The AI ​​then creates a business plan, planning, for example, the product development schedule, budget, and required resources. The AI ​​then proposes branding and marketing strategies, such as an advertising campaign and social media strategy to promote the eco-friendly product. Finally, the AI ​​presents the optimal plan to the user, and the entrepreneur executes it based on the AI's guidance. This allows the entrepreneurial support system to promote the creation and growth of new businesses and contribute to the promotion of innovation. It can efficiently analyze entrepreneurs' ideas and visions and provide comprehensive support, from the necessary skills and procedures to selecting a business domain, business planning, branding, and marketing. For example, with AI support, entrepreneurs can run their businesses more efficiently, increasing their chances of success.

[0105] An entrepreneurial support system according to an embodiment includes a reception unit, an analysis unit, a business planning unit, and a proposal unit. The reception unit inputs an entrepreneur's idea or vision. The entrepreneur's idea or vision may be, for example, a business idea, a technical vision, or a creative vision, but is not limited to these examples. The reception unit may receive the idea or vision via, for example, text input, voice input, or image input. The analysis unit analyzes the information input by the reception unit and selects necessary skills, procedures, and a business domain. The analysis may be performed using, for example, text analysis, data mining, or a machine learning algorithm, but is not limited to these examples. For example, the analysis unit may use text analysis to extract keywords from the input idea or vision and identify related skills and procedures. The analysis unit may also use data mining to analyze past success stories and select an optimal business domain. The analysis unit may also use a machine learning algorithm to select skills and procedures based on the input information. The business planning unit creates a business plan based on the information selected by the analysis unit. The business plan may include, but is not limited to, a financial plan, a marketing plan, an operation plan, etc. For example, the business planning department may prepare a financial plan and propose a budget and fundraising methods. The business planning department may also prepare a marketing plan and propose a target market and a promotion strategy. The business planning department may also prepare an operation plan and plan the necessary resources and schedule. The proposal department may propose branding and marketing strategies based on the business plan prepared by the business planning department. Branding may include, but is not limited to, building a brand image, a brand strategy, and evaluating brand value. For example, the proposal department may build a brand image and send an effective message to the target market. The proposal department may also formulate a brand strategy and differentiate the company from competitors. The proposal department may also evaluate brand value and analyze the strengths and weaknesses of the brand. The marketing strategy may include, but is not limited to, selecting a target market, selecting a marketing channel, and developing a promotion strategy.For example, the proposal unit selects a target market and optimal marketing channels. The proposal unit can also formulate a promotion strategy and propose advertising campaigns and social media strategies. As a result, the business startup support system according to the embodiment can efficiently analyze the ideas and visions of entrepreneurs and provide comprehensive support, including the necessary skills and procedures, business domain selection, business planning, branding, and marketing.

[0106] The analysis unit includes a skill selection unit that identifies required skills. The skill selection unit identifies the required skills. The required skills include, but are not limited to, technical skills, business skills, and marketing skills. For example, the skill selection unit identifies programming, data analysis, machine learning, and the like as technical skills. The skill selection unit can also identify financial management, project management, leadership, and the like as business skills. The skill selection unit can also identify market research, advertising strategy, brand management, and the like as marketing skills. In this way, the analysis unit can identify the required skills and provide appropriate skills to entrepreneurs.

[0107] The analysis unit includes a procedure selection unit that identifies necessary procedures. The procedure selection unit identifies necessary procedures. The necessary procedures include, but are not limited to, legal procedures, business procedures, and technical procedures. For example, the procedure selection unit identifies legal procedures such as company formation, patent application, and contract creation. The procedure selection unit can also identify business procedures such as market research, business model development, and fundraising. The procedure selection unit can also identify technical procedures such as prototype development, technology evaluation, and quality control. In this way, the analysis unit can identify necessary procedures, thereby providing appropriate procedures to entrepreneurs.

[0108] The analysis unit includes a domain selection unit that selects a business domain. The domain selection unit selects the business domain. Examples of business domains include, but are not limited to, an IT domain, a healthcare domain, and a manufacturing domain. For example, the domain selection unit selects software development, cloud computing, cybersecurity, etc. as the IT domain. The domain selection unit can also select medical device development, biotechnology, digital health, etc. as the healthcare domain. The domain selection unit can also select automation technology, smart factories, supply chain management, etc. as the manufacturing domain. In this way, the analysis unit's selection of a business domain can provide an appropriate business domain to an entrepreneur.

[0109] The business planning unit includes a plan creation unit that creates a specific business plan. The plan creation unit creates the specific business plan. The specific business plan may include, but is not limited to, a financial plan, a marketing plan, and an operation plan. For example, the plan creation unit creates a financial plan and proposes a budget and fundraising methods. The plan creation unit may also create a marketing plan and propose a target market and a promotion strategy. The plan creation unit may also create an operation plan and plan the necessary resources and schedule. In this way, the business planning unit can create a specific business plan and provide it to the entrepreneur.

[0110] The proposal unit includes a market analysis unit that analyzes the target market. The market analysis unit analyzes the target market. The target market includes, but is not limited to, demographics, consumer behavior, and competitive analysis. For example, the market analysis unit analyzes demographics to identify the age group, gender, income, and so on of the target market. The market analysis unit can also analyze consumer behavior to identify the purchasing patterns and preferences of the target market. The market analysis unit can also perform competitive analysis to identify the strengths and weaknesses of competitors. As a result, the proposal unit can analyze the target market and provide the entrepreneur with an appropriate market analysis.

[0111] The proposal unit includes a competitor analysis unit that analyzes competitors. The competitor analysis unit analyzes competitors. The competitors include, but are not limited to, competitor market shares, competitor strengths and weaknesses, and competitor strategies. For example, the competitor analysis unit analyzes competitor market shares and identifies competitor market shares. The competitor analysis unit can also analyze competitor strengths and weaknesses and identify competitor advantages and challenges. The competitor analysis unit can also analyze competitor strategies and identify competitor marketing strategies and product strategies. This allows the proposal unit to analyze competitors and provide entrepreneurs with an appropriate competitor analysis.

[0112] The reception unit can estimate the user's emotions and adjust the timing of inputting ideas and visions based on the emotions. For example, if the user is feeling stressed, the reception unit can prompt the user to input ideas and visions at a time when the user can relax. Furthermore, if the user is concentrating, the reception unit can encourage the user to input ideas and visions by taking advantage of the user's concentration. Furthermore, if the user is tired, the reception unit can prompt the user to input ideas and visions after a break. By adjusting the input timing based on the user's emotions, ideas and visions can be input at a more appropriate time. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using AI, or may be performed without AI. For example, the reception unit can input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0113] The reception unit can analyze the user's past idea submission history and select an appropriate input method. For example, if the user has preferred text input in the past, the reception unit can preferentially suggest text input. Furthermore, if the user has frequently used voice input in the past, the reception unit can also recommend voice input. Furthermore, if the user has previously submitted ideas using images, the reception unit can also support image input. This allows the optimal input method to be selected based on the user's past history, providing an input method that is easy for the user to use. 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. For example, the reception unit can input the user's past idea submission history data into a generation AI and have the generation AI select an optimal input method.

[0114] When inputting ideas or visions, the reception unit can filter the ideas or visions based on the user's current project or field of interest. For example, the reception unit prioritizes inputting ideas related to the user's ongoing project. The reception unit can also filter and input related ideas based on the user's field of interest. The reception unit can also suggest ideas related to fields in which the user has shown interest in the past. In this way, by filtering based on the user's current project or field of interest, highly relevant ideas or visions can be input. 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. For example, the reception unit can input the user's project data and field of interest data to the generation AI and have the generation AI perform the filtering.

