System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving business orientation data input by a user; means for analyzing the received business orientation data to extract key keywords and intent; and means for generating an appropriate business model based on the extracted keywords and intent.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Many factors contribute to the success of a business, and selecting an appropriate business model is particularly important. However, determining which business model is optimal is difficult, and many businesses fail because they cannot find the right model. It is also essential to run a business while minimizing risk based on examples of success and failure. The purpose of this invention is to provide a system that efficiently and effectively presents optimal business models and provides comprehensive support, simply by letting users know what they want to do. [Means for solving the problem]

[0005] The present invention is a system that includes a means for receiving business direction data input by a user, a means for analyzing the received data to extract key keywords and intentions, and a means for generating an appropriate business model based on the extracted information. It also includes a means for calculating the required stakeholders and development man-hours based on the business model, a means for extracting past success stories and failure stories from a database, and a means for generating a specific action plan. It also includes a means for comprehensively displaying the generated business model, required stakeholders, development man-hours, success stories, and failure stories. This allows users to quickly and efficiently obtain a comprehensive business execution plan.

[0006] Understood. Below are definitions of important terms included in the claims.

[0007] "Business direction data" is information that indicates the business goals and directions that the user wants to achieve.

[0008] "Analysis" is the process of interpreting received data and extracting specific patterns or meanings.

[0009] "Keywords" are important words or phrases extracted from analyzed data that indicate the direction of a business.

[0010] "Intention" refers to the purpose or goal that a user wants to achieve through a business.

[0011] A "business model" is a concept that includes a plan or method of operating a business, such as a particular business method or fee structure.

[0012] "Stakeholders" are parties or interested parties who play an important role in the operation and execution of a business.

[0013] "Development man-hours" refers to the amount of work time and effort required to execute a business or develop a system.

[0014] "Success stories" are specific cases or examples of past business success.

[0015] "Failure cases" are specific cases or examples of past business failures.

[0016] An "action plan" is a list of specific steps or plans for running a business. [Brief explanation of the drawings]

[0017] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

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

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

[0020] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0021] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0023] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0024] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0025] [First embodiment]

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

[0027] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0028] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0029] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0030] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 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 of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0032] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0034] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0035] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0036] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0037] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0038] This invention relates to a system that, by inputting what a user wants to do and the direction of their business, presents an optimal business model and provides relevant stakeholders, development man-hours, success stories and failure stories, and specific action plans. Specific embodiments of this system are described below.

[0039] Overall flow

[0040] 1. User Input

[0041] Users input the direction of the business they want to run, such as a specific business idea like "I want to run an online learning platform."

[0042] 2. Sending input data

[0043] The terminal transmits the business direction data entered by the user to a server, usually via an API.

[0044] 3. Data Analysis

[0045] The server analyzes the incoming data using natural language processing (NLP) libraries, extracting key keywords and intent.

[0046] 4. Business model generation

[0047] The server generates the optimal business model based on the extracted keywords and intent. This generation uses a generative AI model. For example, if the user enters "online learning platform," the server will suggest the "subscription model" as an appropriate business model.

[0048] 5. Stakeholders and Development Effort Calculation

[0049] The server calculates the required stakeholders and development effort based on the generated business model. This data is retrieved from an internal database.

[0050] 6. Presentation of success and failure cases

[0051] The server extracts past success and failure cases from a database, allowing users to refer to the circumstances that led to success or failure.

[0052] 7. Generate an action plan

[0053] The server generates a specific action plan based on the business model, such as "Month 1: Basic design of the platform, Month 2: Start content creation, Month 3: Start beta testing."

[0054] 8. User Feedback

[0055] The terminal displays to the user the business model, stakeholders, development time, success stories and failure stories, and specific action plans received from the server.

[0056] Specific examples

[0057] Below are some specific examples of how this system can be used.

[0058] 1. A user types, "I want to run an online learning platform."

[0059] 2. The device sends the input in JSON format to the server.

[0060] 3. The server uses an NLP library to extract the keyword "online learning platform" and intent.

[0061] 4. The server uses the generative AI model to generate a "subscription model."

[0062] 5. The stakeholders who require a server are the "educational content creator" and the "IT infrastructure manager," and the development time is calculated to be "6 months."

[0063] 6. The server extracts success stories of famous online learning platforms and failure stories of similar services in the past.

[0064] 7. The server generates a specific action plan and presents it as follows: "Month 1: Basic design of the platform, Month 2: Start content creation, Month 3: Start beta testing."

[0065] 8. The device displays all information to the user.

[0066] In this way, users can obtain a comprehensive business model and concrete implementation plan based on their business idea, which can minimize business risks and increase the chances of success.

[0067] The processing flow will be explained below.

[0068] Step 1:

[0069] Users input data about the business they want to run. For example, they can enter a specific business idea into the input form, such as "I want to run an online learning platform."

[0070] Step 2:

[0071] The terminal sends the business direction data entered by the user to the server, usually via an API, packaging the input in JSON format.

[0072] Step 3:

[0073] The server analyzes the incoming data using natural language processing (NLP) libraries, specifically tokenizing the text and performing morphological analysis to extract key keywords and intent.

[0074] Step 4:

[0075] The server generates the optimal business model based on the extracted keywords and intent. This generation process involves using a generative AI model to select the most suitable business model from a number of options. For example, if the user enters "online learning platform," the server will suggest a "subscription model."

[0076] Step 5:

[0077] The server calculates the required stakeholders and development effort based on the generated business model. To do this, it queries an internal database to obtain the roles of the relevant stakeholders and the development effort. As an example, it presents the stakeholders "educational content creator" and "IT infrastructure manager" and the development effort of "6 months."

[0078] Step 6:

[0079] The server extracts past success stories and failure stories from a database. To do this, it searches the database of success stories and failure stories to collect information that users can use as reference. For example, a success story might be presented as a "success story of a famous online learning platform," and a failure story might be presented as a "reason for the failure of a similar service in the past."

[0080] Step 7:

[0081] The server generates a specific action plan based on the business model. This involves creating steps and a timeline that best fit the business model, and listing specific tasks for each step. For example, a step-by-step plan such as "Month 1: Basic platform design, Month 2: Start content creation, Month 3: Start beta testing" is provided.

[0082] Step 8:

[0083] The terminal receives information from the server, including the business model, stakeholders, development time, success stories and failure stories, and a concrete action plan, and displays it to the user. Comprehensive information is displayed on the user interface, allowing the user to create an action plan based on the information presented.

[0084] In this way, the system starts with user input and goes through various data analysis and generation processes to provide the optimal business model and executable plan.

[0085] Example 1

[0086] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0087] Conventional business model generation systems have difficulty effectively analyzing the data entered by users and providing an optimal business model and detailed implementation plan. As a result, users are unable to obtain appropriate advice or a concrete action plan for their business ideas, which reduces the probability of business success.

[0088] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0089] In this invention, the server includes: means for receiving business direction data input by a user; means for analyzing the received business direction data and extracting key keywords and intentions; means for using a generative AI model to generate an appropriate business model based on the extracted keywords and intentions; means for calculating the required stakeholders and development man-hours based on the generated business model; means for extracting past success stories and failure stories from a database; means for generating a specific action plan tailored to the business model; means for comprehensively displaying the generated business model, stakeholders, development man-hours, success stories, and failure stories; means for converting the user's input data into JSON format and sending it to the server; and means for analyzing the received data using a natural language processing library. This allows users to easily obtain a comprehensive business model and a detailed action plan based on their business idea.

[0090] "Business direction data" is information entered by the user regarding specific business ideas and business goals.

[0091] "Means for receiving" refers to a device or program that provides the function of sending and receiving data entered by the user to the server.

[0092] "Means of analysis" are algorithms, libraries, and software modules that process the received data and extract key keywords and intent.

[0093] "Keywords" are important words or phrases extracted from business direction data that represent business themes or themes.

[0094] "Intent" refers to the purpose or goal that a user is trying to achieve based on business direction data.

[0095] A "generative AI model" is an artificial intelligence model that generates optimal business models and action plans based on extracted keywords and intent.

[0096] "Stakeholders" are people or organizations that play a role necessary to the execution of a business model.

[0097] "Development man-hours" refers to the working time and resources required to execute a business model.

[0098] A "success story" is a specific example of a similar business model being successfully implemented in the past.

[0099] "Failure cases" are specific examples of similar business models that have been implemented in the past and failed.

[0100] A "specific action plan" refers to a step-by-step execution plan or tasks for implementing a business model.

[0101] "JSON format" refers to the JavaScript Object Notation format, which represents data in a structured text format.

[0102] A "natural language processing library" is a software component that analyzes incoming data and understands the linguistic meaning of the text.

[0103] This invention is a system that provides optimal business models and concrete action plans by allowing users to input their business direction and ideas. This system consists of three elements: a server, a terminal, and the user.

[0104] Overall structure

[0105] 1. User Interface

[0106] Users enter data about the direction of the business they want to run into a special input form. For example, they can enter a specific business idea such as "I want to run an online learning platform."

[0107] 2. Data Transmission

[0108] The terminal converts the business direction data entered by the user into JSON format and sends it to the server using an HTTP POST request, using technologies such as Ajax or Fetch API.

[0109] 3. Data Analysis

[0110] The server parses the received JSON data using a natural language processing library (e.g., spaCy, NLTK) to extract key keywords and intent, which clarifies the business's subject matter and goals.

[0111] 4. Business model generation

[0112] The server generates the optimal business model using a generative AI model (e.g., GPT-4) based on the extracted keywords and intent. For example, if the user enters "online learning platform," the server will suggest a "subscription model."

[0113] 5. Stakeholders and Development Effort Calculation

[0114] Based on the generated business model, the server retrieves the necessary stakeholders (e.g., educational content creator, IT infrastructure manager) and development man-hours (e.g., 6 months) from the database and calculates them.

[0115] 6. Presentation of success and failure cases

[0116] The server extracts past success stories and failure stories from a database and provides them to users. For example, it might present "the success story of a famous online learning platform" as a success story, or "the reasons for the failure of similar services in the past" as a failure story.

[0117] 7. Generate a concrete action plan

[0118] The server generates a specific action plan based on the generated business model, such as "Month 1: Basic platform design, Month 2: Start content creation, Month 3: Start beta testing."

[0119] 8. User Feedback

[0120] The terminal displays the information received from the server to the user, who can then use it to get a detailed execution plan based on their business idea.

[0121] Specific example explanation

[0122] Below are some specific examples of how this system can be used.

[0123] 1. A user types, "I want to run an online learning platform."

[0124] 2. The device converts the input into JSON format and sends it to the server.

[0125] 3. The server analyzes the received data using a natural language processing library and extracts the keyword "online learning platform" and the intent.

[0126] 4. The server uses the generative AI model to generate the optimal business model and propose a "subscription model."

[0127] 5. Calculate the stakeholders who will need a server (educational content creators, IT infrastructure managers) and the development time (6 months).

[0128] 6. The server extracts success stories of famous online learning platforms and failure stories of similar services in the past.

[0129] 7. The server generates a specific action plan and presents it as follows: "Month 1: Basic design of the platform, Month 2: Start content creation, Month 3: Start beta testing."

[0130] 8. The device displays all the information to the user.

[0131] Prompt Sentence Examples

[0132] Below are some example prompts to input to the generative AI model:

[0133] Business idea: Run an online learning platform

[0134] Required stakeholders: Educational content creators, IT infrastructure managers

[0135] Development time: 6 months

[0136] Business model: Subscription model

[0137] Success Story: A Popular Online Learning Platform's Success Story

[0138] Failure Case: Reasons for the failure of similar services in the past

[0139] Generate a concrete action plan.

[0140] This system allows users to easily obtain a comprehensive business model and detailed execution plan based on their business idea, which is expected to minimize business risks and increase the chances of success.

[0141] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0142] Processing step details

[0143] Step 1: User Input

[0144] Users enter their business direction data into an input form. For example, let's say they want to run an online learning platform.

[0145] Input: User enters their business idea into a form.

[0146] Output: The user's business idea is input into the system.

[0147] Step 2: Submitting input data

[0148] The terminal converts the business direction data entered by the user into JSON format and sends it to the server using an HTTP POST request, using Ajax or the Fetch API.

[0149] Input: User's business direction data.

[0150] Output: The data is converted to JSON format and sent to the server.

[0151] Step 3: Receiving and analyzing data

[0152] The server parses the received JSON data using a natural language processing library (e.g., spaCy or NLTK) to extract important keywords and intent.

[0153] Input: Business direction data in JSON format.

[0154] Output: Analysis results including key keywords and intent.

[0155] Specific operation: Passes the received data to a natural language processing library to perform morphological analysis and topic modeling.

[0156] Step 4: Generate a business model

[0157] The server inputs the analysis results (keywords and intent) into the generative AI model as prompts to generate the optimal business model. For example, for the input keyword "online learning platform," the model generates "subscription model."

[0158] Input: Primary keywords and intent.

[0159] Output: The generated business model.

[0160] Specific operation: Generate a prompt sentence using the analysis results, send a query to the generative AI model, and obtain a response.

[0161] Step 5: Stakeholders and development effort calculations

[0162] Based on the generated business model, the server retrieves the stakeholders (e.g., educational content creator, IT infrastructure manager) and development time (e.g., 6 months) from an internal database.

[0163] Input: The generated business model.

[0164] Output: Stakeholder list and development effort required.

[0165] Specific operation: Query the database based on the business model to retrieve the relevant stakeholders and effort.

[0166] Step 6: Present success stories and failure stories

[0167] The server extracts relevant success stories and failure stories from its internal database and provides them to users, such as "Success stories: success stories of famous online learning platforms" and "Failure stories: reasons for the failure of similar services in the past."

[0168] Input: Business model related data.

[0169] Output: Success stories and failure stories.

[0170] Specific operation: Retrieves success and failure cases from the database and formats them as relevant information.

[0171] Step 7: Generate a concrete action plan

[0172] The server generates a specific action plan based on the generated business model, such as a step-by-step plan like "Month 1: Basic platform design, Month 2: Start content creation, Month 3: Start beta testing."

[0173] Input: Generated business model and related data.

[0174] Output: A concrete action plan.

[0175] What it does: Incorporates input data into a planning template to generate a step-by-step execution plan.

[0176] Step 8: User feedback

[0177] The terminal displays all the information received from the server (business model, stakeholders, development time, success stories, failure stories, action plans) to the user.

[0178] Input: General information sent from the server.

[0179] Output: Information displayed to the user.

[0180] Specific behavior: Formats the information appropriately and reflects it in the user interface.

[0181] The above is a detailed description of the processing steps of this system, a series of processes that provide the optimal business model and detailed implementation plan based on the user's business idea.

[0182] (Application example 1)

[0183] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0184] Conventional business model generation systems lack sufficient support for users to quickly and comprehensively develop new business directions. They also struggle to present specific action plans based on success and failure cases in specific areas. Furthermore, there is a need for comprehensive business model proposals and displays that can be operated directly on smartphones and other devices. Therefore, there is a need for a system that can help users launch businesses efficiently and increase the likelihood of success.

[0185] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0186] In this invention, the server includes means for receiving business direction data input by a user, means for analyzing the received business direction data and extracting key keywords and intentions, means for generating an appropriate business model based on the extracted keywords and intentions, means for calculating the required stakeholders and development man-hours based on the generated business model, means for extracting past success stories and failure stories, means for generating a specific action plan tailored to the business model, and means for comprehensively displaying the generated business model, stakeholders, development man-hours, success stories, and failure stories. This enables users to quickly and effectively build business models in specific business areas and obtain specific action plans based on past cases.

[0187] "Business direction data" is information that indicates the specific ideas, goals, and strategies of a new business when a user launches the business.

[0188] "Means for receiving" refers to the functions and processes by which a server or device obtains data entered by a user.

[0189] "Means of analysis" refers to the functions and processes that analyze received data using technologies such as machine learning and natural language processing to extract key keywords and intent.

[0190] A "business model" refers to the structure and methodology for realizing a user's business idea, including the mechanism for generating revenue.

[0191] "Stakeholders" refers to all parties involved in business activities, specifically including individuals and organizations that affect the success of a project.

[0192] "Development time" refers to the time, resources, and human resources required to realize a business idea.

[0193] "Success stories" refer to specific cases or examples where similar business models have been successful in the past, and include the results of their analysis.

[0194] "Failure Cases" refers to specific cases or examples where similar business models have failed in the past, including lessons learned and causes.

[0195] An "action plan" is a set of concrete steps and plans for realizing a business model, including a timeline and implementation procedures.

[0196] "Display means" refers to an interface or device for visually presenting the calculated business model and related information to the user.

[0197] A "smartphone" is a type of mobile phone, a mobile device that has advanced computing power and communication functions and can run a variety of applications.

[0198] This invention relates to a system that generates an optimal business model based on business direction data entered by a user and provides a specific action plan based on that model. Specific embodiments of this system are described below.

[0199] The server has a means for receiving business direction data entered by the user. For example, when a user enters a specific business idea such as "I want to launch a new online shopping site," the system receives this data. This data is usually sent via a mobile device such as a smartphone.

[0200] The received business direction data is analyzed using NLP (Natural Language Processing) libraries. This analysis extracts key keywords and their intent from the data, allowing the system to accurately understand the user's business goals.

[0201] Based on the analyzed data, the server uses a generative AI model to generate an optimal business model. This model changes depending on the extracted keywords and intent. For example, if the user enters "online shopping site," the system will suggest a "subscription model" as an appropriate business model. At the same time, the system also calculates the required stakeholders and development time.

[0202] In addition, the server also has a means to extract past success and failure cases from the database, allowing users to understand the factors that led to the success or failure of a specific business model in the past. This information helps users evaluate their own business models and minimize risks.

[0203] The server then generates a specific action plan, such as "Month 1: Platform basic design, Month 2: Start content creation, Month 3: Start beta testing." This action plan provides users with concrete steps to take toward realizing their business.

[0204] The generated business model, stakeholders, development time, success stories, and failure stories are all displayed comprehensively. Users are provided with an interface that allows them to view this information at a glance on their smartphones or other devices. This allows users to grasp the overall picture for efficiently launching a business.

[0205] As a concrete example, if a user inputs a business idea such as "I want to launch a new online shopping site," the system will process it as follows:

[0206] Example prompt sentence:

[0207] "I want to launch a new online shopping site."

[0208] Based on this input, the system proposes an optimal business model, provides stakeholders, development time, success stories, and failure stories, and generates and displays a concrete action plan to users, allowing them to launch their business quickly and effectively.

[0209] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0210] Step 1:

[0211] Users input business direction

[0212] Users input their business ideas using devices such as smartphones. This input data is specific, such as "I want to launch a new online shopping site." The input data is sent from the device to the server.

[0213] Input: User's business idea (e.g., "I want to launch a new online shopping site")

[0214] Output: Input data is sent from the device to the server

[0215] Step 2:

[0216] The server analyzes the received data

[0217] The server analyzes the received business direction data using an NLP library, specifically extracting key keywords (e.g., "online shopping site") and intent (e.g., "I want to run one") using natural language processing.

