system

Abstract

We provide the system. [Solution] Means for collecting information and building a database, A means for training a model based on the aforementioned database, A means of automatically generating a plan based on information received from the user, A means of generating feedback on automatically generated plans, A means for presenting the aforementioned feedback to the user, A system that includes this.

Description

【Technical Field】 , , , , 【0004】 , , , , 【0005】 , , , , , 【0003】 , , 【0001】 The technology of the present disclosure relates to a system. 【Background Art】 【0002】 Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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 Unexamined Patent Application Publication No. 2022-180282 【Summary of the Invention】 【Problems to be Solved by the Invention】 【0004】 Conventionally, in the planning stage of a new business, entrepreneurs often require a lot of time and effort to create an appropriate business plan and prepare a presentation. Also, there has been a problem that the success rate decreases because plan revisions based on feedback from investors and related parties cannot be smoothly made. For this reason, there is a demand for a system that supports effective planning and revision based on prompt feedback of new businesses. 【Means for Solving the Problems】 【0005】 This invention provides a means for collecting success stories and lessons learned from new businesses, segmenting the information, and building a database. It also provides a means for training a model based on the database and automatically generating business plans based on user-generated information. Furthermore, it includes a function to provide feedback on the generated plans from the perspective of virtual investors and partners, enabling users to quickly and effectively revise their plans. This allows entrepreneurs to efficiently formulate and improve business plans, thereby increasing their chances of success. 【0006】 "Information" refers to success stories and lessons learned regarding new businesses, as well as related background data, obtained through data collection. 【0007】 A "database" refers to a collection of information that is organized, structured, and stored in a format that makes it easy to search and refer to. 【0008】 A "model" refers to an algorithm or program that has been trained to analyze information in a database and generate new business plans. 【0009】 "Users" refer to entrepreneurs and business owners who use this system to create new business plans and obtain feedback. 【0010】 "Plan" refers to a document or proposal that includes an overview, goals, and strategies for a new business, generated based on information obtained from users. 【0011】 "Feedback" refers to information that includes questions, suggestions, and areas for improvement proposed from a hypothetical perspective regarding the generated business plan. 【0012】 A "virtual investor or partner" refers to a simulated entity within this system that takes on the roles of an investor or business partner and provides feedback. [Brief explanation of the drawing] 【0013】 [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention] 【0014】 An example of an embodiment of the system according to the technology of the present disclosure will be described below with reference to the accompanying drawings. 【0015】 First, the terms used in the following description will be explained. 【0016】 In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units 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), and the like. 【0017】 In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor. 【0018】 In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc. 【0019】 In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark). 【0020】 In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or." 【0021】 [First Embodiment] 【0022】 Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment. 【0023】 As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server. 【0024】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0025】 The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52. 【0026】 The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input. 【0027】 The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor. 【0028】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54. 【0029】 Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14. 【0030】 As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30. 【0031】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0032】 In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0033】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal". 【0034】 This invention provides a system that automatically generates new business plans and supports entrepreneurs by providing feedback. This system is implemented by a program that handles everything from information gathering to plan generation and feedback provision in an integrated manner. 【0035】 First, the server collects information on successful case studies and lessons learned from new business ventures from around the world via the internet and specialized databases. This allows the server to acquire diverse and reliable information, which it then uses to build a database for training. 【0036】 Next, the server trains a generative AI model based on the constructed database. This model performs case analysis based on the collected information, learning success factors and commonalities. In this process, the server updates the model as new cases are added, ensuring that it always provides analysis results based on the latest information. 【0037】 Users input basic information about their business via a terminal. For example, if a user wants to launch an "environmentally friendly home product" into the market, they would input information such as the product's features, target market, and challenges. Based on the information received from the user, the server uses a generation AI model to automatically generate a business plan based on similar past cases. The plan includes business objectives, strategies, and market analysis. 【0038】 Next, the server generates feedback on the created business plan from the perspective of a virtual investor or partner. This feedback includes virtual questions, points of concern, and suggestions for improvement regarding the user's plan, which can be used to revise the business plan. For example, a virtual investor might point out that "the competitive analysis in the target market is insufficient." 【0039】 Finally, the device presents the user with the generated business plan and feedback. Based on this feedback, the user can revise the business plan, improving its completeness and quality. This process allows the user to enhance the quality of their business plan and deliver more compelling presentations to investors and stakeholders. 【0040】 This invention enables a reduction in the time required to launch a new business and an improvement in the probability of success. 【0041】 The following describes the processing flow. 【0042】 Step 1: 【0043】 The server collects information on new business ventures from external sources. Specifically, it obtains information on successful case studies and lessons learned from publicly available databases on the internet, academic papers, and industry reports. 【0044】 Step 2: 【0045】 The server organizes the collected information, extracts the necessary elements, and builds a database. The information is categorized into business areas and success factors, and tagged to improve searchability. 【0046】 Step 3: 【0047】 The server trains a generative AI model based on the constructed database. In this process, the server learns trends and patterns in the dataset and establishes an algorithm for automatically generating business plans. 【0048】 Step 4: 【0049】 Users will use a terminal to input basic information about their business. For example, they might input information such as the characteristics of a new product, the target market, and current challenges. 【0050】 Step 5: 【0051】 The server automatically generates business plans using AI based on information received from users. The AI ​​model provides draft plans that include appropriate business strategies and market analysis, referencing similar past cases. 【0052】 Step 6: 【0053】 The server evaluates the automatically generated business plan from the perspective of a virtual investor or partner and generates feedback. This includes questions and suggestions for improvement regarding the plan, such as "the financial plan is unclear." 【0054】 Step 7: 【0055】 The device presents the user with the generated business plan and feedback. Based on this information, the user revises the plan and improves its quality. 【0056】 This series of steps allows users to quickly and effectively develop business plans and increase their chances of success. 【0057】 (Example 1) 【0058】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0059】 In modern entrepreneurial activities, it is difficult to quickly and effectively develop new business plans and obtain appropriate feedback. This can lead to insufficient quality business plans and missed opportunities to effectively persuade investors and collaborators. In particular, there is a need for means to leverage diverse success stories and lessons learned, automatically create optimal plans using generative AI models, and obtain feedback. 【0060】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0061】 In this invention, the server includes means for collecting information from global communication networks and specialized data aggregates, means for cleansing and organizing the information to construct a data warehouse for learning, and means for training a generative AI model constructed based on the data warehouse. This enables users to quickly create new business plans and receive high-quality feedback on them from the perspective of virtual investors or collaborators. 【0062】 "Information" refers to data on success stories, lessons learned, and industry trends related to new businesses, obtained from global communication networks and specialized data collections. 【0063】 A "data warehouse" refers to a large-capacity data storage system where pre-processed information is stored and organized for training purposes. 【0064】 A "generative AI model" refers to artificial intelligence technology that learns from collected data and is used for business planning and generating feedback. 【0065】 A "user" refers to anyone who uses this system to input information about their work plan and receives the results. 【0066】 A "virtual investor or collaborator" refers to a fictitious stakeholder simulated within the system to evaluate the generated business plan and provide improvement suggestions. 【0067】 A "terminal" refers to a device used by users to input business plans and receive feedback. 【0068】 This invention relates to a system that automatically generates plans for new businesses and supports entrepreneurs through feedback. This system includes the following elements: 【0069】 The server gathers information by leveraging global communication networks and specialized data aggregations. This information includes success stories and lessons learned from new ventures, which are then cleansed and organized to build a data repository. In this process, the server uses data cleansing tools and web crawlers to extract reliable information. 【0070】 Next, the server trains a generative AI model based on the constructed data warehouse. At this stage, a GPU cluster is used to improve the model's training speed. The generative AI model employs industry-standard natural language processing techniques (e.g., GPT-3® and BERT). This builds a knowledge base that can be used to generate new business plans and feedback. 【0071】 Users input information related to their work into the system via a terminal. The terminal accepts the user's input in an intuitive format and verifies the validity of the input as needed. For example, if a user wants to launch an "environmentally friendly household product" into the market, they can input its features, target market, and challenges. 【0072】 The server inputs the received information into a generating AI model and automatically generates a business plan, referencing similar past cases. This business plan is designed to include business objectives, strategies, and market analysis. An example of a prompt message it accepts is, "Please tell me about your market entry plan for environmentally friendly products." 【0073】 Subsequently, the server generates feedback on the generated business plan from the perspective of virtual investors and collaborators. This feedback is designed to include questions, comments, and suggestions for improvement. Finally, the terminal presents the generated business plan and feedback to the user, providing them with the material to improve the plan. Through this process, users can create higher-quality business plans and deliver more compelling presentations to investors and stakeholders. 【0074】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0075】 Step 1: 【0076】 The server collects information from communication networks and specialized data collections worldwide. Its inputs include multiple data sources (e.g., news sites, research papers, business forums), and its output is a collection of raw, unprocessed data. Specifically, the server runs a web crawler, visiting target sites at specified time intervals to retrieve updated information. 【0077】 Step 2: 【0078】 The server cleanses and organizes the collected data. The input is the raw data collected in step 1, and the output is the cleansed and organized data. In this step, the server uses data cleansing tools to remove noise and unnecessary data, storing only the most relevant information in the data repository. 【0079】 Step 3: 【0080】 The server trains a generative AI model based on a data repository. The input is cleansed and organized data, and the output is the trained generative AI model. Specifically, the server utilizes a GPU cluster to efficiently process large datasets, thereby improving the accuracy of the model. 【0081】 Step 4: 【0082】 Users input information about their work through a terminal. This input includes business characteristics, target markets, and challenges, and this information is then transferred to a server as output. The terminal has a function to verify the validity of the data entered by the user by providing an intuitive UI. 【0083】 Step 5: 【0084】 The server inputs information received from the user into a generating AI model to automatically generate a business plan. The input is the user's business information, and the output is a new business plan. In this step, the server utilizes the model and generates a plan that includes business objectives, strategies, and market analysis, while comparing it with similar past cases. 【0085】 Step 6: 【0086】 The server generates feedback on the automatically generated business plan. This feedback includes questions and comments from the perspectives of virtual investors and collaborators. The input is the business plan, and the output is structured feedback. Specifically, an AI-based rule engine identifies shortcomings and areas for improvement in the plan. 【0087】 Step 7: 【0088】 The terminal presents the generated business plan and feedback to the user. Input is data received from the server, and output is a visual or documentary presentation to the user. The terminal displays the results clearly and supports the user in revising the plan. 【0089】 (Application Example 1) 【0090】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0091】 In today's advertising industry, planning and executing effective advertising campaigns requires complex data analysis and specialized knowledge. However, this is time-consuming and costly, placing a significant burden on small and medium-sized businesses and individual advertisers. Furthermore, while multifaceted feedback is needed to enhance the effectiveness of advertising campaigns, there is a lack of efficient methods for collecting it. 【0092】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0093】 In this invention, the server includes means for collecting information and building a database, means for training a model based on the database, and means for automatically generating plans based on information received from users. As a result, users can automatically generate advertising plans simply by inputting advertising campaign information, and can also receive feedback from virtual consumers and marketing experts. 【0094】 "Means of collecting information" refers to methods for obtaining necessary data through the internet or specialized databases. 【0095】 "Methods for constructing a database" refers to methods for creating a database to organize and efficiently manage collected information. 【0096】 "Methods for training a model" refer to methods for training a generative AI model using a learning algorithm based on a constructed database. 【0097】 "Methods for automatically generating plans" refer to methods that automatically create appropriate plans by analyzing past data based on information provided by the user. 【0098】 "Means of generating feedback" refers to methods of providing evaluations and opinions on automatically generated plans from the perspectives of hypothetical experts or consumers. 【0099】 "Means of providing a user interface" refers to a method of providing a screen for users to input advertising campaign information and interact with the system. 【0100】 "Methods for generating advertising plans" refer to methods that automatically create effective advertising strategies based on entered campaign information. 【0101】 To realize this application, the system will collect new business information and successful advertising campaign case studies from the internet and build a database to manage them efficiently. The server will use a Python-based framework (e.g., Flask or Django) to receive campaign information entered by users and automatically generate advertising plans using a generative AI model based on that information. 【0102】 Specifically, the server uses AI models built with TENSORFLOW® or PyTorch to analyze information from the database and user-provided information. Natural language processing (NLP) techniques are then used to generate feedback from virtual consumers and marketing experts on the automatically generated advertising plans. This process provides strategic suggestions to improve the effectiveness of the advertising. 【0103】 The user's device, such as a smartphone, receives advertising plans and feedback from the server and presents them in an easy-to-understand format. The user can then use this information to improve their advertising campaigns. 【0104】 As a concrete example, consider a case where a user plans a "promotion of health foods for middle-aged and elderly people in a specific region." The system receives input information via the following prompt: 【0105】 "We are planning an advertising campaign for a new health food product. Our target audience is middle-aged and older adults aged 40 to 60, and we will be using TV commercials and web advertising. Our budget is 3,000,000 yen, and the target area is limited to the Kansai region. Please propose an appropriate strategy." 