[0115] The reception unit can select the optimal input means according to the user's input method when inputting an idea or vision. For example, if the user selects voice input, the reception unit inputs the idea using voice recognition technology. Furthermore, if the user selects text input, the reception unit can also support keyboard input. Furthermore, if the user selects image input, the reception unit can also input the idea using image analysis technology. In this way, by selecting the optimal input means according to the user's input method, it is possible to provide an input means that is easy for the user to use. 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. For example, the reception unit can input the user's input method data to the generation AI and have the generation AI select the optimal input means.

[0116] The reception unit can estimate the user's emotions and determine the priority of the ideas and visions to be input based on the estimated user emotions. For example, when the user is excited, the reception unit can prioritize inputting important ideas. Furthermore, when the user is relaxed, the reception unit can prioritize inputting detailed visions. Furthermore, when the user is stressed, the reception unit can prioritize inputting simple ideas. In this way, by determining the priority based on the user's emotions, more important ideas and visions can be prioritized. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the reception unit can input the user's emotion data to the generation AI and have the generation AI determine the priority.

[0117] When inputting ideas or visions, the reception unit can prioritize inputting highly relevant information by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize inputting ideas related to that area. Furthermore, if the user is traveling, the reception unit can prioritize inputting visions related to the travel destination. Furthermore, if the user is participating in a specific event, the reception unit can prioritize inputting ideas related to the event. In this way, highly relevant information can be prioritized by taking the user's geographical location information into account. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information data to the generation AI and cause the generation AI to select highly relevant information.

[0118] The reception unit can analyze the user's social media activity and input related information when inputting an idea or vision. For example, the reception unit automatically inputs ideas shared by the user on social media. The reception unit can also analyze the content of the user's social media posts and input related visions. The reception unit can also input related ideas by referring to the activity of the user's friends on social media. In this way, related information can be input by analyzing the user's social media activity. 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. For example, the reception unit can input the user's social media data to the generation AI and cause the generation AI to select related information.

[0119] The reception unit can customize the input method by reflecting the user's past feedback when inputting ideas or visions. For example, the reception unit preferentially suggests input methods that the user has provided feedback on in the past. The reception unit can also customize the input interface based on the user's past feedback. The reception unit can also select the optimal input means by referring to the user's past feedback. In this way, the optimal input method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's feedback data into a generation AI and have the generation AI customize the input method.

[0120] The analysis unit can estimate the user's emotions and adjust the presentation method of the analysis based on the estimated user's emotions. For example, the analysis unit can provide detailed analysis results when the user is relaxed. The analysis unit can also provide concise analysis results when the user is in a hurry. The analysis unit can also provide visually appealing analysis results when the user is excited. By adjusting the presentation method of the analysis based on the user's emotions, more appropriate analysis results can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the presentation method of the analysis.

[0121] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the idea or vision. For example, the analysis unit performs a detailed analysis for important ideas. The analysis unit can also perform a concise analysis for general visions. The analysis unit can also perform a basic analysis for low-priority ideas. In this way, by adjusting the level of detail of the analysis based on the importance of the idea or vision, more important information can be provided. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input importance data of the idea or vision to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0122] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the idea or vision. For example, the analysis unit can apply a technical analysis algorithm to a technical idea. The analysis unit can also apply a business analysis algorithm to a business vision. The analysis unit can also apply a creative analysis algorithm to a creative idea. In this way, by applying different analysis algorithms depending on the category of the idea or vision, more appropriate analysis results can be provided. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, AI, or can be performed without using AI. For example, the analysis unit can input category data of the idea or vision into the generation AI and cause the generation AI to apply the analysis algorithm.

[0123] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit, for example, adjusts the current analysis based on the user's past analysis results. The analysis unit can also optimize the analysis algorithm by referring to the user's past analysis results. The analysis unit can also improve the accuracy of the analysis by using the user's past analysis results. In this way, the accuracy of the analysis can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and have the generation AI improve the accuracy of the analysis.

[0124] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated user emotions. For example, the analysis unit can provide a short analysis result when the user is in a hurry. The analysis unit can also provide a detailed analysis result when the user is relaxed. The analysis unit can also provide a visually appealing analysis result when the user is excited. By adjusting the length of the analysis based on the user's emotions, more appropriate analysis results can be provided. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the analysis.

[0125] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of ideas and visions. For example, the analysis unit prioritizes analysis of recently submitted ideas. The analysis unit can also postpone analysis of ideas that were submitted earlier. The analysis unit can also adjust the order of analysis based on the time of submission. In this way, by determining the priority of analysis based on the time of submission of ideas and visions, more important information can be analyzed preferentially. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time of submission of ideas and visions to the generation AI and have the generation AI determine the priority of analysis.

[0126] During analysis, the analysis unit can adjust the order of analysis based on the relevance of ideas and visions. For example, the analysis unit prioritizes analysis of highly relevant ideas. The analysis unit can also postpone analysis of less relevant ideas. The analysis unit can also adjust the order of analysis based on the relevance of ideas and visions. In this way, by adjusting the order of analysis based on the relevance of ideas and visions, more relevant information can be analyzed preferentially. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of ideas and visions to the generation AI and cause the generation AI to adjust the order of analysis.

[0127] During analysis, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. For example, if the user has specialized knowledge, the analysis unit uses a lot of technical terminology. Furthermore, if the user is a beginner, the analysis unit can provide analysis results in simple language. Furthermore, the analysis unit can adjust the use of technical terminology in the analysis according to the user's level of expertise. By adjusting the use of technical terminology in the analysis according to the user's level of expertise, more understandable analysis results can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's level of expertise data into the generation AI and have the generation AI execute the use of technical terminology.

[0128] The business planning unit can estimate the user's emotions and adjust the presentation of the business plan based on the estimated user emotions. For example, if the user is relaxed, the business planning unit can provide a detailed business plan. If the user is in a hurry, the business planning unit can provide a concise business plan. If the user is excited, the business planning unit can provide a visually appealing business plan. This allows for adjusting the presentation of the business plan based on the user's emotions, thereby providing a more appropriate business plan. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the business planning unit can be performed using AI, for example, or without AI. For example, the business planning unit can input the user's emotion data into the generation AI and have the generation AI adjust the presentation of the business plan.

[0129] When creating a business plan, the business planning department can adjust the level of detail of the plan based on the importance of the business. For example, the business planning department creates a detailed plan for an important business. The business planning department can also create a concise plan for a general business. The business planning department can also create a basic plan for a low-priority business. In this way, by adjusting the level of detail of the plan based on the importance of the business, a detailed plan can be provided for a more important business. Some or all of the above-mentioned processing in the business planning department may be performed using, for example, AI, or may be performed without using AI. For example, the business planning department can input business importance data into the generation AI and have the generation AI adjust the level of detail of the plan.

[0130] When creating a business plan, the business planning department can apply different planning algorithms depending on the business category. For example, the business planning department can apply a technical planning algorithm to a technical business. The business planning department can also apply a business planning algorithm to a business business. The business planning department can also apply a creative planning algorithm to a creative business. In this way, by applying different planning algorithms depending on the business category, a more appropriate business plan can be provided. Some or all of the above-mentioned processing in the business planning department can be performed using, for example, AI, or can be performed without using AI. For example, the business planning department can input business category data into a generation AI and have the generation AI apply a planning algorithm.

[0131] When creating a business plan, the business planning unit can improve the accuracy of the plan by referring to the user's past planning results. For example, the business planning unit adjusts the current plan based on the user's past planning results. The business planning unit can also optimize the planning algorithm by referring to the user's past planning results. The business planning unit can also improve the accuracy of the plan by using the user's past planning results. In this way, the accuracy of the plan can be improved by referring to the user's past planning results. Some or all of the above-mentioned processing in the business planning unit may be performed using, for example, AI, or may be performed without using AI. For example, the business planning unit can input the user's past planning result data into the generation AI and have the generation AI improve the accuracy of the plan.

