system

Abstract

We provide the system. [Solution] A method for collecting data on past business success and failure cases and analyzing it using natural language processing, A means of structuring the analyzed data and selecting relevant information according to the user's business category and goals, A means of automatically generating a business plan based on a template using selected information, A means of providing the generated business plan to the user's terminal and obtaining feedback from the user, A means of revising the business plan based on user feedback and creating a final plan, A system that includes means to obtain subsidy and advertising strategy information suitable for the user's business and add it to the business plan.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] Companies and individuals starting a new business need a great deal of time and effort to create a business plan, conduct market analysis, and identify success factors. In particular, startup companies need to quickly launch their businesses with limited resources, and means for generating and improving an efficient and effective business plan are required. Also, it is difficult to effectively incorporate lessons from past success and failure cases, and many companies have problems in terms of how to reflect this information in the current business plan.

Means for Solving the Problems

[0005] This invention solves the aforementioned problems by providing a means to automatically collect relevant information from past business success and failure cases, and to analyze and structure it using natural language processing. By having the user input business categories and goals, a business plan suitable for that input information is automatically generated based on a template. This plan is provided to the user, and based on the user's feedback, the plan is modified. Furthermore, specific subsidy and advertising strategy information is automatically acquired and added to the business plan, enabling the generation of a more complete business plan. Ultimately, the user can comprehensively obtain the information necessary to efficiently launch a business, reducing the burden during the initial stages of entrepreneurship.

[0006] The definition statement is created below.

[0007] "Business success / failure case studies" refer to specific examples or situations in the past where a business or company succeeded or failed.

[0008] "Natural language processing" is a technology that uses computers to understand, interpret, and generate human language, and is a technique used in the analysis of text data, among other things.

[0009] "Structured data" refers to data that is organized and classified in a specific format, making it easily accessible, searchable, and analyzable.

[0010] A "user" is an individual or company that intends to use this system to create a business plan.

[0011] A "business category" is a classification that indicates the type of business belonging to a particular industry or field.

[0012] A "template" is a model that has a specific format or structure and is used to assemble information in a predetermined format.

[0013] "Feedback" refers to information provided by users, such as opinions, suggestions for improvement, evaluations, and requests for corrections.

[0014] "Subsidy" refers to the economic assistance or support funds provided by the government or organizations for a certain purpose.

[0015] "Advertising strategy information" refers to the information related to the plans and activities for efficiently promoting a business, product, or service in the market.

[0016] "Brush-up" refers to making corrections and adjustments to improve existing plans or ideas and enhance their completeness.

Brief Explanation of Drawings

[0017] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when combined with an emotion engine. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when combined with an emotion engine.

Mode for Carrying Out the Invention

[0018] Hereinafter, an example of an embodiment of the system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0019] First, the language used in the following description will be explained.

[0020] 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.

[0021] 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.

[0022] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0023] 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).

[0024] 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."

[0025] [First Embodiment]

[0026] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0027] 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.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0034] As shown in Figure 2, in the data processing device 12, 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.

[0035] 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.

[0036] 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.

[0037] 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".

[0038] This invention is a system for efficiently generating and improving business plans. Specifically, a server plays a major role in collecting data on past business successes and failures and analyzing this data using natural language processing technology. Through this analysis, key features that contribute to success or failure are extracted and stored as structured data.

[0039] Users access this system to create business plans using their terminals, entering detailed information about their business ideas and goals. The terminals facilitate user input through an input interface. This information is sent to a server, which searches for relevant business success and failure stories based on the user's business category and goals.

[0040] The server leverages structured data to automatically generate business plans based on templates. The generated plans are sent to the terminal, where users can review them in detail. If the user provides feedback, the server analyzes that feedback and modifies the business plan as needed.

[0041] As part of this process, the server extracts the latest subsidy information and advertising strategy data relevant to the user's business. This information is then added to the user's business plan, resulting in a more specific and actionable plan.

[0042] As a concrete example, let's consider a scenario where a user wants to launch a retail business selling a new product. The user inputs business details (e.g., target market, budget, competitor analysis) into a terminal, and the server analyzes past successful retail business examples, automatically generating a business plan based on its success factors. The user reviews the plan and provides feedback on areas for improvement, and the server updates the plan accordingly, adding any available subsidy information. This entire process allows the user to quickly and efficiently prepare for a higher-quality business venture.

[0043] The following describes the processing flow.

[0044] Step 1:

[0045] The server collects past business success and failure stories, pitch materials, and related documents from online databases and public repositories. The collected data is analyzed using natural language processing to identify factors contributing to success and failure, and then stored as structured data.

[0046] Step 2:

[0047] The terminal presents the user with an input interface, allowing them to enter the information necessary to generate a business plan, such as business category, target market, and goals. The user enters this information and sends it to the server via the terminal.

[0048] Step 3:

[0049] The server analyzes the information received from the user and searches for relevant cases in the database. Using the analyzed data, it automatically generates an initial business plan based on a template.

[0050] Step 4:

[0051] The server sends the generated business plan to the terminal and presents it to the user. The terminal displays the plan in a visually organized format to make it easy for the user to review.

[0052] Step 5:

[0053] Users check the contents of the plan generated through their device and enter feedback and requests for revisions. This feedback information is then sent from the device to the server.

[0054] Step 6:

[0055] The server analyzes user feedback and generates an updated business plan with necessary modifications. Furthermore, it retrieves subsidy and advertising strategy information from relevant databases and integrates it into the plan.

[0056] Step 7:

[0057] The final version of the business plan, incorporating revisions and additional information, is resent to the terminal and displayed to the user. The user can then review this final version and download or print it as needed to complete the business preparations.

[0058] (Example 1)

[0059] 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."

[0060] In business, creating an effective business plan quickly is a challenging task. In particular, efficiently utilizing past success and failure stories requires expertise and time, making it a significant burden for many small and medium-sized enterprises (SMEs). Furthermore, incorporating the latest information while updating plans is not easy. This hinders the creation of high-quality business plans, leading to a decrease in the success rate of businesses.

[0061] 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.

[0062] In this invention, the server includes means for acquiring a collection of information on past business success and failure cases and analyzing it using natural language processing technology; means for structuring the analyzed information and extracting relevant items according to the user's business category and objectives; and means for automatically constructing a business plan using a plan template based on the extracted items. This enables the rapid, effective creation of high-quality business plans.

[0063] An "information set" is a collection of data gathered for a specific purpose, which may include past examples of business successes and failures.

[0064] "Natural language processing technology" is a technology that enables computers to understand, interpret, and generate human language, and in this system, it is used for analyzing text data.

[0065] A "business category" is a category used to classify specific business activities and refers to the field related to the user's business.

[0066] A "business plan template" is a template used to form the foundation of a business plan, providing the basic structure of the plan to be constructed.

[0067] A "terminal" is a device used by a user to input information, and includes an interface for accessing the system.

[0068] "Opinions" refer to feedback and revision requests provided by users, which are used to improve the generated business plans.

[0069] A "subsidy" is funding provided to projects that meet specific conditions, and it is important information for obtaining financial support in a business plan.

[0070] A "marketing strategy" is a plan designed to increase awareness of a particular product or service in the market, and it is an important element of any plan.

[0071] A description of the embodiment for carrying out the invention will be provided.

[0072] This system operates through the collaboration of servers, terminals, and users. The servers are built, for example, in a cloud computing environment. In this environment, the servers process vast amounts of data and use natural language processing techniques to analyze past business successes and failures. This analysis process utilizes generative AI models such as BERT and GPT, which are used to understand text data and extract business-related information.

[0073] The server uses the analyzed information to structure the data and automatically generates a business plan tailored to the user's business category and objectives. Because this plan is created using a template, consistency and quality are ensured. The generated business plan is sent to the user's terminal, where they review it and provide feedback as needed. These terminals include PCs and tablets, equipped with the necessary functions for secure communication with the server.

[0074] Furthermore, the server collects user feedback and analyzes it using AI technology. Based on this feedback analysis, the server revises the business plan for improvement and adds the latest information on subsidies and advertising strategies if necessary. This allows users to obtain a concrete and actionable business plan.

[0075] As a concrete example, consider a user who is launching a retail business selling a new product. The user uses a terminal to input information such as the target market, budget, and competitor information, and the server generates a plan based on past success stories. The user can review the plan and provide feedback on areas for improvement, thereby receiving an even more optimized plan.

[0076] An example of a prompt might be, "Generate a business plan for a new product based on successful retail business examples." This prompt allows the server to begin generating an appropriate business plan.

[0077] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0078] Step 1:

[0079] The server collects data on past business success and failure cases from the internet and dedicated databases. This process efficiently retrieves relevant text data using APIs and web scraping techniques. The input requires business-related data source URLs and API keys, and the output is a raw dataset of business case studies.

[0080] Step 2:

[0081] The server analyzes the collected data using natural language processing techniques. Specifically, it inputs text data into a generative AI model (e.g., BERT or GPT) to extract features that indicate factors for success or failure. The generative AI model analyzes the structure of the text and extracts highly relevant keywords and phrases. As a result, the input becomes a raw dataset, and the output becomes structured feature data.

[0082] Step 3:

[0083] The server extracts relevant information that matches the user's business category and objectives based on structured feature data. The business category and goals provided by the user to the system are used as filtering criteria. The input consists of structured feature data and user-specified category information, and the output is a list of relevant items that match the user's needs.

[0084] Step 4:

[0085] Users input business ideas and goals through a terminal, which are then sent to the server. The terminal allows users to easily input specific business information through forms and guides. User-provided business information is the input, and that data is sent to the server as output.

[0086] Step 5:

[0087] The server applies the extracted items to a template and automatically builds a user-specific work plan. It utilizes a generation AI model to generate the plan by embedding the necessary details into the plan template. The input consists of relevant items and a template, and the output is a customized work plan.

[0088] Step 6:

[0089] The server sends the generated work plan to the user's terminal, and the user reviews its contents. The user displays the work plan on their terminal and provides feedback as needed. The generated work plan is the input, and the user's feedback is the output.

[0090] Step 7:

[0091] The server analyzes user feedback and modifies the business plan accordingly. It makes the plan more practical by adding the latest subsidy information and advertising strategies. The server re-analyzes the feedback and generates a final version of the plan with necessary revisions. The input is user feedback, and the output is the improved business plan.

[0092] (Application Example 1)

[0093] 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."

[0094] Traditional commercial planning processes fail to fully utilize past successes and failures, making it difficult to quickly generate specific plans tailored to individual stores. Furthermore, they lack mechanisms for efficiently incorporating user feedback and continuously improving plans. Therefore, there is a need for a faster and more flexible planning and improvement process.

[0095] 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.

[0096] This invention includes a server that collects information on past successful and unsuccessful commercial activities and processes it using natural language processing; a server that formalizes the processed information and selects relevant information according to the user's commercial category and purpose; and a server that uses a smartphone to generate and improve plans based on physical store placement, customer analysis, and budget allocation. This enables users to quickly and easily generate and improve specific commercial plans tailored to individual physical stores while leveraging past success stories.

[0097] "Success and failure cases of commercial activities" refer to specific cases of success and failure resulting from past commercial activities, and are information used to extract the factors behind them through analysis.

[0098] "Natural language processing" is a technology that converts natural language into a form that computers can understand and analyze, allowing for a high level of analysis of the content of information.

[0099] "Formalization" is the process of converting processed information into data in a unified format, enabling further processing and retrieval.

[0100] "User" refers to an individual or organization that uses this system to create or improve commercial plans.

[0101] A "commercial category" is a classification related to specific commercial activities or markets, indicating the scope to which a user's business activities belong.

[0102] "Purpose" refers to the specific goals or outcomes that users intend to achieve by using this system.

[0103] A "smartphone" is a type of portable information terminal equipped with computing power and connectivity, and is used for creating and improving commercial plans.

[0104] A "physical store" refers to a store that exists in a physical location and provides goods or services to consumers.

[0105] "Customer analysis" is the process of analyzing the characteristics and behavior of target customers in commercial activities and formulating sales strategies based on that analysis.

[0106] "Budget allocation" is the process by which users effectively distribute funds allocated for commercial activities across different activities and projects.

[0107] This invention provides a system for efficiently generating and improving commercial plans by leveraging past successes and failures in commercial activities. The server collects information on successful and unsuccessful commercial activities and processes this information using natural language processing technology. Python libraries such as spaCy and NLTK can be used for this analysis. The server formalizes the analyzed information and selects relevant information according to the user's commercial category and purpose. This formalized data is processed through AWS® cloud servers.

[0108] Users can begin creating a business plan using a smartphone application. Through the application, users input information about their business vision, objectives, and budget, and the server supports them in generating and improving an optimal business plan based on this information. As a concrete example, consider a case where a store owner wants to open a cafe in the suburbs. In this case, the user inputs information about their objectives and target market via their smartphone, and the server presents the user with a business plan based on automatically generated templates that refer to past cases.