【0106】 This approach allows users to quickly receive actionable advertising strategies, which in turn enables them to conduct more effective advertising campaigns. 【0107】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0108】 Step 1: 【0109】 Users use their devices to enter basic information about their advertising campaign. This information includes target audience, budget, media used, and promotional regions. The entered information is sent to the server in JSON format. Basic validation is performed on the input data to ensure accuracy. 【0110】 Step 2: 【0111】 The server parses the received JSON data and extracts details of the advertising campaign. Based on this extracted data, it interacts with a constructed database to train a generative AI model. Specifically, it matches similar cases in the database and applies machine learning algorithms (e.g., TensorFlow) to detect successful patterns. 【0112】 Step 3: 【0113】 The server uses a trained generative AI model to generate ad plans suitable for the user's advertising campaign. This generative model predicts effective marketing strategies based on historical data. The generated results are output as ad plans in JSON format. 【0114】 Step 4: 【0115】 Based on the generated advertising plan, the server uses natural language processing (NLP) techniques to generate feedback from virtual consumers and marketing experts. Specifically, it extracts potential areas for improvement and points to note regarding the generated plan and puts them into written form as advice from virtual advisors. 【0116】 Step 5: 【0117】 Feedback and advertising plans are sent to the user's device and presented in a visually easy-to-understand format (graphs and lists). Users can then modify and optimize their advertising campaigns based on the suggested plans and feedback. 【0118】 Step 6: 【0119】 Users review the final advertising strategy on their device and, if necessary, send the information back to the server to request further improvements. This resubmission allows the server to update its AI model using the latest information and provide more specific suggestions. 【0120】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0121】 This invention is a system that automatically generates new business plans and provides feedback that takes user emotions into consideration. This system is realized by combining the functions of information gathering, model training, plan generation, emotion recognition, and feedback adjustment. 【0122】 First, the server collects success stories and lessons learned from startups from the internet and specialized data resources. This information is organized based on business areas and success factors and incorporated into a database. The server uses this database to train a generative AI model, completing an algorithm that automatically builds new business plans while learning from past examples. 【0123】 Next, the user enters basic information about their business via a terminal. This information includes product features, target market, and business objectives. Based on this information, the server uses a trained AI model to automatically generate a new business plan. The plan includes a business model, marketing strategy, and competitive analysis. 【0124】 Furthermore, a key feature of this system is the incorporation of an emotion engine. The server recognizes the user's emotions in real time through interaction with the user. This emotion recognition is analyzed from the user's input data and operation patterns, and an emotion profile is generated. 【0125】 For the generated business plan, the server produces feedback from the perspectives of virtual investors and partners. This feedback is adjusted to take into account the user's emotional state. For example, if the user is feeling anxious or stressed, the tone of the feedback will be changed to be more understandable and supportive. On the other hand, if the user is confident, more detailed and challenging improvement suggestions will be presented. 【0126】 Finally, the device presents the user with an improved business plan and emotionally-responsive feedback. The user can then revise the plan based on this feedback, further enhancing its quality. Through this process, the user can develop a more effective business plan and visualize the path to success. 【0127】 Thus, the present invention aims to support the development of new business plans and boost the success of entrepreneurs through feedback that reflects the emotions of users. 【0128】 The following describes the processing flow. 【0129】 Step 1: 【0130】 The server collects success stories and lessons learned from new business ventures from publicly available databases and expert reports on the internet. This information is categorized based on business type and success factors and stored in the database. 【0131】 Step 2: 【0132】 The server trains a generative AI model based on data from the database. This model learns patterns from successful cases and builds algorithms to automatically generate plans for new businesses. 【0133】 Step 3: 【0134】 Users input basic information about their planned business through a terminal. Specifically, they provide information such as the business objectives, target market, and product features. 【0135】 Step 4: 【0136】 The server uses user-provided information and leverages a generative AI model to automatically generate business plans. These plans include key business components such as strategy and market analysis. 【0137】 Step 5: 【0138】 The server uses an emotion engine to analyze input data from the user's device and understand the user's emotional state. In this process, factors such as the speed of operation and the content of input become influences for emotion recognition. 【0139】 Step 6: 【0140】 The server generates feedback on the created business plan from the perspective of virtual investors and partners. The feedback is tailored to the user's emotional state and delivered in a tone appropriate to the user's mental state. 【0141】 Step 7: 【0142】 The device presents the user with an improved business plan and tailored feedback. The user then revises the business plan based on the feedback provided, aiming to improve the quality of its content. 【0143】 Through these steps, users will be able to effectively improve their business plans while receiving emotionally sensitive support. 【0144】 (Example 2) 【0145】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal." 【0146】 In planning new businesses, traditional methods require significant time and effort for information gathering and analysis. Furthermore, the accuracy of the plan depends on the user's subjective opinion, making it difficult to obtain objective and appropriate feedback. Additionally, because plans are developed without considering user emotions, timely advice and support tailored to the user may not be provided, potentially lowering the success rate of the business. 【0147】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0148】 In this invention, the server includes means for collecting information and building a database, means for training a generation AI model, means for automatically generating a plan using prompt statements based on the user's business, and means for recognizing and adjusting the user's feelings towards the generated plan and generating feedback. This makes it possible to automatically generate plans for new businesses objectively and efficiently, and to quickly provide feedback that is adapted to the user's feelings. 【0149】 "Means of information gathering" refers to the process or techniques for collecting and organizing success stories and lessons learned related to new businesses from the internet and specialized data sources. 【0150】 A "database" is an information management system that systematically stores collected information based on business areas and success factors, making it easy to search and analyze. 【0151】 A "generative AI model" refers to a machine learning or artificial intelligence program that includes an algorithm that automatically generates plans for new businesses based on collected data. 【0152】 A "prompt statement" is an instruction text generated by the AI ​​model from user input data, and it functions as a guideline for outputting a concrete business plan. 【0153】 "Means of recognizing emotions" refers to technologies and methods that analyze user operation patterns and input content to determine the user's emotional state in real time. 【0154】 "Means of adjusting feedback" refers to a process or mechanism for providing adaptive feedback that takes into account the user's feelings regarding the generated plan. 【0155】 This invention is an automated system for generating new business plans that takes user emotions into consideration. The system consists of a server, terminals, and a user interface. The following describes each element of the system and its operation in detail. 【0156】 Server Functions and Configuration 【0157】 The server is responsible for collecting information on successful startup cases and lessons learned from the internet and specialized data sources. This information is stored in a database. The server uses this database to train a generative AI model. Specifically, it uses machine learning frameworks such as TensorFlow and PyTorch to train algorithms that build new business plans while learning from past cases. Through this process, the server efficiently processes large amounts of data and generates highly accurate plans. 【0158】 Device functionality and user interaction 【0159】 Users input basic information about their business through a terminal. This information includes product features, target market, and business objectives. The terminal acts as a context, processing user input and sending it to the server. The server generates prompts based on this information and inputs them into a generative AI model. Through this process, users can quickly receive new business plans. 【0160】 Emotion recognition and feedback generation 【0161】 The server uses an emotion engine to recognize the user's emotions in real time. This recognition utilizes the user's input data and behavioral patterns, resulting in the generation of an emotion profile. The server leverages this profile to adaptively adjust feedback. Specifically, if the user is feeling anxious, gentle-toned feedback is provided; if the user is confident, challenging suggestions are offered. 【0162】 Examples of specific cases and prompt statements 【0163】 For example, if a user wants to open a new cafe, they might enter "A cafe offering Scandinavian-style design and a menu using natural ingredients" into the terminal. The prompt would then be something like, "I'm planning to open a new cafe. My target audience is working adults in their 30s, and I will offer Scandinavian-style design and a menu using natural ingredients. Please create a business plan based on this concept." Based on this prompt, the server would provide the user with a suitable business plan through the terminal, presenting an overall picture including feedback. 【0164】 In this way, the present invention is a system that supports users in formulating efficient and appropriate business plans and provides a path to success. 【0165】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0166】 Step 1: 【0167】 The server collects information and builds a database. The server gathers startup success stories and lessons learned from the internet and specialized data resources. Specifically, it uses scraping tools and APIs to collect and filter data. The input to this process is raw data from the internet, and the output is organized, structured data. 【0168】 Step 2: 【0169】 The server trains the generation AI model. The server prepares training data based on the constructed database and trains the AI ​​model using a machine learning framework (e.g., TensorFlow, PyTorch). The input is an organized dataset, and the output is a trained model of the business plan generation algorithm. In this step, the model learns patterns from past cases and improves its prediction accuracy. 【0170】 Step 3: 【0171】 The user inputs business information via a terminal. The user inputs information about their business (e.g., product features, target market, objectives) through the terminal. The input is text data from the user, and the output is data sent to the server. The terminal receives the input through the user interface and appropriately transmits it to the server. 【0172】 Step 4: 【0173】 The server automatically generates business plans. Based on information received from the user, the server generates prompt messages and inputs them into a trained AI model to generate the business plan. The input is prompt messages generated from user information, and the output is text data of the new business plan. This allows the server to quickly provide detailed business plans. 【0174】 Step 5: 【0175】 The server recognizes the user's emotions and generates feedback. The server uses an emotion engine to analyze user actions and inputs to generate an emotion profile. Inputs are user data and action logs, and output is tailored feedback. This process generates feedback that takes the user's emotional state into account, providing advice that leads to improvements in planning. 【0176】 Step 6: 【0177】 The terminal presents feedback to the user. The terminal displays the business plan and feedback sent from the server to the user. The input is feedback data from the server, and the output is the information displayed to the user. The terminal supports the user in reviewing the feedback and making revisions or improvements to the business plan. 【0178】 (Application Example 2) 【0179】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal". 【0180】 In today's entrepreneurial environment, developing a business plan is extremely complex, requiring the analysis of a large amount of information and effective feedback. However, existing systems lack the technology to provide appropriate feedback that takes user sentiment into account. This presents a challenge for entrepreneurs, making it difficult to confidently develop competitive business plans. 【0181】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0182】 In this invention, the server includes means for collecting information and building a database, means for training a model based on the database, means for automatically generating a plan based on information received from the user, means for recognizing and analyzing the user's emotions, means for generating feedback on the automatically generated plan while considering the user's emotional state, and means for presenting the feedback to the user. This enables entrepreneurs to develop more effective and sophisticated business plans while receiving appropriate feedback tailored to their emotions. 【0183】 "Collecting information" means gathering necessary data from the internet or specialized data resources. 【0184】 "Building a database" means organizing collected information and making it into a format that allows for efficient searching and use of the data. 【0185】 "Training a model" means using collected data to train a machine learning algorithm so that it can recognize data patterns. 【0186】 "Automatically generating a plan" means that an AI model automatically creates a business plan based on user input information. 【0187】 "Recognizing and analyzing emotions" means determining the user's emotional state from input data and operation patterns, and then analyzing those emotions. 【0188】 "Generating feedback" means creating opinions and suggestions that reflect the user's emotions regarding the generated plan. 【0189】 "Providing feedback" means displaying the generated feedback to the user to help improve the plan. 【0190】 The system that realizes this invention generates new business plans and provides feedback through interaction between a server, terminals, and users. The server first collects information about startups from the internet and databases, and builds a database based on that information. This database functions as a repository of data, including success stories and lessons learned, and serves as the foundation for training the generative AI model. 【0191】 Next, the user uses a terminal to input necessary information such as product features, target market, and business objectives. Based on this information, the server automatically generates a new business plan using a trained AI model. This plan includes a business model, marketing strategy, and competitive analysis, and is customized according to the user's specific business goals. 【0192】 Furthermore, the server detects and analyzes the user's emotions in real time through an emotion recognition engine. Based on this analysis, feedback on the plan is generated taking the user's emotional state into consideration. The feedback assumes the perspective of virtual investors and collaborators, providing accurate advice and suggestions. 【0193】 Ultimately, the device presents the user with an improved business plan and emotionally appropriate feedback. Through this process, the user can work on further improving the plan. In particular, receiving positive feedback helps users gain confidence, and if they have negative emotions, receiving supportive feedback helps alleviate anxiety and doubts. 【0194】 For example, when a virtual store operator plans a new product line, this system can be used to develop specifications tailored to customer needs and receive feedback. Examples of prompts input to the generating AI model are as follows: 【0195】 "Please generate a new business plan based on the following information: Product features: {product_features}, Target market: {target_market}, Business purpose: {business_purpose}." 【0196】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0197】 Step 1: 【0198】 The server collects startup-related data from the internet and specialized databases. This input data includes success stories and lessons learned. The collected data is placed in a database and organized for use in subsequent processes. 【0199】 Step 2: 【0200】 The server trains the AI ​​model based on the database built in Step 1. This training process adjusts the model to recognize patterns in the collected data and enable the automatic generation of business plans. The output is the trained AI model. 【0201】 Step 3: 【0202】 Users use a terminal to input business information such as product features, target market, and business objectives. This input information is managed digitally because it will be used in the next step. 【0203】 Step 4: 【0204】 The server utilizes the AI ​​model trained in Step 2 to automatically generate a new business plan based on the information entered by the user. For data processing, prompts are input to the AI ​​model, and various data calculations necessary for plan generation are performed. The output is the automatically generated business plan. 