[0132] The business planning unit can estimate the user's emotions and adjust the length of the business plan based on the estimated user emotions. For example, if the user is in a hurry, the business planning unit can provide a short, concise business plan. If the user is relaxed, the business planning unit can provide a longer business plan with detailed explanations. If the user is excited, the business planning unit can provide a business plan with visually stimulating effects. This allows for adjusting the length of the business plan based on the user's emotions to provide a more appropriate business plan. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the business planning unit can be performed using, for example, an AI, or without an AI. For example, the business planning unit can input the user's emotion data into the generation AI and have the generation AI adjust the length of the business plan.

[0133] When creating business plans, the business planning department can determine the priority of the plans based on the time of submission of the projects. For example, the business planning department can prioritize the creation of recently submitted business plans. The business planning department can also postpone business plans that were submitted earlier. The business planning department can also adjust the order of plans based on the time of submission. In this way, by determining the priority of plans based on the time of submission of the projects, more important projects can be planned with priority. Some or all of the above-mentioned processing in the business planning department may be performed using, for example, AI, or may be performed without using AI. For example, the business planning department can input project submission time data into the generation AI and have the generation AI determine the priority of the plans.

[0134] When creating business plans, the business planning department can adjust the order of the plans based on the relevance of the businesses. For example, the business planning department prioritizes creating business plans with high relevance. The business planning department can also postpone business plans with low relevance. The business planning department can also adjust the order of the plans based on the relevance of the businesses. In this way, by adjusting the order of the plans based on the relevance of the businesses, it is possible to plan more highly relevant businesses with priority. Some or all of the above-mentioned processing in the business planning department may be performed using, for example, AI, or may be performed without using AI. For example, the business planning department can input business relevance data into a generation AI and have the generation AI adjust the order of the plans.

[0135] When creating a business plan, the business planning unit can adjust the use of technical terminology in the plan according to the user's level of expertise. For example, if the user has technical expertise, the business planning unit uses a lot of technical terminology. Furthermore, if the user is a beginner, the business planning unit can provide a business plan in simple language. Furthermore, the business planning unit can adjust the use of technical terminology in the plan according to the user's level of expertise. This allows for the provision of a more understandable business plan by adjusting the use of technical terminology in the plan according to the user's level of expertise. Some or all of the above-described processing in the business planning unit may be performed using, for example, AI, or may be performed without using AI. For example, the business planning unit can input the user's level of expertise data into the generation AI and have the generation AI execute the use of technical terminology.

[0136] The suggestion unit can estimate the user's emotions and adjust the way the suggestions are expressed based on the estimated user's emotions. For example, the suggestion unit can provide detailed suggestions when the user is relaxed. The suggestion unit can also provide concise suggestions when the user is in a hurry. The suggestion unit can also provide visually appealing suggestions when the user is excited. This allows for more appropriate suggestions to be provided by adjusting the way the suggestions are expressed based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way the suggestions are expressed.

[0137] The suggestion unit can adjust the level of detail of the proposal based on the importance of the proposal content when making a proposal. For example, the suggestion unit provides a detailed proposal for an important proposal. The suggestion unit can also provide a concise proposal for a general proposal. The suggestion unit can also provide a basic proposal for a proposal with a low priority. In this way, by adjusting the level of detail of the proposal based on the importance of the proposal content, a detailed proposal can be provided for a more important proposal. Some or all of the above-described processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input importance data of the proposal content to the generation AI and cause the generation AI to adjust the level of detail of the proposal.

[0138] When making a proposal, the suggestion unit can apply different suggestion algorithms depending on the category of the proposal content. For example, the suggestion unit can apply a technical suggestion algorithm to a technical proposal. The suggestion unit can also apply a business suggestion algorithm to a business proposal. The suggestion unit can also apply a creative suggestion algorithm to a creative proposal. In this way, by applying different suggestion algorithms depending on the category of the proposal content, more appropriate suggestions can be provided. Some or all of the above-mentioned processing in the suggestion unit can be performed using, for example, AI, or can be performed without using AI. For example, the suggestion unit can input category data of the proposal content to the generation AI and cause the generation AI to apply the suggestion algorithm.

[0139] When making a proposal, the suggestion unit can improve the accuracy of the proposal by referring to the user's past proposal results. The suggestion unit, for example, adjusts the current proposal based on the user's past proposal results. The suggestion unit can also optimize the proposal algorithm by referring to the user's past proposal results. The suggestion unit can also improve the accuracy of the proposal by using the user's past proposal results. In this way, the accuracy of the proposal can be improved by referring to the user's past proposal results. Some or all of the above-mentioned processing in the suggestion unit may be performed using, for example, AI, or may be performed without using AI. For example, the suggestion unit can input the user's past proposal result data into the generation AI and cause the generation AI to improve the accuracy of the proposal.

[0140] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated user emotions. For example, if the user is in a hurry, the suggestion unit can provide short, to-the-point suggestions. If the user is relaxed, the suggestion unit can provide longer suggestions with detailed explanations. If the user is excited, the suggestion unit can provide suggestions with visually stimulating effects. By adjusting the length of the suggestions based on the user's emotions, more appropriate suggestions can be provided. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the suggestion unit can be performed using, for example, an AI, or without an AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the length of the suggestions.

[0141] When making a proposal, the proposal unit can determine the priority of the proposal based on the submission time of the proposal content. For example, the proposal unit can prioritize the most recently submitted proposal. The proposal unit can also postpone proposals that were submitted earlier. The proposal unit can also adjust the order of the proposals based on the submission time. In this way, by determining the priority of the proposals based on the submission time of the proposal content, more important proposals can be given priority. Some or all of the above-mentioned processing in the proposal unit may be performed using AI, for example, or may be performed without using AI. For example, the proposal unit can input submission time data of the proposal content to the generation AI and have the generation AI determine the priority of the proposals.

[0142] The suggestion unit can adjust the order of the suggestions based on the relevance of the suggestions when making suggestions. For example, the suggestion unit prioritizes highly relevant suggestions. The suggestion unit can also postpone less relevant suggestions. The suggestion unit can also adjust the order of the suggestions based on the relevance of the suggestions. In this way, by adjusting the order of the suggestions based on the relevance of the suggestions, more relevant suggestions can be prioritized. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input relevance data of the suggestions to the generation AI and cause the generation AI to adjust the order of the suggestions.

[0143] When making a proposal, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. For example, if the user has technical expertise, the suggestion unit uses a lot of technical terminology. Furthermore, if the user is a beginner, the suggestion unit can provide a proposal in simple language. Furthermore, the suggestion unit can adjust the use of technical terminology in the proposal according to the user's level of expertise. In this way, by adjusting the use of technical terminology in the proposal according to the user's level of expertise, it is possible to provide a proposal that is easier to understand. Some or all of the above-described processing in the suggestion unit may be performed using AI, for example, or may be performed without using AI. For example, the suggestion unit can input the user's level of expertise data into a generation AI and cause the generation AI to execute the use of technical terminology.

[0144] The skill selection unit can estimate the user's emotions and adjust the required skill selection method based on the estimated user emotions. For example, the skill selection unit can select detailed skills when the user is relaxed. Furthermore, the skill selection unit can select simple skills when the user is in a hurry. Furthermore, the skill selection unit can select visually appealing skills when the user is excited. This allows for adjusting the skill selection method based on the user's emotions, thereby providing more appropriate skills. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the skill selection unit can be performed using, for example, an AI, or without an AI. For example, the skill selection unit can input the user's emotion data into the generation AI and have the generation AI adjust the skill selection method.