[0218] Input: User's business idea (e.g., "I want to launch a new online shopping site")

[0219] Output: Primary keywords and intent (e.g., "online shopping site" and "want to run")

[0220] Step 3:

[0221] The server generates the business model

[0222] The server uses a generative AI model to generate an appropriate business model based on the extracted keywords and intent, including specific revenue models such as subscription models.

[0223] Input: Primary keywords and intent (e.g., "online shopping site" and "want to run")

[0224] Output: Generated business model (e.g. "Subscription Model")

[0225] Step 4:

[0226] The server calculates development time with stakeholders

[0227] Based on the generated business model, the server calculates the required stakeholders (e.g., "content creator" and "IT infrastructure manager") and the development time (e.g., "6 months"), which are obtained from an internal database.

[0228] Input: Generated business model (e.g. "Subscription model")

[0229] Output: Required stakeholders and development effort (e.g., "Content Creator," "IT Infrastructure Manager," and "6 months")

[0230] Step 5:

[0231] The server extracts past success stories and failure stories

[0232] The server extracts past success stories (e.g., "Success stories of famous online learning platforms") and failure stories (e.g., "Reasons for the failure of similar services in the past") from the database.

[0233] Input: Generated business model (e.g. "Subscription model")

[0234] Output: Success stories and failure stories (e.g., "Success stories," "Failure factors")

[0235] Step 6:

[0236] The server generates a concrete action plan

[0237] The server generates a specific action plan based on the business model, including step-by-step plans such as "Month 1: Platform basic design," "Month 2: Content creation begins," and "Month 3: Beta testing begins."

[0238] Input: Generated business model (e.g. "Subscription model")

[0239] Output: A concrete action plan (e.g., "Month 1: Platform basic design," "Month 2: Content creation begins," "Month 3: Beta testing begins")

[0240] Step 7:

[0241] The server displays all information to the user

[0242] The terminal comprehensively displays the business model, stakeholders, development time, success stories, failure stories, and specific action plans received from the server to the user, allowing the user to intuitively grasp the overall picture of their business and specific implementation plans.

[0243] Input: Business model, stakeholders, development time, success stories, failure stories, specific action plans

[0244] Output: All information displayed to the user

[0245] These are the main processing steps of this system.

[0246] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0247] The present invention relates to a system that generates an optimal business model based on a user's input of their goals and business direction, and provides relevant stakeholders, development man-hours, success stories, and failure stories, as well as a concrete action plan. Furthermore, the present invention is characterized by incorporating an emotion engine that recognizes the user's emotions, and by adjusting the business model and proposal content based on the user's emotion data. A specific embodiment of this system is described below.

[0248] Overall flow

[0249] 1. User Input

[0250] Users input data about the direction of the business they want to run, for example, a specific business idea such as "I want to run an online learning platform."

[0251] 2. Sending input data

[0252] The terminal sends the business direction data entered by the user to the server, usually via an API, packaging the input in JSON format.

[0253] 3. Data Analysis

[0254] The server analyzes the incoming data using natural language processing (NLP) libraries, specifically tokenizing the text and performing morphological analysis to extract key keywords and intent.

[0255] 4. Business model generation

[0256] The server generates the optimal business model based on the extracted keywords and intent. This generation process involves using a generative AI model to select the most suitable business model from a number of options. For example, if the user enters "online learning platform," the server will suggest a "subscription model."

[0257] 5. Stakeholders and Development Effort Calculation

[0258] The server calculates the required stakeholders and development time based on the generated business model. This data is retrieved from an internal database. For example, the stakeholders "educational content creator" and "IT infrastructure manager" are presented, and the development time is "6 months."

[0259] 6. Presentation of success and failure cases

[0260] The server extracts past success stories and failure stories from a database. To do this, it searches the database of success stories and failure stories to collect information that users can use as reference. For example, a success story might be presented as a "success story of a famous online learning platform," and a failure story might be presented as a "reason for the failure of a similar service in the past."

[0261] 7. Generate an action plan

[0262] The server generates a specific action plan based on the business model. This involves creating steps and a timeline that best fit the business model, and listing specific tasks for each step. For example, a step-by-step plan such as "Month 1: Basic platform design, Month 2: Start content creation, Month 3: Start beta testing" is provided.

[0263] 8. Collecting Emotional Data with an Emotion Engine

[0264] The device collects the user's emotional data. The emotion engine analyzes the user's emotions based on facial recognition, voice analysis, and the tone of the input content, and generates emotional data. For example, if the user is excited or depressed while typing, that emotional data is collected in real time.

[0265] 9. Emotion Data Analysis

[0266] The server analyzes the collected emotional data to determine the user's emotional state, which is then used to generate business models and prioritize action plans.

[0267] 10. Emotional Adjustment

[0268] The server adjusts business models and action plans based on emotional data, for example, presenting simplified plans and motivational suggestions if the user is feeling stressed.

[0269] 11. User Feedback

[0270] The terminal displays the business model, stakeholders, development time, success stories and failure stories, and specific action plans received from the server to the user, and also reflects adjustments based on the analysis results of the emotion engine.

[0271] Specific examples

[0272] Below are some specific examples of how this system can be used.

[0273] 1. A user types, "I want to run an online learning platform."

[0274] 2. The device sends the input in JSON format to the server.

[0275] 3. The server uses an NLP library to extract the keyword "online learning platform" and intent.

[0276] 4. The server uses the generative AI model to generate a "subscription model."

[0277] 5. The stakeholders who require a server are the "educational content creator" and the "IT infrastructure manager," and the development time is calculated to be "6 months."

[0278] 6. The server extracts success stories of famous online learning platforms and failure stories of similar services in the past.

[0279] 7. The server generates a specific action plan and presents it as follows: "Month 1: Basic design of the platform, Month 2: Start content creation, Month 3: Start beta testing."

[0280] 8. The device collects emotional data as the user types and detects when the user is nervous.

[0281] 9. The server analyzes the emotional data and suggests additional support to help the user relax.

[0282] 10. The device displays all information to the user and also makes adjustments based on emotional data.

[0283] In this way, the system starts with user input, goes through various data analysis and generation processes, and then provides an optimal business model and feasible plan. It also incorporates an emotion engine to flexibly adjust according to the user's emotional state.

[0284] The processing flow will be explained below.

[0285] Step 1:

[0286] Users input data about the business they want to run. For example, they can enter a specific business idea into the input form, such as "I want to run an online learning platform."

[0287] Step 2:

[0288] The terminal sends the business direction data entered by the user to the server, usually via an API, packaging the input in JSON format.

[0289] Step 3:

[0290] The server parses the incoming data using natural language processing (NLP) libraries, tokenizing the text and performing morphological analysis to extract key keywords and intent.

[0291] Step 4:

[0292] The server generates the optimal business model based on the extracted keywords and intent. This generation process uses a generative AI model to select the most suitable business model from a number of options. For example, if the user enters "online learning platform," the server will suggest a "subscription model."

[0293] Step 5:

[0294] The server calculates the required stakeholders and development time based on the generated business model. This data is obtained by querying an internal database. As an example, the stakeholders "educational content creator" and "IT infrastructure manager" are presented, and the development time is "6 months."

[0295] Step 6:

[0296] The server extracts past success stories and failure stories from a database. To do this, it searches the database of success stories and failure stories to collect information that users can use as reference. For example, a success story might be presented as a "success story of a famous online learning platform," and a failure story might be presented as a "reason for the failure of a similar service in the past."

[0297] Step 7:

[0298] The server generates a specific action plan based on the business model. This involves creating steps and a timeline that best fit the business model, and listing specific tasks for each step. For example, a step-by-step plan such as "Month 1: Basic platform design, Month 2: Start content creation, Month 3: Start beta testing" is provided.

[0299] Step 8:

[0300] The device collects the user's emotional data. The emotion engine analyzes the user's emotions based on facial recognition, voice analysis, and the tone of the input content, and generates emotional data. For example, if the user is excited or depressed while typing, that emotional data is collected in real time.

[0301] Step 9:

[0302] The server analyzes the collected emotional data to determine the user's emotional state, which is then used to generate business models and prioritize action plans.

[0303] Step 10:

[0304] The server adjusts business models and action plans based on emotional data, for example, presenting simplified plans and motivational suggestions if the user is feeling stressed.

[0305] Step 11:

[0306] The terminal displays the business model, stakeholders, development time, success stories and failure stories, and specific action plans received from the server to the user, and also reflects adjustments based on the analysis results of the emotion engine.

[0307] This concrete flow allows users to obtain a comprehensive business model and a concrete implementation plan based on their business idea. Furthermore, by incorporating an emotion engine, the system can flexibly adjust according to the user's emotional state, providing more personalized recommendations.

[0308] Example 2

[0309] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0310] While conventional business model generation systems were capable of generating models based on the user's business direction, they lacked the ability to make optimal adjustments based on the user's emotional state. Furthermore, they lacked the ability to comprehensively provide the stakeholders and workload involved in the generated business model, as well as past successes and failures, and to present a feasible implementation plan for the user. This can lead to users feeling anxious about implementing the generated business model and lowering their motivation.

[0311] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0312] In this invention, the server includes means for receiving business direction data input by a user, means for analyzing the received business direction data and extracting main keywords and intentions, means for generating an appropriate business model based on the extracted keywords and intentions, means for collecting emotional data based on the generated business model, and means for analyzing the collected emotional data and adjusting the business model and action plan based on the emotional state of the user. This makes it possible to adjust the optimal business model according to the emotional state of the user, eliminating problems such as anxiety and decreased motivation, and providing a more feasible and effective business model.

[0313] "Business direction data" is information including the business objectives, goals, strategies, and specific ideas proposed by the user.

[0314] "Keywords" are particularly important words or phrases in business direction data, and are terms that play an important role in generating and analyzing business models.

[0315] "Intent" refers to the purpose or goal that the user wants to convey in the business direction data, as well as the thinking behind it.

[0316] A "business model" is a specific summary of the value of the products or services offered, revenue structure, customer base, and management methods.

[0317] "Emotional data" is information that indicates the emotional state of the user, and is data collected through facial recognition, voice analysis, tone analysis of input content, and the like.

[0318] "Stakeholders" refers to the people and organizations necessary to execute and maintain the business model, such as creators of educational content and IT infrastructure managers.

[0319] "Work volume" refers to the labor and time required to execute a business model, such as development man-hours.

[0320] "Success stories" are specific examples or data showing that similar business models or strategies have been successful in the past.

[0321] "Failure cases" are specific examples or data of similar business models or strategies that have failed in the past.

[0322] An "action plan" is a plan that includes specific steps and a timeline for realizing a business model, as well as tasks for each step.

[0323] A "generative AI model" is an algorithm or system that uses artificial intelligence technology to generate business models, and outputs an appropriate business model by inputting, for example, a prompt statement.

[0324] MODE FOR CARRYING OUT THE INVENTION

[0325] This invention relates to a system that allows users to input their business direction, generates an optimal business model based on that, and provides information on the people involved, workload, success stories, failure stories, and specific implementation plans.Furthermore, this invention combines an emotion engine that recognizes the user's emotions, and automatically adjusts the business model and proposal content based on the user's emotion data.

[0326] Hardware and software used

[0327] The system of the present invention uses the following hardware and software:

[0328] Server: Analyzes data, generates business models, analyzes sentiment data. The server hosts the database and AI models.

[0329] Terminal: A device where the user can provide input and receive feedback from the system. It is equipped with an emotion engine to collect user emotion data.

[0330] Natural Language Processing (NLP) libraries, such as NLTK or spaCy, to analyze user-entered text.

[0331] Generative AI model: For example, use OpenAI's GPT-3 to generate an appropriate business model.

[0332] Database: A database for storing data such as information on stakeholders, success stories, and failure stories.

[0333] System processing overview

[0334] 1. User input: The user inputs the business direction from the device. For example, the user inputs a specific idea such as "I want to operate an online learning platform."

[0335] 2. Sending input data: The terminal packages the business direction data entered by the user in JSON format and sends it to the server via API.

[0336] 3. Data Analysis: The server uses a natural language processing library to analyze the incoming data, tokenizing it and performing morphological analysis to extract key keywords and intent.

[0337] 4. Business model generation: The server uses the extracted keywords and intent to generate an optimal business model using a generative AI model. For example, if the input is "online learning platform," the server will suggest a "subscription model."

[0338] 5. Calculation of stakeholders and workload: Based on the generated business model, the server retrieves and calculates the necessary stakeholders (e.g., educational content creator, IT infrastructure administrator) and workload (e.g., 6 months) from its internal database.

[0339] 6. Presentation of success and failure cases: The server extracts relevant success and failure cases from the database. For example, it displays "success stories of famous online learning platforms" as success cases and "reasons for failure of similar services in the past" as failure cases.

[0340] 7. Generate a detailed execution plan: The server generates an execution plan with specific steps and a timeline. For example, a step-by-step plan such as "Month 1: Platform basic design, Month 2: Content creation begins, Month 3: Beta testing begins" is provided.

[0341] 8. Emotional data collection and analysis: The device collects the user's emotional data and sends it to the server. The server analyzes the collected emotional data and determines the user's emotional state.

[0342] 9. Emotional Adjustment: The server adjusts business models and execution plans based on emotional data. For example, if a user is feeling stressed, it will provide simplified plans and motivational suggestions.

[0343] Specific examples

[0344] Here are some concrete examples of how this system can be used:

[0345] 1. A user types, "I want to run an online learning platform."

[0346] 2. The device sends the input in JSON format to the server.

[0347] 3. The server uses a natural language processing library to extract the keyword "online learning platform" and the intent.

[0348] 4. The server uses the generative AI model to generate a "subscription model."

[0349] 5. The parties who will need a server are the "educational content creator" and the "IT infrastructure manager," and the amount of work is calculated to be "6 months."

[0350] 6. The server extracts success stories of famous online learning platforms and failure stories of similar services in the past.

[0351] 7. The server generates a specific execution plan and presents it as follows: "Month 1: Basic design of the platform, Month 2: Start content creation, Month 3: Start beta testing."

[0352] 8. The device collects emotional data as the user types and detects when the user is nervous.

[0353] 9. The server analyzes the emotional data and suggests additional support to help the user relax.

[0354] 10. The device displays all information to the user and also makes adjustments based on emotional data.

[0355] Prompt Sentence Examples

[0356] Below are some example prompts to input to a generative AI model:

[0357] A user has entered, "I want to run an online learning platform." Please generate the optimal business model based on this direction. Also, please calculate the necessary stakeholders and the amount of work, provide examples of past successes and failures, and even provide a concrete implementation plan.

[0358] In this way, the system of the present invention starts with user input, goes through various data analysis and generation processes, and provides an optimal business model and a feasible plan. Furthermore, by incorporating an emotion engine, it can make flexible adjustments according to the user's emotional state.

[0359] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0360] Step 1:

[0361] Accepting user input

[0362] The user enters business direction data into an input form on the device. For example, they enter a specific idea such as "I want to operate an online learning platform." The input data is temporarily stored in the device's memory and used for the next processing step.

[0363] Input: Business direction data (e.g., "I want to run an online learning platform")

[0364] Output: Business direction data stored in the device's memory

[0365] Step 2:

[0366] Sending input data

[0367] The terminal packages the business direction data entered by the user in JSON format and sends it to the server using an HTTP POST request to the specified API endpoint, where the data is stored in the server's receive buffer.

[0368] Input: User orientation data (data stored in device memory)

[0369] Output: JSON format data stored in the server's receive buffer

[0370] Step 3:

[0371] Data analysis

[0372] The server unpacks the received JSON data and parses it using a natural language processing (NLP) library (e.g., NLTK or spaCy) to tokenize the text, perform morphological analysis, and extract key keywords and intent.

[0373] Input: JSON format data stored in the server's receive buffer

[0374] Output: Key keywords and user intent (stored in server memory)

[0375] Step 4:

[0376] Business model generation

[0377] The server generates an optimal business model using a generative AI model (e.g., OpenAI's GPT-3) based on the extracted keywords and intent. The extracted intent and keywords are input into the generative AI model as prompt statements, and the model outputs an optimal business model, such as a "subscription model."

[0378] Input: Key keywords and user intent (stored in server memory)

[0379] Output: Generated business model (e.g. "Subscription model")

[0380] Step 5:

[0381] Calculating stakeholders and effort

[0382] Based on the generated business model, the server retrieves the necessary stakeholders (e.g., educational content creator, IT infrastructure manager) and the amount of work (e.g., 6 months) from an internal database and calculates them. The calculation is performed by querying the existing project database.

[0383] Input: Generated business model (stored in server memory)

[0384] Output: The stakeholders and effort required (e.g., "Educational content creator, 6 months")

[0385] Step 6:

[0386] Presentation of success and failure cases

[0387] The server searches the internal database and extracts success stories and failure stories related to the business model. Specific examples include a success story, "The success story of a famous online learning platform," and a failure story, "The reasons for the failure of similar services in the past."

[0388] Input: Generated business model (stored in server memory)

[0389] Output: Success stories and failure stories (e.g., "Success stories of famous online learning platforms")

[0390] Step 7:

[0391] Generate a concrete execution plan

[0392] The server creates specific steps and a timeline based on the business model. This plan has a step-by-step structure, listing specific tasks and the duration of each step. For example, "Month 1: Platform basic design, Month 2: Content creation begins, Month 3: Beta testing begins."

[0393] Input: Generated business model (stored in server memory)

[0394] Output: A concrete action plan (e.g., "Month 1: Basic design of the platform, Month 2: Start creating content")

[0395] Step 8:

[0396] Emotion data collection and analysis

[0397] The device uses facial recognition, voice analysis, and tone analysis of input content to collect user emotional data, thereby capturing the user's emotional state (e.g., nervousness, excitement, etc.) in real time and sending the data in JSON format to the server.

[0398] Input: User's facial expressions, voice, and input (device sensor data)

[0399] Output: Emotion data (sent to server in JSON format)

[0400] Step 9:

[0401] Emotional Data Analysis

[0402] The server analyzes the collected emotional data to determine the user's emotional state. It processes the data using an emotion analysis algorithm to identify the user's emotional state (e.g., nervous, stressed, relaxed).

[0403] Input: Emotion data (stored on the server in JSON format)

[0404] Output: User's emotional state (e.g., "tense," "relaxed")

[0405] Step 10:

[0406] Emotional adjustment

[0407] The server adjusts business models and action plans based on the analyzed emotional data, for example, presenting simplified plans and motivational suggestions if the user is feeling stressed.

[0408] Input: User's emotional state (stored in server memory)

[0409] Output: A tailored business model and implementation plan (e.g., a "simplified plan")

[0410] Step 11:

[0411] User feedback

[0412] The device receives information from the server and displays it to the user, including the business model, stakeholders, workload, success stories, failure stories, and specific implementation plans, along with adjustments based on emotion data.