[0109] In this plan generation process, the server utilizes information on physical store locations, customer analysis, and budget allocation to provide actionable suggestions tailored to the user's specific business situation. Through this process, users can quickly obtain an appropriate commercial plan and use it to guide their subsequent business development.

[0110] An example of a prompt for a generative AI model is: "Analyze retail business case studies and generate a customized commercial plan based on the target market. Utilize a template that includes success factors for cafes and suggest ways to maximize return on investment within the budget."

[0111] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0112] Step 1:

[0113] The server collects information on past successful and unsuccessful commercial activities. The input is accumulated data on commercial activities, which is analyzed using natural language processing techniques (such as the Python libraries spaCy and NLTK) to extract factors contributing to success and failure. The output is structured data containing this factor information.

[0114] Step 2:

[0115] The server formalizes structured information and selects relevant information based on the user's business category and purpose. Input is the user's business category and vision entered via a terminal, and the server searches the database for appropriate success stories. Output is a template containing the selected relevant information.

[0116] Step 3:

[0117] The user inputs their business objectives and budget via a terminal. The terminal sends the input information to a server, which provides the data necessary for generating a business plan. The input consists of the user's business ideas and goals, and the output forms a request for a generated plan template.

[0118] Step 4:

[0119] The server automatically generates a commercial plan using a template based on the selected information. The input consists of the template and user input information, which is then processed into specific execution steps and recommendations suitable for the commercial plan. The output is provided to the user as the generated commercial plan.

[0120] Step 5:

[0121] Users review the generated commercial plan displayed on their terminal and provide feedback. The input consists of suggestions for improvements and revisions the user proposes for the commercial plan, and the feedback information is sent to the server as output.

[0122] Step 6:

[0123] The server modifies the commercial plan based on user feedback to form the final plan. The input is feedback information, and a generative AI model is used to adjust the plan and generate the final plan document. The output is the modified final commercial plan.

[0124] Step 7:

[0125] The server retrieves subsidy and advertising strategy information suitable for the user's commercial activities and adds it to the commercial plan. The input is information about the user's commercial content and target market, and the server retrieves the latest subsidy information from an external database. The output is the commercial plan with the added information.

[0126] 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.

[0127] This invention is a system that recognizes user emotions when generating and improving business plans, and utilizes that information to make more appropriate business proposals. The system's main components are a server, terminals, and a user interface. In particular, an emotion engine analyzes user emotional feedback and uses that data to optimize the plan.

[0128] The server collects past business success and failure stories from online databases and public repositories, and analyzes the data using natural language processing. The results of this analysis are stored as structured data. Users can input the necessary information for business plan generation via their terminals and send their business ideas and goals to the server.

[0129] Furthermore, the terminal is equipped with an interface for analyzing user emotions, and an emotion engine detects the user's emotions during input and feedback. This information is sent to the server and taken into consideration when generating and improving business plans. Specifically, if the user's emotions are positive, the server can incorporate proactive strategies and new suggestions, while if negative emotions are detected, it strengthens suggestions to mitigate risks.

[0130] The generated business plan is displayed on the terminal and provided in a user-friendly format. Users can review the plan and provide feedback, including emotional responses. This feedback is then analyzed again by the emotion engine, and the server uses the analysis results to optimize the business plan and adjust subsidy information and advertising strategies.

[0131] As a concrete example, let's assume a user uses this system while considering the market launch of a new product. If the user wishes to incorporate the sentiment analysis of potential customers into their business plan, the sentiment engine will make suggestions that also reflect market sentiment trends, allowing the business plan to be more market-oriented. Through this invention, users will be able to utilize sentiment information to develop highly personalized business plans.

[0132] The following describes the processing flow.

[0133] Step 1:

[0134] The server collects business success and failure stories, pitch materials, and related documents from online databases and public repositories. This data is analyzed using natural language processing techniques to identify factors contributing to success and failure, and then stored as structured data.

[0135] Step 2:

[0136] The terminal provides an interface to the user, allowing them to input the information necessary to create a business plan (such as business category, target market, and budget). The user enters this information into the terminal and sends it to the server.

[0137] Step 3:

[0138] The device uses an emotion engine to analyze the user's emotions in real time based on user input. This identifies the user's emotional state, and that information is sent to the server.

[0139] Step 4:

[0140] The server analyzes user input and sentiment data, and searches a database of past related cases for similar success stories. It automatically generates business plans based on templates and makes suggestions and adjustments that take user sentiment into account.

[0141] Step 5:

[0142] The server sends the generated business plan to the terminal, which then displays it to the user. The user reviews the plan in detail and provides feedback, along with emotional responses.

[0143] Step 6:

[0144] The server receives user feedback and analysis results from the emotion engine, optimizing the business plan for each task. It also retrieves subsidy information and advertising strategy details as needed, and incorporates them into the plan.

[0145] Step 7:

[0146] The final version of the business plan will be resent to the terminal. After reviewing it, the user can download or print the plan and use it to further develop their business.

[0147] (Example 2)

[0148] 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 as the "terminal".

[0149] Conventional business plan generation systems have struggled to formulate plans that adequately consider user sentiment and market sentiment trends. As a result, they have been unable to generate highly accurate plans that reflect the individual needs and emotions of users, making it difficult to formulate optimal business strategies.

[0150] 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.

[0151] In this invention, the server includes means for collecting information on past industrial success and failure cases and analyzing it using natural language processing; means for automatically generating a plan based on a template, taking into account the analyzed information and the user's emotional information; and means for analyzing the user's emotional feedback and utilizing that information to optimize the plan. This makes it possible to incorporate the user's emotional information and generate highly personalized business plans that meet individual needs.

[0152] "Natural language processing" is a technology that analyzes, understands, and generates natural language used by humans in order to make textual information easier to process by computers.

[0153] "Emotional information" refers to data that indicates the emotional state of a user, extracted from their input and responses.

[0154] "Structured information" refers to data that has been organized through analysis and is managed based on specific patterns or formats.

[0155] A "plan" is a document or information that outlines a set of strategies and guidelines for action generated to achieve a specific objective.

[0156] "Emotional feedback" refers to responses that include emotional opinions and impressions expressed by users as a reaction to a plan or system.

[0157] A "template" refers to a pre-prepared model or format that is referenced when generating a plan.

[0158] "Grant information" refers to information about financial assistance and economic support available for business activities.

[0159] "Advertising strategy" refers to advertising activities and promotional methods planned to effectively communicate a product or service to the market.

[0160] This system generates business plans that take user emotions into consideration and consists of a server, terminals, a user interface, and an emotion engine.

[0161] The server collects information on industry success and failure stories from online databases and public repositories. This information is analyzed using natural language processing tools (e.g., NLTK and Spacy) and stored as structured data. This data is useful for generating and improving business plans.

[0162] Users can input detailed business ideas and goals through the terminal's interface. This information is sent to the server based on technologies used as generative AI models (e.g., GPT or other similar models).

[0163] The terminal is equipped with sentiment analysis software to analyze the user's emotions, such as IBM Watson® Tone Analyzer or Microsoft® Azure® Text Analytics. This allows the terminal to detect the user's emotions during input and feedback, and send the results to the server.

[0164] The generated business plan is structured to include proactive suggestions when positive emotions are detected, and risk mitigation suggestions when negative emotions are detected. This allows the terminal to present the plan to the user in a visually easy-to-understand format. The user reviews the plan and provides emotional feedback. This feedback is then analyzed again on the server side to further optimize the business plan.

[0165] A concrete example is a user considering launching a new product who wants to incorporate the sentiment analysis of potential customers into their business plan. In this case, the sentiment engine can provide suggestions that reflect market sentiment trends, presenting a plan that is more adapted to the market.

[0166] As an example of a prompt, it is possible to utilize a generative AI model in the form of a message such as, "Develop a strategy for entering a new market. Please state your business idea with positive emotions."

[0167] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0168] Step 1:

[0169] The server collects past industry success and failure stories from external sources into a database. It references online databases and public repositories as input and analyzes the data using natural language processing tools. This analysis outputs the case study data in a structured format, which is then stored within the server. This organizes the information that forms the basis for generating business plans.

[0170] Step 2:

[0171] Users input business ideas and goals through the terminal's interface. The terminal receives detailed information about the user's business concept and achievement goals as input, and sends this information to a generating AI model. The AI ​​model analyzes this information and outputs basic data for planning. This prepares the system to create a plan tailored to the user's specific needs.

[0172] Step 3:

[0173] The terminal uses sentiment analysis software to detect user emotions during input and feedback. User actions via keyboard and mouse are passed to the sentiment analysis engine and output as positive or negative sentiment indicators. This allows the system to reflect emotional information in plan generation.

[0174] Step 4:

[0175] The server uses a generative AI model to create a business plan based on user input data and sentiment analysis results. Emotional states and business data are fed to the model as input, and emotion-based plan proposals are output. Positive emotions will result in proactive suggestions, while negative emotions will emphasize risk mitigation.

[0176] Step 5:

[0177] The generated business plan is displayed on the terminal, making it accessible to the user. The user reviews the visually displayed plan and provides feedback, including satisfaction levels and suggestions for improvement. This feedback is again emotionally analyzed on the terminal and sent to the server for consideration during the plan adjustment phase.

[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 recent years, there has been a growing need for individuals and businesses to utilize sentiment analysis technology to make more accurate decisions in online environments. However, current systems have the problem of not being able to effectively utilize sentiment information and automatically and precisely provide appropriate business proposals and business plans. The present invention aims to solve these problems and provide a system that enables strategic business proposals based on sentiment information.

[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 data on past business success and failure cases and analyzing it using natural language processing; means for detecting the user's emotions and adjusting recommendation information based on the analyzed emotion data; and means for analyzing the emotion analysis results in real time and providing highly relevant alternative information. This enables personalized business proposals based on the user's emotions and the optimization of those proposals.

[0183] "Data on past business successes and failures" refers to historical information about business successes and failures, and serves as foundational data for formulating new business plans based on that information.

[0184] Natural language processing is a technology that enables computers to understand, analyze, and process human language, and it is the process of converting text data into useful information.

[0185] "Structured data" refers to data arranged in a specific format, making it easy to search and analyze.

[0186] "User's device" refers to an electronic device used by the user, including smartphones and tablets.

[0187] "Feedback" refers to the reactions and opinions that users provide to a system, and is data used to improve and optimize the system.

[0188] A "template-based business plan" is a template that provides a basic structure for efficiently creating a business plan, and is customized based on the information entered by the user.

[0189] "Support funds" refer to financial assistance provided to business activities that meet specific conditions.

[0190] A "promotional strategy" is a plan of advertising activities designed to increase awareness of a service or product.

[0191] "Emotion detection" is the process of determining the user's current emotional state through their facial expressions and voice.

[0192] "Recommended information" refers to specific recommendations that the system provides, taking into account the user's needs and preferences.

[0193] "Real-time analysis" is a technology that enables immediate information provision without delay by processing and analyzing data instantly.

[0194] "Alternative information" refers to alternative options or suggestions presented when the original information is inappropriate or unsatisfactory to the user.

[0195] The system for implementing this invention will be built as an e-commerce system that utilizes sentiment analysis to provide users with appropriate product recommendations. Specifically, by integrating this system into a smartphone application, the system will detect the emotions a user feels while browsing products and use the obtained sentiment data to provide product recommendations.

[0196] The smartphone uses its front camera to capture the user's facial expressions and runs software for emotion analysis. Emotion detection software, such as the Affectiva SDK, is used to identify positive or negative emotions in real time from the facial expressions. The data obtained from the emotion analysis is immediately sent to a cloud server, where a recommendation algorithm generates appropriate product information. This process enables product suggestions that better match the user's preferences.

[0197] The servers utilize cloud platforms such as Amazon Web Services and Google Cloud AI to rapidly process large amounts of data. The recommendation engine integrates analyzed sentiment data with the user's past purchase history data. This engine selects the optimal products by considering current market trends and the user's potential purchasing intent.

[0198] For example, if a user is browsing a new camera on their smartphone and shows a smiling expression, the system might suggest a high-quality camera case. On the other hand, if they show a confused expression, the system might explain other camera specifications in detail and even offer a 20% discount coupon.

[0199] An example of a prompt message would be: "When a user opens a specific product page, use the front camera to detect their facial expression and perform emotion analysis, then recommend products based on the result. If they are smiling, suggest related products; if they are confused, notify them of alternative products and discount information." In this way, users can receive highly relevant information about products that interest them in real time.

[0200] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0201] Step 1:

[0202] The device captures the user's face using its front camera. The input is image data taken while the user is using their smartphone. This image data is sent to emotion analysis software based on the Affectiva SDK to generate the user's emotional information. The output is data with emotional labels such as positive, negative, and neutral.

[0203] Step 2:

[0204] The emotion engine analyzes the obtained emotion label data and adjusts the relevant recommendation information. It receives emotion labels as input and uses a generative AI model to create prompt sentences that provide product recommendations according to each emotion. Here, as part of the data processing, the user's emotions are integrated with past purchase data, and the adjusted prompt sentences are generated as output.