[0145] When selecting skills, the skill selection unit can select optimal skills by referring to the user's past skill history. The skill selection unit, for example, selects current skills based on the user's past skill history. The skill selection unit can also optimize the skill selection algorithm by referring to the user's past skill history. The skill selection unit can also improve the accuracy of skill selection by using the user's past skill history. This allows more appropriate skills to be selected by referring to the user's past skill history. Some or all of the above-described processing in the skill selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the skill selection unit can input the user's past skill history data into the generation AI and cause the generation AI to select optimal skills.

[0146] When selecting skills, the skill selection unit can select skills based on the user's current project or area of ​​interest. For example, the skill selection unit preferentially selects skills related to the user's current project. The skill selection unit can also select related skills based on the user's area of ​​interest. The skill selection unit can also suggest skills related to areas in which the user has previously shown interest. This allows for the provision of more relevant skills by selecting skills based on the user's current project or area of ​​interest. Some or all of the above-described processing in the skill selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the skill selection unit can input the user's project data and area of ​​interest data into the generation AI and cause the generation AI to select skills.

[0147] The skill selection unit can customize the skill selection method by reflecting the user's past feedback when selecting a skill. For example, the skill selection unit preferentially suggests a skill selection method that the user has provided feedback on in the past. The skill selection unit can also customize the skill selection interface based on the user's past feedback. The skill selection unit can also select an optimal skill selection means by referring to the user's past feedback. This makes it possible to provide a more appropriate skill selection method by reflecting the user's past feedback. Some or all of the above-described processing in the skill selection unit can be performed using, for example, AI, or can be performed without using AI. For example, the skill selection unit can input the user's feedback data into the generation AI and cause the generation AI to customize the skill selection method.

[0148] The skill selection unit can estimate the user's emotions and determine the priority of skills based on the estimated user emotions. For example, when the user is excited, the skill selection unit can prioritize important skills. Furthermore, when the user is relaxed, the skill selection unit can prioritize detailed skills. Furthermore, when the user is stressed, the skill selection unit can prioritize simple skills. By determining the priority of skills based on the user's emotions, more important skills can be provided preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the skill selection unit can be performed using, for example, an AI, or without an AI. For example, the skill selection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of skills.

[0149] When selecting skills, the skill selection unit can prioritize highly relevant skills by taking into account the user's geographical location information. For example, if the user is in a specific area, the skill selection unit can prioritize selecting skills related to that area. Furthermore, if the user is traveling, the skill selection unit can also prioritize selecting skills related to the travel destination. Furthermore, if the user is participating in a specific event, the skill selection unit can also prioritize selecting skills related to the event. In this way, by taking the user's geographical location information into account, more relevant skills can be provided. Some or all of the above-described processing in the skill selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the skill selection unit can input the user's geographical location information data into the generation AI and cause the generation AI to select highly relevant skills.

[0150] When selecting skills, the skill selection unit can analyze the user's social media activities and select relevant skills. For example, the skill selection unit automatically selects skills shared by the user on social media. The skill selection unit can also analyze the content of the user's social media posts and select relevant skills. The skill selection unit can also select relevant skills based on the activities of the user's friends on social media. In this way, by analyzing the user's social media activities, more relevant skills can be provided. Some or all of the above-described processing in the skill selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the skill selection unit can input the user's social media data into the generation AI and cause the generation AI to select relevant skills.

[0151] The skill selection unit can customize the skill selection method by reflecting the user's past feedback when selecting a skill. For example, the skill selection unit preferentially suggests a skill selection method that the user has provided feedback on in the past. The skill selection unit can also customize the skill selection interface based on the user's past feedback. The skill selection unit can also select an optimal skill selection means by referring to the user's past feedback. This makes it possible to provide a more appropriate skill selection method by reflecting the user's past feedback. Some or all of the above-described processing in the skill selection unit can be performed using, for example, AI, or can be performed without using AI. For example, the skill selection unit can input the user's feedback data into the generation AI and cause the generation AI to customize the skill selection method.

[0152] The procedure selection unit can estimate the user's emotions and adjust the required procedure selection method based on the estimated user emotions. For example, if the user is relaxed, the procedure selection unit can select a detailed procedure. If the user is in a hurry, the procedure selection unit can also select a concise procedure. If the user is excited, the procedure selection unit can also select a visually appealing procedure. This allows for adjusting the procedure selection method based on the user's emotions, thereby providing more appropriate procedures. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the procedure selection unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the procedure selection unit can input the user's emotion data into the generation AI and have the generation AI adjust the procedure selection method.

[0153] When selecting a procedure, the procedure selection unit can select the optimal procedure by referring to the user's past procedure history. The procedure selection unit, for example, selects the current procedure based on the user's past procedure history. The procedure selection unit can also optimize the procedure selection algorithm by referring to the user's past procedure history. The procedure selection unit can also improve the accuracy of procedure selection by using the user's past procedure history. In this way, by referring to the user's past procedure history, a more appropriate procedure can be selected. Some or all of the above-described processing in the procedure selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the procedure selection unit can input the user's past procedure history data into a generation AI and have the generation AI select the optimal procedure.

[0154] When selecting a procedure, the procedure selection unit can select a procedure based on the user's current project or area of ​​interest. For example, the procedure selection unit preferentially selects procedures related to the user's current project. The procedure selection unit can also select related procedures based on the user's area of ​​interest. The procedure selection unit can also suggest procedures related to areas in which the user has previously shown interest. This makes it possible to provide more relevant procedures by selecting procedures based on the user's current project or area of ​​interest. Some or all of the above-described processing in the procedure selection unit may be performed using, or without, AI, for example. For example, the procedure selection unit can input the user's project data and area of ​​interest data into a generation AI and have the generation AI select a procedure.

[0155] When selecting a procedure, the procedure selection unit can customize the procedure selection method by reflecting the user's past feedback. For example, the procedure selection unit preferentially suggests procedure selection methods that the user has provided feedback on in the past. The procedure selection unit can also customize the procedure selection interface based on the user's past feedback. The procedure selection unit can also select the optimal procedure selection means by referring to the user's past feedback. In this way, a more appropriate procedure selection method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the procedure selection unit may be performed using, or without, AI, for example. For example, the procedure selection unit can input user feedback data into a generation AI and have the generation AI customize the procedure selection method.

[0156] The procedure selection unit can estimate the user's emotions and determine the priority of procedures based on the estimated user emotions. For example, if the user is excited, the procedure selection unit can prioritize important procedures. Furthermore, if the user is relaxed, the procedure selection unit can also prioritize detailed procedures. Furthermore, if the user is stressed, the procedure selection unit can also prioritize simple procedures. By determining the priority of procedures based on the user's emotions, more important procedures can be provided preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the procedure selection unit can be performed using, for example, an AI, or without an AI. For example, the procedure selection unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of procedures.

[0157] When selecting a procedure, the procedure selection unit can prioritize selecting a highly relevant procedure by taking into account the user's geographical location information. For example, if the user is in a specific area, the procedure selection unit can prioritize selecting a procedure related to that area. Furthermore, if the user is traveling, the procedure selection unit can also prioritize selecting a procedure related to the travel destination. Furthermore, if the user is participating in a specific event, the procedure selection unit can also prioritize selecting a procedure related to the event. In this way, by taking the user's geographical location information into account, more relevant procedures can be provided. Some or all of the above-described processing in the procedure selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the procedure selection unit can input the user's geographical location information data into the generation AI and cause the generation AI to select a highly relevant procedure.

[0158] When selecting a procedure, the procedure selection unit can analyze the user's social media activity and select relevant procedures. For example, the procedure selection unit automatically selects procedures shared by the user on social media. The procedure selection unit can also analyze the content of the user's social media posts and select relevant procedures. The procedure selection unit can also select relevant procedures based on the activities of the user's friends on social media. In this way, by analyzing the user's social media activity, more relevant procedures can be provided. Some or all of the above-described processing in the procedure selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the procedure selection unit can input the user's social media data into a generation AI and cause the generation AI to select relevant procedures.