[0413] Input: Adjusted business model and execution plan (data sent from the server)

[0414] Output: Information displayed to the user (displayed on the device display)

[0415] As mentioned above, the program processing of this system is carried out through a series of steps, starting with user input, data analysis, business model generation, and adjustments based on emotion data. This process makes it possible to provide a more specific and feasible business model.

[0416] (Application example 2)

[0417] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0418] Conventional business model generation systems provide models and action plans based on business direction data entered by users, but they are unable to consider the user's emotional state. As a result, there are problems such as users feeling stressed or being unable to make appropriate proposals based on their emotional state. Furthermore, when it comes to immediately applying improvement ideas on the factory floor, it is difficult to provide appropriate plans that take emotions into account in real time.

[0419] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0420] In this invention, the server includes means for receiving business direction data input by a user, means for analyzing the received business direction data and extracting main keywords and intentions, means for generating an appropriate business model based on the extracted keywords and intentions, means for generating a specific action plan based on the business model, means for collecting and analyzing user emotion data, and means for adjusting the business model and action plan based on the emotion data. This not only appropriately reflects the user's input, but also makes it possible to provide a real-time improvement action plan that is adjusted according to the user's emotional state.

[0421] "Business direction data" is information about the business direction the user is aiming for, the processes they want to improve, their goals, etc.

[0422] "Key Keywords" are important words and phrases extracted from business direction data.

[0423] "Intent" refers to the user's purpose or goal that is inferred based on the business direction data entered by the user.

[0424] A "business model" is an optimal business structure and revenue mechanism proposed based on business direction data.

[0425] An "action plan" is a specific action plan and timeline for realizing a business model.

[0426] "Emotional data" refers to information that indicates a user's emotional state and is collected through facial recognition, voice analysis, tone of input, etc.

[0427] An "emotion engine" is a system or algorithm that collects and analyzes a user's emotional data and determines their emotional state.

[0428] "Stakeholders" are those interested in implementing the business model and action plan.

[0429] "Development man-hours" refers to the working hours and labor required to realize a business model or action plan.

[0430] A "success story" is a specific example of a similar business model or action plan that was successful in the past.

[0431] "Failure cases" are specific examples of similar business models or action plans that have failed in the past.

[0432] The system realizing this invention includes various means for receiving and analyzing business direction data input by users, which not only generates an appropriate business model and a specific action plan, but also makes adjustments taking into account the emotional state of the user.

[0433] Hardware and software used

[0434] Hardware: smart glasses, server, camera, microphone.

[0435] Software: NLP libraries (e.g. spaCy), generative AI models (e.g. OpenAI GPT), databases (e.g. PostgreSQL), sentiment analysis libraries (e.g. Affectiva).

[0436] System Operation Overview

[0437] User Input

[0438] Users input the business process they want to improve through the smart glasses using voice or text, which is first captured by the smart glasses and then sent to the server in JSON format.

[0439] Data analysis and business model generation

[0440] The server uses an NLP library (e.g., spaCy) to tokenize the received data and extract key keywords and intent, as shown in the example below.

[0441] Example prompt sentence:

[0442] "We want to improve the production efficiency of our line. Which step is causing the bottleneck in our current setup?"

[0443] Next, a generative AI model (e.g., OpenAI GPT) is used to generate optimal business models and action plans based on the extracted keywords and intent.

[0444] Stakeholders and development effort calculations

[0445] Based on the generated business model, the required stakeholders and development effort are retrieved from a database (e.g., PostgreSQL) and calculated.

[0446] Extraction of success and failure cases

[0447] Past examples of successful and unsuccessful similar business models are extracted from a database and presented to the user.

[0448] Generate and view an action plan

[0449] Generate specific action plans based on your business model, including timelines for completing each task by when.

[0450] Emotion data collection and analysis

[0451] The smart glasses use a built-in camera and microphone to collect and analyze the user's emotional data, which is then analyzed in real time using an emotion analysis library (e.g., Affectiva) to determine the user's emotional state.

[0452] Emotional adjustment

[0453] Based on the collected emotional data, the generated business model and action plan can be adjusted accordingly. For example, if the user is feeling stressed, a simplified plan or an encouraging message can be provided.

[0454] User feedback

[0455] Finally, the adjusted business model, action plan, stakeholders, development time, success stories, and failure stories are displayed on the user's smart glasses.

[0456] Through the above process, starting from user input and going through various data analysis and generation processes, it becomes possible to provide optimal business models and action plans. In addition, by combining it with an emotion engine, it is possible to flexibly adjust according to the user's emotional state.

[0457] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0458] Step 1:

[0459] Users can input the business process they want to improve through voice or text input via smart glasses, which is captured by the device, converted into JSON format, and sent to the server.

[0460] Input: User voice or text input

[0461] Output: JSON format data

[0462] Step 2:

[0463] The server parses the received JSON-formatted business direction data using a natural language processing (NLP) library (e.g., spaCy), tokenizing the data and extracting key keywords and intent.

[0464] Input: Business direction data in JSON format

[0465] Output: Primary keywords and intent

[0466] Step 3:

[0467] Based on the extracted keywords and intent, the server uses a generative AI model (e.g., OpenAI GPT) to generate an optimal business model. This generation process takes into account the conditions and options necessary for business success.

[0468] Input: Extracted keywords and intent

[0469] Output: Generated business model

[0470] Step 4:

[0471] Based on the generated business model, the server retrieves and calculates the required stakeholders and development effort from an internal database (e.g., PostgreSQL).

[0472] Input: Generated business model

[0473] Output: Required stakeholders and development effort

[0474] Step 5:

[0475] The server extracts past success stories and failure stories from a related database and collects information that users can use as reference.

[0476] Input: Generated business model

[0477] Output: Success stories and failure stories

[0478] Step 6:

[0479] The server generates a specific action plan based on the business model and related information (stakeholders, development time, success stories, failure stories). The action plan includes specific tasks and timelines for each step.

[0480] Input: Business model, stakeholders, development time, success stories, failure stories

[0481] Output: A concrete action plan

[0482] Step 7:

[0483] The device collects the user's emotional data using the camera and microphone built into the smart glasses, which are then used to collect emotions through facial recognition and voice analysis. This data is then sent to a server in real time.

[0484] Input: User's video and audio data

[0485] Output: Real-time emotion data

[0486] Step 8:

[0487] The server analyzes the collected emotional data using an emotion analysis library (e.g., Affectiva) to determine the user's emotional state.

[0488] Input: Real-time emotion data

[0489] Output: Parsed emotion data

[0490] Step 9:

[0491] The server then adjusts the generated business model and action plan based on the analyzed emotional data, for example, providing a simplified plan or encouraging message if the user is feeling stressed.

[0492] Input: Analyzed emotion data and action plan

[0493] Output: A tailored business model and action plan

[0494] Step 10:

[0495] The device compiles information on the business model, stakeholders, development time, success stories, failure stories, specific action plans, and adjusted content, and displays it on the smart glasses.

[0496] Input: Adjusted business model, stakeholders, development time, success stories, failure stories, concrete action plan

[0497] Output: Display feedback to the user

[0498] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0499] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0500] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0501] [Second embodiment]

[0502] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0503] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0504] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0505] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0506] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0507] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0508] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0509] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0510] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0511] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0512] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0513] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0514] This invention relates to a system that, by inputting what a user wants to do and the direction of their business, presents an optimal business model and provides relevant stakeholders, development man-hours, success stories and failure stories, and specific action plans. Specific embodiments of this system are described below.

[0515] Overall flow

[0516] 1. User Input

[0517] Users input the direction of the business they want to run, such as a specific business idea like "I want to run an online learning platform."

[0518] 2. Sending input data

[0519] The terminal transmits the business direction data entered by the user to a server, usually via an API.

[0520] 3. Data Analysis

[0521] The server analyzes the incoming data using natural language processing (NLP) libraries, extracting key keywords and intent.

[0522] 4. Business model generation

[0523] The server generates the optimal business model based on the extracted keywords and intent. This generation uses a generative AI model. For example, if the user enters "online learning platform," the server will suggest the "subscription model" as an appropriate business model.

[0524] 5. Stakeholders and Development Effort Calculation

[0525] The server calculates the required stakeholders and development effort based on the generated business model. This data is retrieved from an internal database.

[0526] 6. Presentation of success and failure cases

[0527] The server extracts past success and failure cases from a database, allowing users to refer to the circumstances that led to success or failure.

[0528] 7. Generate an action plan

[0529] The server generates a specific action plan based on the business model, such as "Month 1: Basic design of the platform, Month 2: Start content creation, Month 3: Start beta testing."

[0530] 8. User Feedback

[0531] The terminal displays to the user the business model, stakeholders, development time, success stories and failure stories, and specific action plans received from the server.

[0532] Specific examples

[0533] Below are some specific examples of how this system can be used.

[0534] 1. A user types, "I want to run an online learning platform."

[0535] 2. The device sends the input in JSON format to the server.

[0536] 3. The server uses an NLP library to extract the keyword "online learning platform" and intent.

[0537] 4. The server uses the generative AI model to generate a "subscription model."

[0538] 5. The stakeholders who require a server are the "educational content creator" and the "IT infrastructure manager," and the development time is calculated to be "6 months."

[0539] 6. The server extracts success stories of famous online learning platforms and failure stories of similar services in the past.

[0540] 7. The server generates a specific action plan and presents it as follows: "Month 1: Basic design of the platform, Month 2: Start content creation, Month 3: Start beta testing."

[0541] 8. The device displays all information to the user.

[0542] In this way, users can obtain a comprehensive business model and concrete implementation plan based on their business idea, which can minimize business risks and increase the chances of success.

[0543] The processing flow will be explained below.

[0544] Step 1:

[0545] Users input data about the business they want to run. For example, they can enter a specific business idea into the input form, such as "I want to run an online learning platform."

[0546] Step 2:

[0547] The terminal sends the business direction data entered by the user to the server, usually via an API, packaging the input in JSON format.

[0548] Step 3:

[0549] The server analyzes the incoming data using natural language processing (NLP) libraries, specifically tokenizing the text and performing morphological analysis to extract key keywords and intent.

[0550] Step 4:

[0551] The server generates the optimal business model based on the extracted keywords and intent. This generation process involves using a generative AI model to select the most suitable business model from a number of options. For example, if the user enters "online learning platform," the server will suggest a "subscription model."

[0552] Step 5:

[0553] The server calculates the required stakeholders and development effort based on the generated business model. To do this, it queries an internal database to obtain the roles of the relevant stakeholders and the development effort. As an example, it presents the stakeholders "educational content creator" and "IT infrastructure manager" and the development effort of "6 months."

[0554] Step 6:

[0555] The server extracts past success stories and failure stories from a database. To do this, it searches the database of success stories and failure stories to collect information that users can use as reference. For example, a success story might be presented as a "success story of a famous online learning platform," and a failure story might be presented as a "reason for the failure of a similar service in the past."

[0556] Step 7:

[0557] The server generates a specific action plan based on the business model. This involves creating steps and a timeline that best fit the business model, and listing specific tasks for each step. For example, a step-by-step plan such as "Month 1: Basic platform design, Month 2: Start content creation, Month 3: Start beta testing" is provided.

[0558] Step 8:

[0559] The terminal receives information from the server, including the business model, stakeholders, development time, success stories and failure stories, and a concrete action plan, and displays it to the user. Comprehensive information is displayed on the user interface, allowing the user to create an action plan based on the information presented.

[0560] In this way, the system starts with user input and goes through various data analysis and generation processes to provide the optimal business model and executable plan.

[0561] Example 1

[0562] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0563] Conventional business model generation systems have difficulty effectively analyzing the data entered by users and providing an optimal business model and detailed implementation plan. As a result, users are unable to obtain appropriate advice or a concrete action plan for their business ideas, which reduces the probability of business success.

[0564] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0565] In this invention, the server includes: means for receiving business direction data input by a user; means for analyzing the received business direction data and extracting key keywords and intentions; means for using a generative AI model to generate an appropriate business model based on the extracted keywords and intentions; means for calculating the required stakeholders and development man-hours based on the generated business model; means for extracting past success stories and failure stories from a database; means for generating a specific action plan tailored to the business model; means for comprehensively displaying the generated business model, stakeholders, development man-hours, success stories, and failure stories; means for converting the user's input data into JSON format and sending it to the server; and means for analyzing the received data using a natural language processing library. This allows users to easily obtain a comprehensive business model and a detailed action plan based on their business idea.

[0566] "Business direction data" is information entered by the user regarding specific business ideas and business goals.

[0567] "Means for receiving" refers to a device or program that provides the function of sending and receiving data entered by the user to the server.

[0568] "Means of analysis" are algorithms, libraries, and software modules that process the received data and extract key keywords and intent.

[0569] "Keywords" are important words or phrases extracted from business direction data that represent business themes or themes.

[0570] "Intent" refers to the purpose or goal that a user is trying to achieve based on business direction data.

[0571] A "generative AI model" is an artificial intelligence model that generates optimal business models and action plans based on extracted keywords and intent.

[0572] "Stakeholders" are people or organizations that play a role necessary to the execution of a business model.

[0573] "Development man-hours" refers to the working time and resources required to execute a business model.

[0574] A "success story" is a specific example of a similar business model being successfully implemented in the past.

[0575] "Failure cases" are specific examples of similar business models that have been implemented in the past and failed.

[0576] A "specific action plan" refers to a step-by-step execution plan or tasks for implementing a business model.

[0577] "JSON format" refers to the JavaScript Object Notation format, which represents data in a structured text format.

[0578] A "natural language processing library" is a software component that analyzes incoming data and understands the linguistic meaning of the text.

[0579] This invention is a system that provides optimal business models and concrete action plans by allowing users to input their business direction and ideas. This system consists of three elements: a server, a terminal, and the user.

[0580] Overall structure

[0581] 1. User Interface

[0582] Users enter data about the direction of the business they want to run into a special input form. For example, they can enter a specific business idea such as "I want to run an online learning platform."

[0583] 2. Data Transmission

[0584] The terminal converts the business direction data entered by the user into JSON format and sends it to the server using an HTTP POST request, using technologies such as Ajax or Fetch API.

[0585] 3. Data Analysis

[0586] The server parses the received JSON data using a natural language processing library (e.g., spaCy, NLTK) to extract key keywords and intent, which clarifies the business's subject matter and goals.

[0587] 4. Business model generation

[0588] The server generates the optimal business model using a generative AI model (e.g., GPT-4) based on the extracted keywords and intent. For example, if the user enters "online learning platform," the server will suggest a "subscription model."

[0589] 5. Stakeholders and Development Effort Calculation

[0590] Based on the generated business model, the server retrieves the necessary stakeholders (e.g., educational content creator, IT infrastructure manager) and development man-hours (e.g., 6 months) from the database and calculates them.

[0591] 6. Presentation of success and failure cases

[0592] The server extracts past success stories and failure stories from a database and provides them to users. For example, it might present "the success story of a famous online learning platform" as a success story, or "the reasons for the failure of similar services in the past" as a failure story.

[0593] 7. Generate a concrete action plan

[0594] The server generates a specific action plan based on the generated business model, such as "Month 1: Basic platform design, Month 2: Start content creation, Month 3: Start beta testing."

[0595] 8. User Feedback

[0596] The terminal displays the information received from the server to the user, who can then use it to get a detailed execution plan based on their business idea.

[0597] Specific example explanation

[0598] Below are some specific examples of how this system can be used.

[0599] 1. A user types, "I want to run an online learning platform."

[0600] 2. The device converts the input into JSON format and sends it to the server.

[0601] 3. The server analyzes the received data using a natural language processing library and extracts the keyword "online learning platform" and the intent.

[0602] 4. The server uses the generative AI model to generate the optimal business model and propose a "subscription model."

[0603] 5. Calculate the stakeholders who will need a server (educational content creators, IT infrastructure managers) and the development time (6 months).

[0604] 6. The server extracts success stories of famous online learning platforms and failure stories of similar services in the past.

[0605] 7. The server generates a specific action plan and presents it as follows: "Month 1: Basic design of the platform, Month 2: Start content creation, Month 3: Start beta testing."

[0606] 8. The device displays all the information to the user.

[0607] Prompt Sentence Examples

[0608] Below are some example prompts to input to the generative AI model:

[0609] Business idea: Run an online learning platform

[0610] Required stakeholders: Educational content creators, IT infrastructure managers

[0611] Development time: 6 months

[0612] Business model: Subscription model

[0613] Success Story: A Popular Online Learning Platform's Success Story

[0614] Failure Case: Reasons for the failure of similar services in the past

[0615] Generate a concrete action plan.

[0616] This system allows users to easily obtain a comprehensive business model and detailed execution plan based on their business idea, which is expected to minimize business risks and increase the chances of success.

[0617] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0618] Processing step details

[0619] Step 1: User Input

[0620] Users enter their business direction data into an input form. For example, let's say they want to run an online learning platform.

[0621] Input: User enters their business idea into a form.

[0622] Output: The user's business idea is input into the system.

[0623] Step 2: Submitting input data

[0624] The terminal converts the business direction data entered by the user into JSON format and sends it to the server using an HTTP POST request, using Ajax or the Fetch API.

[0625] Input: User's business direction data.

[0626] Output: The data is converted to JSON format and sent to the server.

[0627] Step 3: Receiving and analyzing data

[0628] The server parses the received JSON data using a natural language processing library (e.g., spaCy or NLTK) to extract important keywords and intent.

[0629] Input: Business direction data in JSON format.

[0630] Output: Analysis results including key keywords and intent.

[0631] Specific operation: Passes the received data to a natural language processing library to perform morphological analysis and topic modeling.

[0632] Step 4: Generate a business model

[0633] The server inputs the analysis results (keywords and intent) into the generative AI model as prompts to generate the optimal business model. For example, for the input keyword "online learning platform," the model generates "subscription model."

[0634] Input: Primary keywords and intent.

[0635] Output: The generated business model.

[0636] Specific operation: Generate a prompt sentence using the analysis results, send a query to the generative AI model, and obtain a response.

[0637] Step 5: Stakeholders and development effort calculations

[0638] Based on the generated business model, the server retrieves the stakeholders (e.g., educational content creators, IT infrastructure managers) and development man-hours (e.g., 6 months) from an internal database.

[0639] Input: The generated business model.

[0640] Output: Stakeholder list and development effort required.

[0641] Specific operation: Query the database based on the business model to retrieve the relevant stakeholders and effort.

[0642] Step 6: Present success stories and failure stories

[0643] The server extracts relevant success stories and failure stories from its internal database and provides them to users, such as "Success stories: success stories of famous online learning platforms" and "Failure stories: reasons for the failure of similar services in the past."

[0644] Input: Business model related data.

[0645] Output: Success stories and failure stories.

[0646] Specific operation: Retrieves success and failure cases from the database and formats them as relevant information.

[0647] Step 7: Generate a concrete action plan

[0648] The server generates a specific action plan based on the generated business model, such as a step-by-step plan like "Month 1: Basic platform design, Month 2: Start content creation, Month 3: Start beta testing."