【0205】 Step 5: 【0206】 The server uses an emotion recognition engine to analyze the user's emotions. User interaction patterns and input data are input, and an emotion profile is generated in real time. This output is used to generate feedback in the next step. 【0207】 Step 6: 【0208】 The server generates feedback on the automatically generated business plan, taking into account the user's emotional state obtained in step 5. The content is adjusted to reflect the perspectives of hypothetical investors and collaborators. The specific details of the feedback are then output. 【0209】 Step 7: 【0210】 The device presents the user with the improved business plan, along with the feedback generated in step 6. This allows the user to use it as material to further revise and consider their own business plan. 【0211】 The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data. 【0212】 Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0213】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14. 【0214】 [Second Embodiment] 【0215】 Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment. 【0216】 As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server. 【0217】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0218】 The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52. 【0219】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46. 【0220】 Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0221】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner. 【0222】 Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56. 【0223】 The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30. 【0224】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0225】 In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0226】 Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal". 【0227】 This invention provides a system that automatically generates new business plans and supports entrepreneurs by providing feedback. This system is implemented by a program that handles everything from information gathering to plan generation and feedback provision in an integrated manner. 【0228】 First, the server collects information on successful case studies and lessons learned from new business ventures from around the world via the internet and specialized databases. This allows the server to acquire diverse and reliable information, which it then uses to build a database for training. 【0229】 Next, the server trains a generative AI model based on the constructed database. This model performs case analysis based on the collected information, learning success factors and commonalities. In this process, the server updates the model as new cases are added, ensuring that it always provides analysis results based on the latest information. 【0230】 Users input basic information about their business via a terminal. For example, if a user wants to launch an "environmentally friendly home product" into the market, they would input information such as the product's features, target market, and challenges. Based on the information received from the user, the server uses a generation AI model to automatically generate a business plan based on similar past cases. The plan includes business objectives, strategies, and market analysis. 【0231】 Next, the server generates feedback on the created business plan from the perspective of a virtual investor or partner. This feedback includes virtual questions, points of concern, and suggestions for improvement regarding the user's plan, which can be used to revise the business plan. For example, a virtual investor might point out that "the competitive analysis in the target market is insufficient." 【0232】 Finally, the device presents the user with the generated business plan and feedback. Based on this feedback, the user can revise the business plan, improving its completeness and quality. This process allows the user to enhance the quality of their business plan and deliver more compelling presentations to investors and stakeholders. 【0233】 This invention enables a reduction in the time required to launch a new business and an improvement in the probability of success. 【0234】 The following describes the processing flow. 【0235】 Step 1: 【0236】 The server collects information on new business ventures from external sources. Specifically, it obtains information on successful case studies and lessons learned from publicly available databases on the internet, academic papers, and industry reports. 【0237】 Step 2: 【0238】 The server organizes the collected information, extracts the necessary elements, and builds a database. The information is categorized into business areas and success factors, and tagged to improve searchability. 【0239】 Step 3: 【0240】 The server trains a generative AI model based on the constructed database. In this process, the server learns trends and patterns in the dataset and establishes an algorithm for automatically generating business plans. 【0241】 Step 4: 【0242】 Users will use a terminal to input basic information about their business. For example, they might input information such as the characteristics of a new product, the target market, and current challenges. 【0243】 Step 5: 【0244】 The server automatically generates business plans using AI based on information received from users. The AI ​​model provides draft plans that include appropriate business strategies and market analysis, referencing similar past cases. 【0245】 Step 6: 【0246】 The server evaluates the automatically generated business plan from the perspective of a virtual investor or partner and generates feedback. This includes questions and suggestions for improvement regarding the plan, such as "the financial plan is unclear." 【0247】 Step 7: 【0248】 The device presents the user with the generated business plan and feedback. Based on this information, the user revises the plan and improves its quality. 【0249】 This series of steps allows users to quickly and effectively develop business plans and increase their chances of success. 【0250】 (Example 1) 【0251】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0252】 In modern entrepreneurial activities, it is difficult to quickly and effectively develop new business plans and obtain appropriate feedback. This can lead to insufficient quality business plans and missed opportunities to effectively persuade investors and collaborators. In particular, there is a need for means to leverage diverse success stories and lessons learned, automatically create optimal plans using generative AI models, and obtain feedback. 【0253】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0254】 In this invention, the server includes means for collecting information from global communication networks and specialized data aggregates, means for cleansing and organizing the information to construct a data warehouse for learning, and means for training a generative AI model constructed based on the data warehouse. This enables users to quickly create new business plans and receive high-quality feedback on them from the perspective of virtual investors or collaborators. 【0255】 "Information" refers to data on success stories, lessons learned, and industry trends related to new businesses, obtained from global communication networks and specialized data collections. 【0256】 A "data warehouse" refers to a large-capacity data storage system where pre-processed information is stored and organized for training purposes. 【0257】 A "generative AI model" refers to artificial intelligence technology that learns from collected data and is used for business planning and generating feedback. 【0258】 A "user" refers to anyone who uses this system to input information about their work plan and receives the results. 【0259】 A "virtual investor or collaborator" refers to a fictitious stakeholder simulated within the system to evaluate the generated business plan and provide improvement suggestions. 【0260】 A "terminal" refers to a device used by users to input business plans and receive feedback. 【0261】 This invention relates to a system that automatically generates plans for new businesses and supports entrepreneurs through feedback. This system includes the following elements: 【0262】 The server gathers information by leveraging global communication networks and specialized data aggregations. This information includes success stories and lessons learned from new ventures, which are then cleansed and organized to build a data repository. In this process, the server uses data cleansing tools and web crawlers to extract reliable information. 【0263】 Next, the server trains a generative AI model based on the constructed data warehouse. At this stage, a GPU cluster is used to improve the model's training speed. The generative AI model employs a natural language processing technique commonly used in the industry (e.g., GPT-3 or BERT). This builds a knowledge base that can be used to generate new business plans and feedback. 【0264】 Users input information related to their work into the system via a terminal. The terminal accepts the user's input in an intuitive format and verifies the validity of the input as needed. For example, if a user wants to launch an "environmentally friendly household product" into the market, they can input its features, target market, and challenges. 【0265】 The server inputs the received information into a generating AI model and automatically generates a business plan, referencing similar past cases. This business plan is designed to include business objectives, strategies, and market analysis. An example of a prompt message it accepts is, "Please tell me about your market entry plan for environmentally friendly products." 【0266】 Subsequently, the server generates feedback on the generated business plan from the perspective of virtual investors and collaborators. This feedback is designed to include questions, comments, and suggestions for improvement. Finally, the terminal presents the generated business plan and feedback to the user, providing them with the material to improve the plan. Through this process, users can create higher-quality business plans and deliver more compelling presentations to investors and stakeholders. 【0267】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0268】 Step 1: 【0269】 The server collects information from communication networks and specialized data collections worldwide. Its inputs include multiple data sources (e.g., news sites, research papers, business forums), and its output is a collection of raw, unprocessed data. Specifically, the server runs a web crawler, visiting target sites at specified time intervals to retrieve updated information. 【0270】 Step 2: 【0271】 The server cleanses and organizes the collected data. The input is the raw data collected in step 1, and the output is the cleansed and organized data. In this step, the server uses data cleansing tools to remove noise and unnecessary data, storing only the most relevant information in the data repository. 【0272】 Step 3: 【0273】 The server trains a generative AI model based on a data repository. The input is cleansed and organized data, and the output is the trained generative AI model. Specifically, the server utilizes a GPU cluster to efficiently process large datasets, thereby improving the accuracy of the model. 【0274】 Step 4: 【0275】 Users input information about their work through a terminal. This input includes business characteristics, target markets, and challenges, and this information is then transferred to a server as output. The terminal has a function to verify the validity of the data entered by the user by providing an intuitive UI. 【0276】 Step 5: 【0277】 The server inputs information received from the user into a generating AI model to automatically generate a business plan. The input is the user's business information, and the output is a new business plan. In this step, the server utilizes the model and generates a plan that includes business objectives, strategies, and market analysis, while comparing it with similar past cases. 【0278】 Step 6: 【0279】 The server generates feedback on the automatically generated business plan. This feedback includes questions and comments from the perspectives of virtual investors and collaborators. The input is the business plan, and the output is structured feedback. As a specific operation, an AI-based rule engine identifies the drawbacks and improvement points of the plan. 【0280】 Step 7: 【0281】 The terminal presents the generated business plan and feedback to the user. The input is the data received from the server, and the output is a visual or documentary presentation to the user. The terminal displays the results clearly and supports the user in making plan modifications. 【0282】 (Application Example 1) 【0283】 Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal". 【0284】 In the modern advertising industry, complex data analysis and specialized knowledge are required to plan and execute effective advertising campaigns. However, this is time-consuming and costly, posing a significant burden on small and medium-sized enterprises and individual advertisers. Furthermore, to enhance the effectiveness of advertising campaigns, feedback from multiple aspects is required, but there is a lack of efficient methods for collecting it. 【0285】 The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0286】 In this invention, the server includes means for collecting information and constructing a database, means for training a model based on the database, and means for automatically generating a plan based on the information received from the user. As a result, the user can automatically generate an advertising plan by simply inputting advertising campaign information and can also receive feedback from virtual consumers and marketing experts. 【0287】 The "means for collecting information" is a method for acquiring necessary data through the Internet or specialized databases. 【0288】 The "means for constructing a database" is a method for generating a database for organizing and efficiently managing the collected information. 【0289】 The "means for training a model" is a method for training a generated AI model using a learning algorithm based on the constructed database. 【0290】 The "means for automatically generating a plan" is a method for automatically creating an appropriate plan by analyzing past data based on the information provided by the user. 【0291】 The "means for generating feedback" is a method for providing evaluations and opinions from the perspectives of virtual experts or consumers with respect to the automatically generated plan. 【0292】 The "means for providing a user interface" is a method for providing an operation screen for the user to input advertising campaign information and interact with the system. 【0293】 The "means for generating an advertising plan" is a method for automatically creating an effective advertising strategy based on the input campaign information. 【0294】 To realize this application example, the system collects new business information and successful cases related to advertising campaigns from the Internet and constructs a database for efficiently managing them. The server uses a Python-based framework (e.g., Flask or Django) to receive the campaign information input by the user and automatically generate an advertising plan by utilizing the generated AI model based on that information. 【0295】 Specifically, the server uses AI models built with TensorFlow or PyTorch to analyze information from the database and user-provided data. Natural language processing (NLP) techniques are then used to generate feedback from virtual consumers and marketing experts on automatically generated advertising plans. This process provides strategic suggestions to improve the effectiveness of the advertisements. 【0296】 The user's device, such as a smartphone, receives advertising plans and feedback from the server and presents them in an easy-to-understand format. The user can then use this information to improve their advertising campaigns. 【0297】 As a concrete example, consider a case where a user plans a "promotion of health foods for middle-aged and elderly people in a specific region." The system receives input information via the following prompt: 【0298】 "We are planning an advertising campaign for a new health food product. Our target audience is middle-aged and older adults aged 40 to 60, and we will be using TV commercials and web advertising. Our budget is 3,000,000 yen, and the target area is limited to the Kansai region. Please propose an appropriate strategy." 【0299】 This approach allows users to quickly receive actionable advertising strategies, which in turn enables them to conduct more effective advertising campaigns. 【0300】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0301】 Step 1: 【0302】 Users use their devices to enter basic information about their advertising campaign. This information includes target audience, budget, media used, and promotional regions. The entered information is sent to the server in JSON format. Basic validation is performed on the input data to ensure accuracy. 【0303】 Step 2: 【0304】 The server analyzes the received JSON data and extracts the details of the advertising campaign. Based on the extracted data, it cooperates with the constructed database to train the generated AI model. Specifically, it matches similar cases in the database and applies machine learning algorithms (e.g., TensorFlow) to detect successful patterns. 【0305】 Step 3: 【0306】 The server uses the trained generated AI model to generate an advertising plan suitable for the user's advertising campaign. The generation model used here predicts effective marketing strategies based on past data. The generation result is generated in JSON format as an advertising plan. 【0307】 Step 4: 【0308】 Based on the generated advertising plan, the server uses natural language processing (NLP) technology to generate feedback from virtual consumers and marketing experts. Specifically, it extracts possible improvement points and precautions for the generated plan and formulates them as advice from a virtual advisor. 【0309】 Step 5: 【0310】 The feedback and the advertising plan are sent to the user's terminal and presented in a visually understandable form (graphs or lists). The user can modify and optimize the advertising campaign based on the proposed plan and feedback. 【0311】 <000098​​​​Users review the final advertising strategy on their device and, if necessary, send the information back to the server to request further improvements. This resubmission allows the server to update its AI model using the latest information and provide more specific suggestions. 【0313】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0314】 This invention is a system that automatically generates new business plans and provides feedback that takes user emotions into consideration. This system is realized by combining the functions of information gathering, model training, plan generation, emotion recognition, and feedback adjustment. 