[0159] When selecting a procedure, the procedure selection unit can customize the procedure selection method by reflecting the user's past feedback. For example, the procedure selection unit preferentially suggests procedure selection methods that the user has provided feedback on in the past. The procedure selection unit can also customize the procedure selection interface based on the user's past feedback. The procedure selection unit can also select the optimal procedure selection means by referring to the user's past feedback. In this way, a more appropriate procedure selection method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the procedure selection unit may be performed using, or without, AI, for example. For example, the procedure selection unit can input user feedback data into a generation AI and have the generation AI customize the procedure selection method.

[0160] The domain selection unit can estimate the user's emotions and adjust the business domain selection method based on the estimated user emotions. For example, if the user is relaxed, the domain selection unit can select a detailed domain. If the user is in a hurry, the domain selection unit can select a concise domain. If the user is excited, the domain selection unit can select a visually appealing domain. This allows for adjusting the business domain selection method based on the user's emotions to provide a more appropriate business domain. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the domain selection unit can be performed using AI, for example, or without AI. For example, the domain selection unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the business domain selection method.

[0161] When selecting a domain, the domain selection unit can select an optimal domain by referring to the user's past business history. The domain selection unit, for example, selects a current domain based on the user's past business history. The domain selection unit can also optimize the domain selection algorithm by referring to the user's past business history. The domain selection unit can also improve the accuracy of domain selection by using the user's past business history. This allows a more appropriate domain to be selected by referring to the user's past business history. Some or all of the above-described processing in the domain selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the domain selection unit can input the user's past business history data into a generation AI and have the generation AI select an optimal domain.

[0162] When selecting a domain, the domain selection unit can select a domain based on the user's current project or field of interest. For example, the domain selection unit preferentially selects a domain related to the user's current project. The domain selection unit can also select a related domain based on the user's field of interest. The domain selection unit can also suggest domains related to fields in which the user has previously shown interest. By selecting a domain based on the user's current project or field of interest, more relevant domains can be provided. Some or all of the above-described processing in the domain selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the domain selection unit can input the user's project data and field of interest data into a generation AI and have the generation AI select a domain.

[0163] When selecting a domain, the domain selection unit can customize the domain selection method by reflecting the user's past feedback. For example, the domain selection unit preferentially suggests domain selection methods that the user has previously provided feedback on. The domain selection unit can also customize the domain selection interface based on the user's past feedback. The domain selection unit can also select the optimal domain selection method by referring to the user's past feedback. In this way, a more appropriate domain selection method can be provided by reflecting the user's past feedback. Some or all of the above-mentioned processing in the domain selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the domain selection unit can input the user's feedback data into the generation AI and cause the generation AI to customize the domain selection method.

[0164] The domain selection unit can estimate the user's emotions and prioritize domains based on the estimated user emotions. For example, if the user is excited, the domain selection unit can prioritize important domains. Furthermore, if the user is relaxed, the domain selection unit can prioritize detailed domains. Furthermore, if the user is stressed, the domain selection unit can prioritize simple domains. Thus, by prioritizing domains based on the user's emotions, more important domains can be provided preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the domain selection unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the domain selection unit can input the user's emotion data into the generation AI and have the generation AI determine the domain priorities.

[0165] When selecting a domain, the domain selection unit can prioritize selecting highly relevant domains by taking into account the user's geographical location information. For example, if the user is in a specific area, the domain selection unit can prioritize selecting domains related to that area. Furthermore, if the user is traveling, the domain selection unit can prioritize selecting domains related to the travel destination. Furthermore, if the user is participating in a specific event, the domain selection unit can prioritize selecting domains related to the event. This makes it possible to provide more relevant domains by taking the user's geographical location information into account. Some or all of the above-described processing in the domain selection unit may be performed using, or without, AI. For example, the domain selection unit can input the user's geographical location information data into the generation AI and cause the generation AI to select highly relevant domains.

[0166] When selecting a domain, the domain selection unit can analyze the user's social media activity and select a related domain. For example, the domain selection unit can automatically select a domain shared by the user on social media. The domain selection unit can also select a related domain by analyzing the content of the user's social media posts. The domain selection unit can also select a related domain by referring to the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, more relevant domains can be provided. Some or all of the above-described processing by the domain selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the domain selection unit can input the user's social media data into a generation AI and cause the generation AI to select a related domain.

[0167] When selecting a domain, the domain selection unit can customize the domain selection method by reflecting the user's past feedback. For example, the domain selection unit preferentially suggests domain selection methods that the user has previously provided feedback on. The domain selection unit can also customize the domain selection interface based on the user's past feedback. The domain selection unit can also select the optimal domain selection method by referring to the user's past feedback. In this way, a more appropriate domain selection method can be provided by reflecting the user's past feedback. Some or all of the above-mentioned processing in the domain selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the domain selection unit can input the user's feedback data into the generation AI and cause the generation AI to customize the domain selection method.

[0168] The plan creation unit can estimate the user's emotions and adjust the business plan creation method based on the estimated user emotions. For example, the plan creation unit can create a detailed business plan when the user is relaxed. The plan creation unit can also create a concise business plan when the user is in a hurry. The plan creation unit can also create a visually appealing business plan when the user is excited. This allows for adjusting the business plan creation method based on the user's emotions to provide a more appropriate business plan. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the plan creation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the plan creation unit can input the user's emotion data into the generation AI and have the generation AI adjust the business plan creation method.

[0169] When creating a plan, the plan creation unit can create an optimal plan by referring to the user's past plan history. The plan creation unit, for example, creates a current plan based on the user's past plan history. The plan creation unit can also optimize the plan creation algorithm by referring to the user's past plan history. The plan creation unit can also improve the accuracy of plan creation by using the user's past plan history. In this way, a more appropriate plan can be created by referring to the user's past plan history. Some or all of the above-mentioned processing in the plan creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the plan creation unit can input the user's past plan history data into a generation AI and cause the generation AI to create an optimal plan.

[0170] When creating a plan, the plan creation unit can create a plan based on the user's current project or areas of interest. For example, the plan creation unit prioritizes creating plans related to the user's current project. The plan creation unit can also create related plans based on the user's areas of interest. The plan creation unit can also suggest plans related to areas in which the user has previously shown interest. This allows for creating a plan based on the user's current project or areas of interest, thereby providing a more relevant plan. Some or all of the above-described processing in the plan creation unit may be performed using, or without, AI, for example. For example, the plan creation unit can input the user's project data and area of ​​interest data into a generation AI and cause the generation AI to create a plan.

[0171] The plan creation unit can customize the plan creation method by reflecting the user's past feedback when creating a plan. For example, the plan creation unit preferentially suggests a plan creation method that the user has provided feedback on in the past. The plan creation unit can also customize the plan creation interface based on the user's past feedback. The plan creation unit can also select an optimal plan creation means by referring to the user's past feedback. In this way, a more appropriate plan creation method can be provided by reflecting the user's past feedback. Some or all of the above-mentioned processing in the plan creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the plan creation unit can input the user's feedback data into the generation AI and cause the generation AI to customize the plan creation method.

[0172] The plan creation unit can estimate the user's emotions and determine the priority of plans based on the estimated user emotions. For example, if the user is excited, the plan creation unit can prioritize creating important plans. Furthermore, if the user is relaxed, the plan creation unit can prioritize creating detailed plans. Furthermore, if the user is stressed, the plan creation unit can prioritize creating simple plans. By determining the priority of plans based on the user's emotions, more important plans can be provided preferentially. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the plan creation unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the plan creation unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of plans.