[0649] Input: Generated business model and related data.

[0650] Output: A concrete action plan.

[0651] What it does: Incorporates input data into a planning template to generate a step-by-step execution plan.

[0652] Step 8: User feedback

[0653] The terminal displays all the information received from the server (business model, stakeholders, development time, success stories, failure stories, action plans) to the user.

[0654] Input: General information sent from the server.

[0655] Output: Information displayed to the user.

[0656] Specific behavior: Formats the information appropriately and reflects it in the user interface.

[0657] The above is a detailed description of the processing steps of this system, a series of processes that provide the optimal business model and detailed implementation plan based on the user's business idea.

[0658] (Application example 1)

[0659] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0660] Conventional business model generation systems lack sufficient support for users to quickly and comprehensively develop new business directions. They also struggle to present specific action plans based on success and failure cases in specific areas. Furthermore, there is a need for comprehensive business model proposals and displays that can be operated directly on smartphones and other devices. Therefore, there is a need for a system that can help users launch businesses efficiently and increase the likelihood of success.

[0661] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0662] In this invention, the server includes means for receiving business direction data input by a user, means for analyzing the received business direction data and extracting key keywords and intentions, means for generating an appropriate business model based on the extracted keywords and intentions, means for calculating the required stakeholders and development man-hours based on the generated business model, means for extracting past success stories and failure stories, means for generating a specific action plan tailored to the business model, and means for comprehensively displaying the generated business model, stakeholders, development man-hours, success stories, and failure stories. This enables users to quickly and effectively build business models in specific business areas and obtain specific action plans based on past cases.

[0663] "Business direction data" is information that indicates the specific ideas, goals, and strategies of a new business when a user launches the business.

[0664] "Means for receiving" refers to the functions and processes by which a server or device obtains data entered by a user.

[0665] "Means of analysis" refers to the functions and processes that analyze received data using technologies such as machine learning and natural language processing to extract key keywords and intent.

[0666] A "business model" refers to the structure and methodology for realizing a user's business idea, including the mechanism for generating revenue.

[0667] "Stakeholders" refers to all parties involved in business activities, specifically including individuals and organizations that affect the success of a project.

[0668] "Development time" refers to the time, resources, and human resources required to realize a business idea.

[0669] "Success stories" refer to specific cases or examples where similar business models have been successful in the past, and include the results of their analysis.

[0670] "Failure Cases" refers to specific cases or examples where similar business models have failed in the past, including lessons learned and causes.

[0671] An "action plan" is a set of concrete steps and plans for realizing a business model, including a timeline and implementation procedures.

[0672] "Display means" refers to an interface or device for visually presenting the calculated business model and related information to the user.

[0673] A "smartphone" is a type of mobile phone, a mobile device that has advanced computing power and communication functions and can run a variety of applications.

[0674] This invention relates to a system that generates an optimal business model based on business direction data entered by a user and provides a specific action plan based on that model. Specific embodiments of this system are described below.

[0675] The server has a means for receiving business direction data entered by the user. For example, when a user enters a specific business idea such as "I want to launch a new online shopping site," the system receives this data. This data is usually sent via a mobile device such as a smartphone.

[0676] The received business direction data is analyzed using NLP (Natural Language Processing) libraries. This analysis extracts key keywords and their intent from the data, allowing the system to accurately understand the user's business goals.

[0677] Based on the analyzed data, the server uses a generative AI model to generate an optimal business model. This model changes depending on the extracted keywords and intent. For example, if the user enters "online shopping site," the system will suggest a "subscription model" as an appropriate business model. At the same time, the system also calculates the required stakeholders and development time.

[0678] In addition, the server also has a means to extract past success and failure cases from the database, allowing users to understand the factors that led to the success or failure of a specific business model in the past. This information helps users evaluate their own business models and minimize risks.

[0679] The server then generates a specific action plan, such as "Month 1: Platform basic design, Month 2: Start content creation, Month 3: Start beta testing." This action plan provides users with concrete steps to take toward realizing their business.

[0680] The generated business model, stakeholders, development time, success stories, and failure stories are all displayed comprehensively. Users are provided with an interface that allows them to view this information at a glance on their smartphones or other devices. This allows users to grasp the overall picture for efficiently launching a business.

[0681] As a concrete example, if a user inputs a business idea such as "I want to launch a new online shopping site," the system will process it as follows:

[0682] Example prompt sentence:

[0683] "I want to launch a new online shopping site."

[0684] Based on this input, the system proposes an optimal business model, provides stakeholders, development time, success stories, and failure stories, and generates and displays a concrete action plan to users, allowing them to launch their business quickly and effectively.

[0685] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0686] Step 1:

[0687] Users input business direction

[0688] Users input their business ideas using devices such as smartphones. This input data is specific, such as "I want to launch a new online shopping site." The input data is sent from the device to the server.

[0689] Input: User's business idea (e.g., "I want to launch a new online shopping site")

[0690] Output: Input data is sent from the device to the server

[0691] Step 2:

[0692] The server analyzes the received data

[0693] The server analyzes the received business direction data using an NLP library, specifically extracting key keywords (e.g., "online shopping site") and intent (e.g., "I want to run one") using natural language processing.

[0694] Input: User's business idea (e.g., "I want to launch a new online shopping site")

[0695] Output: Primary keywords and intent (e.g., "online shopping site" and "want to run")

[0696] Step 3:

[0697] The server generates the business model

[0698] The server uses a generative AI model to generate an appropriate business model based on the extracted keywords and intent, including specific revenue models such as subscription models.

[0699] Input: Primary keywords and intent (e.g., "online shopping site" and "want to run")

[0700] Output: Generated business model (e.g. "Subscription Model")

[0701] Step 4:

[0702] The server calculates development time with stakeholders

[0703] Based on the generated business model, the server calculates the required stakeholders (e.g., "content creator" and "IT infrastructure manager") and the development time (e.g., "6 months"), which are obtained from an internal database.

[0704] Input: Generated business model (e.g. "Subscription model")

[0705] Output: Required stakeholders and development effort (e.g., "Content Creator," "IT Infrastructure Manager," and "6 months")

[0706] Step 5:

[0707] The server extracts past success stories and failure stories

[0708] The server extracts past success stories (e.g., "Success stories of famous online learning platforms") and failure stories (e.g., "Reasons for the failure of similar services in the past") from the database.

[0709] Input: Generated business model (e.g. "Subscription model")

[0710] Output: Success stories and failure stories (e.g., "Success stories," "Failure factors")

[0711] Step 6:

[0712] The server generates a concrete action plan

[0713] The server generates a specific action plan based on the business model, including step-by-step plans such as "Month 1: Platform basic design," "Month 2: Content creation begins," and "Month 3: Beta testing begins."

[0714] Input: Generated business model (e.g. "Subscription model")

[0715] Output: A concrete action plan (e.g., "Month 1: Platform basic design," "Month 2: Content creation begins," "Month 3: Beta testing begins")

[0716] Step 7:

[0717] The server displays all information to the user

[0718] The terminal comprehensively displays the business model, stakeholders, development time, success stories, failure stories, and specific action plans received from the server to the user, allowing the user to intuitively grasp the overall picture of their business and specific implementation plans.

[0719] Input: Business model, stakeholders, development time, success stories, failure stories, specific action plans

[0720] Output: All information displayed to the user

[0721] These are the main processing steps of this system.

[0722] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0723] The present invention relates to a system that generates an optimal business model based on a user's input of their goals and business direction, and provides relevant stakeholders, development man-hours, success stories, and failure stories, as well as a concrete action plan. Furthermore, the present invention is characterized by incorporating an emotion engine that recognizes the user's emotions, and by adjusting the business model and proposal content based on the user's emotion data. A specific embodiment of this system is described below.

[0724] Overall flow

[0725] 1. User Input

[0726] Users input data about the direction of the business they want to run, for example, a specific business idea such as "I want to run an online learning platform."

[0727] 2. Sending input data

[0728] The terminal sends the business direction data entered by the user to the server, usually via an API, packaging the input in JSON format.

[0729] 3. Data Analysis

[0730] The server analyzes the incoming data using natural language processing (NLP) libraries, specifically tokenizing the text and performing morphological analysis to extract key keywords and intent.

[0731] 4. Business model generation

[0732] The server generates the optimal business model based on the extracted keywords and intent. This generation process involves using a generative AI model to select the most suitable business model from a number of options. For example, if the user enters "online learning platform," the server will suggest a "subscription model."

[0733] 5. Stakeholders and Development Effort Calculation

[0734] The server calculates the required stakeholders and development time based on the generated business model. This data is retrieved from an internal database. For example, the stakeholders "educational content creator" and "IT infrastructure manager" are presented, and the development time is "6 months."

[0735] 6. Presentation of success and failure cases

[0736] The server extracts past success stories and failure stories from a database. To do this, it searches the database of success stories and failure stories to collect information that users can use as reference. For example, a success story might be presented as a "success story of a famous online learning platform," and a failure story might be presented as a "reason for the failure of a similar service in the past."

[0737] 7. Generate an action plan

[0738] The server generates a specific action plan based on the business model. This involves creating steps and a timeline that best fit the business model, and listing specific tasks for each step. For example, a step-by-step plan such as "Month 1: Basic platform design, Month 2: Start content creation, Month 3: Start beta testing" is provided.

[0739] 8. Collecting Emotional Data with an Emotion Engine

[0740] The device collects the user's emotional data. The emotion engine analyzes the user's emotions based on facial recognition, voice analysis, and the tone of the input content, and generates emotional data. For example, if the user is excited or depressed while typing, that emotional data is collected in real time.

[0741] 9. Emotion Data Analysis

[0742] The server analyzes the collected emotional data to determine the user's emotional state, which is then used to generate business models and prioritize action plans.

[0743] 10. Emotional Adjustment

[0744] The server adjusts business models and action plans based on emotional data, for example, presenting simplified plans and motivational suggestions if the user is feeling stressed.

[0745] 11. User Feedback

[0746] The terminal displays the business model, stakeholders, development time, success stories and failure stories, and specific action plans received from the server to the user, and also reflects adjustments based on the analysis results of the emotion engine.

[0747] Specific examples

[0748] Below are some specific examples of how this system can be used.

[0749] 1. A user types, "I want to run an online learning platform."

[0750] 2. The device sends the input in JSON format to the server.

[0751] 3. The server uses an NLP library to extract the keyword "online learning platform" and intent.

[0752] 4. The server uses the generative AI model to generate a "subscription model."

[0753] 5. The stakeholders who require a server are the "educational content creator" and the "IT infrastructure manager," and the development time is calculated to be "6 months."

[0754] 6. The server extracts success stories of famous online learning platforms and failure stories of similar services in the past.

[0755] 7. The server generates a specific action plan and presents it as follows: "Month 1: Basic design of the platform, Month 2: Start content creation, Month 3: Start beta testing."

[0756] 8. The device collects emotional data as the user types and detects when the user is nervous.

[0757] 9. The server analyzes the emotional data and suggests additional support to help the user relax.

[0758] 10. The device displays all information to the user and also makes adjustments based on emotional data.

[0759] In this way, the system starts with user input, goes through various data analysis and generation processes, and then provides an optimal business model and feasible plan. It also incorporates an emotion engine to flexibly adjust according to the user's emotional state.

[0760] The processing flow will be explained below.

[0761] Step 1:

[0762] Users input data about the business they want to run. For example, they can enter a specific business idea into the input form, such as "I want to run an online learning platform."

[0763] Step 2:

[0764] The terminal sends the business direction data entered by the user to the server, usually via an API, packaging the input in JSON format.

[0765] Step 3:

[0766] The server parses the incoming data using natural language processing (NLP) libraries, tokenizing the text and performing morphological analysis to extract key keywords and intent.

[0767] Step 4:

[0768] The server generates the optimal business model based on the extracted keywords and intent. This generation process uses a generative AI model to select the most suitable business model from a number of options. For example, if the user enters "online learning platform," the server will suggest a "subscription model."

[0769] Step 5:

[0770] The server calculates the required stakeholders and development time based on the generated business model. This data is obtained by querying an internal database. As an example, the stakeholders "educational content creator" and "IT infrastructure manager" are presented, and the development time is "6 months."

[0771] Step 6:

[0772] The server extracts past success stories and failure stories from a database. To do this, it searches the database of success stories and failure stories to collect information that users can use as reference. For example, a success story might be presented as a "success story of a famous online learning platform," and a failure story might be presented as a "reason for the failure of a similar service in the past."

[0773] Step 7:

[0774] The server generates a specific action plan based on the business model. This involves creating steps and a timeline that best fit the business model, and listing specific tasks for each step. For example, a step-by-step plan such as "Month 1: Basic platform design, Month 2: Start content creation, Month 3: Start beta testing" is provided.

[0775] Step 8:

[0776] The device collects the user's emotional data. The emotion engine analyzes the user's emotions based on facial recognition, voice analysis, and the tone of the input content, and generates emotional data. For example, if the user is excited or depressed while typing, that emotional data is collected in real time.

[0777] Step 9:

[0778] The server analyzes the collected emotional data to determine the user's emotional state, which is then used to generate business models and prioritize action plans.

[0779] Step 10:

[0780] The server adjusts business models and action plans based on emotional data, for example, presenting simplified plans and motivational suggestions if the user is feeling stressed.

[0781] Step 11:

[0782] The terminal displays the business model, stakeholders, development time, success stories and failure stories, and specific action plans received from the server to the user, and also reflects adjustments based on the analysis results of the emotion engine.

[0783] This concrete flow allows users to obtain a comprehensive business model and a concrete implementation plan based on their business idea. Furthermore, by incorporating an emotion engine, the system can flexibly adjust according to the user's emotional state, providing more personalized recommendations.

[0784] Example 2

[0785] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0786] While conventional business model generation systems were capable of generating models based on the user's business direction, they lacked the ability to make optimal adjustments based on the user's emotional state. Furthermore, they lacked the ability to comprehensively provide the stakeholders and workload involved in the generated business model, as well as past successes and failures, and to present a feasible implementation plan for the user. This can lead to users feeling anxious about implementing the generated business model and lowering their motivation.

[0787] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0788] In this invention, the server includes means for receiving business direction data input by a user, means for analyzing the received business direction data and extracting main keywords and intentions, means for generating an appropriate business model based on the extracted keywords and intentions, means for collecting emotional data based on the generated business model, and means for analyzing the collected emotional data and adjusting the business model and action plan based on the emotional state of the user. This makes it possible to adjust the optimal business model according to the emotional state of the user, eliminating problems such as anxiety and decreased motivation, and providing a more feasible and effective business model.

[0789] "Business direction data" is information including the business objectives, goals, strategies, and specific ideas proposed by the user.

[0790] "Keywords" are particularly important words or phrases in business direction data, and are terms that play an important role in generating and analyzing business models.

[0791] "Intent" refers to the purpose or goal that the user wants to convey in the business direction data, as well as the thinking behind it.

[0792] A "business model" is a specific summary of the value of the products or services offered, revenue structure, customer base, and management methods.

[0793] "Emotional data" is information that indicates the emotional state of the user, and is data collected through facial recognition, voice analysis, tone analysis of input content, and the like.

[0794] "Stakeholders" refers to the people and organizations necessary to execute and maintain the business model, such as creators of educational content and IT infrastructure managers.

[0795] "Work volume" refers to the labor and time required to execute a business model, such as development man-hours.

[0796] "Success stories" are specific examples or data showing that similar business models or strategies have been successful in the past.

[0797] "Failure cases" are specific examples or data of similar business models or strategies that have failed in the past.

[0798] An "action plan" is a plan that includes specific steps and a timeline for realizing a business model, as well as tasks for each step.

[0799] A "generative AI model" is an algorithm or system that uses artificial intelligence technology to generate business models, and outputs an appropriate business model by inputting, for example, a prompt statement.

[0800] MODE FOR CARRYING OUT THE INVENTION

[0801] This invention relates to a system that allows users to input their business direction, generates an optimal business model based on that, and provides information on the people involved, workload, success stories, failure stories, and specific implementation plans.Furthermore, this invention combines an emotion engine that recognizes the user's emotions, and automatically adjusts the business model and proposal content based on the user's emotion data.

[0802] Hardware and software used

[0803] The system of the present invention uses the following hardware and software:

[0804] Server: Analyzes data, generates business models, analyzes sentiment data. The server hosts the database and AI models.

[0805] Terminal: A device where the user can provide input and receive feedback from the system. It is equipped with an emotion engine to collect user emotion data.

[0806] Natural Language Processing (NLP) libraries, such as NLTK or spaCy, to analyze user-entered text.

[0807] Generative AI model: For example, use OpenAI's GPT-3 to generate an appropriate business model.

[0808] Database: A database for storing data such as information on stakeholders, success stories, and failure stories.

[0809] System processing overview

[0810] 1. User input: The user inputs the business direction from the device. For example, the user inputs a specific idea such as "I want to operate an online learning platform."

[0811] 2. Sending input data: The terminal packages the business direction data entered by the user in JSON format and sends it to the server via API.

[0812] 3. Data Analysis: The server uses a natural language processing library to analyze the incoming data, tokenizing it and performing morphological analysis to extract key keywords and intent.

[0813] 4. Business model generation: The server uses the extracted keywords and intent to generate an optimal business model using a generative AI model. For example, if the input is "online learning platform," the server will suggest a "subscription model."

[0814] 5. Calculation of stakeholders and workload: Based on the generated business model, the server retrieves and calculates the necessary stakeholders (e.g., educational content creator, IT infrastructure administrator) and workload (e.g., 6 months) from its internal database.

[0815] 6. Presentation of success and failure cases: The server extracts relevant success and failure cases from the database. For example, it displays "success stories of famous online learning platforms" as success cases and "reasons for failure of similar services in the past" as failure cases.

[0816] 7. Generate a detailed execution plan: The server generates an execution plan with specific steps and a timeline. For example, a step-by-step plan such as "Month 1: Platform basic design, Month 2: Content creation begins, Month 3: Beta testing begins" is provided.

[0817] 8. Emotional data collection and analysis: The device collects the user's emotional data and sends it to the server. The server analyzes the collected emotional data and determines the user's emotional state.

[0818] 9. Emotional Adjustment: The server adjusts business models and execution plans based on emotional data. For example, if a user is feeling stressed, it will provide simplified plans and motivational suggestions.

[0819] Specific examples

[0820] Here are some concrete examples of how this system can be used:

[0821] 1. A user types, "I want to run an online learning platform."

[0822] 2. The device sends the input in JSON format to the server.

[0823] 3. The server uses a natural language processing library to extract the keyword "online learning platform" and the intent.

[0824] 4. The server uses the generative AI model to generate a "subscription model."

[0825] 5. The parties who will need a server are the "educational content creator" and the "IT infrastructure manager," and the amount of work is calculated to be "6 months."

[0826] 6. The server extracts success stories of famous online learning platforms and failure stories of similar services in the past.