【0315】 First, the server collects success stories and lessons learned from startups from the internet and specialized data resources. This information is organized based on business areas and success factors and incorporated into a database. The server uses this database to train a generative AI model, completing an algorithm that automatically builds new business plans while learning from past examples. 【0316】 Next, the user enters basic information about their business via a terminal. This information includes product features, target market, and business objectives. Based on this information, the server uses a trained AI model to automatically generate a new business plan. The plan includes a business model, marketing strategy, and competitive analysis. 【0317】 Furthermore, a key feature of this system is the incorporation of an emotion engine. The server recognizes the user's emotions in real time through interaction with the user. This emotion recognition is analyzed from the user's input data and operation patterns, and an emotion profile is generated. 【0318】 For the generated business plan, the server produces feedback from the perspectives of virtual investors and partners. This feedback is adjusted to take into account the user's emotional state. For example, if the user is feeling anxious or stressed, the tone of the feedback will be changed to be more understandable and supportive. On the other hand, if the user is confident, more detailed and challenging improvement suggestions will be presented. 【0319】 Finally, the device presents the user with an improved business plan and emotionally-responsive feedback. The user can then revise the plan based on this feedback, further enhancing its quality. Through this process, the user can develop a more effective business plan and visualize the path to success. 【0320】 Thus, the present invention aims to support the development of new business plans and boost the success of entrepreneurs through feedback that reflects the emotions of users. 【0321】 The following describes the processing flow. 【0322】 Step 1: 【0323】 The server collects success stories and lessons learned from new business ventures from publicly available databases and expert reports on the internet. This information is categorized based on business type and success factors and stored in the database. 【0324】 Step 2: 【0325】 The server trains a generative AI model based on data from the database. This model learns patterns from successful cases and builds algorithms to automatically generate plans for new businesses. 【0326】 Step 3: 【0327】 Users input basic information about their planned business through a terminal. Specifically, they provide information such as the business objectives, target market, and product features. 【0328】 Step 4: 【0329】 The server uses user-provided information and leverages a generative AI model to automatically generate business plans. These plans include key business components such as strategy and market analysis. 【0330】 Step 5: 【0331】 The server uses an emotion engine to analyze input data from the user's device and understand the user's emotional state. In this process, factors such as the speed of operation and the content of input become influences for emotion recognition. 【0332】 Step 6: 【0333】 The server generates feedback on the created business plan from the perspective of virtual investors and partners. The feedback is tailored to the user's emotional state and delivered in a tone appropriate to the user's mental state. 【0334】 Step 7: 【0335】 The device presents the user with an improved business plan and tailored feedback. The user then revises the business plan based on the feedback provided, aiming to improve the quality of its content. 【0336】 Through these steps, users will be able to effectively improve their business plans while receiving emotionally sensitive support. 【0337】 (Example 2) 【0338】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal". 【0339】 In planning new businesses, traditional methods require significant time and effort for information gathering and analysis. Furthermore, the accuracy of the plan depends on the user's subjective opinion, making it difficult to obtain objective and appropriate feedback. Additionally, because plans are developed without considering user emotions, timely advice and support tailored to the user may not be provided, potentially lowering the success rate of the business. 【0340】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0341】 In this invention, the server includes means for collecting information and building a database, means for training a generation AI model, means for automatically generating a plan using prompt statements based on the user's business, and means for recognizing and adjusting the user's feelings towards the generated plan and generating feedback. This makes it possible to automatically generate plans for new businesses objectively and efficiently, and to quickly provide feedback that is adapted to the user's feelings. 【0342】 "Means of information gathering" refers to the process or techniques for collecting and organizing success stories and lessons learned related to new businesses from the internet and specialized data sources. 【0343】 A "database" is an information management system that systematically stores collected information based on business areas and success factors, making it easy to search and analyze. 【0344】 A "generative AI model" refers to a machine learning or artificial intelligence program that includes an algorithm that automatically generates plans for new businesses based on collected data. 【0345】 A "prompt statement" is an instruction text generated by the AI ​​model from user input data, and it functions as a guideline for outputting a concrete business plan. 【0346】 "Means of recognizing emotions" refers to technologies and methods that analyze user operation patterns and input content to determine the user's emotional state in real time. 【0347】 "Means of adjusting feedback" refers to a process or mechanism for providing adaptive feedback that takes into account the user's feelings regarding the generated plan. 【0348】 This invention is an automated system for generating new business plans that takes user emotions into consideration. The system consists of a server, terminals, and a user interface. The following describes each element of the system and its operation in detail. 【0349】 Server Functions and Configuration 【0350】 The server is responsible for collecting information on successful startup cases and lessons learned from the internet and specialized data sources. This information is stored in a database. The server uses this database to train a generative AI model. Specifically, it uses machine learning frameworks such as TensorFlow and PyTorch to train algorithms that build new business plans while learning from past cases. Through this process, the server efficiently processes large amounts of data and generates highly accurate plans. 【0351】 Device functionality and user interaction 【0352】 Users input basic information about their business through a terminal. This information includes product features, target market, and business objectives. The terminal acts as a context, processing user input and sending it to the server. The server generates prompts based on this information and inputs them into a generative AI model. Through this process, users can quickly receive new business plans. 【0353】 Emotion recognition and feedback generation 【0354】 The server uses an emotion engine to recognize the user's emotions in real time. This recognition utilizes the user's input data and behavioral patterns, resulting in the generation of an emotion profile. The server leverages this profile to adaptively adjust feedback. Specifically, if the user is feeling anxious, gentle-toned feedback is provided; if the user is confident, challenging suggestions are offered. 【0355】 Examples of specific cases and prompt statements 【0356】 For example, if a user wants to open a new cafe, they might enter "A cafe offering Scandinavian-style design and a menu using natural ingredients" into the terminal. The prompt would then be something like, "I'm planning to open a new cafe. My target audience is working adults in their 30s, and I will offer Scandinavian-style design and a menu using natural ingredients. Please create a business plan based on this concept." Based on this prompt, the server would provide the user with a suitable business plan through the terminal, presenting an overall picture including feedback. 【0357】 In this way, the present invention is a system that supports users in formulating efficient and appropriate business plans and provides a path to success. 【0358】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0359】 Step 1: 【0360】 The server collects information and builds a database. The server gathers startup success stories and lessons learned from the internet and specialized data resources. Specifically, it uses scraping tools and APIs to collect and filter data. The input to this process is raw data from the internet, and the output is organized, structured data. 【0361】 Step 2: 【0362】 The server trains the generation AI model. The server prepares training data based on the constructed database and trains the AI ​​model using a machine learning framework (e.g., TensorFlow, PyTorch). The input is an organized dataset, and the output is a trained model of the business plan generation algorithm. In this step, the model learns patterns from past cases and improves its prediction accuracy. 【0363】 Step 3: 【0364】 The user inputs business information via a terminal. The user inputs information about their business (e.g., product features, target market, objectives) through the terminal. The input is text data from the user, and the output is data sent to the server. The terminal receives the input through the user interface and appropriately transmits it to the server. 【0365】 Step 4: 【0366】 The server automatically generates business plans. Based on information received from the user, the server generates prompt messages and inputs them into a trained AI model to generate the business plan. The input is prompt messages generated from user information, and the output is text data of the new business plan. This allows the server to quickly provide detailed business plans. 【0367】 Step 5: 【0368】 The server recognizes the user's emotions and generates feedback. The server uses an emotion engine to analyze user actions and inputs to generate an emotion profile. Inputs are user data and action logs, and output is tailored feedback. This process generates feedback that takes the user's emotional state into account, providing advice that leads to improvements in planning. 【0369】 Step 6: 【0370】 The terminal presents feedback to the user. The terminal displays the business plan and feedback sent from the server to the user. The input is feedback data from the server, and the output is the information displayed to the user. The terminal supports the user in reviewing the feedback and making revisions or improvements to the business plan. 【0371】 (Application Example 2) 【0372】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal." 【0373】 In today's entrepreneurial environment, developing a business plan is extremely complex, requiring the analysis of a large amount of information and effective feedback. However, existing systems lack the technology to provide appropriate feedback that takes user sentiment into account. This presents a challenge for entrepreneurs, making it difficult to confidently develop competitive business plans. 【0374】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0375】 In this invention, the server includes means for collecting information and building a database, means for training a model based on the database, means for automatically generating a plan based on information received from the user, means for recognizing and analyzing the user's emotions, means for generating feedback on the automatically generated plan while considering the user's emotional state, and means for presenting the feedback to the user. This enables entrepreneurs to develop more effective and sophisticated business plans while receiving appropriate feedback tailored to their emotions. 【0376】 "Collecting information" means gathering necessary data from the internet or specialized data resources. 【0377】 "Building a database" means organizing collected information and making it into a format that allows for efficient searching and use of the data. 【0378】 "Training a model" means using collected data to train a machine learning algorithm so that it can recognize data patterns. 【0379】 "Automatically generating a plan" means that an AI model automatically creates a business plan based on user input information. 【0380】 "Recognizing and analyzing emotions" means determining the user's emotional state from input data and operation patterns, and then analyzing those emotions. 【0381】 "Generating feedback" means creating opinions and suggestions that reflect the user's emotions regarding the generated plan. 【0382】 "Providing feedback" means displaying the generated feedback to the user to help improve the plan. 【0383】 The system that realizes this invention generates new business plans and provides feedback through interaction between a server, terminals, and users. The server first collects information about startups from the internet and databases, and builds a database based on that information. This database functions as a repository of data, including success stories and lessons learned, and serves as the foundation for training the generative AI model. 【0384】 Next, the user uses a terminal to input necessary information such as product features, target market, and business objectives. Based on this information, the server automatically generates a new business plan using a trained AI model. This plan includes a business model, marketing strategy, and competitive analysis, and is customized according to the user's specific business goals. 【0385】 Furthermore, the server detects and analyzes the user's emotions in real time through an emotion recognition engine. Based on this analysis, feedback on the plan is generated taking the user's emotional state into consideration. The feedback assumes the perspective of virtual investors and collaborators, providing accurate advice and suggestions. 【0386】 Ultimately, the device presents the user with an improved business plan and emotionally appropriate feedback. Through this process, the user can work on further improving the plan. In particular, receiving positive feedback helps users gain confidence, and if they have negative emotions, receiving supportive feedback helps alleviate anxiety and doubts. 【0387】 For example, when a virtual store operator plans a new product line, this system can be used to develop specifications tailored to customer needs and receive feedback. Examples of prompts input to the generating AI model are as follows: 【0388】 "Please generate a new business plan based on the following information: Product features: {product_features}, Target market: {target_market}, Business purpose: {business_purpose}." 【0389】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0390】 Step 1: 【0391】 The server collects startup-related data from the internet and specialized databases. This input data includes success stories and lessons learned. The collected data is placed in a database and organized for use in subsequent processes. 【0392】 Step 2: 【0393】 The server trains the AI ​​model based on the database built in Step 1. This training process adjusts the model to recognize patterns in the collected data and enable the automatic generation of business plans. The output is the trained AI model. 【0394】 Step 3: 【0395】 Users use a terminal to input business information such as product features, target market, and business objectives. This input information is managed digitally because it will be used in the next step. 【0396】 Step 4: 【0397】 The server utilizes the AI ​​model trained in Step 2 to automatically generate a new business plan based on the information entered by the user. For data processing, prompts are input to the AI ​​model, and various data calculations necessary for plan generation are performed. The output is the automatically generated business plan. 【0398】 Step 5: 【0399】 The server uses an emotion recognition engine to analyze the user's emotions. User interaction patterns and input data are input, and an emotion profile is generated in real time. This output is used to generate feedback in the next step. 【0400】 Step 6: 【0401】 The server generates feedback on the automatically generated business plan, taking into account the user's emotional state obtained in step 5. The content is adjusted to reflect the perspectives of hypothetical investors and collaborators. The specific details of the feedback are then output. 【0402】 Step 7: 【0403】 The device presents the user with the improved business plan, along with the feedback generated in step 6. This allows the user to use it as material to further revise and consider their own business plan. 【0404】 The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data. 【0405】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0406】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214. 【0407】 [Third Embodiment] 【0408】 Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment. 【0409】 As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server. 【0410】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0411】 The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52. 【0412】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46. 【0413】 Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0414】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner. 【0415】 Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56. 【0416】 The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30. 【0417】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0418】 In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0419】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal". 【0420】 This invention provides a system that automatically generates new business plans and supports entrepreneurs by providing feedback. This system is implemented by a program that handles everything from information gathering to plan generation and feedback provision in an integrated manner. 【0421】 First, the server collects information on successful case studies and lessons learned from new business ventures from around the world via the internet and specialized databases. This allows the server to acquire diverse and reliable information, which it then uses to build a database for training. 【0422】 Next, the server trains a generative AI model based on the constructed database. This model performs case analysis based on the collected information, learning success factors and commonalities. In this process, the server updates the model as new cases are added, ensuring that it always provides analysis results based on the latest information. 