[0173] When creating a plan, the plan creation unit can prioritize creating highly relevant plans by taking into account the user's geographical location information. For example, if the user is in a specific area, the plan creation unit can prioritize creating plans related to that area. Furthermore, if the user is traveling, the plan creation unit can prioritize creating plans related to the travel destination. Furthermore, if the user is participating in a specific event, the plan creation unit can prioritize creating plans related to that event. In this way, by taking the user's geographical location information into account, a more relevant plan can be provided. Some or all of the above-described processing in the plan creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the plan creation unit can input the user's geographical location information data into the generation AI and cause the generation AI to create a highly relevant plan.

[0174] The plan creation unit can analyze the user's social media activity and create a related plan when creating a plan. For example, the plan creation unit automatically creates a plan shared by the user on social media. The plan creation unit can also analyze the content of the user's social media posts and create a related plan. The plan creation unit can also create a related plan by referring to the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, it is possible to provide a more relevant plan. Some or all of the above-mentioned processing in the plan creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the plan creation unit can input the user's social media data into a generation AI and cause the generation AI to create a related plan.

[0175] The plan creation unit can customize the plan creation method by reflecting the user's past feedback when creating a plan. For example, the plan creation unit preferentially suggests a plan creation method that the user has provided feedback on in the past. The plan creation unit can also customize the plan creation interface based on the user's past feedback. The plan creation unit can also select an optimal plan creation means by referring to the user's past feedback. In this way, a more appropriate plan creation method can be provided by reflecting the user's past feedback. Some or all of the above-mentioned processing in the plan creation unit may be performed using, for example, AI, or may be performed without using AI. For example, the plan creation unit can input the user's feedback data into the generation AI and cause the generation AI to customize the plan creation method.

[0176] The market analysis unit can estimate the user's emotions and adjust the market analysis method based on the estimated user emotions. For example, the market analysis unit can perform a detailed market analysis when the user is relaxed. The market analysis unit can also perform a concise market analysis when the user is in a hurry. The market analysis unit can also perform a visually appealing market analysis when the user is excited. This allows for adjusting the market analysis method based on the user's emotions to provide a more appropriate market analysis. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the market analysis unit can be performed using, for example, an AI, or without an AI. For example, the market analysis unit can input the user's emotion data into the generative AI and have the generative AI adjust the market analysis method.

[0177] During market analysis, the market analysis unit can perform optimal analysis by referring to the user's past market analysis history. For example, the market analysis unit performs current market analysis based on the user's past market analysis history. The market analysis unit can also optimize the market analysis algorithm by referring to the user's past market analysis history. The market analysis unit can also improve the accuracy of the market analysis by using the user's past market analysis history. In this way, by referring to the user's past market analysis history, more appropriate market analysis can be provided. Some or all of the above-described processing in the market analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the market analysis unit can input the user's past market analysis history data into the generation AI and cause the generation AI to perform optimal market analysis.

[0178] During market analysis, the market analysis unit can analyze the market based on the user's current project and areas of interest. For example, the market analysis unit prioritizes analysis of markets related to the user's current project. The market analysis unit can also analyze related markets based on the user's areas of interest. The market analysis unit can also suggest markets related to areas in which the user has previously shown interest. This allows for more relevant market analysis by analyzing the market based on the user's current project and areas of interest. Some or all of the above-described processing in the market analysis unit may be performed using, or without, AI, for example. For example, the market analysis unit can input the user's project data and area of ​​interest data into the generation AI and have the generation AI perform market analysis.

[0179] The market analysis unit can customize the market analysis method by reflecting the user's past feedback when analyzing the market. For example, the market analysis unit preferentially suggests market analysis methods that the user has provided feedback on in the past. The market analysis unit can also customize the market analysis interface based on the user's past feedback. The market analysis unit can also select the optimal market analysis means by referring to the user's past feedback. In this way, a more appropriate market analysis method can be provided by reflecting the user's past feedback. Some or all of the above-mentioned processing in the market analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the market analysis unit can input the user's feedback data into the generation AI and have the generation AI customize the market analysis method.

[0180] The market analysis unit can estimate the user's emotions and determine the priorities of market analysis based on the estimated user emotions. For example, if the user is excited, the market analysis unit can prioritize analyzing important markets. Furthermore, if the user is relaxed, the market analysis unit can prioritize analyzing detailed markets. Furthermore, if the user is stressed, the market analysis unit can prioritize analyzing simple markets. Thus, by determining the priorities of market analysis based on the user's emotions, more important markets can be prioritized for analysis. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative 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 market analysis unit can be performed using, for example, an AI, or without an AI. For example, the market analysis unit can input the user's emotion data into the generative AI and have the generative AI determine the priorities of market analysis.

[0181] During market analysis, the market analysis unit can prioritize analysis of highly relevant markets by taking into account the user's geographical location information. For example, if the user is in a specific region, the market analysis unit can prioritize analysis of markets related to that region. Furthermore, if the user is traveling, the market analysis unit can prioritize analysis of markets related to the travel destination. Furthermore, if the user is participating in a specific event, the market analysis unit can prioritize analysis of markets related to the event. This makes it possible to provide more relevant markets by taking the user's geographical location information into account. Some or all of the above-described processing in the market analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the market analysis unit can input the user's geographical location information data into the generation AI and cause the generation AI to perform an analysis of highly relevant markets.

[0182] During market analysis, the market analysis unit can analyze the user's social media activity and analyze related markets. For example, the market analysis unit automatically analyzes markets shared by the user on social media. The market analysis unit can also analyze the content of the user's social media posts and analyze related markets. The market analysis unit can also analyze related markets by referring to the activities of the user's friends on social media. This makes it possible to provide more relevant markets by analyzing the user's social media activity. Some or all of the above-described processing in the market analysis unit may be performed using AI, for example, or may be performed without using AI. For example, the market analysis unit can input the user's social media data into a generation AI and have the generation AI perform an analysis of the related markets.

[0183] The market analysis unit can customize the market analysis method by reflecting the user's past feedback when analyzing the market. For example, the market analysis unit preferentially suggests market analysis methods that the user has provided feedback on in the past. The market analysis unit can also customize the market analysis interface based on the user's past feedback. The market analysis unit can also select the optimal market analysis means by referring to the user's past feedback. In this way, a more appropriate market analysis method can be provided by reflecting the user's past feedback. Some or all of the above-mentioned processing in the market analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the market analysis unit can input the user's feedback data into the generation AI and have the generation AI customize the market analysis method.

[0184] The competitive analysis unit can estimate the user's emotions and adjust the competitive analysis method based on the estimated user emotions. For example, the competitive analysis unit can perform a detailed competitive analysis when the user is relaxed. The competitive analysis unit can also perform a concise competitive analysis when the user is in a hurry. The competitive analysis unit can also perform a visually appealing competitive analysis when the user is excited. This allows for adjusting the competitive analysis method based on the user's emotions to provide a more appropriate competitive analysis. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative 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 competitive analysis unit can be performed using, for example, an AI, or without an AI. For example, the competitive analysis unit can input user emotion data into the generative AI and have the generative AI adjust the competitive analysis method.

[0185] During competitive analysis, the competitive analysis unit can perform optimal analysis by referring to the user's past competitive analysis history. For example, the competitive analysis unit performs a current competitive analysis based on the user's past competitive analysis history. The competitive analysis unit can also optimize the competitive analysis algorithm by referring to the user's past competitive analysis history. The competitive analysis unit can also improve the accuracy of the competitive analysis by using the user's past competitive analysis history. Thus, by referring to the user's past competitive analysis history, a more appropriate competitive analysis can be provided. Some or all of the above-described processing in the competitive analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the competitive analysis unit can input the user's past competitive analysis history data into the generation AI and cause the generation AI to perform an optimal competitive analysis.