[0205] Step 3:

[0206] The server uses prompts to send data to a cloud-based AI platform to generate appropriate product information. The input consists of the tailored prompts and associated product data. Data processing on the AI ​​platform generates a list of products best suited to the user. The output is a product recommendation list.

[0207] Step 4:

[0208] The terminal displays the generated product recommendation list on the user's screen. The input is the product recommendation list, which is visually presented to the user through the user interface. As output, the user is shown information about related products and discount coupons, providing information that leads to the next purchase action.

[0209] 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.

[0210] 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.

[0211] 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.

[0212] [Second Embodiment]

[0213] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0214] 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.

[0215] 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).

[0216] 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.

[0217] 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.

[0218] 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).

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] 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".

[0225] This invention is a system for efficiently generating and improving business plans. Specifically, a server plays a major role in collecting data on past business successes and failures and analyzing this data using natural language processing technology. Through this analysis, key features that contribute to success or failure are extracted and stored as structured data.

[0226] Users access this system to create business plans using their terminals, entering detailed information about their business ideas and goals. The terminals facilitate user input through an input interface. This information is sent to a server, which searches for relevant business success and failure stories based on the user's business category and goals.

[0227] The server leverages structured data to automatically generate business plans based on templates. The generated plans are sent to the terminal, where users can review them in detail. If the user provides feedback, the server analyzes that feedback and modifies the business plan as needed.

[0228] As part of this process, the server extracts the latest subsidy information and advertising strategy data relevant to the user's business. This information is then added to the user's business plan, resulting in a more specific and actionable plan.

[0229] As a concrete example, let's consider a scenario where a user wants to launch a retail business selling a new product. The user inputs business details (e.g., target market, budget, competitor analysis) into a terminal, and the server analyzes past successful retail business examples, automatically generating a business plan based on its success factors. The user reviews the plan and provides feedback on areas for improvement, and the server updates the plan accordingly, adding any available subsidy information. This entire process allows the user to quickly and efficiently prepare for a higher-quality business venture.

[0230] The following describes the processing flow.

[0231] Step 1:

[0232] The server collects past business success and failure stories, pitch materials, and related documents from online databases and public repositories. The collected data is analyzed using natural language processing to identify factors contributing to success and failure, and then stored as structured data.

[0233] Step 2:

[0234] The terminal presents the user with an input interface, allowing them to enter the information necessary to generate a business plan, such as business category, target market, and goals. The user enters this information and sends it to the server via the terminal.

[0235] Step 3:

[0236] The server analyzes the information received from the user and searches for relevant cases in the database. Using the analyzed data, it automatically generates an initial business plan based on a template.

[0237] Step 4:

[0238] The server sends the generated business plan to the terminal and presents it to the user. The terminal displays the plan in a visually organized format to make it easy for the user to review.

[0239] Step 5:

[0240] Users check the contents of the plan generated through their device and enter feedback and requests for revisions. This feedback information is then sent from the device to the server.

[0241] Step 6:

[0242] The server analyzes user feedback and generates an updated business plan with necessary modifications. Furthermore, it retrieves subsidy and advertising strategy information from relevant databases and integrates it into the plan.

[0243] Step 7:

[0244] The final version of the business plan, incorporating revisions and additional information, is resent to the terminal and displayed to the user. The user can then review this final version and download or print it as needed to complete the business preparations.

[0245] (Example 1)

[0246] 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."

[0247] In business, creating an effective business plan quickly is a challenging task. In particular, efficiently utilizing past success and failure stories requires expertise and time, making it a significant burden for many small and medium-sized enterprises (SMEs). Furthermore, incorporating the latest information while updating plans is not easy. This hinders the creation of high-quality business plans, leading to a decrease in the success rate of businesses.

[0248] 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.

[0249] In this invention, the server includes means for acquiring a collection of information on past business success and failure cases and analyzing it using natural language processing technology; means for structuring the analyzed information and extracting relevant items according to the user's business category and objectives; and means for automatically constructing a business plan using a plan template based on the extracted items. This enables the rapid, effective creation of high-quality business plans.

[0250] An "information set" is a collection of data gathered for a specific purpose, which may include past examples of business successes and failures.

[0251] "Natural language processing technology" is a technology that enables computers to understand, interpret, and generate human language, and in this system, it is used for analyzing text data.

[0252] A "business category" is a category used to classify specific business activities and refers to the field related to the user's business.

[0253] A "business plan template" is a template used to form the foundation of a business plan, providing the basic structure of the plan to be constructed.

[0254] A "terminal" is a device used by a user to input information, and includes an interface for accessing the system.

[0255] "Opinions" refer to feedback and revision requests provided by users, which are used to improve the generated business plans.

[0256] A "subsidy" is funding provided to projects that meet specific conditions, and it is important information for obtaining financial support in a business plan.

[0257] A "marketing strategy" is a plan designed to increase awareness of a particular product or service in the market, and it is an important element of any plan.

[0258] A description of the embodiment for carrying out the invention will be provided.

[0259] This system operates through the collaboration of servers, terminals, and users. The servers are built, for example, in a cloud computing environment. In this environment, the servers process vast amounts of data and use natural language processing techniques to analyze past business successes and failures. This analysis process utilizes generative AI models such as BERT and GPT, which are used to understand text data and extract business-related information.

[0260] The server uses the analyzed information to structure the data and automatically generates a business plan tailored to the user's business category and objectives. Because this plan is created using a template, consistency and quality are ensured. The generated business plan is sent to the user's terminal, where they review it and provide feedback as needed. These terminals include PCs and tablets, equipped with the necessary functions for secure communication with the server.

[0261] Furthermore, the server collects user feedback and analyzes it using AI technology. Based on this feedback analysis, the server revises the business plan for improvement and adds the latest information on subsidies and advertising strategies if necessary. This allows users to obtain a concrete and actionable business plan.

[0262] As a concrete example, consider a user who is launching a retail business selling a new product. The user uses a terminal to input information such as the target market, budget, and competitor information, and the server generates a plan based on past success stories. The user can review the plan and provide feedback on areas for improvement, thereby receiving an even more optimized plan.

[0263] An example of a prompt might be, "Generate a business plan for a new product based on successful retail business examples." This prompt allows the server to begin generating an appropriate business plan.

[0264] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0265] Step 1:

[0266] The server collects data on past business success and failure cases from the internet and dedicated databases. This process efficiently retrieves relevant text data using APIs and web scraping techniques. The input requires business-related data source URLs and API keys, and the output is a raw dataset of business case studies.

[0267] Step 2:

[0268] The server analyzes the collected data using natural language processing techniques. Specifically, it inputs text data into a generative AI model (e.g., BERT or GPT) to extract features that indicate factors for success or failure. The generative AI model analyzes the structure of the text and extracts highly relevant keywords and phrases. As a result, the input becomes a raw dataset, and the output becomes structured feature data.

[0269] Step 3:

[0270] The server extracts relevant information that matches the user's business category and objectives based on structured feature data. The business category and goals provided by the user to the system are used as filtering criteria. The input consists of structured feature data and user-specified category information, and the output is a list of relevant items that match the user's needs.

[0271] Step 4:

[0272] Users input business ideas and goals through a terminal, which are then sent to the server. The terminal allows users to easily input specific business information through forms and guides. User-provided business information is the input, and that data is sent to the server as output.

[0273] Step 5:

[0274] The server applies the extracted items to a template and automatically builds a user-specific work plan. It utilizes a generation AI model to generate the plan by embedding the necessary details into the plan template. The input consists of relevant items and a template, and the output is a customized work plan.

[0275] Step 6:

[0276] The server sends the generated work plan to the user's terminal, and the user reviews its contents. The user displays the work plan on their terminal and provides feedback as needed. The generated work plan is the input, and the user's feedback is the output.

[0277] Step 7:

[0278] The server analyzes user feedback and modifies the business plan accordingly. It makes the plan more practical by adding the latest subsidy information and advertising strategies. The server re-analyzes the feedback and generates a final version of the plan with necessary revisions. The input is user feedback, and the output is the improved business plan.

[0279] (Application Example 1)

[0280] 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 glasses 214 will be referred to as the "terminal."

[0281] Traditional commercial planning processes fail to fully utilize past successes and failures, making it difficult to quickly generate specific plans tailored to individual stores. Furthermore, they lack mechanisms for efficiently incorporating user feedback and continuously improving plans. Therefore, there is a need for a faster and more flexible planning and improvement process.

[0282] 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.

[0283] In this invention, the server includes means for collecting information on past commercial activity success and failure cases and processing it through natural language analysis, means for formalizing the processed information and selecting relevant information according to the user's commercial category and purpose, and means for generating and improving plans based on physical store placement, customer target analysis, and budget allocation by utilizing smartphones. As a result, users can quickly and easily generate and improve specific commercial plans suitable for individual physical stores while making use of past success cases.

[0284] "Commercial activity success and failure cases" refer to specific cases of success and failure as the results of past commercial activities, and are information for extracting their factors through analysis.

[0285] "Natural language analysis" is a technology that converts natural language into a form that can be understood by a computer for analysis, and can highly analyze the content of information.

[0286] "Formalization" is a process of converting the processed information into data in a unified format to enable further processing and searching.

[0287] "User" refers to an individual or organization that creates and improves a commercial plan using this system.

[0288] "Commercial category" is a classification related to specific commercial activities or markets, and indicates the scope to which the user's business activities belong.

[0289] "Purpose" refers to a specific goal or outcome that the user intends to achieve by using this system.

[0290] "Smartphone" is a type of mobile information terminal, and because it has computing power and connection functions, it is a device used for creating and improving commercial plans.

[0291] "Physical store" refers to a store that exists in a physical location and provides goods or services to consumers.

[0292] "Customer analysis" is the process of analyzing the characteristics and behavior of target customers in commercial activities and formulating sales strategies based on that analysis.

[0293] "Budget allocation" is the process by which users effectively distribute funds allocated for commercial activities across different activities and projects.

[0294] This invention provides a system for efficiently generating and improving commercial plans by leveraging past successes and failures in commercial activities. The server collects information on successful and unsuccessful commercial activities and processes this information using natural language processing technology. Python libraries such as spaCy and NLTK can be used for this analysis. The server formalizes the analyzed information and selects relevant information according to the user's commercial category and purpose. This formalized data is processed through an AWS cloud server.

[0295] Users can begin creating a business plan using a smartphone application. Through the application, users input information about their business vision, objectives, and budget, and the server supports them in generating and improving an optimal business plan based on this information. As a concrete example, consider a case where a store owner wants to open a cafe in the suburbs. In this case, the user inputs information about their objectives and target market via their smartphone, and the server presents the user with a business plan based on automatically generated templates that refer to past cases.

[0296] In this plan generation process, the server utilizes information on physical store locations, customer analysis, and budget allocation to provide actionable suggestions tailored to the user's specific business situation. Through this process, users can quickly obtain an appropriate commercial plan and use it to guide their subsequent business development.

[0297] An example of a prompt for a generative AI model is: "Analyze retail business case studies and generate a customized commercial plan based on the target market. Utilize a template that includes success factors for cafes and suggest ways to maximize return on investment within the budget."

[0298] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0299] Step 1:

[0300] The server collects information on past successful and unsuccessful commercial activities. The input is accumulated data on commercial activities, which is analyzed using natural language processing techniques (such as the Python libraries spaCy and NLTK) to extract factors contributing to success and failure. The output is structured data containing this factor information.

[0301] Step 2:

[0302] The server formalizes structured information and selects relevant information based on the user's business category and purpose. Input is the user's business category and vision entered via a terminal, and the server searches the database for appropriate success stories. Output is a template containing the selected relevant information.

[0303] Step 3:

[0304] The user inputs their business objectives and budget via a terminal. The terminal sends the input information to a server, which provides the data necessary for generating a business plan. The input consists of the user's business ideas and goals, and the output forms a request for a generated plan template.

[0305] Step 4:

[0306] The server automatically generates a business plan using a template based on the selected information. The input is the template and the user's input information, and the data is processed into specific implementation procedures and recommendations suitable for the business plan. The output is provided to the user as the generated business plan.

[0307] Step 5:

[0308] The user checks the generated business plan displayed on the terminal and provides feedback. The input is the improvements and modification requests proposed by the user for the business plan, and the feedback information is sent to the server as the output.

[0309] Step 6:

[0310] The server modifies the business plan based on the user's feedback and forms the final plan proposal. The input is the feedback information, and the plan is adjusted using the generation AI model to generate the final plan document. The output is the revised final business plan proposal.

[0311] Step 7:

[0312] The server obtains subsidy and advertising strategy information suitable for the user's business activities and adds it to the business plan. The input is the information on the user's business content and the target market, and the latest subsidy information is obtained from an external database. The output is the business plan document with the added information.

[0313] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform specific processing using the user's emotion.

[0314] This invention is a system that recognizes user emotions when generating and improving business plans, and utilizes that information to make more appropriate business proposals. The system's main components are a server, terminals, and a user interface. In particular, an emotion engine analyzes user emotional feedback and uses that data to optimize the plan.