【0423】 Users input basic information about their business via a terminal. For example, if a user wants to launch an "environmentally friendly home product" into the market, they would input information such as the product's features, target market, and challenges. Based on the information received from the user, the server uses a generation AI model to automatically generate a business plan based on similar past cases. The plan includes business objectives, strategies, and market analysis. 【0424】 Next, the server generates feedback on the created business plan from the perspective of a virtual investor or partner. This feedback includes virtual questions, points of concern, and suggestions for improvement regarding the user's plan, which can be used to revise the business plan. For example, a virtual investor might point out that "the competitive analysis in the target market is insufficient." 【0425】 Finally, the device presents the user with the generated business plan and feedback. Based on this feedback, the user can revise the business plan, improving its completeness and quality. This process allows the user to enhance the quality of their business plan and deliver more compelling presentations to investors and stakeholders. 【0426】 This invention enables a reduction in the time required to launch a new business and an improvement in the probability of success. 【0427】 The following describes the processing flow. 【0428】 Step 1: 【0429】 The server collects information on new business ventures from external sources. Specifically, it obtains information on successful case studies and lessons learned from publicly available databases on the internet, academic papers, and industry reports. 【0430】 Step 2: 【0431】 The server organizes the collected information, extracts the necessary elements, and builds a database. The information is categorized into business areas and success factors, and tagged to improve searchability. 【0432】 Step 3: 【0433】 The server trains a generative AI model based on the constructed database. In this process, the server learns trends and patterns in the dataset and establishes an algorithm for automatically generating business plans. 【0434】 Step 4: 【0435】 Users will use a terminal to input basic information about their business. For example, they might input information such as the characteristics of a new product, the target market, and current challenges. 【0436】 Step 5: 【0437】 The server automatically generates business plans using AI based on information received from users. The AI ​​model provides draft plans that include appropriate business strategies and market analysis, referencing similar past cases. 【0438】 Step 6: 【0439】 The server evaluates the automatically generated business plan from the perspective of a virtual investor or partner and generates feedback. This includes questions and suggestions for improvement regarding the plan, such as "the financial plan is unclear." 【0440】 Step 7: 【0441】 The device presents the user with the generated business plan and feedback. Based on this information, the user revises the plan and improves its quality. 【0442】 This series of steps allows users to quickly and effectively develop business plans and increase their chances of success. 【0443】 (Example 1) 【0444】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0445】 In modern entrepreneurial activities, it is difficult to quickly and effectively develop new business plans and obtain appropriate feedback. This can lead to insufficient quality business plans and missed opportunities to effectively persuade investors and collaborators. In particular, there is a need for means to leverage diverse success stories and lessons learned, automatically create optimal plans using generative AI models, and obtain feedback. 【0446】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0447】 In this invention, the server includes means for collecting information from global communication networks and specialized data aggregates, means for cleansing and organizing the information to construct a data warehouse for learning, and means for training a generative AI model constructed based on the data warehouse. This enables users to quickly create new business plans and receive high-quality feedback on them from the perspective of virtual investors or collaborators. 【0448】 "Information" refers to data on success stories, lessons learned, and industry trends related to new businesses, obtained from global communication networks and specialized data collections. 【0449】 A "data warehouse" refers to a large-capacity data storage system where pre-processed information is stored and organized for training purposes. 【0450】 A "generative AI model" refers to artificial intelligence technology that learns from collected data and is used for business planning and generating feedback. 【0451】 A "user" refers to anyone who uses this system to input information about their work plan and receives the results. 【0452】 A "virtual investor or collaborator" refers to a fictitious stakeholder simulated within the system to evaluate the generated business plan and provide improvement suggestions. 【0453】 A "terminal" refers to a device used by users to input business plans and receive feedback. 【0454】 This invention relates to a system that automatically generates plans for new businesses and supports entrepreneurs through feedback. This system includes the following elements: 【0455】 The server gathers information by leveraging global communication networks and specialized data aggregations. This information includes success stories and lessons learned from new ventures, which are then cleansed and organized to build a data repository. In this process, the server uses data cleansing tools and web crawlers to extract reliable information. 【0456】 Next, the server trains a generative AI model based on the constructed data warehouse. At this stage, a GPU cluster is used to improve the model's training speed. The generative AI model employs a natural language processing technique commonly used in the industry (e.g., GPT-3 or BERT). This builds a knowledge base that can be used to generate new business plans and feedback. 【0457】 Users input information related to their work into the system via a terminal. The terminal accepts the user's input in an intuitive format and verifies the validity of the input as needed. For example, if a user wants to launch an "environmentally friendly household product" into the market, they can input its features, target market, and challenges. 【0458】 The server inputs the received information into a generating AI model and automatically generates a business plan, referencing similar past cases. This business plan is designed to include business objectives, strategies, and market analysis. An example of a prompt message it accepts is, "Please tell me about your market entry plan for environmentally friendly products." 【0459】 Subsequently, the server generates feedback on the generated business plan from the perspective of virtual investors and collaborators. This feedback is designed to include questions, comments, and suggestions for improvement. Finally, the terminal presents the generated business plan and feedback to the user, providing them with the material to improve the plan. Through this process, users can create higher-quality business plans and deliver more compelling presentations to investors and stakeholders. 【0460】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0461】 Step 1: 【0462】 The server collects information from communication networks and specialized data collections worldwide. Its inputs include multiple data sources (e.g., news sites, research papers, business forums), and its output is a collection of raw, unprocessed data. Specifically, the server runs a web crawler, visiting target sites at specified time intervals to retrieve updated information. 【0463】 Step 2: 【0464】 The server cleanses and organizes the collected data. The input is the raw data collected in step 1, and the output is the cleansed and organized data. In this step, the server uses data cleansing tools to remove noise and unnecessary data, storing only the most relevant information in the data repository. 【0465】 Step 3: 【0466】 The server trains a generative AI model based on a data repository. The input is cleansed and organized data, and the output is the trained generative AI model. Specifically, the server utilizes a GPU cluster to efficiently process large datasets, thereby improving the accuracy of the model. 【0467】 Step 4: 【0468】 Users input information about their work through a terminal. This input includes business characteristics, target markets, and challenges, and this information is then transferred to a server as output. The terminal has a function to verify the validity of the data entered by the user by providing an intuitive UI. 【0469】 Step 5: 【0470】 The server inputs information received from the user into a generating AI model to automatically generate a business plan. The input is the user's business information, and the output is a new business plan. In this step, the server utilizes the model and generates a plan that includes business objectives, strategies, and market analysis, while comparing it with similar past cases. 【0471】 Step 6: 【0472】 The server generates feedback on the automatically generated business plan. This feedback includes questions and comments from the perspectives of virtual investors and collaborators. The input is the business plan, and the output is structured feedback. Specifically, an AI-based rule engine identifies shortcomings and areas for improvement in the plan. 【0473】 Step 7: 【0474】 The terminal presents the generated business plan and feedback to the user. Input is data received from the server, and output is a visual or documentary presentation to the user. The terminal displays the results clearly and supports the user in revising the plan. 【0475】 (Application Example 1) 【0476】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0477】 In today's advertising industry, planning and executing effective advertising campaigns requires complex data analysis and specialized knowledge. However, this is time-consuming and costly, placing a significant burden on small and medium-sized businesses and individual advertisers. Furthermore, while multifaceted feedback is needed to enhance the effectiveness of advertising campaigns, there is a lack of efficient methods for collecting it. 【0478】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0479】 In this invention, the server includes means for collecting information and building a database, means for training a model based on the database, and means for automatically generating plans based on information received from users. As a result, users can automatically generate advertising plans simply by inputting advertising campaign information, and can also receive feedback from virtual consumers and marketing experts. 【0480】 "Means of collecting information" refers to methods for obtaining necessary data through the internet or specialized databases. 【0481】 "Methods for constructing a database" refers to methods for creating a database to organize and efficiently manage collected information. 【0482】 "Methods for training a model" refer to methods for training a generative AI model using a learning algorithm based on a constructed database. 【0483】 "Methods for automatically generating plans" refer to methods that automatically create appropriate plans by analyzing past data based on information provided by the user. 【0484】 "Means of generating feedback" refers to methods of providing evaluations and opinions on automatically generated plans from the perspectives of hypothetical experts or consumers. 【0485】 "Means of providing a user interface" refers to a method of providing a screen for users to input advertising campaign information and interact with the system. 【0486】 "Methods for generating advertising plans" refer to methods that automatically create effective advertising strategies based on entered campaign information. 【0487】 To realize this application, the system will collect new business information and successful advertising campaign case studies from the internet and build a database to manage them efficiently. The server will use a Python-based framework (e.g., Flask or Django) to receive campaign information entered by users and automatically generate advertising plans using a generative AI model based on that information. 【0488】 Specifically, the server uses AI models built with TensorFlow or PyTorch to analyze information from the database and user-provided data. Natural language processing (NLP) techniques are then used to generate feedback from virtual consumers and marketing experts on automatically generated advertising plans. This process provides strategic suggestions to improve the effectiveness of the advertisements. 【0489】 The user's device, such as a smartphone, receives advertising plans and feedback from the server and presents them in an easy-to-understand format. The user can then use this information to improve their advertising campaigns. 【0490】 As a concrete example, consider a case where a user plans a "promotion of health foods for middle-aged and elderly people in a specific region." The system receives input information via the following prompt: 【0491】 "We are planning an advertising campaign for a new health food product. Our target audience is middle-aged and older adults aged 40 to 60, and we will be using TV commercials and web advertising. Our budget is 3,000,000 yen, and the target area is limited to the Kansai region. Please propose an appropriate strategy." 【0492】 This approach allows users to quickly receive actionable advertising strategies, which in turn enables them to conduct more effective advertising campaigns. 【0493】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0494】 Step 1: 【0495】 Users use their devices to enter basic information about their advertising campaign. This information includes target audience, budget, media used, and promotional regions. The entered information is sent to the server in JSON format. Basic validation is performed on the input data to ensure accuracy. 【0496】 Step 2: 【0497】 The server parses the received JSON data and extracts details of the advertising campaign. Based on this extracted data, it interacts with a constructed database to train a generative AI model. Specifically, it matches similar cases in the database and applies machine learning algorithms (e.g., TensorFlow) to detect successful patterns. 【0498】 Step 3: 【0499】 The server uses a trained generative AI model to generate ad plans suitable for the user's advertising campaign. This generative model predicts effective marketing strategies based on historical data. The generated results are output as ad plans in JSON format. 【0500】 Step 4: 【0501】 Based on the generated advertising plan, the server uses natural language processing (NLP) techniques to generate feedback from virtual consumers and marketing experts. Specifically, it extracts potential areas for improvement and points to note regarding the generated plan and puts them into written form as advice from virtual advisors. 【0502】 Step 5: 【0503】 Feedback and advertising plans are sent to the user's device and presented in a visually easy-to-understand format (graphs and lists). Users can then modify and optimize their advertising campaigns based on the suggested plans and feedback. 【0504】 Step 6: 【0505】 Users review the final advertising strategy on their device and, if necessary, send the information back to the server to request further improvements. This resubmission allows the server to update its AI model using the latest information and provide more specific suggestions. 【0506】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0507】 This invention is a system that automatically generates new business plans and provides feedback that takes user emotions into consideration. This system is realized by combining the functions of information gathering, model training, plan generation, emotion recognition, and feedback adjustment. 【0508】 First, the server collects success stories and lessons learned from startups from the internet and specialized data resources. This information is organized based on business areas and success factors and incorporated into a database. The server uses this database to train a generative AI model, completing an algorithm that automatically builds new business plans while learning from past examples. 【0509】 Next, the user enters basic information about their business via a terminal. This information includes product features, target market, and business objectives. Based on this information, the server uses a trained AI model to automatically generate a new business plan. The plan includes a business model, marketing strategy, and competitive analysis. 【0510】 Furthermore, a key feature of this system is the incorporation of an emotion engine. The server recognizes the user's emotions in real time through interaction with the user. This emotion recognition is analyzed from the user's input data and operation patterns, and an emotion profile is generated. 【0511】 For the generated business plan, the server produces feedback from the perspectives of virtual investors and partners. This feedback is adjusted to take into account the user's emotional state. For example, if the user is feeling anxious or stressed, the tone of the feedback will be changed to be more understandable and supportive. On the other hand, if the user is confident, more detailed and challenging improvement suggestions will be presented. 【0512】 Finally, the device presents the user with an improved business plan and emotionally-responsive feedback. The user can then revise the plan based on this feedback, further enhancing its quality. Through this process, the user can develop a more effective business plan and visualize the path to success. 【0513】 Thus, the present invention aims to support the development of new business plans and boost the success of entrepreneurs through feedback that reflects the emotions of users. 【0514】 The following describes the processing flow. 【0515】 Step 1: 【0516】 The server collects success stories and lessons learned from new business ventures from publicly available databases and expert reports on the internet. This information is categorized based on business type and success factors and stored in the database. 【0517】 Step 2: 【0518】 The server trains a generative AI model based on data from the database. This model learns patterns from successful cases and builds algorithms to automatically generate plans for new businesses. 【0519】 Step 3: 【0520】 Users input basic information about their planned business through a terminal. Specifically, they provide information such as the business objectives, target market, and product features. 【0521】 Step 4: 【0522】 The server uses user-provided information and leverages a generative AI model to automatically generate business plans. These plans include key business components such as strategy and market analysis. 