[0186] During the competitive analysis, the competitive analysis unit can analyze competitive issues based on the user's current project or area of ​​interest. For example, the competitive analysis unit prioritizes analysis of competitive issues related to the user's current project. The competitive analysis unit can also analyze relevant competitive issues based on the user's area of ​​interest. The competitive analysis unit can also suggest competitive issues related to areas in which the user has previously shown interest. This allows for more relevant competitive analysis by analyzing competitive issues based on the user's current project or area of ​​interest. Some or all of the above-described processing in the competitive analysis unit may be performed using, or without, AI. For example, the competitive analysis unit can input the user's project data or area of ​​interest data into a generation AI and have the generation AI perform a competitive analysis.

[0187] During competitive analysis, the competitive analysis unit can customize the competitive analysis method by reflecting the user's past feedback. For example, the competitive analysis unit prioritizes the proposal of competitive analysis methods for which the user has provided feedback in the past. The competitive analysis unit can also customize the competitive analysis interface based on the user's past feedback. The competitive analysis unit can also select the optimal competitive analysis means by referring to the user's past feedback. This makes it possible to provide a more appropriate competitive analysis method by reflecting the user's past feedback. Some or all of the above-described processing in the competitive analysis unit may be performed using, or without, AI, for example. For example, the competitive analysis unit can input user feedback data into a generation AI and have the generation AI customize the competitive analysis method.

[0188] The conflict analysis unit can estimate the user's emotions and determine the priorities of the conflict analysis based on the estimated user emotions. For example, when the user is excited, the conflict analysis unit prioritizes analyzing important conflicts. Furthermore, when the user is relaxed, the conflict analysis unit can prioritize analyzing detailed conflicts. Furthermore, when the user is stressed, the conflict analysis unit can prioritize analyzing simple conflicts. Thus, by determining the priorities of the conflict analysis based on the user's emotions, more important conflicts can be prioritized for analysis. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI may 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 conflict analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the conflict analysis unit can input the user's emotion data into the generative AI and have the generative AI determine the priorities of the conflict analysis.

[0189] During the competitor analysis, the competitor analysis unit can prioritize analysis of highly relevant competitors by taking into account the user's geographical location information. For example, if the user is in a specific region, the competitor analysis unit can prioritize analysis of competitors related to that region. Furthermore, if the user is traveling, the competitor analysis unit can prioritize analysis of competitors related to the travel destination. Furthermore, if the user is participating in a specific event, the competitor analysis unit can prioritize analysis of competitions related to the event. In this way, by taking the user's geographical location information into account, more relevant competitions can be provided. Some or all of the above-described processing in the competitor analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the competitor analysis unit can input the user's geographical location information data into the generation AI and cause the generation AI to analyze highly relevant competitors.

[0190] During the competitor analysis, the competitor analysis unit can analyze the user's social media activity and analyze related competitors. For example, the competitor analysis unit automatically analyzes competitors shared by the user on social media. The competitor analysis unit can also analyze the content of the user's social media posts and analyze related competitors. The competitor analysis unit can also analyze related competitors by referring to the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, more relevant competitors can be provided. Some or all of the above-described processing in the competitor analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the competitor analysis unit can input the user's social media data into a generation AI and cause the generation AI to analyze related competitors.

[0191] During competitive analysis, the competitive analysis unit can customize the competitive analysis method by reflecting the user's past feedback. For example, the competitive analysis unit prioritizes the proposal of competitive analysis methods for which the user has provided feedback in the past. The competitive analysis unit can also customize the competitive analysis interface based on the user's past feedback. The competitive analysis unit can also select the optimal competitive analysis means by referring to the user's past feedback. This makes it possible to provide a more appropriate competitive analysis method by reflecting the user's past feedback. Some or all of the above-described processing in the competitive analysis unit may be performed using, or without, AI, for example. For example, the competitive analysis unit can input user feedback data into a generation AI and have the generation AI customize the competitive analysis method. === Hard Collateral 1-1 === Each of the multiple elements, including the reception unit, analysis unit, business planning unit, and proposal unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit of the entrepreneurial support system can input an entrepreneur's idea or vision using the reception device 38 of the smart device 14. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12, analyzes the input information, and selects the necessary skills, procedures, and business domain. For example, the business planning unit is realized by the specific processing unit 290 of the data processing device 12, and creates a business plan based on the information selected by the analysis unit. For example, the proposal unit is realized by the control unit 46A of the smart device 14, and proposes branding and marketing strategies based on the business plan created by the business planning unit. === Hard Collateral 1-2 === Each of the multiple elements, including the reception unit, analysis unit, business planning unit, and proposal unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit of the entrepreneurial support system can input an entrepreneur's idea or vision using the microphone 238 of the smart glasses 214. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12, analyzes the input information, and selects the necessary skills, procedures, and business domain. For example, the business planning unit is realized by the specific processing unit 290 of the data processing device 12, and creates a business plan based on the information selected by the analysis unit. For example, the proposal unit is realized by the control unit 46A of the smart glasses 214, and proposes branding and marketing strategies based on the business plan created by the business planning unit. === Hard Collateral 1-3 === Each of the multiple elements including the reception unit, analysis unit, business planning unit, and proposal unit described above is realized, for example, by at least one of the headset terminal 314 and the data processing device 12. For example, the reception unit of the entrepreneurial support system can input an entrepreneur's idea or vision using the microphone 238 of the headset terminal 314. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information to select necessary skills, procedures, and business domains. For example, the business planning unit is realized by the specific processing unit 290 of the data processing device 12 and creates a business plan based on the information selected by the analysis unit. For example, the proposal unit is realized by the control unit 46A of the headset terminal 314 and proposes branding and marketing strategies based on the business plan created by the business planning unit. === Hard Collateral 1-4 === Each of the multiple elements including the reception unit, analysis unit, business planning unit, and proposal unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit of the entrepreneurial support system can input an entrepreneur's idea or vision using the microphone 238 of the robot 414. For example, the analysis unit is realized by the specific processing unit 290 of the data processing device 12, analyzes the input information, and selects the necessary skills, procedures, and business domain. For example, the business planning unit is realized by the specific processing unit 290 of the data processing device 12, and creates a business plan based on the information selected by the analysis unit. For example, the proposal unit is realized by the control unit 46A of the robot 414, and proposes branding and marketing strategies based on the business plan created by the business planning unit.

[0192] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0193] The reception unit can analyze the user's past idea submission history and select an appropriate input method. For example, if the user has preferred text input in the past, the reception unit can preferentially suggest text input. Furthermore, if the user has frequently used voice input in the past, the reception unit can also recommend voice input. Furthermore, if the user has previously submitted ideas using images, the reception unit can also support image input. This allows the optimal input method to be selected based on the user's past history, providing an input method that is easy for the user to use. 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. For example, the reception unit can input the user's past idea submission history data into the generation AI and have the generation AI select the optimal input method.

[0194] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated user's emotions. For example, if the user is relaxed, the analysis unit can provide detailed analysis results. If the user is in a hurry, the analysis unit can provide concise analysis results. If the user is excited, the analysis unit can provide visually appealing analysis results. This allows for more appropriate analysis results to be provided by adjusting the presentation of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit can be performed using AI, or can be performed without AI. For example, the analysis unit can input the user's emotion data into the generation AI and have the generation AI adjust the presentation of the analysis.