[0315] The server collects past business success and failure stories from online databases and public repositories, and analyzes the data using natural language processing. The results of this analysis are stored as structured data. Users can input the necessary information for business plan generation via their terminals and send their business ideas and goals to the server.

[0316] Furthermore, the terminal is equipped with an interface for analyzing user emotions, and an emotion engine detects the user's emotions during input and feedback. This information is sent to the server and taken into consideration when generating and improving business plans. Specifically, if the user's emotions are positive, the server can incorporate proactive strategies and new suggestions, while if negative emotions are detected, it strengthens suggestions to mitigate risks.

[0317] The generated business plan is displayed on the terminal and provided in a user-friendly format. Users can review the plan and provide feedback, including emotional responses. This feedback is then analyzed again by the emotion engine, and the server uses the analysis results to optimize the business plan and adjust subsidy information and advertising strategies.

[0318] As a concrete example, let's assume a user uses this system while considering the market launch of a new product. If the user wishes to incorporate the sentiment analysis of potential customers into their business plan, the sentiment engine will make suggestions that also reflect market sentiment trends, allowing the business plan to be more market-oriented. Through this invention, users will be able to utilize sentiment information to develop highly personalized business plans.

[0319] The following describes the processing flow.

[0320] Step 1:

[0321] The server collects business success and failure stories, pitch materials, and related documents from online databases and public repositories. This data is analyzed using natural language processing techniques to identify factors contributing to success and failure, and then stored as structured data.

[0322] Step 2:

[0323] The terminal provides an interface to the user, allowing them to input the information necessary to create a business plan (such as business category, target market, and budget). The user enters this information into the terminal and sends it to the server.

[0324] Step 3:

[0325] The device uses an emotion engine to analyze the user's emotions in real time based on user input. This identifies the user's emotional state, and that information is sent to the server.

[0326] Step 4:

[0327] The server analyzes user input and sentiment data, and searches a database of past related cases for similar success stories. It automatically generates business plans based on templates and makes suggestions and adjustments that take user sentiment into account.

[0328] Step 5:

[0329] The server sends the generated business plan to the terminal, which then displays it to the user. The user reviews the plan in detail and provides feedback, along with emotional responses.

[0330] Step 6:

[0331] The server receives user feedback and analysis results from the emotion engine, optimizing the business plan for each task. It also retrieves subsidy information and advertising strategy details as needed, and incorporates them into the plan.

[0332] Step 7:

[0333] The final version of the business plan will be resent to the terminal. After reviewing it, the user can download or print the plan and use it to further develop their business.

[0334] (Example 2)

[0335] 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".

[0336] Conventional business plan generation systems have struggled to formulate plans that adequately consider user sentiment and market sentiment trends. As a result, they have been unable to generate highly accurate plans that reflect the individual needs and emotions of users, making it difficult to formulate optimal business strategies.

[0337] 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.

[0338] In this invention, the server includes means for collecting information on past industrial success and failure cases and analyzing it using natural language processing; means for automatically generating a plan based on a template, taking into account the analyzed information and the user's emotional information; and means for analyzing the user's emotional feedback and utilizing that information to optimize the plan. This makes it possible to incorporate the user's emotional information and generate highly personalized business plans that meet individual needs.

[0339] "Natural language processing" is a technology that analyzes, understands, and generates natural language used by humans in order to make textual information easier to process by computers.

[0340] "Emotional information" refers to data that indicates the emotional state of a user, extracted from their input and responses.

[0341] "Structured information" refers to data that has been organized through analysis and is managed based on specific patterns or formats.

[0342] A "plan" is a document or information that outlines a set of strategies and guidelines for action generated to achieve a specific objective.

[0343] "Emotional feedback" refers to responses that include emotional opinions and impressions expressed by users as a reaction to a plan or system.

[0344] A "template" refers to a pre-prepared model or format that is referenced when generating a plan.

[0345] "Grant information" refers to information about financial assistance and economic support available for business activities.

[0346] "Advertising strategy" refers to advertising activities and promotional methods planned to effectively communicate a product or service to the market.

[0347] This system generates business plans that take user emotions into consideration and consists of a server, terminals, a user interface, and an emotion engine.

[0348] The server collects information on industry success and failure stories from online databases and public repositories. This information is analyzed using natural language processing tools (e.g., NLTK and Spacy) and stored as structured data. This data is useful for generating and improving business plans.

[0349] Users can input detailed business ideas and goals through the terminal's interface. This information is sent to the server based on technologies used as generative AI models (e.g., GPT or other similar models).

[0350] The terminal is equipped with sentiment analysis software to analyze the user's emotions, such as IBM Watson Tone Analyzer or Microsoft Azure Text Analytics. This allows the terminal to detect the user's emotions during input and feedback, and send the results to the server.

[0351] The generated business plan is structured to include proactive suggestions when positive emotions are detected, and risk mitigation suggestions when negative emotions are detected. This allows the terminal to present the plan to the user in a visually easy-to-understand format. The user reviews the plan and provides emotional feedback. This feedback is then analyzed again on the server side to further optimize the business plan.

[0352] A concrete example is a user considering launching a new product who wants to incorporate the sentiment analysis of potential customers into their business plan. In this case, the sentiment engine can provide suggestions that reflect market sentiment trends, presenting a plan that is more adapted to the market.

[0353] As an example of a prompt, it is possible to utilize a generative AI model in the form of a message such as, "Develop a strategy for entering a new market. Please state your business idea with positive emotions."

[0354] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0355] Step 1:

[0356] The server collects past industry success and failure stories from external sources into a database. It references online databases and public repositories as input and analyzes the data using natural language processing tools. This analysis outputs the case study data in a structured format, which is then stored within the server. This organizes the information that forms the basis for generating business plans.

[0357] Step 2:

[0358] Users input business ideas and goals through the terminal's interface. The terminal receives detailed information about the user's business concept and achievement goals as input, and sends this information to a generating AI model. The AI ​​model analyzes this information and outputs basic data for planning. This prepares the system to create a plan tailored to the user's specific needs.

[0359] Step 3:

[0360] The terminal uses sentiment analysis software to detect user emotions during input and feedback. User actions via keyboard and mouse are passed to the sentiment analysis engine and output as positive or negative sentiment indicators. This allows the system to reflect emotional information in plan generation.

[0361] Step 4:

[0362] The server uses a generative AI model to create a business plan based on user input data and sentiment analysis results. Emotional states and business data are fed to the model as input, and emotion-based plan proposals are output. Positive emotions will result in proactive suggestions, while negative emotions will emphasize risk mitigation.

[0363] Step 5:

[0364] The generated business plan is displayed on the terminal, making it accessible to the user. The user reviews the visually displayed plan and provides feedback, including satisfaction levels and suggestions for improvement. This feedback is again emotionally analyzed on the terminal and sent to the server for consideration during the plan adjustment phase.

[0365] (Application Example 2)

[0366] 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."

[0367] In recent years, there has been a growing need for individuals and businesses to utilize sentiment analysis technology to make more accurate decisions in online environments. However, current systems have the problem of not being able to effectively utilize sentiment information and automatically and precisely provide appropriate business proposals and business plans. The present invention aims to solve these problems and provide a system that enables strategic business proposals based on sentiment information.

[0368] 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.

[0369] In this invention, the server includes means for collecting data on past business success and failure cases and analyzing it using natural language processing; means for detecting the user's emotions and adjusting recommendation information based on the analyzed emotion data; and means for analyzing the emotion analysis results in real time and providing highly relevant alternative information. This enables personalized business proposals based on the user's emotions and the optimization of those proposals.

[0370] "Data on past business successes and failures" refers to historical information about business successes and failures, and serves as foundational data for formulating new business plans based on that information.

[0371] Natural language processing is a technology that enables computers to understand, analyze, and process human language, and it is the process of converting text data into useful information.

[0372] "Structured data" refers to data arranged in a specific format, making it easy to search and analyze.

[0373] "User's device" refers to an electronic device used by the user, including smartphones and tablets.

[0374] "Feedback" refers to the reactions and opinions that users provide to a system, and is data used to improve and optimize the system.

[0375] A "template-based business plan" is a template that provides a basic structure for efficiently creating a business plan, and is customized based on the information entered by the user.

[0376] "Support funds" refer to financial assistance provided to business activities that meet specific conditions.

[0377] A "publicity strategy" is a plan of advertising activities designed to increase awareness of a service or product.

[0378] "Emotion detection" is the process of determining the user's current emotional state through their facial expressions and voice.

[0379] "Recommended information" refers to specific recommendations that the system provides, taking into account the user's needs and preferences.

[0380] "Real-time analysis" is a technology that enables immediate information provision without delay by processing and analyzing data instantly.

[0381] "Alternative information" refers to alternative options or suggestions presented when the original information is inappropriate or unsatisfactory to the user.

[0382] The system for implementing this invention will be built as an e-commerce system that utilizes sentiment analysis to provide users with appropriate product recommendations. Specifically, by integrating this system into a smartphone application, the system will detect the emotions a user feels while browsing products and use the obtained sentiment data to provide product recommendations.

[0383] The smartphone uses its front camera to capture the user's facial expressions and runs software for emotion analysis. Emotion detection software, such as the Affectiva SDK, is used to identify positive or negative emotions in real time from the facial expressions. The data obtained from the emotion analysis is immediately sent to a cloud server, where a recommendation algorithm generates appropriate product information. This process enables product suggestions that better match the user's preferences.

[0384] The servers utilize cloud platforms such as Amazon Web Services and Google Cloud AI to rapidly process large amounts of data. The recommendation engine integrates analyzed sentiment data with users' past purchase history data. This engine selects the optimal products by considering current market trends and users' potential purchasing intent.

[0385] For example, if a user is browsing a new camera on their smartphone and shows a smiling expression, the system might suggest a high-quality camera case. On the other hand, if they show a confused expression, the system might explain other camera specifications in detail and even offer a 20% discount coupon.

[0386] An example of a prompt message would be: "When a user opens a specific product page, use the front camera to detect their facial expression and perform emotion analysis, then recommend products based on the result. If they are smiling, suggest related products; if they are confused, notify them of alternative products and discount information." In this way, users can receive highly relevant information about products that interest them in real time.

[0387] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0388] Step 1:

[0389] The device captures the user's face using its front camera. The input is image data taken while the user is using their smartphone. This image data is sent to emotion analysis software based on the Affectiva SDK to generate the user's emotional information. The output is data with emotional labels such as positive, negative, and neutral.

[0390] Step 2:

[0391] The emotion engine analyzes the obtained emotion label data and adjusts the relevant recommendation information. It receives emotion labels as input and uses a generative AI model to create prompt sentences that provide product recommendations according to each emotion. Here, as part of the data processing, the user's emotions are integrated with past purchase data, and the adjusted prompt sentences are generated as output.

[0392] Step 3:

[0393] The server uses prompts to send data to a cloud-based AI platform to generate appropriate product information. The input consists of the tailored prompts and associated product data. Data processing on the AI ​​platform generates a list of products best suited to the user. The output is a product recommendation list.

[0394] Step 4:

[0395] The terminal displays the generated product recommendation list on the user's screen. The input is the product recommendation list, which is visually presented to the user through the user interface. As output, the user is shown information about related products and discount coupons, providing information that leads to the next purchase action.

[0396] 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.

[0397] 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.

[0398] 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.

[0399] [Third Embodiment]

[0400] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0401] 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.

[0402] 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).

[0403] 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.

[0404] 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.

[0405] 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).

[0406] 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.

[0407] 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.

[0408] 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.

[0409] 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.

[0410] 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.

[0411] 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".

[0412] This invention is a system for efficiently generating and improving business plans. Specifically, a server plays a major role in collecting data on past business successes and failures and analyzing this data using natural language processing technology. Through this analysis, key features that contribute to success or failure are extracted and stored as structured data.

[0413] Users access this system to create business plans using their terminals, entering detailed information about their business ideas and goals. The terminals facilitate user input through an input interface. This information is sent to a server, which searches for relevant business success and failure stories based on the user's business category and goals.

[0414] The server leverages structured data to automatically generate business plans based on templates. The generated plans are sent to the terminal, where users can review them in detail. If the user provides feedback, the server analyzes that feedback and modifies the business plan as needed.

[0415] As part of this process, the server extracts the latest subsidy information and advertising strategy data relevant to the user's business. This information is then added to the user's business plan, resulting in a more specific and actionable plan.

[0416] As a concrete example, let's consider a scenario where a user wants to launch a retail business selling a new product. The user inputs business details (e.g., target market, budget, competitor analysis) into a terminal, and the server analyzes past successful retail business examples, automatically generating a business plan based on its success factors. The user reviews the plan and provides feedback on areas for improvement, and the server updates the plan accordingly, adding any available subsidy information. This entire process allows the user to quickly and efficiently prepare for a higher-quality business venture.

[0417] The following describes the processing flow.

[0418] Step 1:

[0419] The server collects past business success and failure stories, pitch materials, and related documents from online databases and public repositories. The collected data is analyzed using natural language processing to identify factors contributing to success and failure, and then stored as structured data.