【0523】 Step 5: 【0524】 The server uses an emotion engine to analyze input data from the user's device and understand the user's emotional state. In this process, factors such as the speed of operation and the content of input become influences for emotion recognition. 【0525】 Step 6: 【0526】 The server generates feedback on the created business plan from the perspective of virtual investors and partners. The feedback is tailored to the user's emotional state and delivered in a tone appropriate to the user's mental state. 【0527】 Step 7: 【0528】 The device presents the user with an improved business plan and tailored feedback. The user then revises the business plan based on the feedback provided, aiming to improve the quality of its content. 【0529】 Through these steps, users will be able to effectively improve their business plans while receiving emotionally sensitive support. 【0530】 (Example 2) 【0531】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0532】 In planning new businesses, traditional methods require significant time and effort for information gathering and analysis. Furthermore, the accuracy of the plan depends on the user's subjective opinion, making it difficult to obtain objective and appropriate feedback. Additionally, because plans are developed without considering user emotions, timely advice and support tailored to the user may not be provided, potentially lowering the success rate of the business. 【0533】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0534】 In this invention, the server includes means for collecting information and building a database, means for training a generation AI model, means for automatically generating a plan using prompt statements based on the user's business, and means for recognizing and adjusting the user's feelings towards the generated plan and generating feedback. This makes it possible to automatically generate plans for new businesses objectively and efficiently, and to quickly provide feedback that is adapted to the user's feelings. 【0535】 "Means of information gathering" refers to the process or techniques for collecting and organizing success stories and lessons learned related to new businesses from the internet and specialized data sources. 【0536】 A "database" is an information management system that systematically stores collected information based on business areas and success factors, making it easy to search and analyze. 【0537】 A "generative AI model" refers to a machine learning or artificial intelligence program that includes an algorithm that automatically generates plans for new businesses based on collected data. 【0538】 A "prompt statement" is an instruction text generated by the AI ​​model from user input data, and it functions as a guideline for outputting a concrete business plan. 【0539】 "Means of recognizing emotions" refers to technologies and methods that analyze user operation patterns and input content to determine the user's emotional state in real time. 【0540】 "Means of adjusting feedback" refers to a process or mechanism for providing adaptive feedback that takes into account the user's feelings regarding the generated plan. 【0541】 This invention is an automated system for generating new business plans that takes user emotions into consideration. The system consists of a server, terminals, and a user interface. The following describes each element of the system and its operation in detail. 【0542】 Server Functions and Configuration 【0543】 The server is responsible for collecting information on successful startup cases and lessons learned from the internet and specialized data sources. This information is stored in a database. The server uses this database to train a generative AI model. Specifically, it uses machine learning frameworks such as TensorFlow and PyTorch to train algorithms that build new business plans while learning from past cases. Through this process, the server efficiently processes large amounts of data and generates highly accurate plans. 【0544】 Device functionality and user interaction 【0545】 Users input basic information about their business through a terminal. This information includes product features, target market, and business objectives. The terminal acts as a context, processing user input and sending it to the server. The server generates prompts based on this information and inputs them into a generative AI model. Through this process, users can quickly receive new business plans. 【0546】 Emotion recognition and feedback generation 【0547】 The server uses an emotion engine to recognize the user's emotions in real time. This recognition utilizes the user's input data and behavioral patterns, resulting in the generation of an emotion profile. The server leverages this profile to adaptively adjust feedback. Specifically, if the user is feeling anxious, gentle-toned feedback is provided; if the user is confident, challenging suggestions are offered. 【0548】 Examples of specific cases and prompt statements 【0549】 For example, if a user wants to open a new cafe, they might enter "A cafe offering Scandinavian-style design and a menu using natural ingredients" into the terminal. The prompt would then be something like, "I'm planning to open a new cafe. My target audience is working adults in their 30s, and I will offer Scandinavian-style design and a menu using natural ingredients. Please create a business plan based on this concept." Based on this prompt, the server would provide the user with a suitable business plan through the terminal, presenting an overall picture including feedback. 【0550】 In this way, the present invention is a system that supports users in formulating efficient and appropriate business plans and provides a path to success. 【0551】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0552】 Step 1: 【0553】 The server collects information and builds a database. The server gathers startup success stories and lessons learned from the internet and specialized data resources. Specifically, it uses scraping tools and APIs to collect and filter data. The input to this process is raw data from the internet, and the output is organized, structured data. 【0554】 Step 2: 【0555】 The server trains the generation AI model. The server prepares training data based on the constructed database and trains the AI ​​model using a machine learning framework (e.g., TensorFlow, PyTorch). The input is an organized dataset, and the output is a trained model of the business plan generation algorithm. In this step, the model learns patterns from past cases and improves its prediction accuracy. 【0556】 Step 3: 【0557】 The user inputs business information via a terminal. The user inputs information about their business (e.g., product features, target market, objectives) through the terminal. The input is text data from the user, and the output is data sent to the server. The terminal receives the input through the user interface and appropriately transmits it to the server. 【0558】 Step 4: 【0559】 The server automatically generates business plans. Based on information received from the user, the server generates prompt messages and inputs them into a trained AI model to generate the business plan. The input is prompt messages generated from user information, and the output is text data of the new business plan. This allows the server to quickly provide detailed business plans. 【0560】 Step 5: 【0561】 The server recognizes the user's emotions and generates feedback. The server uses an emotion engine to analyze user actions and inputs to generate an emotion profile. Inputs are user data and action logs, and output is tailored feedback. This process generates feedback that takes the user's emotional state into account, providing advice that leads to improvements in planning. 【0562】 Step 6: 【0563】 The terminal presents feedback to the user. The terminal displays the business plan and feedback sent from the server to the user. The input is feedback data from the server, and the output is the information displayed to the user. The terminal supports the user in reviewing the feedback and making revisions or improvements to the business plan. 【0564】 (Application Example 2) 【0565】 Next, we will explain Application Example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal." 【0566】 In today's entrepreneurial environment, developing a business plan is extremely complex, requiring the analysis of a large amount of information and effective feedback. However, existing systems lack the technology to provide appropriate feedback that takes user sentiment into account. This presents a challenge for entrepreneurs, making it difficult to confidently develop competitive business plans. 【0567】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0568】 In this invention, the server includes means for collecting information and building a database, means for training a model based on the database, means for automatically generating a plan based on information received from the user, means for recognizing and analyzing the user's emotions, means for generating feedback on the automatically generated plan while considering the user's emotional state, and means for presenting the feedback to the user. This enables entrepreneurs to develop more effective and sophisticated business plans while receiving appropriate feedback tailored to their emotions. 【0569】 "Collecting information" means gathering necessary data from the internet or specialized data resources. 【0570】 "Building a database" means organizing collected information and making it into a format that allows for efficient searching and use of the data. 【0571】 "Training a model" means using collected data to train a machine learning algorithm so that it can recognize data patterns. 【0572】 "Automatically generating a plan" means that an AI model automatically creates a business plan based on user input information. 【0573】 "Recognizing and analyzing emotions" means determining the user's emotional state from input data and operation patterns, and then analyzing those emotions. 【0574】 "Generating feedback" means creating opinions and suggestions that reflect the user's emotions regarding the generated plan. 【0575】 "Providing feedback" means displaying the generated feedback to the user to help improve the plan. 【0576】 The system that realizes this invention generates new business plans and provides feedback through interaction between a server, terminals, and users. The server first collects information about startups from the internet and databases, and builds a database based on that information. This database functions as a repository of data, including success stories and lessons learned, and serves as the foundation for training the generative AI model. 【0577】 Next, the user uses a terminal to input necessary information such as product features, target market, and business objectives. Based on this information, the server automatically generates a new business plan using a trained AI model. This plan includes a business model, marketing strategy, and competitive analysis, and is customized according to the user's specific business goals. 【0578】 Furthermore, the server detects and analyzes the user's emotions in real time through an emotion recognition engine. Based on this analysis, feedback on the plan is generated taking the user's emotional state into consideration. The feedback assumes the perspective of virtual investors and collaborators, providing accurate advice and suggestions. 【0579】 Ultimately, the device presents the user with an improved business plan and emotionally appropriate feedback. Through this process, the user can work on further improving the plan. In particular, receiving positive feedback helps users gain confidence, and if they have negative emotions, receiving supportive feedback helps alleviate anxiety and doubts. 【0580】 For example, when a virtual store operator plans a new product line, this system can be used to develop specifications tailored to customer needs and receive feedback. Examples of prompts input to the generating AI model are as follows: 【0581】 "Please generate a new business plan based on the following information: Product features: {product_features}, Target market: {target_market}, Business purpose: {business_purpose}." 【0582】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0583】 Step 1: 【0584】 The server collects startup-related data from the internet and specialized databases. This input data includes success stories and lessons learned. The collected data is placed in a database and organized for use in subsequent processes. 【0585】 Step 2: 【0586】 The server trains the AI ​​model based on the database built in Step 1. This training process adjusts the model to recognize patterns in the collected data and enable the automatic generation of business plans. The output is the trained AI model. 【0587】 Step 3: 【0588】 Users use a terminal to input business information such as product features, target market, and business objectives. This input information is managed digitally because it will be used in the next step. 【0589】 Step 4: 【0590】 The server utilizes the AI ​​model trained in Step 2 to automatically generate a new business plan based on the information entered by the user. For data processing, prompts are input to the AI ​​model, and various data calculations necessary for plan generation are performed. The output is the automatically generated business plan. 【0591】 Step 5: 【0592】 The server uses an emotion recognition engine to analyze the user's emotions. User interaction patterns and input data are input, and an emotion profile is generated in real time. This output is used to generate feedback in the next step. 【0593】 Step 6: 【0594】 The server generates feedback on the automatically generated business plan, taking into account the user's emotional state obtained in step 5. The content is adjusted to reflect the perspectives of hypothetical investors and collaborators. The specific details of the feedback are then output. 【0595】 Step 7: 【0596】 The device presents the user with the improved business plan, along with the feedback generated in step 6. This allows the user to use it as material to further revise and consider their own business plan. 【0597】 The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data. 【0598】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0599】 In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314. 【0600】 [Fourth Embodiment] 【0601】 Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment. 【0602】 As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server. 【0603】 The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network). 【0604】 The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52. 【0605】 The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46. 【0606】 Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision). 【0607】 Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner. 【0608】 The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes. 【0609】 Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56. 【0610】 The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30. 【0611】 The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. 【0612】 In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48. 【0613】 Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0614】 This invention provides a system that automatically generates new business plans and supports entrepreneurs by providing feedback. This system is implemented by a program that handles everything from information gathering to plan generation and feedback provision in an integrated manner. 【0615】 First, the server collects information on successful case studies and lessons learned from new business ventures from around the world via the internet and specialized databases. This allows the server to acquire diverse and reliable information, which it then uses to build a database for training. 【0616】 Next, the server trains a generative AI model based on the constructed database. This model performs case analysis based on the collected information, learning success factors and commonalities. In this process, the server updates the model as new cases are added, ensuring that it always provides analysis results based on the latest information. 【0617】 Users input basic information about their business via a terminal. For example, if a user wants to launch an "environmentally friendly home product" into the market, they would input information such as the product's features, target market, and challenges. Based on the information received from the user, the server uses a generation AI model to automatically generate a business plan based on similar past cases. The plan includes business objectives, strategies, and market analysis. 【0618】 Next, the server generates feedback on the created business plan from the perspective of a virtual investor or partner. This feedback includes virtual questions, points of concern, and suggestions for improvement regarding the user's plan, which can be used to revise the business plan. For example, a virtual investor might point out that "the competitive analysis in the target market is insufficient." 【0619】 Finally, the device presents the user with the generated business plan and feedback. Based on this feedback, the user can revise the business plan, improving its completeness and quality. This process allows the user to enhance the quality of their business plan and deliver more compelling presentations to investors and stakeholders. 【0620】 This invention enables a reduction in the time required to launch a new business and an improvement in the probability of success. 【0621】 The following describes the processing flow. 【0622】 Step 1: 【0623】 The server collects information on new business ventures from external sources. Specifically, it obtains information on successful case studies and lessons learned from publicly available databases on the internet, academic papers, and industry reports. 【0624】 Step 2: 【0625】 The server organizes the collected information, extracts the necessary elements, and builds a database. The information is categorized into business areas and success factors, and tagged to improve searchability. 【0626】 Step 3: 【0627】 The server trains a generative AI model based on the constructed database. In this process, the server learns trends and patterns in the dataset and establishes an algorithm for automatically generating business plans. 【0628】 Step 4: 【0629】 Users will use a terminal to input basic information about their business. For example, they might input information such as the characteristics of a new product, the target market, and current challenges. 【0630】 Step 5: 【0631】 The server automatically generates business plans using AI based on information received from users. The AI ​​model provides draft plans that include appropriate business strategies and market analysis, referencing similar past cases. 