[0827] 7. The server generates a specific execution plan and presents it as follows: "Month 1: Basic design of the platform, Month 2: Start content creation, Month 3: Start beta testing."

[0828] 8. The device collects emotional data as the user types and detects when the user is nervous.

[0829] 9. The server analyzes the emotional data and suggests additional support to help the user relax.

[0830] 10. The device displays all information to the user and also makes adjustments based on emotional data.

[0831] Prompt Sentence Examples

[0832] Below are some example prompts to input to a generative AI model:

[0833] A user has entered, "I want to run an online learning platform." Please generate the optimal business model based on this direction. Also, please calculate the necessary stakeholders and the amount of work, provide examples of past successes and failures, and even provide a concrete implementation plan.

[0834] In this way, the system of the present invention starts with user input, goes through various data analysis and generation processes, and provides an optimal business model and a feasible plan. Furthermore, by incorporating an emotion engine, it can make flexible adjustments according to the user's emotional state.

[0835] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0836] Step 1:

[0837] Accepting user input

[0838] The user enters business direction data into an input form on the device. For example, they enter a specific idea such as "I want to operate an online learning platform." The input data is temporarily stored in the device's memory and used for the next processing step.

[0839] Input: Business direction data (e.g., "I want to run an online learning platform")

[0840] Output: Business direction data stored in the device's memory

[0841] Step 2:

[0842] Sending input data

[0843] The terminal packages the business direction data entered by the user in JSON format and sends it to the server using an HTTP POST request to the specified API endpoint, where the data is stored in the server's receive buffer.

[0844] Input: User orientation data (data stored in device memory)

[0845] Output: JSON format data stored in the server's receive buffer

[0846] Step 3:

[0847] Data analysis

[0848] The server unpacks the received JSON data and parses it using a natural language processing (NLP) library (e.g., NLTK or spaCy) to tokenize the text, perform morphological analysis, and extract key keywords and intent.

[0849] Input: JSON format data stored in the server's receive buffer

[0850] Output: Key keywords and user intent (stored in server memory)

[0851] Step 4:

[0852] Business model generation

[0853] The server generates an optimal business model using a generative AI model (e.g., OpenAI's GPT-3) based on the extracted keywords and intent. The extracted intent and keywords are input into the generative AI model as prompt statements, and the model outputs an optimal business model, such as a "subscription model."

[0854] Input: Key keywords and user intent (stored in server memory)

[0855] Output: Generated business model (e.g. "Subscription model")

[0856] Step 5:

[0857] Calculating stakeholders and effort

[0858] Based on the generated business model, the server retrieves the necessary stakeholders (e.g., educational content creator, IT infrastructure manager) and the amount of work (e.g., 6 months) from an internal database and calculates them. The calculation is performed by querying the existing project database.

[0859] Input: Generated business model (stored in server memory)

[0860] Output: The stakeholders and effort required (e.g., "Educational content creator, 6 months")

[0861] Step 6:

[0862] Presentation of success and failure cases

[0863] The server searches the internal database and extracts success stories and failure stories related to the business model. Specific examples include a success story, "The success story of a famous online learning platform," and a failure story, "The reasons for the failure of similar services in the past."

[0864] Input: Generated business model (stored in server memory)

[0865] Output: Success stories and failure stories (e.g., "Success stories of famous online learning platforms")

[0866] Step 7:

[0867] Generate a concrete execution plan

[0868] The server creates specific steps and a timeline based on the business model. This plan has a step-by-step structure, listing specific tasks and the duration of each step. For example, "Month 1: Platform basic design, Month 2: Content creation begins, Month 3: Beta testing begins."

[0869] Input: Generated business model (stored in server memory)

[0870] Output: A concrete action plan (e.g., "Month 1: Basic design of the platform, Month 2: Start creating content")

[0871] Step 8:

[0872] Emotion data collection and analysis

[0873] The device uses facial recognition, voice analysis, and tone analysis of input content to collect user emotional data, thereby capturing the user's emotional state (e.g., nervousness, excitement, etc.) in real time and sending the data in JSON format to the server.

[0874] Input: User's facial expressions, voice, and input (device sensor data)

[0875] Output: Emotion data (sent to server in JSON format)

[0876] Step 9:

[0877] Emotional Data Analysis

[0878] The server analyzes the collected emotional data to determine the user's emotional state. It processes the data using an emotion analysis algorithm to identify the user's emotional state (e.g., nervous, stressed, relaxed).

[0879] Input: Emotion data (stored on the server in JSON format)

[0880] Output: User's emotional state (e.g., "tense," "relaxed")

[0881] Step 10:

[0882] Emotional adjustment

[0883] The server adjusts business models and action plans based on the analyzed emotional data, for example, presenting simplified plans and motivational suggestions if the user is feeling stressed.

[0884] Input: User's emotional state (stored in server memory)

[0885] Output: A tailored business model and implementation plan (e.g., a "simplified plan")

[0886] Step 11:

[0887] User feedback

[0888] The device receives information from the server and displays it to the user, including the business model, stakeholders, workload, success stories, failure stories, and specific implementation plans, along with adjustments based on emotion data.

[0889] Input: Adjusted business model and execution plan (data sent from the server)

[0890] Output: Information displayed to the user (displayed on the device display)

[0891] As mentioned above, the program processing of this system is carried out through a series of steps, starting with user input, data analysis, business model generation, and adjustments based on emotion data. This process makes it possible to provide a more specific and feasible business model.

[0892] (Application example 2)

[0893] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0894] Conventional business model generation systems provide models and action plans based on business direction data entered by users, but they are unable to consider the user's emotional state. As a result, there are problems such as users feeling stressed or being unable to make appropriate proposals based on their emotional state. Furthermore, when it comes to immediately applying improvement ideas on the factory floor, it is difficult to provide appropriate plans that take emotions into account in real time.

[0895] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0896] In this invention, the server includes means for receiving business direction data input by a user, means for analyzing the received business direction data and extracting main keywords and intentions, means for generating an appropriate business model based on the extracted keywords and intentions, means for generating a specific action plan based on the business model, means for collecting and analyzing user emotion data, and means for adjusting the business model and action plan based on the emotion data. This not only appropriately reflects the user's input, but also makes it possible to provide a real-time improvement action plan that is adjusted according to the user's emotional state.

[0897] "Business direction data" is information about the business direction the user is aiming for, the processes they want to improve, their goals, etc.

[0898] "Key Keywords" are important words and phrases extracted from business direction data.

[0899] "Intent" refers to the user's purpose or goal that is inferred based on the business direction data entered by the user.

[0900] A "business model" is an optimal business structure and revenue mechanism proposed based on business direction data.

[0901] An "action plan" is a specific action plan and timeline for realizing a business model.

[0902] "Emotional data" refers to information that indicates a user's emotional state and is collected through facial recognition, voice analysis, tone of input, etc.

[0903] An "emotion engine" is a system or algorithm that collects and analyzes a user's emotional data and determines their emotional state.

[0904] "Stakeholders" are those interested in implementing the business model and action plan.

[0905] "Development man-hours" refers to the working hours and labor required to realize a business model or action plan.

[0906] A "success story" is a specific example of a similar business model or action plan that was successful in the past.

[0907] "Failure cases" are specific examples of similar business models or action plans that have failed in the past.

[0908] The system realizing this invention includes various means for receiving and analyzing business direction data input by users, which not only generates an appropriate business model and a specific action plan, but also makes adjustments taking into account the emotional state of the user.

[0909] Hardware and software used

[0910] Hardware: smart glasses, server, camera, microphone.

[0911] Software: NLP libraries (e.g. spaCy), generative AI models (e.g. OpenAI GPT), databases (e.g. PostgreSQL), sentiment analysis libraries (e.g. Affectiva).

[0912] System Operation Overview

[0913] User Input

[0914] Users input the business process they want to improve through the smart glasses using voice or text, which is first captured by the smart glasses and then sent to the server in JSON format.

[0915] Data analysis and business model generation

[0916] The server uses an NLP library (e.g., spaCy) to tokenize the received data and extract key keywords and intent, as shown in the example below.

[0917] Example prompt sentence:

[0918] "We want to improve the production efficiency of our line. Which step is causing the bottleneck in our current setup?"

[0919] Next, a generative AI model (e.g., OpenAI GPT) is used to generate optimal business models and action plans based on the extracted keywords and intent.

[0920] Stakeholders and development effort calculations

[0921] Based on the generated business model, the required stakeholders and development effort are retrieved from a database (e.g., PostgreSQL) and calculated.

[0922] Extraction of success and failure cases

[0923] Past examples of successful and unsuccessful similar business models are extracted from a database and presented to the user.

[0924] Generate and view an action plan

[0925] Generate specific action plans based on your business model, including timelines for completing each task by when.

[0926] Emotion data collection and analysis

[0927] The smart glasses use a built-in camera and microphone to collect and analyze the user's emotional data, which is then analyzed in real time using an emotion analysis library (e.g., Affectiva) to determine the user's emotional state.

[0928] Emotional adjustment

[0929] Based on the collected emotional data, the generated business model and action plan can be adjusted accordingly. For example, if the user is feeling stressed, a simplified plan or an encouraging message can be provided.

[0930] User feedback

[0931] Finally, the adjusted business model, action plan, stakeholders, development time, success stories, and failure stories are displayed on the user's smart glasses.

[0932] Through the above process, starting from user input and going through various data analysis and generation processes, it becomes possible to provide optimal business models and action plans. In addition, by combining it with an emotion engine, it is possible to flexibly adjust according to the user's emotional state.

[0933] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0934] Step 1:

[0935] Users can input the business process they want to improve through voice or text input via smart glasses, which is captured by the device, converted into JSON format, and sent to the server.

[0936] Input: User voice or text input

[0937] Output: JSON format data

[0938] Step 2:

[0939] The server parses the received JSON-formatted business direction data using a natural language processing (NLP) library (e.g., spaCy), tokenizing the data and extracting key keywords and intent.

[0940] Input: Business direction data in JSON format

[0941] Output: Primary keywords and intent

[0942] Step 3:

[0943] Based on the extracted keywords and intent, the server uses a generative AI model (e.g., OpenAI GPT) to generate an optimal business model. This generation process takes into account the conditions and options necessary for business success.

[0944] Input: Extracted keywords and intent

[0945] Output: Generated business model

[0946] Step 4:

[0947] Based on the generated business model, the server retrieves and calculates the required stakeholders and development effort from an internal database (e.g., PostgreSQL).

[0948] Input: Generated business model

[0949] Output: Required stakeholders and development effort

[0950] Step 5:

[0951] The server extracts past success stories and failure stories from a related database and collects information that users can use as reference.

[0952] Input: Generated business model

[0953] Output: Success stories and failure stories

[0954] Step 6:

[0955] The server generates a specific action plan based on the business model and related information (stakeholders, development time, success stories, failure stories). The action plan includes specific tasks and timelines for each step.

[0956] Input: Business model, stakeholders, development time, success stories, failure stories

[0957] Output: A concrete action plan

[0958] Step 7:

[0959] The device collects the user's emotional data using the camera and microphone built into the smart glasses, which are then used to collect emotions through facial recognition and voice analysis. This data is then sent to a server in real time.

[0960] Input: User's video and audio data

[0961] Output: Real-time emotion data

[0962] Step 8:

[0963] The server analyzes the collected emotional data using an emotion analysis library (e.g., Affectiva) to determine the user's emotional state.

[0964] Input: Real-time emotion data

[0965] Output: Parsed emotion data

[0966] Step 9:

[0967] The server then adjusts the generated business model and action plan based on the analyzed emotional data, for example, providing a simplified plan or encouraging message if the user is feeling stressed.

[0968] Input: Analyzed emotion data and action plan

[0969] Output: A tailored business model and action plan

[0970] Step 10:

[0971] The device compiles information on the business model, stakeholders, development time, success stories, failure stories, specific action plans, and adjusted content, and displays it on the smart glasses.

[0972] Input: Adjusted business model, stakeholders, development time, success stories, failure stories, concrete action plan

[0973] Output: Display feedback to the user

[0974] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0975] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0976] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0977] [Third embodiment]

[0978] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0979] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0980] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0981] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0982] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0983] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0984] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0985] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0986] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[0987] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0988] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0989] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. 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."

[0990] This invention relates to a system that, by inputting what a user wants to do and the direction of their business, presents an optimal business model and provides relevant stakeholders, development man-hours, success stories and failure stories, and specific action plans. Specific embodiments of this system are described below.

[0991] Overall flow

[0992] 1. User Input

[0993] Users input the direction of the business they want to run, such as a specific business idea like "I want to run an online learning platform."

[0994] 2. Sending input data

[0995] The terminal transmits the business direction data entered by the user to a server, usually via an API.

[0996] 3. Data Analysis

[0997] The server analyzes the incoming data using natural language processing (NLP) libraries, extracting key keywords and intent.

[0998] 4. Business model generation

[0999] The server generates the optimal business model based on the extracted keywords and intent. This generation uses a generative AI model. For example, if the user enters "online learning platform," the server will suggest the "subscription model" as an appropriate business model.

[1000] 5. Stakeholders and Development Effort Calculation

[1001] The server calculates the required stakeholders and development effort based on the generated business model. This data is retrieved from an internal database.

[1002] 6. Presentation of success and failure cases

[1003] The server extracts past success and failure cases from a database, allowing users to refer to the circumstances that led to success or failure.

[1004] 7. Generate an action plan

[1005] The server generates a specific action plan based on the business model, such as "Month 1: Basic design of the platform, Month 2: Start content creation, Month 3: Start beta testing."

[1006] 8. User Feedback

[1007] The terminal displays to the user the business model, stakeholders, development time, success stories and failure stories, and specific action plans received from the server.

[1008] Specific examples

[1009] Below are some specific examples of how this system can be used.

[1010] 1. A user types, "I want to run an online learning platform."

[1011] 2. The device sends the input in JSON format to the server.

[1012] 3. The server uses an NLP library to extract the keyword "online learning platform" and intent.

[1013] 4. The server uses the generative AI model to generate a "subscription model."

[1014] 5. The stakeholders who require a server are the "educational content creator" and the "IT infrastructure manager," and the development time is calculated to be "6 months."

[1015] 6. The server extracts success stories of famous online learning platforms and failure stories of similar services in the past.

[1016] 7. The server generates a specific action plan and presents it as follows: "Month 1: Basic design of the platform, Month 2: Start content creation, Month 3: Start beta testing."

[1017] 8. The device displays all information to the user.

[1018] In this way, users can obtain a comprehensive business model and concrete implementation plan based on their business idea, which can minimize business risks and increase the chances of success.

[1019] The processing flow will be explained below.

[1020] Step 1:

[1021] Users input data about the business they want to run. For example, they can enter a specific business idea into the input form, such as "I want to run an online learning platform."

[1022] Step 2:

[1023] The terminal sends the business direction data entered by the user to the server, usually via an API, packaging the input in JSON format.

[1024] Step 3:

[1025] The server analyzes the incoming data using natural language processing (NLP) libraries, specifically tokenizing the text and performing morphological analysis to extract key keywords and intent.

[1026] Step 4:

[1027] The server generates the optimal business model based on the extracted keywords and intent. This generation process involves using a generative AI model to select the most suitable business model from a number of options. For example, if the user enters "online learning platform," the server will suggest a "subscription model."

[1028] Step 5:

[1029] The server calculates the required stakeholders and development effort based on the generated business model. To do this, it queries an internal database to obtain the roles of the relevant stakeholders and the development effort. As an example, it presents the stakeholders "educational content creator" and "IT infrastructure manager" and the development effort of "6 months."

[1030] Step 6:

[1031] The server extracts past success stories and failure stories from a database. To do this, it searches the database of success stories and failure stories to collect information that users can use as reference. For example, a success story might be presented as a "success story of a famous online learning platform," and a failure story might be presented as a "reason for the failure of a similar service in the past."

[1032] Step 7:

[1033] The server generates a specific action plan based on the business model. This involves creating steps and a timeline that best fit the business model, and listing specific tasks for each step. For example, a step-by-step plan such as "Month 1: Basic platform design, Month 2: Start content creation, Month 3: Start beta testing" is provided.

[1034] Step 8:

[1035] The terminal receives information from the server, including the business model, stakeholders, development time, success stories and failure stories, and a concrete action plan, and displays it to the user. Comprehensive information is displayed on the user interface, allowing the user to create an action plan based on the information presented.

[1036] In this way, the system starts with user input and goes through various data analysis and generation processes to provide the optimal business model and executable plan.

[1037] Example 1

[1038] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1039] Conventional business model generation systems have difficulty effectively analyzing the data entered by users and providing an optimal business model and detailed implementation plan. As a result, users are unable to obtain appropriate advice or a concrete action plan for their business ideas, which reduces the probability of business success.

[1040] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1041] In this invention, the server includes: means for receiving business direction data input by a user; means for analyzing the received business direction data and extracting key keywords and intentions; means for using a generative AI model to generate an appropriate business model based on the extracted keywords and intentions; means for calculating the required stakeholders and development man-hours based on the generated business model; means for extracting past success stories and failure stories from a database; means for generating a specific action plan tailored to the business model; means for comprehensively displaying the generated business model, stakeholders, development man-hours, success stories, and failure stories; means for converting the user's input data into JSON format and sending it to the server; and means for analyzing the received data using a natural language processing library. This allows users to easily obtain a comprehensive business model and a detailed action plan based on their business idea.

[1042] "Business direction data" is information entered by the user regarding specific business ideas and business goals.

[1043] "Means for receiving" refers to a device or program that provides the function of sending and receiving data entered by the user to the server.

[1044] "Means of analysis" are algorithms, libraries, and software modules that process the received data and extract key keywords and intent.

[1045] "Keywords" are important words or phrases extracted from business direction data that represent business themes or themes.

[1046] "Intent" refers to the purpose or goal that a user is trying to achieve based on business direction data.

[1047] A "generative AI model" is an artificial intelligence model that generates optimal business models and action plans based on extracted keywords and intent.

[1048] "Stakeholders" are people or organizations that play a role necessary to the execution of a business model.

[1049] "Development man-hours" refers to the working time and resources required to execute a business model.

[1050] A "success story" is a specific example of a similar business model being successfully implemented in the past.

[1051] "Failure cases" are specific examples of similar business models that have been implemented in the past and failed.

[1052] A "specific action plan" refers to a step-by-step execution plan or tasks for implementing a business model.

[1053] "JSON format" refers to the JavaScript Object Notation format, which represents data in a structured text format.

[1054] A "natural language processing library" is a software component that analyzes incoming data and understands the linguistic meaning of the text.

[1055] This invention is a system that provides optimal business models and concrete action plans by allowing users to input their business direction and ideas. This system consists of three elements: a server, a terminal, and the user.

[1056] Overall structure

[1057] 1. User Interface

[1058] Users enter data about the direction of the business they want to run into a special input form. For example, they can enter a specific business idea such as "I want to run an online learning platform."

[1059] 2. Data Transmission

[1060] The terminal converts the business direction data entered by the user into JSON format and sends it to the server using an HTTP POST request, using technologies such as Ajax or Fetch API.