[0195] When creating a business plan, the business planning department can adjust the level of detail of the plan based on the importance of the business. For example, a detailed plan can be created for an important business. The business planning department can also create a concise plan for a general business. The business planning department can also create a basic plan for a low-priority business. In this way, by adjusting the level of detail of the plan based on the importance of the business, a detailed plan can be provided for a more important business. Some or all of the above-mentioned processing in the business planning department may be performed using, for example, AI, or may be performed without using AI. For example, the business planning department can input business importance data into the generation AI and have the generation AI adjust the level of detail of the plan.

[0196] The suggestion unit can estimate the user's emotions and adjust the way suggestions are expressed based on the estimated user's emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is in a hurry, the suggestion unit can provide concise suggestions. If the user is excited, the suggestion unit can provide visually appealing suggestions. This allows for adjusting the way suggestions are expressed based on the user's emotions to provide more appropriate suggestions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit can be performed using AI, or can be performed without AI. For example, the suggestion unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way suggestions are expressed.

[0197] When selecting skills, the skill selection unit can select optimal skills by referring to the user's past skill history. For example, the current skill selection is performed based on the user's past skill history. The skill selection unit can also optimize the skill selection algorithm by referring to the user's past skill history. The skill selection unit can also improve the accuracy of skill selection by using the user's past skill history. This allows more appropriate skills to be selected by referring to the user's past skill history. Some or all of the above-described processing in the skill selection unit may be performed using, for example, AI, or may be performed without using AI. For example, the skill selection unit can input the user's past skill history data into the generation AI and cause the generation AI to select optimal skills.

[0198] The procedure selection unit can estimate the user's emotions and adjust the selection method of the required procedures based on the estimated user emotions. For example, if the user is relaxed, the procedure selection unit can select a detailed procedure. If the user is in a hurry, the procedure selection unit can select a concise procedure. If the user is excited, the procedure selection unit can select a visually appealing procedure. This allows for adjusting the procedure selection method based on the user's emotions to provide more appropriate procedures. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the procedure selection unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the procedure selection unit can input the user's emotion data into the generation AI and have the generation AI adjust the procedure selection method.

[0199] When selecting a domain, the domain selection unit can select the optimal domain by referring to the user's past business history. For example, the current domain selection unit can select a domain based on the user's past business history. The domain selection unit can also optimize the domain selection algorithm by referring to the user's past business history. The domain selection unit can also improve the accuracy of domain selection by using the user's past business history. This allows a more appropriate domain to be selected by referring to the user's past business history. Some or all of the above-mentioned processing in the domain selection unit can be performed using, for example, AI, or can be performed without using AI. For example, the domain selection unit can input the user's past business history data into a generation AI and have the generation AI select the optimal domain.

[0200] The plan creation unit can estimate the user's emotions and adjust the business plan creation method based on the estimated user emotions. For example, if the user is relaxed, the plan creation unit can create a detailed business plan. If the user is in a hurry, the plan creation unit can create a concise business plan. If the user is excited, the plan creation unit can create a visually appealing business plan. This allows the business plan creation method to be adjusted based on the user's emotions, thereby providing a more appropriate business plan. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may 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 plan creation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the plan creation unit can input the user's emotion data into the generation AI and have the generation AI adjust the business plan creation method.

[0201] During market analysis, the market analysis unit can perform optimal analysis by referring to the user's past market analysis history. For example, the current market analysis is performed based on the user's past market analysis history. The market analysis unit can also optimize the market analysis algorithm by referring to the user's past market analysis history. The market analysis unit can also improve the accuracy of the market analysis by using the user's past market analysis history. In this way, by referring to the user's past market analysis history, more appropriate market analysis can be provided. Some or all of the above-described processing in the market analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the market analysis unit can input the user's past market analysis history data into the generation AI and cause the generation AI to perform optimal market analysis.

[0202] The competitive analysis unit can estimate the user's emotions and adjust the competitive analysis method based on the estimated user emotions. For example, if the user is relaxed, the competitive analysis unit can perform a detailed competitive analysis. If the user is in a hurry, the competitive analysis unit can also perform a concise competitive analysis. If the user is excited, the competitive analysis unit can also perform a visually appealing competitive analysis. By adjusting the competitive analysis method based on the user's emotions, more appropriate competitive analysis can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative 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 competitive analysis unit can be performed using, for example, an AI, or without an AI. For example, the competitive analysis unit can input the user's emotion data into the generative AI and have the generative AI adjust the competitive analysis method.

[0203] The processing flow of the second embodiment will be briefly explained below.

[0204] Step 1: The reception unit inputs the entrepreneur's idea or vision. The entrepreneur's idea or vision can include a business idea, a technical vision, a creative vision, etc. The reception unit can accept the idea or vision by text input, voice input, image input, etc. Step 2: The analysis unit analyzes the information entered by the reception unit and selects the necessary skills, procedures, and business domains. The analysis is performed using methods such as text analysis, data mining, and machine learning algorithms. For example, text analysis can be used to extract keywords and identify related skills and procedures. Data mining can also be used to analyze past success stories and select the optimal business domain. Skills and procedures can also be selected using machine learning algorithms. Step 3: The business planning department creates a business plan based on the information selected by the analysis department. The business plan includes a financial plan, marketing plan, and operation plan. For example, a financial plan may be created to propose a budget and fundraising methods. A marketing plan may be created to propose a target market and promotion strategy. An operation plan may be created to plan the necessary resources and schedule. Step 4: The proposal department proposes branding and marketing strategies based on the business plan prepared by the business planning department. Branding includes building a brand image, brand strategy, and brand value evaluation. For example, building a brand image and sending an effective message to the target market. A brand strategy can be formulated to differentiate the brand from competitors. Brand value can also be evaluated and the brand's strengths and weaknesses analyzed. Marketing strategies include selecting a target market, selecting marketing channels, and a promotion strategy. For example, selecting a target market and selecting the optimal marketing channels. A promotion strategy can be formulated and advertising campaigns and social media strategies can be proposed.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0209] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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).

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0223] 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.

[0224] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0225] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0226] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0227] 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.

[0228] 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.

[0229] 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.

[0230] 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).

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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.

[0235] 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.

[0236] 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.

[0237] 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.

[0238] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0239] 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.

[0240] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0241] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0242] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0243] 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.

[0244] 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.

[0245] 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.

[0246] 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).

[0247] 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.

[0248] 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.

[0249] 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.

[0250] 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.

[0251] 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.

[0252] 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.

[0253] 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.

[0254] 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.

[0255] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0256] 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.

[0257] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0258] 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.

[0259] 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.

[0260] 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.

[0261] 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).

[0262] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0263] 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."

[0264] 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.

[0265] 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.

[0266] 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.

[0267] 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.

[0268] 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.

[0269] 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.

[0270] 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.

[0271] 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.

[0272] 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.

[0273] 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.

[0274] 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.

[0275] 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.

[0276] [Explanation of symbols]

[0277] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception desk where entrepreneurs can input their ideas or visions; an analysis unit that analyzes the information input by the reception unit and selects necessary skills, procedures, and business domains; a business planning unit that creates a business plan based on the information selected by the analysis unit; a proposal department that proposes branding and marketing strategies based on the business plan created by the business planning department; Equipped with A system characterized by:

2. The analysis unit Have a skills selection department that identifies the necessary skills 2. The system of claim 1.

3. The analysis unit Equipped with a procedure selection unit that identifies the necessary procedures 2. The system of claim 1.

4. The analysis unit Equipped with a domain selection department that selects business domains 2. The system of claim 1.

5. The business planning department Establish a planning department that creates specific business plans 2. The system of claim 1.

6. The proposal unit Equipping a market analysis department to analyze target markets 2. The system of claim 1.

7. The proposal unit Equipped with a competitive analysis department to analyze competitors 2. The system of claim 1.

8. The reception unit Estimate the user's emotions and adjust the timing of inputting ideas and visions based on those emotions 2. The system of claim 1.

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