【0632】 Step 6: 【0633】 The server evaluates the automatically generated business plan from the perspective of a virtual investor or partner and generates feedback. This includes questions and suggestions for improvement regarding the plan, such as "the financial plan is unclear." 【0634】 Step 7: 【0635】 The device presents the user with the generated business plan and feedback. Based on this information, the user revises the plan and improves its quality. 【0636】 This series of steps allows users to quickly and effectively develop business plans and increase their chances of success. 【0637】 (Example 1) 【0638】 Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0639】 In modern entrepreneurial activities, it is difficult to quickly and effectively develop new business plans and obtain appropriate feedback. This can lead to insufficient quality business plans and missed opportunities to effectively persuade investors and collaborators. In particular, there is a need for means to leverage diverse success stories and lessons learned, automatically create optimal plans using generative AI models, and obtain feedback. 【0640】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means. 【0641】 In this invention, the server includes means for collecting information from global communication networks and specialized data aggregates, means for cleansing and organizing the information to construct a data warehouse for learning, and means for training a generative AI model constructed based on the data warehouse. This enables users to quickly create new business plans and receive high-quality feedback on them from the perspective of virtual investors or collaborators. 【0642】 "Information" refers to data on success stories, lessons learned, and industry trends related to new businesses, obtained from global communication networks and specialized data collections. 【0643】 A "data warehouse" refers to a large-capacity data storage system where pre-processed information is stored and organized for training purposes. 【0644】 A "generative AI model" refers to artificial intelligence technology that learns from collected data and is used for business planning and generating feedback. 【0645】 A "user" refers to anyone who uses this system to input information about their work plan and receives the results. 【0646】 A "virtual investor or collaborator" refers to a fictitious stakeholder simulated within the system to evaluate the generated business plan and provide improvement suggestions. 【0647】 A "terminal" refers to a device used by users to input business plans and receive feedback. 【0648】 This invention relates to a system that automatically generates plans for new businesses and supports entrepreneurs through feedback. This system includes the following elements: 【0649】 The server gathers information by leveraging global communication networks and specialized data aggregations. This information includes success stories and lessons learned from new ventures, which are then cleansed and organized to build a data repository. In this process, the server uses data cleansing tools and web crawlers to extract reliable information. 【0650】 Next, the server trains a generative AI model based on the constructed data warehouse. At this stage, a GPU cluster is used to improve the model's training speed. The generative AI model employs a natural language processing technique commonly used in the industry (e.g., GPT-3 or BERT). This builds a knowledge base that can be used to generate new business plans and feedback. 【0651】 Users input information related to their work into the system via a terminal. The terminal accepts the user's input in an intuitive format and verifies the validity of the input as needed. For example, if a user wants to launch an "environmentally friendly household product" into the market, they can input its features, target market, and challenges. 【0652】 The server inputs the received information into a generating AI model and automatically generates a business plan, referencing similar past cases. This business plan is designed to include business objectives, strategies, and market analysis. An example of a prompt message it accepts is, "Please tell me about your market entry plan for environmentally friendly products." 【0653】 Subsequently, the server generates feedback on the generated business plan from the perspective of virtual investors and collaborators. This feedback is designed to include questions, comments, and suggestions for improvement. Finally, the terminal presents the generated business plan and feedback to the user, providing them with the material to improve the plan. Through this process, users can create higher-quality business plans and deliver more compelling presentations to investors and stakeholders. 【0654】 The flow of the specific processing in Example 1 will be explained using Figure 11. 【0655】 Step 1: 【0656】 The server collects information from communication networks and specialized data collections worldwide. Its inputs include multiple data sources (e.g., news sites, research papers, business forums), and its output is a collection of raw, unprocessed data. Specifically, the server runs a web crawler, visiting target sites at specified time intervals to retrieve updated information. 【0657】 Step 2: 【0658】 The server cleanses and organizes the collected data. The input is the raw data collected in step 1, and the output is the cleansed and organized data. In this step, the server uses data cleansing tools to remove noise and unnecessary data, storing only the most relevant information in the data repository. 【0659】 Step 3: 【0660】 The server trains a generative AI model based on a data repository. The input is cleansed and organized data, and the output is the trained generative AI model. Specifically, the server utilizes a GPU cluster to efficiently process large datasets, thereby improving the accuracy of the model. 【0661】 Step 4: 【0662】 Users input information about their work through a terminal. This input includes business characteristics, target markets, and challenges, and this information is then transferred to a server as output. The terminal has a function to verify the validity of the data entered by the user by providing an intuitive UI. 【0663】 Step 5: 【0664】 The server inputs information received from the user into a generating AI model to automatically generate a business plan. The input is the user's business information, and the output is a new business plan. In this step, the server utilizes the model and generates a plan that includes business objectives, strategies, and market analysis, while comparing it with similar past cases. 【0665】 Step 6: 【0666】 The server generates feedback on the automatically generated business plan. This feedback includes questions and comments from the perspectives of virtual investors and collaborators. The input is the business plan, and the output is structured feedback. Specifically, an AI-based rule engine identifies shortcomings and areas for improvement in the plan. 【0667】 Step 7: 【0668】 The terminal presents the generated business plan and feedback to the user. Input is data received from the server, and output is a visual or documentary presentation to the user. The terminal displays the results clearly and supports the user in revising the plan. 【0669】 (Application Example 1) 【0670】 Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0671】 In today's advertising industry, planning and executing effective advertising campaigns requires complex data analysis and specialized knowledge. However, this is time-consuming and costly, placing a significant burden on small and medium-sized businesses and individual advertisers. Furthermore, while multifaceted feedback is needed to enhance the effectiveness of advertising campaigns, there is a lack of efficient methods for collecting it. 【0672】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means. 【0673】 In this invention, the server includes means for collecting information and building a database, means for training a model based on the database, and means for automatically generating plans based on information received from users. As a result, users can automatically generate advertising plans simply by inputting advertising campaign information, and can also receive feedback from virtual consumers and marketing experts. 【0674】 "Means of collecting information" refers to methods for obtaining necessary data through the internet or specialized databases. 【0675】 "Methods for constructing a database" refers to methods for creating a database to organize and efficiently manage collected information. 【0676】 "Methods for training a model" refer to methods for training a generative AI model using a learning algorithm based on a constructed database. 【0677】 "Methods for automatically generating plans" refer to methods that automatically create appropriate plans by analyzing past data based on information provided by the user. 【0678】 "Means of generating feedback" refers to methods of providing evaluations and opinions on automatically generated plans from the perspectives of hypothetical experts or consumers. 【0679】 "Means of providing a user interface" refers to a method of providing a screen for users to input advertising campaign information and interact with the system. 【0680】 "Methods for generating advertising plans" refer to methods that automatically create effective advertising strategies based on entered campaign information. 【0681】 To realize this application, the system will collect new business information and successful advertising campaign case studies from the internet and build a database to manage them efficiently. The server will use a Python-based framework (e.g., Flask or Django) to receive campaign information entered by users and automatically generate advertising plans using a generative AI model based on that information. 【0682】 Specifically, the server uses AI models built with TensorFlow or PyTorch to analyze information from the database and user-provided data. Natural language processing (NLP) techniques are then used to generate feedback from virtual consumers and marketing experts on automatically generated advertising plans. This process provides strategic suggestions to improve the effectiveness of the advertisements. 【0683】 The user's device, such as a smartphone, receives advertising plans and feedback from the server and presents them in an easy-to-understand format. The user can then use this information to improve their advertising campaigns. 【0684】 As a concrete example, consider a case where a user plans a "promotion of health foods for middle-aged and elderly people in a specific region." The system receives input information via the following prompt: 【0685】 "We are planning an advertising campaign for a new health food product. Our target audience is middle-aged and older adults aged 40 to 60, and we will be using TV commercials and web advertising. Our budget is 3,000,000 yen, and the target area is limited to the Kansai region. Please propose an appropriate strategy." 【0686】 This approach allows users to quickly receive actionable advertising strategies, which in turn enables them to conduct more effective advertising campaigns. 【0687】 The flow of a specific process in Application Example 1 will be explained using Figure 12. 【0688】 Step 1: 【0689】 Users use their devices to enter basic information about their advertising campaign. This information includes target audience, budget, media used, and promotional regions. The entered information is sent to the server in JSON format. Basic validation is performed on the input data to ensure accuracy. 【0690】 Step 2: 【0691】 The server parses the received JSON data and extracts details of the advertising campaign. Based on this extracted data, it interacts with a constructed database to train a generative AI model. Specifically, it matches similar cases in the database and applies machine learning algorithms (e.g., TensorFlow) to detect successful patterns. 【0692】 Step 3: 【0693】 The server uses a trained generative AI model to generate ad plans suitable for the user's advertising campaign. This generative model predicts effective marketing strategies based on historical data. The generated results are output as ad plans in JSON format. 【0694】 Step 4: 【0695】 Based on the generated advertising plan, the server uses natural language processing (NLP) techniques to generate feedback from virtual consumers and marketing experts. Specifically, it extracts potential areas for improvement and points to note regarding the generated plan and puts them into written form as advice from virtual advisors. 【0696】 Step 5: 【0697】 Feedback and advertising plans are sent to the user's device and presented in a visually easy-to-understand format (graphs and lists). Users can then modify and optimize their advertising campaigns based on the suggested plans and feedback. 【0698】 Step 6: 【0699】 Users review the final advertising strategy on their device and, if necessary, send the information back to the server to request further improvements. This resubmission allows the server to update its AI model using the latest information and provide more specific suggestions. 【0700】 Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions. 【0701】 This invention is a system that automatically generates new business plans and provides feedback that takes user emotions into consideration. This system is realized by combining the functions of information gathering, model training, plan generation, emotion recognition, and feedback adjustment. 【0702】 First, the server collects success stories and lessons learned from startups from the internet and specialized data resources. This information is organized based on business areas and success factors and incorporated into a database. The server uses this database to train a generative AI model, completing an algorithm that automatically builds new business plans while learning from past examples. 【0703】 Next, the user enters basic information about their business via a terminal. This information includes product features, target market, and business objectives. Based on this information, the server uses a trained AI model to automatically generate a new business plan. The plan includes a business model, marketing strategy, and competitive analysis. 【0704】 Furthermore, a key feature of this system is the incorporation of an emotion engine. The server recognizes the user's emotions in real time through interaction with the user. This emotion recognition is analyzed from the user's input data and operation patterns, and an emotion profile is generated. 【0705】 For the generated business plan, the server produces feedback from the perspectives of virtual investors and partners. This feedback is adjusted to take into account the user's emotional state. For example, if the user is feeling anxious or stressed, the tone of the feedback will be changed to be more understandable and supportive. On the other hand, if the user is confident, more detailed and challenging improvement suggestions will be presented. 【0706】 Finally, the device presents the user with an improved business plan and emotionally-responsive feedback. The user can then revise the plan based on this feedback, further enhancing its quality. Through this process, the user can develop a more effective business plan and visualize the path to success. 【0707】 Thus, the present invention aims to support the development of new business plans and boost the success of entrepreneurs through feedback that reflects the emotions of users. 【0708】 The following describes the processing flow. 【0709】 Step 1: 【0710】 The server collects success stories and lessons learned from new business ventures from publicly available databases and expert reports on the internet. This information is categorized based on business type and success factors and stored in the database. 【0711】 Step 2: 【0712】 The server trains a generative AI model based on data from the database. This model learns patterns from successful cases and builds algorithms to automatically generate plans for new businesses. 【0713】 Step 3: 【0714】 Users input basic information about their planned business through a terminal. Specifically, they provide information such as the business objectives, target market, and product features. 【0715】 Step 4: 【0716】 The server uses user-provided information and leverages a generative AI model to automatically generate business plans. These plans include key business components such as strategy and market analysis. 【0717】 Step 5: 【0718】 The server uses an emotion engine to analyze input data from the user's device and understand the user's emotional state. In this process, factors such as the speed of operation and the content of input become influences for emotion recognition. 【0719】 Step 6: 【0720】 The server generates feedback on the created business plan from the perspective of virtual investors and partners. The feedback is tailored to the user's emotional state and delivered in a tone appropriate to the user's mental state. 【0721】 Step 7: 【0722】 The device presents the user with an improved business plan and tailored feedback. The user then revises the business plan based on the feedback provided, aiming to improve the quality of its content. 【0723】 Through these steps, users will be able to effectively improve their business plans while receiving emotionally sensitive support. 【0724】 (Example 2) 【0725】 Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0726】 In planning new businesses, traditional methods require significant time and effort for information gathering and analysis. Furthermore, the accuracy of the plan depends on the user's subjective opinion, making it difficult to obtain objective and appropriate feedback. Additionally, because plans are developed without considering user emotions, timely advice and support tailored to the user may not be provided, potentially lowering the success rate of the business. 【0727】 The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. 【0728】 In this invention, the server includes means for collecting information and building a database, means for training a generation AI model, means for automatically generating a plan using prompt statements based on the user's business, and means for recognizing and adjusting the user's feelings towards the generated plan and generating feedback. This makes it possible to automatically generate plans for new businesses objectively and efficiently, and to quickly provide feedback that is adapted to the user's feelings. 【0729】 "Means of information gathering" refers to the process or techniques for collecting and organizing success stories and lessons learned related to new businesses from the internet and specialized data sources. 【0730】 A "database" is an information management system that systematically stores collected information based on business areas and success factors, making it easy to search and analyze. 【0731】 A "generative AI model" refers to a machine learning or artificial intelligence program that includes an algorithm that automatically generates plans for new businesses based on collected data. 