[0420] Step 2:

[0421] The terminal presents the user with an input interface, allowing them to enter the information necessary to generate a business plan, such as business category, target market, and goals. The user enters this information and sends it to the server via the terminal.

[0422] Step 3:

[0423] The server analyzes the information received from the user and searches for relevant cases in the database. Using the analyzed data, it automatically generates an initial business plan based on a template.

[0424] Step 4:

[0425] The server sends the generated business plan to the terminal and presents it to the user. The terminal displays the plan in a visually organized format to make it easy for the user to review.

[0426] Step 5:

[0427] Users check the contents of the plan generated through their device and enter feedback and requests for revisions. This feedback information is then sent from the device to the server.

[0428] Step 6:

[0429] The server analyzes user feedback and generates an updated business plan with necessary modifications. Furthermore, it retrieves subsidy and advertising strategy information from relevant databases and integrates it into the plan.

[0430] Step 7:

[0431] The final version of the business plan, incorporating revisions and additional information, is resent to the terminal and displayed to the user. The user can then review this final version and download or print it as needed to complete the business preparations.

[0432] (Example 1)

[0433] 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."

[0434] In business, creating an effective business plan quickly is a challenging task. In particular, efficiently utilizing past success and failure stories requires expertise and time, making it a significant burden for many small and medium-sized enterprises (SMEs). Furthermore, incorporating the latest information while updating plans is not easy. This hinders the creation of high-quality business plans, leading to a decrease in the success rate of businesses.

[0435] 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.

[0436] In this invention, the server includes means for acquiring a collection of information on past business success and failure cases and analyzing it using natural language processing technology; means for structuring the analyzed information and extracting relevant items according to the user's business category and objectives; and means for automatically constructing a business plan using a plan template based on the extracted items. This enables the rapid, effective creation of high-quality business plans.

[0437] An "information set" is a collection of data gathered for a specific purpose, which may include past examples of business successes and failures.

[0438] "Natural language processing technology" is a technology that enables computers to understand, interpret, and generate human language, and in this system, it is used for analyzing text data.

[0439] A "business category" is a category used to classify specific business activities and refers to the field related to the user's business.

[0440] A "business plan template" is a template used to form the foundation of a business plan, providing the basic structure of the plan to be constructed.

[0441] A "terminal" is a device used by a user to input information, and includes an interface for accessing the system.

[0442] "Opinions" refer to feedback and revision requests provided by users, which are used to improve the generated business plans.

[0443] A "subsidy" is funding provided to projects that meet specific conditions, and it is important information for obtaining financial support in a business plan.

[0444] A "marketing strategy" is a plan designed to increase awareness of a particular product or service in the market, and it is an important element of any plan.

[0445] A description of the embodiment for carrying out the invention will be provided.

[0446] This system operates through the collaboration of servers, terminals, and users. The servers are built, for example, in a cloud computing environment. In this environment, the servers process vast amounts of data and use natural language processing techniques to analyze past business successes and failures. This analysis process utilizes generative AI models such as BERT and GPT, which are used to understand text data and extract business-related information.

[0447] The server uses the analyzed information to structure the data and automatically generates a business plan tailored to the user's business category and objectives. Because this plan is created using a template, consistency and quality are ensured. The generated business plan is sent to the user's terminal, where they review it and provide feedback as needed. These terminals include PCs and tablets, equipped with the necessary functions for secure communication with the server.

[0448] Furthermore, the server collects user feedback and analyzes it using AI technology. Based on this feedback analysis, the server revises the business plan for improvement and adds the latest information on subsidies and advertising strategies if necessary. This allows users to obtain a concrete and actionable business plan.

[0449] As a concrete example, consider a user who is launching a retail business selling a new product. The user uses a terminal to input information such as the target market, budget, and competitor information, and the server generates a plan based on past success stories. The user can review the plan and provide feedback on areas for improvement, thereby receiving an even more optimized plan.

[0450] An example of a prompt might be, "Generate a business plan for a new product based on successful retail business examples." This prompt allows the server to begin generating an appropriate business plan.

[0451] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0452] Step 1:

[0453] The server collects data on past business success and failure cases from the internet and dedicated databases. This process efficiently retrieves relevant text data using APIs and web scraping techniques. The input requires business-related data source URLs and API keys, and the output is a raw dataset of business case studies.

[0454] Step 2:

[0455] The server analyzes the collected data using natural language processing techniques. Specifically, it inputs text data into a generative AI model (e.g., BERT or GPT) to extract features that indicate factors for success or failure. The generative AI model analyzes the structure of the text and extracts highly relevant keywords and phrases. As a result, the input becomes a raw dataset, and the output becomes structured feature data.

[0456] Step 3:

[0457] The server extracts relevant information that matches the user's business category and objectives based on structured feature data. The business category and goals provided by the user to the system are used as filtering criteria. The input consists of structured feature data and user-specified category information, and the output is a list of relevant items that match the user's needs.

[0458] Step 4:

[0459] Users input business ideas and goals through a terminal, which are then sent to the server. The terminal allows users to easily input specific business information through forms and guides. User-provided business information is the input, and that data is sent to the server as output.

[0460] Step 5:

[0461] The server applies the extracted items to a template and automatically builds a user-specific work plan. It utilizes a generation AI model to generate the plan by embedding the necessary details into the plan template. The input consists of relevant items and a template, and the output is a customized work plan.

[0462] Step 6:

[0463] The server sends the generated work plan to the user's terminal, and the user reviews its contents. The user displays the work plan on their terminal and provides feedback as needed. The generated work plan is the input, and the user's feedback is the output.

[0464] Step 7:

[0465] The server analyzes user feedback and modifies the business plan accordingly. It makes the plan more practical by adding the latest subsidy information and advertising strategies. The server re-analyzes the feedback and generates a final version of the plan with necessary revisions. The input is user feedback, and the output is the improved business plan.

[0466] (Application Example 1)

[0467] 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."

[0468] Traditional commercial planning processes fail to fully utilize past successes and failures, making it difficult to quickly generate specific plans tailored to individual stores. Furthermore, they lack mechanisms for efficiently incorporating user feedback and continuously improving plans. Therefore, there is a need for a faster and more flexible planning and improvement process.

[0469] 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.

[0470] This invention includes a server that collects information on past successful and unsuccessful commercial activities and processes it using natural language processing; a server that formalizes the processed information and selects relevant information according to the user's commercial category and purpose; and a server that uses a smartphone to generate and improve plans based on physical store placement, customer analysis, and budget allocation. This enables users to quickly and easily generate and improve specific commercial plans tailored to individual physical stores while leveraging past success stories.

[0471] "Success and failure cases of commercial activities" refer to specific cases of success and failure resulting from past commercial activities, and are information used to extract the factors behind them through analysis.

[0472] "Natural language processing" is a technology that converts natural language into a form that computers can understand and analyze, allowing for a high level of analysis of the content of information.

[0473] "Formalization" is the process of converting processed information into data in a unified format, enabling further processing and retrieval.

[0474] "User" refers to an individual or organization that uses this system to create or improve commercial plans.

[0475] A "commercial category" is a classification related to specific commercial activities or markets, indicating the scope to which a user's business activities belong.

[0476] "Purpose" refers to the specific goals or outcomes that users intend to achieve by using this system.

[0477] A "smartphone" is a type of portable information terminal equipped with computing power and connectivity, and is used for creating and improving commercial plans.

[0478] A "physical store" refers to a store that exists in a physical location and provides goods or services to consumers.

[0479] "Customer analysis" is the process of analyzing the characteristics and behavior of target customers in commercial activities and formulating sales strategies based on that analysis.

[0480] "Budget allocation" is the process by which users effectively distribute funds allocated for commercial activities across different activities and projects.

[0481] This invention provides a system for efficiently generating and improving commercial plans by leveraging past successes and failures in commercial activities. The server collects information on successful and unsuccessful commercial activities and processes this information using natural language processing technology. Python libraries such as spaCy and NLTK can be used for this analysis. The server formalizes the analyzed information and selects relevant information according to the user's commercial category and purpose. This formalized data is processed through an AWS cloud server.

[0482] Users can begin creating a business plan using a smartphone application. Through the application, users input information about their business vision, objectives, and budget, and the server supports them in generating and improving an optimal business plan based on this information. As a concrete example, consider a case where a store owner wants to open a cafe in the suburbs. In this case, the user inputs information about their objectives and target market via their smartphone, and the server presents the user with a business plan based on automatically generated templates that refer to past cases.

[0483] In this plan generation process, the server utilizes information on physical store locations, customer analysis, and budget allocation to provide actionable suggestions tailored to the user's specific business situation. Through this process, users can quickly obtain an appropriate commercial plan and use it to guide their subsequent business development.

[0484] An example of a prompt for a generative AI model is: "Analyze retail business case studies and generate a customized commercial plan based on the target market. Utilize a template that includes success factors for cafes and suggest ways to maximize return on investment within the budget."

[0485] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0486] Step 1:

[0487] The server collects information on past successful and unsuccessful commercial activities. The input is accumulated data on commercial activities, which is analyzed using natural language processing techniques (such as the Python libraries spaCy and NLTK) to extract factors contributing to success and failure. The output is structured data containing this factor information.

[0488] Step 2:

[0489] The server formalizes structured information and selects relevant information based on the user's business category and purpose. Input is the user's business category and vision entered via a terminal, and the server searches the database for appropriate success stories. Output is a template containing the selected relevant information.

[0490] Step 3:

[0491] The user inputs their business objectives and budget via a terminal. The terminal sends the input information to a server, which provides the data necessary for generating a business plan. The input consists of the user's business ideas and goals, and the output forms a request for a generated plan template.

[0492] Step 4:

[0493] The server automatically generates a commercial plan using a template based on the selected information. The input consists of the template and user input information, which is then processed into specific execution steps and recommendations suitable for the commercial plan. The output is provided to the user as the generated commercial plan.

[0494] Step 5:

[0495] Users review the generated commercial plan displayed on their terminal and provide feedback. The input consists of suggestions for improvements and revisions the user proposes for the commercial plan, and the feedback information is sent to the server as output.

[0496] Step 6:

[0497] The server modifies the commercial plan based on user feedback to form the final plan. The input is feedback information, and a generative AI model is used to adjust the plan and generate the final plan document. The output is the modified final commercial plan.

[0498] Step 7:

[0499] The server retrieves subsidy and advertising strategy information suitable for the user's commercial activities and adds it to the commercial plan. The input is information about the user's commercial content and target market, and the server retrieves the latest subsidy information from an external database. The output is the commercial plan with the added information.

[0500] 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.

[0501] This invention is a system that recognizes user emotions when generating and improving business plans, and utilizes that information to make more appropriate business proposals. The system's main components are a server, terminals, and a user interface. In particular, an emotion engine analyzes user emotional feedback and uses that data to optimize the plan.

[0502] The server collects past business success and failure stories from online databases and public repositories, and analyzes the data using natural language processing. The results of this analysis are stored as structured data. Users can input the necessary information for business plan generation via their terminals and send their business ideas and goals to the server.

[0503] Furthermore, the terminal is equipped with an interface for analyzing user emotions, and an emotion engine detects the user's emotions during input and feedback. This information is sent to the server and taken into consideration when generating and improving business plans. Specifically, if the user's emotions are positive, the server can incorporate proactive strategies and new suggestions, while if negative emotions are detected, it strengthens suggestions to mitigate risks.

[0504] The generated business plan is displayed on the terminal and provided in a user-friendly format. Users can review the plan and provide feedback, including emotional responses. This feedback is then analyzed again by the emotion engine, and the server uses the analysis results to optimize the business plan and adjust subsidy information and advertising strategies.

[0505] As a concrete example, let's assume a user uses this system while considering the market launch of a new product. If the user wishes to incorporate the sentiment analysis of potential customers into their business plan, the sentiment engine will make suggestions that also reflect market sentiment trends, allowing the business plan to be more market-oriented. Through this invention, users will be able to utilize sentiment information to develop highly personalized business plans.

[0506] The following describes the processing flow.

[0507] Step 1:

[0508] The server collects business success and failure stories, pitch materials, and related documents from online databases and public repositories. This data is analyzed using natural language processing techniques to identify factors contributing to success and failure, and then stored as structured data.

[0509] Step 2:

[0510] The terminal provides an interface to the user, allowing them to input the information necessary to create a business plan (such as business category, target market, and budget). The user enters this information into the terminal and sends it to the server.

[0511] Step 3:

[0512] The device uses an emotion engine to analyze the user's emotions in real time based on user input. This identifies the user's emotional state, and that information is sent to the server.

[0513] Step 4:

[0514] The server analyzes user input and sentiment data, and searches a database of past related cases for similar success stories. It automatically generates business plans based on templates and makes suggestions and adjustments that take user sentiment into account.

[0515] Step 5:

[0516] The server sends the generated business plan to the terminal, which then displays it to the user. The user reviews the plan in detail and provides feedback, along with emotional responses.