[1061] 3. Data Analysis

[1062] The server parses the received JSON data using a natural language processing library (e.g., spaCy, NLTK) to extract key keywords and intent, which clarifies the business's subject matter and goals.

[1063] 4. Business model generation

[1064] The server generates the optimal business model using a generative AI model (e.g., GPT-4) based on the extracted keywords and intent. For example, if the user enters "online learning platform," the server will suggest a "subscription model."

[1065] 5. Stakeholders and Development Effort Calculation

[1066] Based on the generated business model, the server retrieves the necessary stakeholders (e.g., educational content creator, IT infrastructure manager) and development man-hours (e.g., 6 months) from the database and calculates them.

[1067] 6. Presentation of success and failure cases

[1068] The server extracts past success stories and failure stories from a database and provides them to users. For example, it might present "the success story of a famous online learning platform" as a success story, or "the reasons for the failure of similar services in the past" as a failure story.

[1069] 7. Generate a concrete action plan

[1070] The server generates a specific action plan based on the generated business model, such as "Month 1: Basic platform design, Month 2: Start content creation, Month 3: Start beta testing."

[1071] 8. User Feedback

[1072] The terminal displays the information received from the server to the user, who can then use it to get a detailed execution plan based on their business idea.

[1073] Specific example explanation

[1074] Below are some specific examples of how this system can be used.

[1075] 1. A user types, "I want to run an online learning platform."

[1076] 2. The device converts the input into JSON format and sends it to the server.

[1077] 3. The server analyzes the received data using a natural language processing library and extracts the keyword "online learning platform" and the intent.

[1078] 4. The server uses the generative AI model to generate the optimal business model and propose a "subscription model."

[1079] 5. Calculate the stakeholders who will need a server (educational content creators, IT infrastructure managers) and the development time (6 months).

[1080] 6. The server extracts success stories of famous online learning platforms and failure stories of similar services in the past.

[1081] 7. The server generates a specific action plan and presents it as follows: "Month 1: Basic design of the platform, Month 2: Start content creation, Month 3: Start beta testing."

[1082] 8. The device displays all the information to the user.

[1083] Prompt Sentence Examples

[1084] Below are some example prompts to input to the generative AI model:

[1085] Business idea: Run an online learning platform

[1086] Required stakeholders: Educational content creators, IT infrastructure managers

[1087] Development time: 6 months

[1088] Business model: Subscription model

[1089] Success Story: A Popular Online Learning Platform's Success Story

[1090] Failure Case: Reasons for the failure of similar services in the past

[1091] Generate a concrete action plan.

[1092] This system allows users to easily obtain a comprehensive business model and detailed execution plan based on their business idea, which is expected to minimize business risks and increase the chances of success.

[1093] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1094] Processing step details

[1095] Step 1: User Input

[1096] Users enter their business direction data into an input form. For example, let's say they want to run an online learning platform.

[1097] Input: User enters their business idea into a form.

[1098] Output: The user's business idea is input into the system.

[1099] Step 2: Submitting input data

[1100] The terminal converts the business direction data entered by the user into JSON format and sends it to the server using an HTTP POST request, using Ajax or the Fetch API.

[1101] Input: User's business direction data.

[1102] Output: The data is converted to JSON format and sent to the server.

[1103] Step 3: Receiving and analyzing data

[1104] The server parses the received JSON data using a natural language processing library (e.g., spaCy or NLTK) to extract important keywords and intent.

[1105] Input: Business direction data in JSON format.

[1106] Output: Analysis results including key keywords and intent.

[1107] Specific operation: Passes the received data to a natural language processing library to perform morphological analysis and topic modeling.

[1108] Step 4: Generate a business model

[1109] The server inputs the analysis results (keywords and intent) into the generative AI model as prompts to generate the optimal business model. For example, for the input keyword "online learning platform," the model generates "subscription model."

[1110] Input: Primary keywords and intent.

[1111] Output: The generated business model.

[1112] Specific operation: Generate a prompt sentence using the analysis results, send a query to the generative AI model, and obtain a response.

[1113] Step 5: Stakeholders and development effort calculations

[1114] Based on the generated business model, the server retrieves the stakeholders (e.g., educational content creators, IT infrastructure managers) and development man-hours (e.g., 6 months) from an internal database.

[1115] Input: The generated business model.

[1116] Output: Stakeholder list and development effort required.

[1117] Specific operation: Query the database based on the business model to retrieve the relevant stakeholders and effort.

[1118] Step 6: Present success stories and failure stories

[1119] The server extracts relevant success stories and failure stories from its internal database and provides them to users, such as "Success stories: success stories of famous online learning platforms" and "Failure stories: reasons for the failure of similar services in the past."

[1120] Input: Business model related data.

[1121] Output: Success stories and failure stories.

[1122] Specific operation: Retrieves success and failure cases from the database and formats them as relevant information.

[1123] Step 7: Generate a concrete action plan

[1124] The server generates a specific action plan based on the generated business model, such as a step-by-step plan like "Month 1: Basic platform design, Month 2: Start content creation, Month 3: Start beta testing."

[1125] Input: Generated business model and related data.

[1126] Output: A concrete action plan.

[1127] What it does: Incorporates input data into a planning template to generate a step-by-step execution plan.

[1128] Step 8: User feedback

[1129] The terminal displays all the information received from the server (business model, stakeholders, development time, success stories, failure stories, action plans) to the user.

[1130] Input: General information sent from the server.

[1131] Output: Information displayed to the user.

[1132] Specific behavior: Formats the information appropriately and reflects it in the user interface.

[1133] The above is a detailed description of the processing steps of this system, a series of processes that provide the optimal business model and detailed implementation plan based on the user's business idea.

[1134] (Application example 1)

[1135] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1136] Conventional business model generation systems lack sufficient support for users to quickly and comprehensively develop new business directions. They also struggle to present specific action plans based on success and failure cases in specific areas. Furthermore, there is a need for comprehensive business model proposals and displays that can be operated directly on smartphones and other devices. Therefore, there is a need for a system that can help users launch businesses efficiently and increase the likelihood of success.

[1137] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1138] In this invention, the server includes means for receiving business direction data input by a user, means for analyzing the received business direction data and extracting key keywords and intentions, means for generating an appropriate business model based on the extracted keywords and intentions, means for calculating the required stakeholders and development man-hours based on the generated business model, means for extracting past success stories and failure stories, means for generating a specific action plan tailored to the business model, and means for comprehensively displaying the generated business model, stakeholders, development man-hours, success stories, and failure stories. This enables users to quickly and effectively build business models in specific business areas and obtain specific action plans based on past cases.

[1139] "Business direction data" is information that indicates the specific ideas, goals, and strategies of a new business when a user launches the business.

[1140] "Means for receiving" refers to the functions and processes by which a server or device obtains data entered by a user.

[1141] "Means of analysis" refers to the functions and processes that analyze received data using technologies such as machine learning and natural language processing to extract key keywords and intent.

[1142] A "business model" refers to the structure and methodology for realizing a user's business idea, including the mechanism for generating revenue.

[1143] "Stakeholders" refers to all parties involved in business activities, specifically including individuals and organizations that affect the success of a project.

[1144] "Development time" refers to the time, resources, and human resources required to realize a business idea.

[1145] "Success stories" refer to specific cases or examples where similar business models have been successful in the past, and include the results of their analysis.

[1146] "Failure Cases" refers to specific cases or examples where similar business models have failed in the past, including lessons learned and causes.

[1147] An "action plan" is a set of concrete steps and plans for realizing a business model, including a timeline and implementation procedures.

[1148] "Display means" refers to an interface or device for visually presenting the calculated business model and related information to the user.

[1149] A "smartphone" is a type of mobile phone, a mobile device that has advanced computing power and communication functions and can run a variety of applications.

[1150] This invention relates to a system that generates an optimal business model based on business direction data entered by a user and provides a specific action plan based on that model. Specific embodiments of this system are described below.

[1151] The server has a means for receiving business direction data entered by the user. For example, when a user enters a specific business idea such as "I want to launch a new online shopping site," the system receives this data. This data is usually sent via a mobile device such as a smartphone.

[1152] The received business direction data is analyzed using NLP (Natural Language Processing) libraries. This analysis extracts key keywords and their intent from the data, allowing the system to accurately understand the user's business goals.

[1153] Based on the analyzed data, the server uses a generative AI model to generate an optimal business model. This model changes depending on the extracted keywords and intent. For example, if the user enters "online shopping site," the system will suggest a "subscription model" as an appropriate business model. At the same time, the system also calculates the required stakeholders and development time.

[1154] In addition, the server also has a means to extract past success and failure cases from the database, allowing users to understand the factors that led to the success or failure of a specific business model in the past. This information helps users evaluate their own business models and minimize risks.

[1155] The server then generates a specific action plan, such as "Month 1: Platform basic design, Month 2: Start content creation, Month 3: Start beta testing." This action plan provides users with concrete steps to take toward realizing their business.

[1156] The generated business model, stakeholders, development time, success stories, and failure stories are all displayed comprehensively. Users are provided with an interface that allows them to view this information at a glance on their smartphones or other devices. This allows users to grasp the overall picture for efficiently launching a business.

[1157] As a concrete example, if a user inputs a business idea such as "I want to launch a new online shopping site," the system will process it as follows:

[1158] Example prompt sentence:

[1159] "I want to launch a new online shopping site."

[1160] Based on this input, the system proposes an optimal business model, provides stakeholders, development time, success stories, and failure stories, and generates and displays a concrete action plan to users, allowing them to launch their business quickly and effectively.

[1161] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1162] Step 1:

[1163] Users input business direction

[1164] Users input their business ideas using devices such as smartphones. This input data is specific, such as "I want to launch a new online shopping site." The input data is sent from the device to the server.

[1165] Input: User's business idea (e.g., "I want to launch a new online shopping site")

[1166] Output: Input data is sent from the device to the server

[1167] Step 2:

[1168] The server analyzes the received data

[1169] The server analyzes the received business direction data using an NLP library, specifically extracting key keywords (e.g., "online shopping site") and intent (e.g., "I want to run one") using natural language processing.

[1170] Input: User's business idea (e.g., "I want to launch a new online shopping site")

[1171] Output: Primary keywords and intent (e.g., "online shopping site" and "want to run")

[1172] Step 3:

[1173] The server generates the business model

[1174] The server uses a generative AI model to generate an appropriate business model based on the extracted keywords and intent, including specific revenue models such as subscription models.

[1175] Input: Primary keywords and intent (e.g., "online shopping site" and "want to run")

[1176] Output: Generated business model (e.g. "Subscription Model")

[1177] Step 4:

[1178] The server calculates development time with stakeholders

[1179] Based on the generated business model, the server calculates the required stakeholders (e.g., "content creator" and "IT infrastructure manager") and the development time (e.g., "6 months"), which are obtained from an internal database.

[1180] Input: Generated business model (e.g. "Subscription model")

[1181] Output: Required stakeholders and development effort (e.g., "Content Creator," "IT Infrastructure Manager," and "6 months")

[1182] Step 5:

[1183] The server extracts past success stories and failure stories

[1184] The server extracts past success stories (e.g., "Success stories of famous online learning platforms") and failure stories (e.g., "Reasons for the failure of similar services in the past") from the database.

[1185] Input: Generated business model (e.g. "Subscription model")

[1186] Output: Success stories and failure stories (e.g., "Success stories," "Failure factors")

[1187] Step 6:

[1188] The server generates a concrete action plan

[1189] The server generates a specific action plan based on the business model, including step-by-step plans such as "Month 1: Platform basic design," "Month 2: Content creation begins," and "Month 3: Beta testing begins."

[1190] Input: Generated business model (e.g. "Subscription model")

[1191] Output: A concrete action plan (e.g., "Month 1: Platform basic design," "Month 2: Content creation begins," "Month 3: Beta testing begins")

[1192] Step 7:

[1193] The server displays all information to the user

[1194] The terminal comprehensively displays the business model, stakeholders, development time, success stories, failure stories, and specific action plans received from the server to the user, allowing the user to intuitively grasp the overall picture of their business and specific implementation plans.

[1195] Input: Business model, stakeholders, development time, success stories, failure stories, specific action plans

[1196] Output: All information displayed to the user

[1197] These are the main processing steps of this system.

[1198] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1199] The present invention relates to a system that generates an optimal business model based on a user's input of their goals and business direction, and provides relevant stakeholders, development man-hours, success stories, and failure stories, as well as a concrete action plan. Furthermore, the present invention is characterized by incorporating an emotion engine that recognizes the user's emotions, and by adjusting the business model and proposal content based on the user's emotion data. A specific embodiment of this system is described below.

[1200] Overall flow

[1201] 1. User Input

[1202] Users input data about the direction of the business they want to run, for example, a specific business idea such as "I want to run an online learning platform."

[1203] 2. Sending input data

[1204] The terminal sends the business direction data entered by the user to the server, usually via an API, packaging the input in JSON format.

[1205] 3. Data Analysis

[1206] The server analyzes the incoming data using natural language processing (NLP) libraries, specifically tokenizing the text and performing morphological analysis to extract key keywords and intent.

[1207] 4. Business model generation

[1208] The server generates the optimal business model based on the extracted keywords and intent. This generation process involves using a generative AI model to select the most suitable business model from a number of options. For example, if the user enters "online learning platform," the server will suggest a "subscription model."

[1209] 5. Stakeholders and Development Effort Calculation

[1210] The server calculates the required stakeholders and development time based on the generated business model. This data is retrieved from an internal database. For example, the stakeholders "educational content creator" and "IT infrastructure manager" are presented, and the development time is "6 months."

[1211] 6. Presentation of success and failure cases

[1212] The server extracts past success stories and failure stories from a database. To do this, it searches the database of success stories and failure stories to collect information that users can use as reference. For example, a success story might be presented as a "success story of a famous online learning platform," and a failure story might be presented as a "reason for the failure of a similar service in the past."

[1213] 7. Generate an action plan

[1214] The server generates a specific action plan based on the business model. This involves creating steps and a timeline that best fit the business model, and listing specific tasks for each step. For example, a step-by-step plan such as "Month 1: Basic platform design, Month 2: Start content creation, Month 3: Start beta testing" is provided.

[1215] 8. Collecting Emotional Data with an Emotion Engine

[1216] The device collects the user's emotional data. The emotion engine analyzes the user's emotions based on facial recognition, voice analysis, and the tone of the input content, and generates emotional data. For example, if the user is excited or depressed while typing, that emotional data is collected in real time.

[1217] 9. Emotion Data Analysis

[1218] The server analyzes the collected emotional data to determine the user's emotional state, which is then used to generate business models and prioritize action plans.

[1219] 10. Emotional Adjustment

[1220] The server adjusts business models and action plans based on emotional data, for example, presenting simplified plans and motivational suggestions if the user is feeling stressed.

[1221] 11. User Feedback

[1222] The terminal displays the business model, stakeholders, development time, success stories and failure stories, and specific action plans received from the server to the user, and also reflects adjustments based on the analysis results of the emotion engine.

[1223] Specific examples

[1224] Below are some specific examples of how this system can be used.

[1225] 1. A user types, "I want to run an online learning platform."

[1226] 2. The device sends the input in JSON format to the server.

[1227] 3. The server uses an NLP library to extract the keyword "online learning platform" and intent.

[1228] 4. The server uses the generative AI model to generate a "subscription model."

[1229] 5. The stakeholders who require a server are the "educational content creator" and the "IT infrastructure manager," and the development time is calculated to be "6 months."

[1230] 6. The server extracts success stories of famous online learning platforms and failure stories of similar services in the past.

[1231] 7. The server generates a specific action plan and presents it as follows: "Month 1: Basic design of the platform, Month 2: Start content creation, Month 3: Start beta testing."

[1232] 8. The device collects emotional data as the user types and detects when the user is nervous.

[1233] 9. The server analyzes the emotional data and suggests additional support to help the user relax.

[1234] 10. The device displays all information to the user and also makes adjustments based on emotional data.

[1235] In this way, the system starts with user input, goes through various data analysis and generation processes, and then provides an optimal business model and feasible plan. It also incorporates an emotion engine to flexibly adjust according to the user's emotional state.

[1236] The processing flow will be explained below.

[1237] Step 1:

[1238] Users input data about the business they want to run. For example, they can enter a specific business idea into the input form, such as "I want to run an online learning platform."

[1239] Step 2:

[1240] The terminal sends the business direction data entered by the user to the server, usually via an API, packaging the input in JSON format.

[1241] Step 3:

[1242] The server parses the incoming data using natural language processing (NLP) libraries, tokenizing the text and performing morphological analysis to extract key keywords and intent.

[1243] Step 4:

[1244] The server generates the optimal business model based on the extracted keywords and intent. This generation process uses a generative AI model to select the most suitable business model from a number of options. For example, if the user enters "online learning platform," the server will suggest a "subscription model."

[1245] Step 5:

[1246] The server calculates the required stakeholders and development time based on the generated business model. This data is obtained by querying an internal database. As an example, the stakeholders "educational content creator" and "IT infrastructure manager" are presented, and the development time is "6 months."

[1247] Step 6:

[1248] The server extracts past success stories and failure stories from a database. To do this, it searches the database of success stories and failure stories to collect information that users can use as reference. For example, a success story might be presented as a "success story of a famous online learning platform," and a failure story might be presented as a "reason for the failure of a similar service in the past."

[1249] Step 7:

[1250] The server generates a specific action plan based on the business model. This involves creating steps and a timeline that best fit the business model, and listing specific tasks for each step. For example, a step-by-step plan such as "Month 1: Basic platform design, Month 2: Start content creation, Month 3: Start beta testing" is provided.

[1251] Step 8:

[1252] The device collects the user's emotional data. The emotion engine analyzes the user's emotions based on facial recognition, voice analysis, and the tone of the input content, and generates emotional data. For example, if the user is excited or depressed while typing, that emotional data is collected in real time.

[1253] Step 9:

[1254] The server analyzes the collected emotional data to determine the user's emotional state, which is then used to generate business models and prioritize action plans.

[1255] Step 10:

[1256] The server adjusts business models and action plans based on emotional data, for example, presenting simplified plans and motivational suggestions if the user is feeling stressed.

[1257] Step 11:

[1258] The terminal displays the business model, stakeholders, development time, success stories and failure stories, and specific action plans received from the server to the user, and also reflects adjustments based on the analysis results of the emotion engine.

[1259] This concrete flow allows users to obtain a comprehensive business model and a concrete implementation plan based on their business idea. Furthermore, by incorporating an emotion engine, the system can flexibly adjust according to the user's emotional state, providing more personalized recommendations.

[1260] Example 2

[1261] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1262] While conventional business model generation systems were capable of generating models based on the user's business direction, they lacked the ability to make optimal adjustments based on the user's emotional state. Furthermore, they lacked the ability to comprehensively provide the stakeholders and workload involved in the generated business model, as well as past successes and failures, and to present a feasible implementation plan for the user. This can lead to users feeling anxious about implementing the generated business model and lowering their motivation.