【0732】 A "prompt statement" is an instruction text generated by the AI ​​model from user input data, and it functions as a guideline for outputting a concrete business plan. 【0733】 "Means of recognizing emotions" refers to technologies and methods that analyze user operation patterns and input content to determine the user's emotional state in real time. 【0734】 "Means of adjusting feedback" refers to a process or mechanism for providing adaptive feedback that takes into account the user's feelings regarding the generated plan. 【0735】 This invention is an automated system for generating new business plans that takes user emotions into consideration. The system consists of a server, terminals, and a user interface. The following describes each element of the system and its operation in detail. 【0736】 Server Functions and Configuration 【0737】 The server is responsible for collecting information on successful startup cases and lessons learned from the internet and specialized data sources. This information is stored in a database. The server uses this database to train a generative AI model. Specifically, it uses machine learning frameworks such as TensorFlow and PyTorch to train algorithms that build new business plans while learning from past cases. Through this process, the server efficiently processes large amounts of data and generates highly accurate plans. 【0738】 Device functionality and user interaction 【0739】 Users input basic information about their business through a terminal. This information includes product features, target market, and business objectives. The terminal acts as a context, processing user input and sending it to the server. The server generates prompts based on this information and inputs them into a generative AI model. Through this process, users can quickly receive new business plans. 【0740】 Emotion recognition and feedback generation 【0741】 The server uses an emotion engine to recognize the user's emotions in real time. This recognition utilizes the user's input data and behavioral patterns, resulting in the generation of an emotion profile. The server leverages this profile to adaptively adjust feedback. Specifically, if the user is feeling anxious, gentle-toned feedback is provided; if the user is confident, challenging suggestions are offered. 【0742】 Examples of specific cases and prompt statements 【0743】 For example, if a user wants to open a new cafe, they might enter "A cafe offering Scandinavian-style design and a menu using natural ingredients" into the terminal. The prompt would then be something like, "I'm planning to open a new cafe. My target audience is working adults in their 30s, and I will offer Scandinavian-style design and a menu using natural ingredients. Please create a business plan based on this concept." Based on this prompt, the server would provide the user with a suitable business plan through the terminal, presenting an overall picture including feedback. 【0744】 In this way, the present invention is a system that supports users in formulating efficient and appropriate business plans and provides a path to success. 【0745】 The flow of the specific processing in Example 2 will be explained using Figure 13. 【0746】 Step 1: 【0747】 The server collects information and builds a database. The server gathers startup success stories and lessons learned from the internet and specialized data resources. Specifically, it uses scraping tools and APIs to collect and filter data. The input to this process is raw data from the internet, and the output is organized, structured data. 【0748】 Step 2: 【0749】 The server trains the generation AI model. The server prepares training data based on the constructed database and trains the AI ​​model using a machine learning framework (e.g., TensorFlow, PyTorch). The input is an organized dataset, and the output is a trained model of the business plan generation algorithm. In this step, the model learns patterns from past cases and improves its prediction accuracy. 【0750】 Step 3: 【0751】 The user inputs business information via a terminal. The user inputs information about their business (e.g., product features, target market, objectives) through the terminal. The input is text data from the user, and the output is data sent to the server. The terminal receives the input through the user interface and appropriately transmits it to the server. 【0752】 Step 4: 【0753】 The server automatically generates business plans. Based on information received from the user, the server generates prompt messages and inputs them into a trained AI model to generate the business plan. The input is prompt messages generated from user information, and the output is text data of the new business plan. This allows the server to quickly provide detailed business plans. 【0754】 Step 5: 【0755】 The server recognizes the user's emotions and generates feedback. The server uses an emotion engine to analyze user actions and inputs to generate an emotion profile. Inputs are user data and action logs, and output is tailored feedback. This process generates feedback that takes the user's emotional state into account, providing advice that leads to improvements in planning. 【0756】 Step 6: 【0757】 The terminal presents feedback to the user. The terminal displays the business plan and feedback sent from the server to the user. The input is feedback data from the server, and the output is the information displayed to the user. The terminal supports the user in reviewing the feedback and making revisions or improvements to the business plan. 【0758】 (Application Example 2) 【0759】 Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal". 【0760】 In today's entrepreneurial environment, developing a business plan is extremely complex, requiring the analysis of a large amount of information and effective feedback. However, existing systems lack the technology to provide appropriate feedback that takes user sentiment into account. This presents a challenge for entrepreneurs, making it difficult to confidently develop competitive business plans. 【0761】 The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. 【0762】 In this invention, the server includes means for collecting information and building a database, means for training a model based on the database, means for automatically generating a plan based on information received from the user, means for recognizing and analyzing the user's emotions, means for generating feedback on the automatically generated plan while considering the user's emotional state, and means for presenting the feedback to the user. This enables entrepreneurs to develop more effective and sophisticated business plans while receiving appropriate feedback tailored to their emotions. 【0763】 "Collecting information" means gathering necessary data from the internet or specialized data resources. 【0764】 "Building a database" means organizing collected information and making it into a format that allows for efficient searching and use of the data. 【0765】 "Training a model" means using collected data to train a machine learning algorithm so that it can recognize data patterns. 【0766】 "Automatically generating a plan" means that an AI model automatically creates a business plan based on user input information. 【0767】 "Recognizing and analyzing emotions" means determining the user's emotional state from input data and operation patterns, and then analyzing those emotions. 【0768】 "Generating feedback" means creating opinions and suggestions that reflect the user's emotions regarding the generated plan. 【0769】 "Providing feedback" means displaying the generated feedback to the user to help improve the plan. 【0770】 The system that realizes this invention generates new business plans and provides feedback through interaction between a server, terminals, and users. The server first collects information about startups from the internet and databases, and builds a database based on that information. This database functions as a repository of data, including success stories and lessons learned, and serves as the foundation for training the generative AI model. 【0771】 Next, the user uses a terminal to input necessary information such as product features, target market, and business objectives. Based on this information, the server automatically generates a new business plan using a trained AI model. This plan includes a business model, marketing strategy, and competitive analysis, and is customized according to the user's specific business goals. 【0772】 Furthermore, the server detects and analyzes the user's emotions in real time through an emotion recognition engine. Based on this analysis, feedback on the plan is generated taking the user's emotional state into consideration. The feedback assumes the perspective of virtual investors and collaborators, providing accurate advice and suggestions. 【0773】 Ultimately, the device presents the user with an improved business plan and emotionally appropriate feedback. Through this process, the user can work on further improving the plan. In particular, receiving positive feedback helps users gain confidence, and if they have negative emotions, receiving supportive feedback helps alleviate anxiety and doubts. 【0774】 For example, when a virtual store operator plans a new product line, this system can be used to develop specifications tailored to customer needs and receive feedback. Examples of prompts input to the generating AI model are as follows: 【0775】 "Please generate a new business plan based on the following information: Product features: {product_features}, Target market: {target_market}, Business purpose: {business_purpose}." 【0776】 The flow of a specific process in Application Example 2 will be explained using Figure 14. 【0777】 Step 1: 【0778】 The server collects startup-related data from the internet and specialized databases. This input data includes success stories and lessons learned. The collected data is placed in a database and organized for use in subsequent processes. 【0779】 Step 2: 【0780】 The server trains the AI ​​model based on the database built in Step 1. This training process adjusts the model to recognize patterns in the collected data and enable the automatic generation of business plans. The output is the trained AI model. 【0781】 Step 3: 【0782】 Users use a terminal to input business information such as product features, target market, and business objectives. This input information is managed digitally because it will be used in the next step. 【0783】 Step 4: 【0784】 The server utilizes the AI ​​model trained in Step 2 to automatically generate a new business plan based on the information entered by the user. For data processing, prompts are input to the AI ​​model, and various data calculations necessary for plan generation are performed. The output is the automatically generated business plan. 【0785】 Step 5: 【0786】 The server uses an emotion recognition engine to analyze the user's emotions. User interaction patterns and input data are input, and an emotion profile is generated in real time. This output is used to generate feedback in the next step. 【0787】 Step 6: 【0788】 The server generates feedback on the automatically generated business plan, taking into account the user's emotional state obtained in step 5. The content is adjusted to reflect the perspectives of hypothetical investors and collaborators. The specific details of the feedback are then output. 【0789】 Step 7: 【0790】 The device presents the user with the improved business plan, along with the feedback generated in step 6. This allows the user to use it as material to further revise and consider their own business plan. 【0791】 The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data. 【0792】 Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. 【0793】 In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414. 【0794】 Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion. 【0795】 Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together. 【0796】 These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression. 【0797】 The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become. 【0798】 Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant. 【0799】 The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more." 【0800】 The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values. 【0801】 The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format. 【0802】 In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data. 【0803】 In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56. 【0804】 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. 【0805】 Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56. 【0806】 The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory. 【0807】 The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor. 【0808】 Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources. 【0809】 Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose. 【0810】 The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above. 【0811】 All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference. 【0812】 The following is further disclosed regarding the embodiments described above. 【0813】 (Claim 1) 【0814】 Means for collecting information and building a database, 【0815】 A means for training a model based on the aforementioned database, 【0816】 A means of automatically generating a plan based on information received from the user, 【0817】 A means of generating feedback on automatically generated plans, 【0818】 A means for presenting the aforementioned feedback to the user, 【0819】 A system that includes this. 【0820】 (Claim 2) 【0821】 The system according to claim 1, wherein the information includes success stories and lessons learned related to new businesses. 【0822】 (Claim 3) 【0823】 The system according to claim 1, wherein the feedback includes questions or comments from virtual investors or partners. 【0824】 "Example 1" 【0825】 (Claim 1) 【0826】 Means for collecting information from global communication networks and specialized data collection systems, 【0827】 A means for cleansing and organizing the aforementioned information to construct a data warehouse for learning, 【0828】 A means for training a generative AI model constructed based on the aforementioned data warehouse, 【0829】 A means of automatically generating business plans related to past success stories based on information about business plans received from users, 【0830】 A means of constructing evaluations and improvement suggestions from the perspective of a hypothetical investor or collaborator for an automatically generated business plan, 【0831】 A means via a terminal for presenting the aforementioned evaluation and improvement suggestions to the user, 【0832】 A system that includes this. 【0833】 (Claim 2) 【0834】 The system according to claim 1, wherein the information includes successful cases and lessons learned from experience related to entrepreneurial activities. 【0835】 (Claim 3) 【0836】 The system according to claim 1, wherein the evaluation and improvement suggestions include questions and comments from a hypothetical investor or collaborator. 【0837】 "Application Example 1" 【0838】 (Claim 1) 【0839】 Means for collecting information and building a database, 【0840】 A means for training a model based on the aforementioned database, 【0841】 A means of automatically generating a plan based on information received from the user, 【0842】 A means of generating feedback on automatically generated plans, 【0843】 A means for presenting the aforementioned feedback to the user, 【0844】 A means of providing a user interface that allows users to input advertising campaign information, 【0845】 A means of generating an advertising plan based on the received campaign information, 【0846】 A means of generating feedback from a virtual consumer or marketing expert, 【0847】 A system that includes this. 【0848】 (Claim 2) 【0849】 The system according to claim 1, wherein the aforementioned information includes success stories and lessons learned related to new businesses, and further includes strategic information in advertising campaigns. 【0850】 (Claim 3) 【0851】 The system according to claim 1, wherein the feedback includes questions and comments from virtual investors or partners, and suggestions from virtual consumers or marketing experts. 【0852】 "Example 2 of combining an emotion engine" 【0853】 (Claim 1) 【0854】 Means for collecting information and building a database, 【0855】 A means for training an AI model based on the aforementioned database, 【0856】 A means for automatically generating a plan using prompt messages based on business information received from the user, 【0857】 A means for recognizing the user's feelings towards the generated plan and adjusting and generating feedback based on those feelings, 【0858】 A means for presenting the aforementioned feedback to the user, 【0859】 A system that includes this. 【0860】 (Claim 2) 【0861】 The system according to claim 1, wherein the aforementioned information includes success stories and lessons learned related to new businesses and is used to train a generative AI model. 【0862】 (Claim 3) 【0863】 The system according to claim 1, wherein the feedback includes evaluations from a virtual investor or partner that have been adjusted to take into account the user's emotions. 【0864】 "Application example 2 when combining with an emotional engine" 【0865】 (Claim 1) 【0866】 Means for collecting information and building a database, 【0867】 A means for training a model based on the aforementioned database, 【0868】 A means of automatically generating a plan based on information received from the user, 【0869】 A means of recognizing and analyzing user emotions, 【0870】 A means of generating feedback on an automatically generated plan, taking into account the user's emotional state, 【0871】 A means for presenting the aforementioned feedback to the user, 【0872】 A system that includes this. 【0873】 (Claim 2) 【0874】 The system according to claim 1, wherein the aforementioned information includes success stories and guidance related to new businesses. 【0875】 (Claim 3) 【0876】 The system according to claim 1, wherein the feedback includes questions or suggestions from a virtual investor or collaborator. [Explanation of symbols] 【0877】 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

[Claim 1] Means for collecting information and building a database, A means for training a model based on the aforementioned database, A means of automatically generating a plan based on information received from the user, A means of generating feedback on automatically generated plans, A means for presenting the aforementioned feedback to the user, A system that includes this. [Claim 2] The system according to claim 1, wherein the aforementioned information includes success stories and lessons learned related to new businesses. [Claim 3] The system according to claim 1, wherein the feedback includes questions or comments from virtual investors or partners.

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