[0517] Step 6:

[0518] The server receives user feedback and analysis results from the emotion engine, optimizing the business plan for each task. It also retrieves subsidy information and advertising strategy details as needed, and incorporates them into the plan.

[0519] Step 7:

[0520] The final version of the business plan will be resent to the terminal. After reviewing it, the user can download or print the plan and use it to further develop their business.

[0521] (Example 2)

[0522] 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."

[0523] Conventional business plan generation systems have struggled to formulate plans that adequately consider user sentiment and market sentiment trends. As a result, they have been unable to generate highly accurate plans that reflect the individual needs and emotions of users, making it difficult to formulate optimal business strategies.

[0524] 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.

[0525] In this invention, the server includes means for collecting information on past industrial success and failure cases and analyzing it using natural language processing; means for automatically generating a plan based on a template, taking into account the analyzed information and the user's emotional information; and means for analyzing the user's emotional feedback and utilizing that information to optimize the plan. This makes it possible to incorporate the user's emotional information and generate highly personalized business plans that meet individual needs.

[0526] "Natural language processing" is a technology that analyzes, understands, and generates natural language used by humans in order to make textual information easier to process by computers.

[0527] "Emotional information" refers to data that indicates the emotional state of a user, extracted from their input and responses.

[0528] "Structured information" refers to data that has been organized through analysis and is managed based on specific patterns or formats.

[0529] A "plan" is a document or information that outlines a set of strategies and guidelines for action generated to achieve a specific objective.

[0530] "Emotional feedback" refers to responses that include emotional opinions and impressions expressed by users as a reaction to a plan or system.

[0531] A "template" refers to a pre-prepared model or format that is referenced when generating a plan.

[0532] "Grant information" refers to information about financial assistance and economic support available for business activities.

[0533] "Advertising strategy" refers to advertising activities and promotional methods planned to effectively communicate a product or service to the market.

[0534] This system generates business plans that take user emotions into consideration and consists of a server, terminals, a user interface, and an emotion engine.

[0535] The server collects information on industry success and failure stories from online databases and public repositories. This information is analyzed using natural language processing tools (e.g., NLTK and Spacy) and stored as structured data. This data is useful for generating and improving business plans.

[0536] Users can input detailed business ideas and goals through the terminal's interface. This information is sent to the server based on technologies used as generative AI models (e.g., GPT or other similar models).

[0537] The terminal is equipped with sentiment analysis software to analyze the user's emotions, such as IBM Watson Tone Analyzer or Microsoft Azure Text Analytics. This allows the terminal to detect the user's emotions during input and feedback, and send the results to the server.

[0538] The generated business plan is structured to include proactive suggestions when positive emotions are detected, and risk mitigation suggestions when negative emotions are detected. This allows the terminal to present the plan to the user in a visually easy-to-understand format. The user reviews the plan and provides emotional feedback. This feedback is then analyzed again on the server side to further optimize the business plan.

[0539] A concrete example is a user considering launching a new product who wants to incorporate the sentiment analysis of potential customers into their business plan. In this case, the sentiment engine can provide suggestions that reflect market sentiment trends, presenting a plan that is more adapted to the market.

[0540] As an example of a prompt, it is possible to utilize a generative AI model in the form of a message such as, "Develop a strategy for entering a new market. Please state your business idea with positive emotions."

[0541] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0542] Step 1:

[0543] The server collects past industry success and failure stories from external sources into a database. It references online databases and public repositories as input and analyzes the data using natural language processing tools. This analysis outputs the case study data in a structured format, which is then stored within the server. This organizes the information that forms the basis for generating business plans.

[0544] Step 2:

[0545] Users input business ideas and goals through the terminal's interface. The terminal receives detailed information about the user's business concept and achievement goals as input, and sends this information to a generating AI model. The AI ​​model analyzes this information and outputs basic data for planning. This prepares the system to create a plan tailored to the user's specific needs.

[0546] Step 3:

[0547] The terminal uses sentiment analysis software to detect user emotions during input and feedback. User actions via keyboard and mouse are passed to the sentiment analysis engine and output as positive or negative sentiment indicators. This allows the system to reflect emotional information in plan generation.

[0548] Step 4:

[0549] The server uses a generative AI model to create a business plan based on user input data and sentiment analysis results. Emotional states and business data are fed to the model as input, and emotion-based plan proposals are output. Positive emotions will result in proactive suggestions, while negative emotions will emphasize risk mitigation.

[0550] Step 5:

[0551] The generated business plan is displayed on the terminal, making it accessible to the user. The user reviews the visually displayed plan and provides feedback, including satisfaction levels and suggestions for improvement. This feedback is again emotionally analyzed on the terminal and sent to the server for consideration during the plan adjustment phase.

[0552] (Application Example 2)

[0553] 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."

[0554] In recent years, there has been a growing need for individuals and businesses to utilize sentiment analysis technology to make more accurate decisions in online environments. However, current systems have the problem of not being able to effectively utilize sentiment information and automatically and precisely provide appropriate business proposals and business plans. The present invention aims to solve these problems and provide a system that enables strategic business proposals based on sentiment information.

[0555] 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.

[0556] In this invention, the server includes means for collecting data on past business success and failure cases and analyzing it using natural language processing; means for detecting the user's emotions and adjusting recommendation information based on the analyzed emotion data; and means for analyzing the emotion analysis results in real time and providing highly relevant alternative information. This enables personalized business proposals based on the user's emotions and the optimization of those proposals.

[0557] "Data on past business successes and failures" refers to historical information about business successes and failures, and serves as foundational data for formulating new business plans based on that information.

[0558] Natural language processing is a technology that enables computers to understand, analyze, and process human language, and it is the process of converting text data into useful information.

[0559] "Structured data" refers to data arranged in a specific format, making it easy to search and analyze.

[0560] "User's device" refers to an electronic device used by the user, including smartphones and tablets.

[0561] "Feedback" refers to the reactions and opinions that users provide to a system, and is data used to improve and optimize the system.

[0562] A "template-based business plan" is a template that provides a basic structure for efficiently creating a business plan, and is customized based on the information entered by the user.

[0563] "Support funds" refer to financial assistance provided to business activities that meet specific conditions.

[0564] A "promotional strategy" is a plan of advertising activities designed to increase awareness of a service or product.

[0565] "Emotion detection" is the process of determining the user's current emotional state through their facial expressions and voice.

[0566] "Recommended information" refers to specific recommendations that the system provides, taking into account the user's needs and preferences.

[0567] "Real-time analysis" is a technology that enables immediate information provision without delay by processing and analyzing data instantly.

[0568] "Alternative information" refers to alternative options or suggestions presented when the original information is inappropriate or unsatisfactory to the user.

[0569] The system for implementing this invention will be built as an e-commerce system that utilizes sentiment analysis to provide users with appropriate product recommendations. Specifically, by integrating this system into a smartphone application, the system will detect the emotions a user feels while browsing products and use the obtained sentiment data to provide product recommendations.

[0570] The smartphone uses its front camera to capture the user's facial expressions and runs software for emotion analysis. Emotion detection software, such as the Affectiva SDK, is used to identify positive or negative emotions in real time from the facial expressions. The data obtained from the emotion analysis is immediately sent to a cloud server, where a recommendation algorithm generates appropriate product information. This process enables product suggestions that better match the user's preferences.

[0571] The servers utilize cloud platforms such as Amazon Web Services and Google Cloud AI to rapidly process large amounts of data. The recommendation engine integrates analyzed sentiment data with users' past purchase history data. This engine selects the optimal products by considering current market trends and users' potential purchasing intent.

[0572] For example, if a user is browsing a new camera on their smartphone and shows a smiling expression, the system might suggest a high-quality camera case. On the other hand, if they show a confused expression, the system might explain other camera specifications in detail and even offer a 20% discount coupon.

[0573] An example of a prompt message would be: "When a user opens a specific product page, use the front camera to detect their facial expression and perform emotion analysis, then recommend products based on the result. If they are smiling, suggest related products; if they are confused, notify them of alternative products and discount information." In this way, users can receive highly relevant information about products that interest them in real time.

[0574] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0575] Step 1:

[0576] The device captures the user's face using its front camera. The input is image data taken while the user is using their smartphone. This image data is sent to emotion analysis software based on the Affectiva SDK to generate the user's emotional information. The output is data with emotional labels such as positive, negative, and neutral.

[0577] Step 2:

[0578] The emotion engine analyzes the obtained emotion label data and adjusts the relevant recommendation information. It receives emotion labels as input and uses a generative AI model to create prompt sentences that provide product recommendations according to each emotion. Here, as part of the data processing, the user's emotions are integrated with past purchase data, and the adjusted prompt sentences are generated as output.

[0579] Step 3:

[0580] The server uses prompts to send data to a cloud-based AI platform to generate appropriate product information. The input consists of the tailored prompts and associated product data. Data processing on the AI ​​platform generates a list of products best suited to the user. The output is a product recommendation list.

[0581] Step 4:

[0582] The terminal displays the generated product recommendation list on the user's screen. The input is the product recommendation list, which is visually presented to the user through the user interface. As output, the user is shown information about related products and discount coupons, providing information that leads to the next purchase action.

[0583] 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.

[0584] 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.

[0585] 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.

[0586] [Fourth Embodiment]

[0587] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0588] 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.

[0589] 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).

[0590] 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.

[0591] 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.

[0592] 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).

[0593] 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.

[0594] 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.

[0595] 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.

[0596] 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.

[0597] 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.

[0598] 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.

[0599] 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".

[0600] This invention is a system for efficiently generating and improving business plans. Specifically, a server plays a major role in collecting data on past business successes and failures and analyzing this data using natural language processing technology. Through this analysis, key features that contribute to success or failure are extracted and stored as structured data.

[0601] Users access this system to create business plans using their terminals, entering detailed information about their business ideas and goals. The terminals facilitate user input through an input interface. This information is sent to a server, which searches for relevant business success and failure stories based on the user's business category and goals.

[0602] The server leverages structured data to automatically generate business plans based on templates. The generated plans are sent to the terminal, where users can review them in detail. If the user provides feedback, the server analyzes that feedback and modifies the business plan as needed.

[0603] As part of this process, the server extracts the latest subsidy information and advertising strategy data relevant to the user's business. This information is then added to the user's business plan, resulting in a more specific and actionable plan.

[0604] As a concrete example, let's consider a scenario where a user wants to launch a retail business selling a new product. The user inputs business details (e.g., target market, budget, competitor analysis) into a terminal, and the server analyzes past successful retail business examples, automatically generating a business plan based on its success factors. The user reviews the plan and provides feedback on areas for improvement, and the server updates the plan accordingly, adding any available subsidy information. This entire process allows the user to quickly and efficiently prepare for a higher-quality business venture.

[0605] The following describes the processing flow.

[0606] Step 1:

[0607] The server collects past business success and failure stories, pitch materials, and related documents from online databases and public repositories. The collected data is analyzed using natural language processing to identify factors contributing to success and failure, and then stored as structured data.

[0608] Step 2:

[0609] The terminal presents the user with an input interface, allowing them to enter the information necessary to generate a business plan, such as business category, target market, and goals. The user enters this information and sends it to the server via the terminal.

[0610] Step 3:

[0611] The server analyzes the information received from the user and searches for relevant cases in the database. Using the analyzed data, it automatically generates an initial business plan based on a template.

[0612] Step 4:

[0613] The server sends the generated business plan to the terminal and presents it to the user. The terminal displays the plan in a visually organized format to make it easy for the user to review.

[0614] Step 5:

[0615] Users check the contents of the plan generated through their device and enter feedback and requests for revisions. This feedback information is then sent from the device to the server.

[0616] Step 6:

[0617] The server analyzes user feedback and generates an updated business plan with necessary modifications. Furthermore, it retrieves subsidy and advertising strategy information from relevant databases and integrates it into the plan.

[0618] Step 7:

[0619] The final version of the business plan, incorporating revisions and additional information, is resent to the terminal and displayed to the user. The user can then review this final version and download or print it as needed to complete the business preparations.

[0620] (Example 1)

[0621] 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".

[0622] In business, creating an effective business plan quickly is a challenging task. In particular, efficiently utilizing past success and failure stories requires expertise and time, making it a significant burden for many small and medium-sized enterprises (SMEs). Furthermore, incorporating the latest information while updating plans is not easy. This hinders the creation of high-quality business plans, leading to a decrease in the success rate of businesses.

[0623] 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.

[0624] In this invention, the server includes means for acquiring a collection of information on past business success and failure cases and analyzing it using natural language processing technology; means for structuring the analyzed information and extracting relevant items according to the user's business category and objectives; and means for automatically constructing a business plan using a plan template based on the extracted items. This enables the rapid, effective creation of high-quality business plans.

[0625] An "information set" is a collection of data gathered for a specific purpose, which may include past examples of business successes and failures.

[0626] "Natural language processing technology" is a technology that enables computers to understand, interpret, and generate human language, and in this system, it is used for analyzing text data.