[1263] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1264] In this invention, the server includes means for receiving business direction data input by a user, means for analyzing the received business direction data and extracting main keywords and intentions, means for generating an appropriate business model based on the extracted keywords and intentions, means for collecting emotional data based on the generated business model, and means for analyzing the collected emotional data and adjusting the business model and action plan based on the emotional state of the user. This makes it possible to adjust the optimal business model according to the emotional state of the user, eliminating problems such as anxiety and decreased motivation, and providing a more feasible and effective business model.

[1265] "Business direction data" is information including the business objectives, goals, strategies, and specific ideas proposed by the user.

[1266] "Keywords" are particularly important words or phrases in business direction data, and are terms that play an important role in generating and analyzing business models.

[1267] "Intent" refers to the purpose or goal that the user wants to convey in the business direction data, as well as the thinking behind it.

[1268] A "business model" is a specific summary of the value of the products or services offered, revenue structure, customer base, and management methods.

[1269] "Emotional data" is information that indicates the emotional state of the user, and is data collected through facial recognition, voice analysis, tone analysis of input content, and the like.

[1270] "Stakeholders" refers to the people and organizations necessary to execute and maintain the business model, such as creators of educational content and IT infrastructure managers.

[1271] "Work volume" refers to the labor and time required to execute a business model, such as development man-hours.

[1272] "Success stories" are specific examples or data showing that similar business models or strategies have been successful in the past.

[1273] "Failure cases" are specific examples or data of similar business models or strategies that have failed in the past.

[1274] An "action plan" is a plan that includes specific steps and a timeline for realizing a business model, as well as tasks for each step.

[1275] A "generative AI model" is an algorithm or system that uses artificial intelligence technology to generate business models, and outputs an appropriate business model by inputting, for example, a prompt statement.

[1276] MODE FOR CARRYING OUT THE INVENTION

[1277] This invention relates to a system that allows users to input their business direction, generates an optimal business model based on that, and provides information on the people involved, workload, success stories, failure stories, and specific implementation plans.Furthermore, this invention combines an emotion engine that recognizes the user's emotions, and automatically adjusts the business model and proposal content based on the user's emotion data.

[1278] Hardware and software used

[1279] The system of the present invention uses the following hardware and software:

[1280] Server: Analyzes data, generates business models, analyzes sentiment data. The server hosts the database and AI models.

[1281] Terminal: A device where the user can provide input and receive feedback from the system. It is equipped with an emotion engine to collect user emotion data.

[1282] Natural Language Processing (NLP) libraries, such as NLTK or spaCy, to analyze user-entered text.

[1283] Generative AI model: For example, use OpenAI's GPT-3 to generate an appropriate business model.

[1284] Database: A database for storing data such as information on stakeholders, success stories, and failure stories.

[1285] System processing overview

[1286] 1. User input: The user inputs the business direction from the device. For example, the user inputs a specific idea such as "I want to operate an online learning platform."

[1287] 2. Sending input data: The terminal packages the business direction data entered by the user in JSON format and sends it to the server via API.

[1288] 3. Data Analysis: The server uses a natural language processing library to analyze the incoming data, tokenizing it and performing morphological analysis to extract key keywords and intent.

[1289] 4. Business model generation: The server uses the extracted keywords and intent to generate an optimal business model using a generative AI model. For example, if the input is "online learning platform," the server will suggest a "subscription model."

[1290] 5. Calculation of stakeholders and workload: Based on the generated business model, the server retrieves and calculates the necessary stakeholders (e.g., educational content creator, IT infrastructure administrator) and workload (e.g., 6 months) from its internal database.

[1291] 6. Presentation of success and failure cases: The server extracts relevant success and failure cases from the database. For example, it displays "success stories of famous online learning platforms" as success cases and "reasons for failure of similar services in the past" as failure cases.

[1292] 7. Generate a detailed execution plan: The server generates an execution plan with specific steps and a timeline. For example, a step-by-step plan such as "Month 1: Platform basic design, Month 2: Content creation begins, Month 3: Beta testing begins" is provided.

[1293] 8. Emotional data collection and analysis: The device collects the user's emotional data and sends it to the server. The server analyzes the collected emotional data and determines the user's emotional state.

[1294] 9. Emotional Adjustment: The server adjusts business models and execution plans based on emotional data. For example, if a user is feeling stressed, it will provide simplified plans and motivational suggestions.

[1295] Specific examples

[1296] Here are some concrete examples of how this system can be used:

[1297] 1. A user types, "I want to run an online learning platform."

[1298] 2. The device sends the input in JSON format to the server.

[1299] 3. The server uses a natural language processing library to extract the keyword "online learning platform" and the intent.

[1300] 4. The server uses the generative AI model to generate a "subscription model."

[1301] 5. The parties who will need a server are the "educational content creator" and the "IT infrastructure manager," and the amount of work is calculated to be "6 months."

[1302] 6. The server extracts success stories of famous online learning platforms and failure stories of similar services in the past.

[1303] 7. The server generates a specific execution plan and presents it as follows: "Month 1: Basic design of the platform, Month 2: Start content creation, Month 3: Start beta testing."

[1304] 8. The device collects emotional data as the user types and detects when the user is nervous.

[1305] 9. The server analyzes the emotional data and suggests additional support to help the user relax.

[1306] 10. The device displays all information to the user and also makes adjustments based on emotional data.

[1307] Prompt Sentence Examples

[1308] Below are some example prompts to input to a generative AI model:

[1309] A user has entered, "I want to run an online learning platform." Please generate the optimal business model based on this direction. Also, please calculate the necessary stakeholders and the amount of work, provide examples of past successes and failures, and even provide a concrete implementation plan.

[1310] In this way, the system of the present invention starts with user input, goes through various data analysis and generation processes, and provides an optimal business model and a feasible plan. Furthermore, by incorporating an emotion engine, it can make flexible adjustments according to the user's emotional state.

[1311] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1312] Step 1:

[1313] Accepting user input

[1314] The user enters business direction data into an input form on the device. For example, they enter a specific idea such as "I want to operate an online learning platform." The input data is temporarily stored in the device's memory and used for the next processing step.

[1315] Input: Business direction data (e.g., "I want to run an online learning platform")

[1316] Output: Business direction data stored in the device's memory

[1317] Step 2:

[1318] Sending input data

[1319] The terminal packages the business direction data entered by the user in JSON format and sends it to the server using an HTTP POST request to the specified API endpoint, where the data is stored in the server's receive buffer.

[1320] Input: User orientation data (data stored in device memory)

[1321] Output: JSON format data stored in the server's receive buffer

[1322] Step 3:

[1323] Data analysis

[1324] The server unpacks the received JSON data and parses it using a natural language processing (NLP) library (e.g., NLTK or spaCy) to tokenize the text, perform morphological analysis, and extract key keywords and intent.

[1325] Input: JSON format data stored in the server's receive buffer

[1326] Output: Key keywords and user intent (stored in server memory)

[1327] Step 4:

[1328] Business model generation

[1329] The server generates an optimal business model using a generative AI model (e.g., OpenAI's GPT-3) based on the extracted keywords and intent. The extracted intent and keywords are input into the generative AI model as prompt statements, and the model outputs an optimal business model, such as a "subscription model."

[1330] Input: Key keywords and user intent (stored in server memory)

[1331] Output: Generated business model (e.g. "Subscription model")

[1332] Step 5:

[1333] Calculating stakeholders and effort

[1334] Based on the generated business model, the server retrieves the necessary stakeholders (e.g., educational content creator, IT infrastructure manager) and the amount of work (e.g., 6 months) from an internal database and calculates them. The calculation is performed by querying the existing project database.

[1335] Input: Generated business model (stored in server memory)

[1336] Output: The stakeholders and effort required (e.g., "Educational content creator, 6 months")

[1337] Step 6:

[1338] Presentation of success and failure cases

[1339] The server searches the internal database and extracts success stories and failure stories related to the business model. Specific examples include a success story, "The success story of a famous online learning platform," and a failure story, "The reasons for the failure of similar services in the past."

[1340] Input: Generated business model (stored in server memory)

[1341] Output: Success stories and failure stories (e.g., "Success stories of famous online learning platforms")

[1342] Step 7:

[1343] Generate a concrete execution plan

[1344] The server creates specific steps and a timeline based on the business model. This plan has a step-by-step structure, listing specific tasks and the duration of each step. For example, "Month 1: Platform basic design, Month 2: Content creation begins, Month 3: Beta testing begins."

[1345] Input: Generated business model (stored in server memory)

[1346] Output: A concrete action plan (e.g., "Month 1: Basic design of the platform, Month 2: Start creating content")

[1347] Step 8:

[1348] Emotion data collection and analysis

[1349] The device uses facial recognition, voice analysis, and tone analysis of input content to collect user emotional data, thereby capturing the user's emotional state (e.g., nervousness, excitement, etc.) in real time and sending the data in JSON format to the server.

[1350] Input: User's facial expressions, voice, and input (device sensor data)

[1351] Output: Emotion data (sent to server in JSON format)

[1352] Step 9:

[1353] Emotional Data Analysis

[1354] The server analyzes the collected emotional data to determine the user's emotional state. It processes the data using an emotion analysis algorithm to identify the user's emotional state (e.g., nervous, stressed, relaxed).

[1355] Input: Emotion data (stored on the server in JSON format)

[1356] Output: User's emotional state (e.g., "tense," "relaxed")

[1357] Step 10:

[1358] Emotional adjustment

[1359] The server adjusts business models and action plans based on the analyzed emotional data, for example, presenting simplified plans and motivational suggestions if the user is feeling stressed.

[1360] Input: User's emotional state (stored in server memory)

[1361] Output: A tailored business model and implementation plan (e.g., a "simplified plan")

[1362] Step 11:

[1363] User feedback

[1364] The device receives information from the server and displays it to the user, including the business model, stakeholders, workload, success stories, failure stories, and specific implementation plans, along with adjustments based on emotion data.

[1365] Input: Adjusted business model and execution plan (data sent from the server)

[1366] Output: Information displayed to the user (displayed on the device display)

[1367] As mentioned above, the program processing of this system is carried out through a series of steps, starting with user input, data analysis, business model generation, and adjustments based on emotion data. This process makes it possible to provide a more specific and feasible business model.

[1368] (Application example 2)

[1369] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1370] Conventional business model generation systems provide models and action plans based on business direction data entered by users, but they are unable to consider the user's emotional state. As a result, there are problems such as users feeling stressed or being unable to make appropriate proposals based on their emotional state. Furthermore, when it comes to immediately applying improvement ideas on the factory floor, it is difficult to provide appropriate plans that take emotions into account in real time.

[1371] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1372] In this invention, the server includes means for receiving business direction data input by a user, means for analyzing the received business direction data and extracting main keywords and intentions, means for generating an appropriate business model based on the extracted keywords and intentions, means for generating a specific action plan based on the business model, means for collecting and analyzing user emotion data, and means for adjusting the business model and action plan based on the emotion data. This not only appropriately reflects the user's input, but also makes it possible to provide a real-time improvement action plan that is adjusted according to the user's emotional state.

[1373] "Business direction data" is information about the business direction the user is aiming for, the processes they want to improve, their goals, etc.

[1374] "Key Keywords" are important words and phrases extracted from business direction data.

[1375] "Intent" refers to the user's purpose or goal that is inferred based on the business direction data entered by the user.

[1376] A "business model" is an optimal business structure and revenue mechanism proposed based on business direction data.

[1377] An "action plan" is a specific action plan and timeline for realizing a business model.

[1378] "Emotional data" refers to information that indicates a user's emotional state and is collected through facial recognition, voice analysis, tone of input, etc.

[1379] An "emotion engine" is a system or algorithm that collects and analyzes a user's emotional data and determines their emotional state.

[1380] "Stakeholders" are those interested in implementing the business model and action plan.

[1381] "Development man-hours" refers to the working hours and labor required to realize a business model or action plan.

[1382] A "success story" is a specific example of a similar business model or action plan that was successful in the past.

[1383] "Failure cases" are specific examples of similar business models or action plans that have failed in the past.

[1384] The system realizing this invention includes various means for receiving and analyzing business direction data input by users, which not only generates an appropriate business model and a specific action plan, but also makes adjustments taking into account the emotional state of the user.

[1385] Hardware and software used

[1386] Hardware: smart glasses, server, camera, microphone.

[1387] Software: NLP libraries (e.g. spaCy), generative AI models (e.g. OpenAI GPT), databases (e.g. PostgreSQL), sentiment analysis libraries (e.g. Affectiva).

[1388] System Operation Overview

[1389] User Input

[1390] Users input the business process they want to improve through the smart glasses using voice or text, which is first captured by the smart glasses and then sent to the server in JSON format.

[1391] Data analysis and business model generation

[1392] The server uses an NLP library (e.g., spaCy) to tokenize the received data and extract key keywords and intent, as shown in the example below.

[1393] Example prompt sentence:

[1394] "We want to improve the production efficiency of our line. Which step is causing the bottleneck in our current setup?"

[1395] Next, a generative AI model (e.g., OpenAI GPT) is used to generate optimal business models and action plans based on the extracted keywords and intent.

[1396] Stakeholders and development effort calculations

[1397] Based on the generated business model, the required stakeholders and development effort are retrieved from a database (e.g., PostgreSQL) and calculated.

[1398] Extraction of success and failure cases

[1399] Past examples of successful and unsuccessful similar business models are extracted from a database and presented to the user.

[1400] Generate and view an action plan

[1401] Generate specific action plans based on your business model, including timelines for completing each task by when.

[1402] Emotion data collection and analysis

[1403] The smart glasses use a built-in camera and microphone to collect and analyze the user's emotional data, which is then analyzed in real time using an emotion analysis library (e.g., Affectiva) to determine the user's emotional state.

[1404] Emotional adjustment

[1405] Based on the collected emotional data, the generated business model and action plan can be adjusted accordingly. For example, if the user is feeling stressed, a simplified plan or an encouraging message can be provided.

[1406] User feedback

[1407] Finally, the adjusted business model, action plan, stakeholders, development time, success stories, and failure stories are displayed on the user's smart glasses.

[1408] Through the above process, starting from user input and going through various data analysis and generation processes, it becomes possible to provide optimal business models and action plans. In addition, by combining it with an emotion engine, it is possible to flexibly adjust according to the user's emotional state.

[1409] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1410] Step 1:

[1411] Users can input the business process they want to improve through voice or text input via smart glasses, which is captured by the device, converted into JSON format, and sent to the server.

[1412] Input: User voice or text input

[1413] Output: JSON format data

[1414] Step 2:

[1415] The server parses the received JSON-formatted business direction data using a natural language processing (NLP) library (e.g., spaCy), tokenizing the data and extracting key keywords and intent.

[1416] Input: Business direction data in JSON format

[1417] Output: Primary keywords and intent

[1418] Step 3:

[1419] Based on the extracted keywords and intent, the server uses a generative AI model (e.g., OpenAI GPT) to generate an optimal business model. This generation process takes into account the conditions and options necessary for business success.

[1420] Input: Extracted keywords and intent

[1421] Output: Generated business model

[1422] Step 4:

[1423] Based on the generated business model, the server retrieves and calculates the required stakeholders and development effort from an internal database (e.g., PostgreSQL).

[1424] Input: Generated business model

[1425] Output: Required stakeholders and development effort

[1426] Step 5:

[1427] The server extracts past success stories and failure stories from a related database and collects information that users can use as reference.

[1428] Input: Generated business model

[1429] Output: Success stories and failure stories

[1430] Step 6:

[1431] The server generates a specific action plan based on the business model and related information (stakeholders, development time, success stories, failure stories). The action plan includes specific tasks and timelines for each step.

[1432] Input: Business model, stakeholders, development time, success stories, failure stories

[1433] Output: A concrete action plan

[1434] Step 7:

[1435] The device collects the user's emotional data using the camera and microphone built into the smart glasses, which are then used to collect emotions through facial recognition and voice analysis. This data is then sent to a server in real time.

[1436] Input: User's video and audio data

[1437] Output: Real-time emotion data

[1438] Step 8:

[1439] The server analyzes the collected emotional data using an emotion analysis library (e.g., Affectiva) to determine the user's emotional state.

[1440] Input: Real-time emotion data

[1441] Output: Parsed emotion data

[1442] Step 9:

[1443] The server then adjusts the generated business model and action plan based on the analyzed emotional data, for example, providing a simplified plan or encouraging message if the user is feeling stressed.

[1444] Input: Analyzed emotion data and action plan

[1445] Output: A tailored business model and action plan

[1446] Step 10:

[1447] The device compiles information on the business model, stakeholders, development time, success stories, failure stories, specific action plans, and adjusted content, and displays it on the smart glasses.

[1448] Input: Adjusted business model, stakeholders, development time, success stories, failure stories, concrete action plan

[1449] Output: Display feedback to the user

[1450] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1451] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1452] 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 the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1453] [Fourth embodiment]

[1454] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1455] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1456] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1457] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1458] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1459] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1460] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1461] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1462] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1463] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific 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.

[1464] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1465] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. 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 process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1466] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1467] This invention relates to a system that, by inputting what a user wants to do and the direction of their business, presents an optimal business model and provides relevant stakeholders, development man-hours, success stories and failure stories, and specific action plans. Specific embodiments of this system are described below.

[1468] Overall flow

[1469] 1. User Input

[1470] Users input the direction of the business they want to run, such as a specific business idea like "I want to run an online learning platform."

[1471] 2. Sending input data

[1472] The terminal transmits the business direction data entered by the user to a server, usually via an API.

[1473] 3. Data Analysis

[1474] The server analyzes the incoming data using natural language processing (NLP) libraries, extracting key keywords and intent.

[1475] 4. Business model generation

[1476] The server generates the optimal business model based on the extracted keywords and intent. This generation uses a generative AI model. For example, if the user enters "online learning platform," the server will suggest the "subscription model" as an appropriate business model.

[1477] 5. Stakeholders and Development Effort Calculation

[1478] The server calculates the required stakeholders and development effort based on the generated business model. This data is retrieved from an internal database.

[1479] 6. Presentation of success and failure cases

[1480] The server extracts past success and failure cases from a database, allowing users to refer to the circumstances that led to success or failure.

[1481] 7. Generate an action plan

[1482] The server generates a specific action plan based on the business model, such as "Month 1: Basic design of the platform, Month 2: Start content creation, Month 3: Start beta testing."

[1483] 8. User Feedback

[1484] The terminal displays to the user the business model, stakeholders, development time, success stories and failure stories, and specific action plans received from the server.

[1485] Specific examples

[1486] Below are some specific examples of how this system can be used.

[1487] 1. A user types, "I want to run an online learning platform."

[1488] 2. The device sends the input in JSON format to the server.

[1489] 3. The server uses an NLP library to extract the keyword "online learning platform" and intent.

[1490] 4. The server uses the generative AI model to generate a "subscription model."