[0627] A "business category" is a category used to classify specific business activities and refers to the field related to the user's business.

[0628] A "business plan template" is a template used to form the foundation of a business plan, providing the basic structure of the plan to be constructed.

[0629] A "terminal" is a device used by a user to input information, and includes an interface for accessing the system.

[0630] "Opinions" refer to feedback and revision requests provided by users, which are used to improve the generated business plans.

[0631] A "subsidy" is funding provided to projects that meet specific conditions, and it is important information for obtaining financial support in a business plan.

[0632] A "marketing strategy" is a plan designed to increase awareness of a particular product or service in the market, and it is an important element of any plan.

[0633] A description of the embodiment for carrying out the invention will be provided.

[0634] This system operates through the collaboration of servers, terminals, and users. The servers are built, for example, in a cloud computing environment. In this environment, the servers process vast amounts of data and use natural language processing techniques to analyze past business successes and failures. This analysis process utilizes generative AI models such as BERT and GPT, which are used to understand text data and extract business-related information.

[0635] The server uses the analyzed information to structure the data and automatically generates a business plan tailored to the user's business category and objectives. Because this plan is created using a template, consistency and quality are ensured. The generated business plan is sent to the user's terminal, where they review it and provide feedback as needed. These terminals include PCs and tablets, equipped with the necessary functions for secure communication with the server.

[0636] Furthermore, the server collects user feedback and analyzes it using AI technology. Based on this feedback analysis, the server revises the business plan for improvement and adds the latest information on subsidies and advertising strategies if necessary. This allows users to obtain a concrete and actionable business plan.

[0637] As a concrete example, consider a user who is launching a retail business selling a new product. The user uses a terminal to input information such as the target market, budget, and competitor information, and the server generates a plan based on past success stories. The user can review the plan and provide feedback on areas for improvement, thereby receiving an even more optimized plan.

[0638] An example of a prompt might be, "Generate a business plan for a new product based on successful retail business examples." This prompt allows the server to begin generating an appropriate business plan.

[0639] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0640] Step 1:

[0641] The server collects data on past business success and failure cases from the internet and dedicated databases. This process efficiently retrieves relevant text data using APIs and web scraping techniques. The input requires business-related data source URLs and API keys, and the output is a raw dataset of business case studies.

[0642] Step 2:

[0643] The server analyzes the collected data using natural language processing techniques. Specifically, it inputs text data into a generative AI model (e.g., BERT or GPT) to extract features that indicate factors for success or failure. The generative AI model analyzes the structure of the text and extracts highly relevant keywords and phrases. As a result, the input becomes a raw dataset, and the output becomes structured feature data.

[0644] Step 3:

[0645] The server extracts relevant information that matches the user's business category and objectives based on structured feature data. The business category and goals provided by the user to the system are used as filtering criteria. The input consists of structured feature data and user-specified category information, and the output is a list of relevant items that match the user's needs.

[0646] Step 4:

[0647] Users input business ideas and goals through a terminal, which are then sent to the server. The terminal allows users to easily input specific business information through forms and guides. User-provided business information is the input, and that data is sent to the server as output.

[0648] Step 5:

[0649] The server applies the extracted items to a template and automatically builds a user-specific work plan. It utilizes a generation AI model to generate the plan by embedding the necessary details into the plan template. The input consists of relevant items and a template, and the output is a customized work plan.

[0650] Step 6:

[0651] The server sends the generated work plan to the user's terminal, and the user reviews its contents. The user displays the work plan on their terminal and provides feedback as needed. The generated work plan is the input, and the user's feedback is the output.

[0652] Step 7:

[0653] The server analyzes user feedback and modifies the business plan accordingly. It makes the plan more practical by adding the latest subsidy information and advertising strategies. The server re-analyzes the feedback and generates a final version of the plan with necessary revisions. The input is user feedback, and the output is the improved business plan.

[0654] (Application Example 1)

[0655] 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".

[0656] Traditional commercial planning processes fail to fully utilize past successes and failures, making it difficult to quickly generate specific plans tailored to individual stores. Furthermore, they lack mechanisms for efficiently incorporating user feedback and continuously improving plans. Therefore, there is a need for a faster and more flexible planning and improvement process.

[0657] 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.

[0658] This invention includes a server that collects information on past successful and unsuccessful commercial activities and processes it using natural language processing; a server that formalizes the processed information and selects relevant information according to the user's commercial category and purpose; and a server that uses a smartphone to generate and improve plans based on physical store placement, customer analysis, and budget allocation. This enables users to quickly and easily generate and improve specific commercial plans tailored to individual physical stores while leveraging past success stories.

[0659] "Success and failure cases of commercial activities" refer to specific cases of success and failure resulting from past commercial activities, and are information used to extract the factors behind them through analysis.

[0660] "Natural language processing" is a technology that converts natural language into a form that computers can understand and analyze, allowing for a high level of analysis of the content of information.

[0661] "Formalization" is the process of converting processed information into data in a unified format, enabling further processing and retrieval.

[0662] "User" refers to an individual or organization that uses this system to create or improve commercial plans.

[0663] A "commercial category" is a classification related to specific commercial activities or markets, indicating the scope to which a user's business activities belong.

[0664] "Purpose" refers to the specific goals or outcomes that users intend to achieve by using this system.

[0665] A "smartphone" is a type of portable information terminal equipped with computing power and connectivity, and is used for creating and improving commercial plans.

[0666] A "physical store" refers to a store that exists in a physical location and provides goods or services to consumers.

[0667] "Customer analysis" is the process of analyzing the characteristics and behavior of target customers in commercial activities and formulating sales strategies based on that analysis.

[0668] "Budget allocation" is the process by which users effectively distribute funds allocated for commercial activities across different activities and projects.

[0669] This invention provides a system for efficiently generating and improving commercial plans by leveraging past successes and failures in commercial activities. The server collects information on successful and unsuccessful commercial activities and processes this information using natural language processing technology. Python libraries such as spaCy and NLTK can be used for this analysis. The server formalizes the analyzed information and selects relevant information according to the user's commercial category and purpose. This formalized data is processed through an AWS cloud server.

[0670] Users can begin creating a business plan using a smartphone application. Through the application, users input information about their business vision, objectives, and budget, and the server supports them in generating and improving an optimal business plan based on this information. As a concrete example, consider a case where a store owner wants to open a cafe in the suburbs. In this case, the user inputs information about their objectives and target market via their smartphone, and the server presents the user with a business plan based on automatically generated templates that refer to past cases.

[0671] In this plan generation process, the server utilizes information on physical store locations, customer analysis, and budget allocation to provide actionable suggestions tailored to the user's specific business situation. Through this process, users can quickly obtain an appropriate commercial plan and use it to guide their subsequent business development.

[0672] An example of a prompt for a generative AI model is: "Analyze retail business case studies and generate a customized commercial plan based on the target market. Utilize a template that includes success factors for cafes and suggest ways to maximize return on investment within the budget."

[0673] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0674] Step 1:

[0675] The server collects information on past successful and unsuccessful commercial activities. The input is accumulated data on commercial activities, which is analyzed using natural language processing techniques (such as the Python libraries spaCy and NLTK) to extract factors contributing to success and failure. The output is structured data containing this factor information.

[0676] Step 2:

[0677] The server formalizes structured information and selects relevant information based on the user's business category and purpose. Input is the user's business category and vision entered via a terminal, and the server searches the database for appropriate success stories. Output is a template containing the selected relevant information.

[0678] Step 3:

[0679] The user inputs their business objectives and budget via a terminal. The terminal sends the input information to a server, which provides the data necessary for generating a business plan. The input consists of the user's business ideas and goals, and the output forms a request for a generated plan template.

[0680] Step 4:

[0681] The server automatically generates a commercial plan using a template based on the selected information. The input consists of the template and user input information, which is then processed into specific execution steps and recommendations suitable for the commercial plan. The output is provided to the user as the generated commercial plan.

[0682] Step 5:

[0683] Users review the generated commercial plan displayed on their terminal and provide feedback. The input consists of suggestions for improvements and revisions the user proposes for the commercial plan, and the feedback information is sent to the server as output.

[0684] Step 6:

[0685] The server modifies the commercial plan based on user feedback to form the final plan. The input is feedback information, and a generative AI model is used to adjust the plan and generate the final plan document. The output is the modified final commercial plan.

[0686] Step 7:

[0687] The server retrieves subsidy and advertising strategy information suitable for the user's commercial activities and adds it to the commercial plan. The input is information about the user's commercial content and target market, and the server retrieves the latest subsidy information from an external database. The output is the commercial plan with the added information.

[0688] 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.

[0689] This invention is a system that recognizes user emotions when generating and improving business plans, and utilizes that information to make more appropriate business proposals. The system's main components are a server, terminals, and a user interface. In particular, an emotion engine analyzes user emotional feedback and uses that data to optimize the plan.

[0690] The server collects past business success and failure stories from online databases and public repositories, and analyzes the data using natural language processing. The results of this analysis are stored as structured data. Users can input the necessary information for business plan generation via their terminals and send their business ideas and goals to the server.

[0691] Furthermore, the terminal is equipped with an interface for analyzing user emotions, and an emotion engine detects the user's emotions during input and feedback. This information is sent to the server and taken into consideration when generating and improving business plans. Specifically, if the user's emotions are positive, the server can incorporate proactive strategies and new suggestions, while if negative emotions are detected, it strengthens suggestions to mitigate risks.

[0692] The generated business plan is displayed on the terminal and provided in a user-friendly format. Users can review the plan and provide feedback, including emotional responses. This feedback is then analyzed again by the emotion engine, and the server uses the analysis results to optimize the business plan and adjust subsidy information and advertising strategies.

[0693] As a concrete example, let's assume a user uses this system while considering the market launch of a new product. If the user wishes to incorporate the sentiment analysis of potential customers into their business plan, the sentiment engine will make suggestions that also reflect market sentiment trends, allowing the business plan to be more market-oriented. Through this invention, users will be able to utilize sentiment information to develop highly personalized business plans.

[0694] The following describes the processing flow.

[0695] Step 1:

[0696] The server collects business success and failure stories, pitch materials, and related documents from online databases and public repositories. This data is analyzed using natural language processing techniques to identify factors contributing to success and failure, and then stored as structured data.

[0697] Step 2:

[0698] The terminal provides an interface to the user, allowing them to input the information necessary to create a business plan (such as business category, target market, and budget). The user enters this information into the terminal and sends it to the server.

[0699] Step 3:

[0700] The device uses an emotion engine to analyze the user's emotions in real time based on user input. This identifies the user's emotional state, and that information is sent to the server.

[0701] Step 4:

[0702] The server analyzes user input and sentiment data, and searches a database of past related cases for similar success stories. It automatically generates business plans based on templates and makes suggestions and adjustments that take user sentiment into account.

[0703] Step 5:

[0704] The server sends the generated business plan to the terminal, which then displays it to the user. The user reviews the plan in detail and provides feedback, along with emotional responses.

[0705] Step 6:

[0706] The server receives user feedback and analysis results from the emotion engine, optimizing the business plan for each task. It also retrieves subsidy information and advertising strategy details as needed, and incorporates them into the plan.

[0707] Step 7:

[0708] The final version of the business plan will be resent to the terminal. After reviewing it, the user can download or print the plan and use it to further develop their business.

[0709] (Example 2)

[0710] 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".

[0711] Conventional business plan generation systems have struggled to formulate plans that adequately consider user sentiment and market sentiment trends. As a result, they have been unable to generate highly accurate plans that reflect the individual needs and emotions of users, making it difficult to formulate optimal business strategies.

[0712] 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.

[0713] In this invention, the server includes means for collecting information on past industrial success and failure cases and analyzing it using natural language processing; means for automatically generating a plan based on a template, taking into account the analyzed information and the user's emotional information; and means for analyzing the user's emotional feedback and utilizing that information to optimize the plan. This makes it possible to incorporate the user's emotional information and generate highly personalized business plans that meet individual needs.

[0714] "Natural language processing" is a technology that analyzes, understands, and generates natural language used by humans in order to make textual information easier to process by computers.

[0715] "Emotional information" refers to data that indicates the emotional state of a user, extracted from their input and responses.

[0716] "Structured information" refers to data that has been organized through analysis and is managed based on specific patterns or formats.

[0717] A "plan" is a document or information that outlines a set of strategies and guidelines for action generated to achieve a specific objective.

[0718] "Emotional feedback" refers to responses that include emotional opinions and impressions expressed by users as a reaction to a plan or system.

[0719] A "template" refers to a pre-prepared model or format that is referenced when generating a plan.

[0720] "Grant information" refers to information about financial assistance and economic support available for business activities.

[0721] "Advertising strategy" refers to advertising activities and promotional methods planned to effectively communicate a product or service to the market.

[0722] This system generates business plans that take user emotions into consideration and consists of a server, terminals, a user interface, and an emotion engine.

[0723] The server collects information on industry success and failure stories from online databases and public repositories. This information is analyzed using natural language processing tools (e.g., NLTK and Spacy) and stored as structured data. This data is useful for generating and improving business plans.

[0724] Users can input detailed business ideas and goals through the terminal's interface. This information is sent to the server based on technologies used as generative AI models (e.g., GPT or other similar models).

[0725] The terminal is equipped with sentiment analysis software to analyze the user's emotions, such as IBM Watson Tone Analyzer or Microsoft Azure Text Analytics. This allows the terminal to detect the user's emotions during input and feedback, and send the results to the server.

[0726] The generated business plan is structured to include proactive suggestions when positive emotions are detected, and risk mitigation suggestions when negative emotions are detected. This allows the terminal to present the plan to the user in a visually easy-to-understand format. The user reviews the plan and provides emotional feedback. This feedback is then analyzed again on the server side to further optimize the business plan.

[0727] A concrete example is a user considering launching a new product who wants to incorporate the sentiment analysis of potential customers into their business plan. In this case, the sentiment engine can provide suggestions that reflect market sentiment trends, presenting a plan that is more adapted to the market.

[0728] As an example of a prompt, it is possible to utilize a generative AI model in the form of a message such as, "Develop a strategy for entering a new market. Please state your business idea with positive emotions."

[0729] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0730] Step 1:

[0731] The server collects past industry success and failure stories from external sources into a database. It references online databases and public repositories as input and analyzes the data using natural language processing tools. This analysis outputs the case study data in a structured format, which is then stored within the server. This organizes the information that forms the basis for generating business plans.

[0732] Step 2:

[0733] Users input business ideas and goals through the terminal's interface. The terminal receives detailed information about the user's business concept and achievement goals as input, and sends this information to a generating AI model. The AI ​​model analyzes this information and outputs basic data for planning. This prepares the system to create a plan tailored to the user's specific needs.

[0734] Step 3:

[0735] The terminal uses sentiment analysis software to detect user emotions during input and feedback. User actions via keyboard and mouse are passed to the sentiment analysis engine and output as positive or negative sentiment indicators. This allows the system to reflect emotional information in plan generation.

[0736] Step 4:

[0737] The server uses a generative AI model to create a business plan based on user input data and sentiment analysis results. Emotional states and business data are fed to the model as input, and emotion-based plan proposals are output. Positive emotions will result in proactive suggestions, while negative emotions will emphasize risk mitigation.

[0738] Step 5:

[0739] The generated business plan is displayed on the terminal, making it accessible to the user. The user reviews the visually displayed plan and provides feedback, including satisfaction levels and suggestions for improvement. This feedback is again emotionally analyzed on the terminal and sent to the server for consideration during the plan adjustment phase.

[0740] (Application Example 2)

[0741] 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".

[0742] In recent years, there has been a growing need for individuals and businesses to utilize sentiment analysis technology to make more accurate decisions in online environments. However, current systems have the problem of not being able to effectively utilize sentiment information and automatically and precisely provide appropriate business proposals and business plans. The present invention aims to solve these problems and provide a system that enables strategic business proposals based on sentiment information.

[0743] 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.

[0744] In this invention, the server includes means for collecting data on past business success and failure cases and analyzing it using natural language processing; means for detecting the user's emotions and adjusting recommendation information based on the analyzed emotion data; and means for analyzing the emotion analysis results in real time and providing highly relevant alternative information. This enables personalized business proposals based on the user's emotions and the optimization of those proposals.

[0745] "Data on past business successes and failures" refers to historical information about business successes and failures, and serves as foundational data for formulating new business plans based on that information.

[0746] Natural language processing is a technology that enables computers to understand, analyze, and process human language, and it is the process of converting text data into useful information.

[0747] "Structured data" refers to data arranged in a specific format, making it easy to search and analyze.

[0748] "User's device" refers to an electronic device used by the user, including smartphones and tablets.

[0749] "Feedback" refers to the reactions and opinions that users provide to a system, and is data used to improve and optimize the system.

[0750] A "template-based business plan" is a template that provides a basic structure for efficiently creating a business plan, and is customized based on the information entered by the user.

[0751] "Support funds" refer to financial assistance provided to business activities that meet specific conditions.

[0752] A "promotional strategy" is a plan of advertising activities designed to increase awareness of a service or product.

[0753] "Emotion detection" is the process of determining the user's current emotional state through their facial expressions and voice.

[0754] "Recommended information" refers to specific recommendations that the system provides, taking into account the user's needs and preferences.

[0755] "Real-time analysis" is a technology that enables immediate information provision without delay by processing and analyzing data instantly.

[0756] "Alternative information" refers to alternative options or suggestions presented when the original information is inappropriate or unsatisfactory to the user.

[0757] The system for implementing this invention will be built as an e-commerce system that utilizes sentiment analysis to provide users with appropriate product recommendations. Specifically, by integrating this system into a smartphone application, the system will detect the emotions a user feels while browsing products and use the obtained sentiment data to provide product recommendations.

[0758] The smartphone uses its front camera to capture the user's facial expressions and runs software for emotion analysis. Emotion detection software, such as the Affectiva SDK, is used to identify positive or negative emotions in real time from the facial expressions. The data obtained from the emotion analysis is immediately sent to a cloud server, where a recommendation algorithm generates appropriate product information. This process enables product suggestions that better match the user's preferences.

[0759] The servers utilize cloud platforms such as Amazon Web Services and Google Cloud AI to rapidly process large amounts of data. The recommendation engine integrates analyzed sentiment data with users' past purchase history data. This engine selects the optimal products by considering current market trends and users' potential purchasing intent.

[0760] For example, if a user is browsing a new camera on their smartphone and shows a smiling expression, the system might suggest a high-quality camera case. On the other hand, if they show a confused expression, the system might explain other camera specifications in detail and even offer a 20% discount coupon.

[0761] An example of a prompt message would be: "When a user opens a specific product page, use the front camera to detect their facial expression and perform emotion analysis, then recommend products based on the result. If they are smiling, suggest related products; if they are confused, notify them of alternative products and discount information." In this way, users can receive highly relevant information about products that interest them in real time.

[0762] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0763] Step 1:

[0764] The device captures the user's face using its front camera. The input is image data taken while the user is using their smartphone. This image data is sent to emotion analysis software based on the Affectiva SDK to generate the user's emotional information. The output is data with emotional labels such as positive, negative, and neutral.

[0765] Step 2:

[0766] The emotion engine analyzes the obtained emotion label data and adjusts the relevant recommendation information. It receives emotion labels as input and uses a generative AI model to create prompt sentences that provide product recommendations according to each emotion. Here, as part of the data processing, the user's emotions are integrated with past purchase data, and the adjusted prompt sentences are generated as output.

[0767] Step 3:

[0768] The server uses prompts to send data to a cloud-based AI platform to generate appropriate product information. The input consists of the tailored prompts and associated product data. Data processing on the AI ​​platform generates a list of products best suited to the user. The output is a product recommendation list.

[0769] Step 4:

[0770] The terminal displays the generated product recommendation list on the user's screen. The input is the product recommendation list, which is visually presented to the user through the user interface. As output, the user is shown information about related products and discount coupons, providing information that leads to the next purchase action.

[0771] 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.

[0772] 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.

[0773] 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.

[0774] 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.

[0775] 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.

[0776] 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.

[0777] 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.

[0778] 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.

[0779] 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."

[0780] 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.

[0781] 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.

[0782] 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.

[0783] 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.

[0784] 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.

[0785] 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.

[0786] 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.

[0787] 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.

[0788] 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.

[0789] 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.

[0790] 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.

[0791] 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.

[0792] The following is further disclosed regarding the embodiments described above.

[0793] (Claim 1)

[0794] A method for collecting data on past business success and failure cases and analyzing it using natural language processing,

[0795] A means of structuring the analyzed data and selecting relevant information according to the user's business category and goals,

[0796] A means of automatically generating a business plan based on a template using selected information,

[0797] A means of providing the generated business plan to the user's terminal and obtaining feedback from the user,

[0798] A means of revising the business plan based on user feedback and creating a final plan,

[0799] A system that includes means to obtain subsidy and advertising strategy information suitable for the user's business and add it to the business plan.

[0800] (Claim 2)

[0801] The system according to claim 1, which provides the user with guidance on input information via an interface and allows them to input details of a business idea.

[0802] (Claim 3)

[0803] The system according to claim 1, which identifies success factors and failure factors based on the analyzed data and incorporates these into the user feedback analysis.

[0804] "Example 1"

[0805] (Claim 1)

[0806] A method for obtaining a collection of information on past business success and failure cases and analyzing it using natural language processing technology,

[0807] A means of structuring the analyzed information and extracting relevant items according to the user's business category and purpose,

[0808] A means of automatically constructing a business plan using a plan template based on the extracted items,

[0809] A means of presenting the constructed business plan to the user's device and collecting feedback from the user,

[0810] A means of adjusting the business plan based on user feedback and forming the final plan,

[0811] A system that includes means to obtain subsidy and advertising policy information suitable for the user's business and add it to the business plan.

[0812] (Claim 2)

[0813] The system according to claim 1, which provides the user with input information via a display device and allows them to fill in the details of their business idea.

[0814] (Claim 3)

[0815] The system according to claim 1, which extracts success factors and failure factors based on the analyzed information and incorporates them into the user opinion analysis.

[0816] "Application Example 1"

[0817] (Claim 1)

[0818] A means of collecting information on past successful and unsuccessful commercial activities and processing it using natural language processing,

[0819] A means of formalizing processed information and selecting relevant information according to the user's commercial category and purpose,

[0820] A means of automatically generating a template-based commercial plan based on selected information,

[0821] A means of providing the generated commercial plan to the user's computer and obtaining feedback from the user,

[0822] A means of revising the commercial plan based on user feedback and forming the final plan,

[0823] A means of obtaining financial support and information strategies suitable for the user's business and adding them to the business plan.

[0824] A system that utilizes smartphones to generate and improve plans based on physical store placement, customer analysis, and budget allocation.

[0825] (Claim 2)

[0826] The system according to claim 1, which provides users with guidance on inputting information through support, allows them to input details of their commercial concept, and supports users in creating a commercial plan in a short period of time based on past successful store examples.

[0827] (Claim 3)

[0828] The system according to claim 1, which identifies success factors and failure factors based on processed information, integrates these with user opinion analysis, and automatically generates a commercial plan.

[0829] "Example 2 of combining an emotion engine"

[0830] (Claim 1)

[0831] A method for collecting information on past industrial success and failure cases and analyzing it using natural language processing,

[0832] A means of structuring the analyzed information and selecting relevant information according to the user's purpose category and goals,

[0833] A means for automatically generating a plan based on a template, taking into account selected information and user sentiment information,

[0834] A means for providing the generated plan to the user's device and obtaining feedback from the user,

[0835] A means of revising the plan based on user feedback and creating the final plan,

[0836] A means of obtaining grant and advertising strategy information suitable for the user's objectives and adding it to the plan,

[0837] A system that includes means for analyzing users' emotional feedback and utilizing that information to optimize the proposed plan.

[0838] (Claim 2)

[0839] The system according to claim 1, which provides the user with guidelines for input information via an interface and allows them to input details of their ideas.

[0840] (Claim 3)

[0841] The system according to claim 1, which identifies success factors and failure factors based on the analyzed information and incorporates this into the analysis of the user's emotional feedback.

[0842] "Application example 2 when combining with an emotional engine"

[0843] (Claim 1)

[0844] A method for collecting data on past business success and failure cases and analyzing it using natural language processing,

[0845] A means of structuring the analyzed data and selecting relevant information according to the user's business category and goals,

[0846] A means of automatically generating a business plan based on a template using selected information,

[0847] A means of providing the generated business plan to the user's terminal and obtaining feedback from the user,

[0848] A means of revising the business plan based on user feedback and creating a final plan,

[0849] A means to obtain information on suitable funding and advertising strategies for the user's business and add it to the business plan,

[0850] A means for detecting user emotions and adjusting recommendation information based on the analyzed emotion data,

[0851] A system that includes means for analyzing emotion analysis results in real time and providing highly relevant alternative information.

[0852] (Claim 2)

[0853] The system according to claim 1, which provides the user with guidance on input information via an interface and allows them to input details of their business concept.

[0854] (Claim 3)

[0855] The system according to claim 1, which identifies success factors and failure factors based on the analyzed data and incorporates these into the user feedback analysis. [Explanation of Symbols]

[0856] 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

1. A method for collecting data on past business success and failure cases and analyzing it using natural language processing, A means of structuring the analyzed data and selecting relevant information according to the user's business category and goals, A means of automatically generating a business plan based on a template using selected information, A means of providing the generated business plan to the user's terminal and obtaining feedback from the user, A means of revising the business plan based on user feedback and creating a final plan, A system that includes means to obtain subsidy and advertising strategy information suitable for the user's business and add it to the business plan.

2. The system according to claim 1, which provides the user with guidance on input information via an interface and allows them to input details of a business idea.

3. The system according to claim 1, which identifies success factors and failure factors based on the analyzed data and incorporates these into the user feedback analysis.

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