[1491] 5. The stakeholders who require a server are the "educational content creator" and the "IT infrastructure manager," and the development time is calculated to be "6 months."

[1492] 6. The server extracts success stories of famous online learning platforms and failure stories of similar services in the past.

[1493] 7. The server generates a specific action plan and presents it as follows: "Month 1: Basic design of the platform, Month 2: Start content creation, Month 3: Start beta testing."

[1494] 8. The device displays all information to the user.

[1495] In this way, users can obtain a comprehensive business model and concrete implementation plan based on their business idea, which can minimize business risks and increase the chances of success.

[1496] The processing flow will be explained below.

[1497] Step 1:

[1498] Users input data about the business they want to run. For example, they can enter a specific business idea into the input form, such as "I want to run an online learning platform."

[1499] Step 2:

[1500] The terminal sends the business direction data entered by the user to the server, usually via an API, packaging the input in JSON format.

[1501] Step 3:

[1502] The server analyzes the incoming data using natural language processing (NLP) libraries, specifically tokenizing the text and performing morphological analysis to extract key keywords and intent.

[1503] Step 4:

[1504] The server generates the optimal business model based on the extracted keywords and intent. This generation process involves using a generative AI model to select the most suitable business model from a number of options. For example, if the user enters "online learning platform," the server will suggest a "subscription model."

[1505] Step 5:

[1506] The server calculates the required stakeholders and development effort based on the generated business model. To do this, it queries an internal database to obtain the roles of the relevant stakeholders and the development effort. As an example, it presents the stakeholders "educational content creator" and "IT infrastructure manager" and the development effort of "6 months."

[1507] Step 6:

[1508] The server extracts past success stories and failure stories from a database. To do this, it searches the database of success stories and failure stories to collect information that users can use as reference. For example, a success story might be presented as a "success story of a famous online learning platform," and a failure story might be presented as a "reason for the failure of a similar service in the past."

[1509] Step 7:

[1510] The server generates a specific action plan based on the business model. This involves creating steps and a timeline that best fit the business model, and listing specific tasks for each step. For example, a step-by-step plan such as "Month 1: Basic platform design, Month 2: Start content creation, Month 3: Start beta testing" is provided.

[1511] Step 8:

[1512] The terminal receives information from the server, including the business model, stakeholders, development time, success stories and failure stories, and a concrete action plan, and displays it to the user. Comprehensive information is displayed on the user interface, allowing the user to create an action plan based on the information presented.

[1513] In this way, the system starts with user input and goes through various data analysis and generation processes to provide the optimal business model and executable plan.

[1514] Example 1

[1515] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1516] Conventional business model generation systems have difficulty effectively analyzing the data entered by users and providing an optimal business model and detailed implementation plan. As a result, users are unable to obtain appropriate advice or a concrete action plan for their business ideas, which reduces the probability of business success.

[1517] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1518] In this invention, the server includes: means for receiving business direction data input by a user; means for analyzing the received business direction data and extracting key keywords and intentions; means for using a generative AI model to generate an appropriate business model based on the extracted keywords and intentions; means for calculating the required stakeholders and development man-hours based on the generated business model; means for extracting past success stories and failure stories from a database; means for generating a specific action plan tailored to the business model; means for comprehensively displaying the generated business model, stakeholders, development man-hours, success stories, and failure stories; means for converting the user's input data into JSON format and sending it to the server; and means for analyzing the received data using a natural language processing library. This allows users to easily obtain a comprehensive business model and a detailed action plan based on their business idea.

[1519] "Business direction data" is information entered by the user regarding specific business ideas and business goals.

[1520] "Means for receiving" refers to a device or program that provides the function of sending and receiving data entered by the user to the server.

[1521] "Means of analysis" are algorithms, libraries, and software modules that process the received data and extract key keywords and intent.

[1522] "Keywords" are important words or phrases extracted from business direction data that represent business themes or themes.

[1523] "Intent" refers to the purpose or goal that a user is trying to achieve based on business direction data.

[1524] A "generative AI model" is an artificial intelligence model that generates optimal business models and action plans based on extracted keywords and intent.

[1525] "Stakeholders" are people or organizations that play a role necessary to the execution of a business model.

[1526] "Development man-hours" refers to the working time and resources required to execute a business model.

[1527] A "success story" is a specific example of a similar business model being successfully implemented in the past.

[1528] "Failure cases" are specific examples of similar business models that have been implemented in the past and failed.

[1529] A "specific action plan" refers to a step-by-step execution plan or tasks for implementing a business model.

[1530] "JSON format" refers to the JavaScript Object Notation format, which represents data in a structured text format.

[1531] A "natural language processing library" is a software component that analyzes incoming data and understands the linguistic meaning of the text.

[1532] This invention is a system that provides optimal business models and concrete action plans by allowing users to input their business direction and ideas. This system consists of three elements: a server, a terminal, and the user.

[1533] Overall structure

[1534] 1. User Interface

[1535] Users enter data about the direction of the business they want to run into a special input form. For example, they can enter a specific business idea such as "I want to run an online learning platform."

[1536] 2. Data Transmission

[1537] The terminal converts the business direction data entered by the user into JSON format and sends it to the server using an HTTP POST request, using technologies such as Ajax or Fetch API.

[1538] 3. Data Analysis

[1539] The server parses the received JSON data using a natural language processing library (e.g., spaCy, NLTK) to extract key keywords and intent, which clarifies the business's subject matter and goals.

[1540] 4. Business model generation

[1541] The server generates the optimal business model using a generative AI model (e.g., GPT-4) based on the extracted keywords and intent. For example, if the user enters "online learning platform," the server will suggest a "subscription model."

[1542] 5. Stakeholders and Development Effort Calculation

[1543] Based on the generated business model, the server retrieves the necessary stakeholders (e.g., educational content creator, IT infrastructure manager) and development man-hours (e.g., 6 months) from the database and calculates them.

[1544] 6. Presentation of success and failure cases

[1545] The server extracts past success stories and failure stories from a database and provides them to users. For example, it might present "the success story of a famous online learning platform" as a success story, or "the reasons for the failure of similar services in the past" as a failure story.

[1546] 7. Generate a concrete action plan

[1547] The server generates a specific action plan based on the generated business model, such as "Month 1: Basic platform design, Month 2: Start content creation, Month 3: Start beta testing."

[1548] 8. User Feedback

[1549] The terminal displays the information received from the server to the user, who can then use it to get a detailed execution plan based on their business idea.

[1550] Specific example explanation

[1551] Below are some specific examples of how this system can be used.

[1552] 1. A user types, "I want to run an online learning platform."

[1553] 2. The device converts the input into JSON format and sends it to the server.

[1554] 3. The server analyzes the received data using a natural language processing library and extracts the keyword "online learning platform" and the intent.

[1555] 4. The server uses the generative AI model to generate the optimal business model and propose a "subscription model."

[1556] 5. Calculate the stakeholders who will need a server (educational content creators, IT infrastructure managers) and the development time (6 months).

[1557] 6. The server extracts success stories of famous online learning platforms and failure stories of similar services in the past.

[1558] 7. The server generates a specific action plan and presents it as follows: "Month 1: Basic design of the platform, Month 2: Start content creation, Month 3: Start beta testing."

[1559] 8. The device displays all the information to the user.

[1560] Prompt Sentence Examples

[1561] Below are some example prompts to input to the generative AI model:

[1562] Business idea: Run an online learning platform

[1563] Required stakeholders: Educational content creators, IT infrastructure managers

[1564] Development time: 6 months

[1565] Business model: Subscription model

[1566] Success Story: A Popular Online Learning Platform's Success Story

[1567] Failure Case: Reasons for the failure of similar services in the past

[1568] Generate a concrete action plan.

[1569] This system allows users to easily obtain a comprehensive business model and detailed execution plan based on their business idea, which is expected to minimize business risks and increase the chances of success.

[1570] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1571] Processing step details

[1572] Step 1: User Input

[1573] Users enter their business direction data into an input form. For example, let's say they want to run an online learning platform.

[1574] Input: User enters their business idea into a form.

[1575] Output: The user's business idea is input into the system.

[1576] Step 2: Submitting input data

[1577] The terminal converts the business direction data entered by the user into JSON format and sends it to the server using an HTTP POST request, using Ajax or the Fetch API.

[1578] Input: User's business direction data.

[1579] Output: The data is converted to JSON format and sent to the server.

[1580] Step 3: Receiving and analyzing data

[1581] The server parses the received JSON data using a natural language processing library (e.g., spaCy or NLTK) to extract important keywords and intent.

[1582] Input: Business direction data in JSON format.

[1583] Output: Analysis results including key keywords and intent.

[1584] Specific operation: Passes the received data to a natural language processing library to perform morphological analysis and topic modeling.

[1585] Step 4: Generate a business model

[1586] The server inputs the analysis results (keywords and intent) into the generative AI model as prompts to generate the optimal business model. For example, for the input keyword "online learning platform," the model generates "subscription model."

[1587] Input: Primary keywords and intent.

[1588] Output: The generated business model.

[1589] Specific operation: Generate a prompt sentence using the analysis results, send a query to the generative AI model, and obtain a response.

[1590] Step 5: Stakeholders and development effort calculations

[1591] Based on the generated business model, the server retrieves the stakeholders (e.g., educational content creators, IT infrastructure managers) and development man-hours (e.g., 6 months) from an internal database.

[1592] Input: The generated business model.

[1593] Output: Stakeholder list and development effort required.

[1594] Specific operation: Query the database based on the business model to retrieve the relevant stakeholders and effort.

[1595] Step 6: Present success stories and failure stories

[1596] The server extracts relevant success stories and failure stories from its internal database and provides them to users, such as "Success stories: success stories of famous online learning platforms" and "Failure stories: reasons for the failure of similar services in the past."

[1597] Input: Business model related data.

[1598] Output: Success stories and failure stories.

[1599] Specific operation: Retrieves success and failure cases from the database and formats them as relevant information.

[1600] Step 7: Generate a concrete action plan

[1601] The server generates a specific action plan based on the generated business model, such as a step-by-step plan like "Month 1: Basic platform design, Month 2: Start content creation, Month 3: Start beta testing."

[1602] Input: Generated business model and related data.

[1603] Output: A concrete action plan.

[1604] What it does: Incorporates input data into a planning template to generate a step-by-step execution plan.

[1605] Step 8: User feedback

[1606] The terminal displays all the information received from the server (business model, stakeholders, development time, success stories, failure stories, action plans) to the user.

[1607] Input: General information sent from the server.

[1608] Output: Information displayed to the user.

[1609] Specific behavior: Formats the information appropriately and reflects it in the user interface.

[1610] The above is a detailed description of the processing steps of this system, a series of processes that provide the optimal business model and detailed implementation plan based on the user's business idea.

[1611] (Application example 1)

[1612] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1613] Conventional business model generation systems lack sufficient support for users to quickly and comprehensively develop new business directions. They also struggle to present specific action plans based on success and failure cases in specific areas. Furthermore, there is a need for comprehensive business model proposals and displays that can be operated directly on smartphones and other devices. Therefore, there is a need for a system that can help users launch businesses efficiently and increase the likelihood of success.

[1614] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1615] In this invention, the server includes means for receiving business direction data input by a user, means for analyzing the received business direction data and extracting key keywords and intentions, means for generating an appropriate business model based on the extracted keywords and intentions, means for calculating the required stakeholders and development man-hours based on the generated business model, means for extracting past success stories and failure stories, means for generating a specific action plan tailored to the business model, and means for comprehensively displaying the generated business model, stakeholders, development man-hours, success stories, and failure stories. This enables users to quickly and effectively build business models in specific business areas and obtain specific action plans based on past cases.

[1616] "Business direction data" is information that indicates the specific ideas, goals, and strategies of a new business when a user launches the business.

[1617] "Means for receiving" refers to the functions and processes by which a server or device obtains data entered by a user.

[1618] "Means of analysis" refers to the functions and processes that analyze received data using technologies such as machine learning and natural language processing to extract key keywords and intent.

[1619] A "business model" refers to the structure and methodology for realizing a user's business idea, including the mechanism for generating revenue.

[1620] "Stakeholders" refers to all parties involved in business activities, specifically including individuals and organizations that affect the success of a project.

[1621] "Development time" refers to the time, resources, and human resources required to realize a business idea.

[1622] "Success stories" refer to specific cases or examples where similar business models have been successful in the past, and include the results of their analysis.

[1623] "Failure Cases" refers to specific cases or examples where similar business models have failed in the past, including lessons learned and causes.

[1624] An "action plan" is a set of concrete steps and plans for realizing a business model, including a timeline and implementation procedures.

[1625] "Display means" refers to an interface or device for visually presenting the calculated business model and related information to the user.

[1626] A "smartphone" is a type of mobile phone, a mobile device that has advanced computing power and communication functions and can run a variety of applications.

[1627] This invention relates to a system that generates an optimal business model based on business direction data entered by a user and provides a specific action plan based on that model. Specific embodiments of this system are described below.

[1628] The server has a means for receiving business direction data entered by the user. For example, when a user enters a specific business idea such as "I want to launch a new online shopping site," the system receives this data. This data is usually sent via a mobile device such as a smartphone.

[1629] The received business direction data is analyzed using NLP (Natural Language Processing) libraries. This analysis extracts key keywords and their intent from the data, allowing the system to accurately understand the user's business goals.

[1630] Based on the analyzed data, the server uses a generative AI model to generate an optimal business model. This model changes depending on the extracted keywords and intent. For example, if the user enters "online shopping site," the system will suggest a "subscription model" as an appropriate business model. At the same time, the system also calculates the required stakeholders and development time.

[1631] In addition, the server also has a means to extract past success and failure cases from the database, allowing users to understand the factors that led to the success or failure of a specific business model in the past. This information helps users evaluate their own business models and minimize risks.

[1632] The server then generates a specific action plan, such as "Month 1: Platform basic design, Month 2: Start content creation, Month 3: Start beta testing." This action plan provides users with concrete steps to take toward realizing their business.

[1633] The generated business model, stakeholders, development time, success stories, and failure stories are all displayed comprehensively. Users are provided with an interface that allows them to view this information at a glance on their smartphones or other devices. This allows users to grasp the overall picture for efficiently launching a business.

[1634] As a concrete example, if a user inputs a business idea such as "I want to launch a new online shopping site," the system will process it as follows:

[1635] Example prompt sentence:

[1636] "I want to launch a new online shopping site."

[1637] Based on this input, the system proposes an optimal business model, provides stakeholders, development time, success stories, and failure stories, and generates and displays a concrete action plan to users, allowing them to launch their business quickly and effectively.

[1638] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1639] Step 1:

[1640] Users input business direction

[1641] Users input their business ideas using devices such as smartphones. This input data is specific, such as "I want to launch a new online shopping site." The input data is sent from the device to the server.

[1642] Input: User's business idea (e.g., "I want to launch a new online shopping site")

[1643] Output: Input data is sent from the device to the server

[1644] Step 2:

[1645] The server analyzes the received data

[1646] The server analyzes the received business direction data using an NLP library, specifically extracting key keywords (e.g., "online shopping site") and intent (e.g., "I want to run one") using natural language processing.

[1647] Input: User's business idea (e.g., "I want to launch a new online shopping site")

[1648] Output: Primary keywords and intent (e.g., "online shopping site" and "want to run")

[1649] Step 3:

[1650] The server generates the business model

[1651] The server uses a generative AI model to generate an appropriate business model based on the extracted keywords and intent, including specific revenue models such as subscription models.

[1652] Input: Primary keywords and intent (e.g., "online shopping site" and "want to run")

[1653] Output: Generated business model (e.g. "Subscription Model")

[1654] Step 4:

[1655] The server calculates development time with stakeholders

[1656] Based on the generated business model, the server calculates the required stakeholders (e.g., "content creator" and "IT infrastructure manager") and the development time (e.g., "6 months"), which are obtained from an internal database.

[1657] Input: Generated business model (e.g. "Subscription model")

[1658] Output: Required stakeholders and development effort (e.g., "Content Creator," "IT Infrastructure Manager," and "6 months")

[1659] Step 5:

[1660] The server extracts past success stories and failure stories

[1661] The server extracts past success stories (e.g., "Success stories of famous online learning platforms") and failure stories (e.g., "Reasons for the failure of similar services in the past") from the database.

[1662] Input: Generated business model (e.g. "Subscription model")

[1663] Output: Success stories and failure stories (e.g., "Success stories," "Failure factors")

[1664] Step 6:

[1665] The server generates a concrete action plan

[1666] The server generates a specific action plan based on the business model, including step-by-step plans such as "Month 1: Platform basic design," "Month 2: Content creation begins," and "Month 3: Beta testing begins."

[1667] Input: Generated business model (e.g. "Subscription model")

[1668] Output: A concrete action plan (e.g., "Month 1: Platform basic design," "Month 2: Content creation begins," "Month 3: Beta testing begins")

[1669] Step 7:

[1670] The server displays all information to the user

[1671] The terminal comprehensively displays the business model, stakeholders, development time, success stories, failure stories, and specific action plans received from the server to the user, allowing the user to intuitively grasp the overall picture of their business and specific implementation plans.

[1672] Input: Business model, stakeholders, development time, success stories, failure stories, specific action plans

[1673] Output: All information displayed to the user

[1674] These are the main processing steps of this system.

[1675] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1676] The present invention relates to a system that generates an optimal business model based on a user's input of their goals and business direction, and provides relevant stakeholders, development man-hours, success stories, and failure stories, as well as a concrete action plan. Furthermore, the present invention is characterized by incorporating an emotion engine that recognizes the user's emotions, and by adjusting the business model and proposal content based on the user's emotion data. A specific embodiment of this system is described below.

[1677] Overall flow

[1678] 1. User Input

[1679] Users input data about the direction of the business they want to run, for example, a specific business idea such as "I want to run an online learning platform."

[1680] 2. Sending input data

[1681] The terminal sends the business direction data entered by the user to the server, usually via an API, packaging the input in JSON format.

[1682] 3. Data Analysis

[1683] The server analyzes the incoming data using natural language processing (NLP) libraries, specifically tokenizing the text and performing morphological analysis to extract key keywords and intent.

[1684] 4. Business model generation

[1685] The server generates the optimal business model based on the extracted keywords and intent. This generation process involves using a generative AI model to select the most suitable business model from a number of options. For example, if the user enters "online learning platform," the server will suggest a "subscription model."

[1686] 5. Stakeholders and Development Effort Calculation

[1687] The server calculates the required stakeholders and development time based on the generated business model. This data is retrieved from an internal database. For example, the stakeholders "educational content creator" and "IT infrastructure manager" are presented, and the development time is "6 months."

[1688] 6. Presentation of success and failure cases

[1689] The server extracts past success stories and failure stories from a database. To do this, it searches the database of success stories and failure stories to collect information that users can use as reference. For example, a success story might be presented as a "success story of a famous online learning platform," and a failure story might be presented as a "reason for the failure of a similar service in the past."

[1690] 7. Generate an action plan

[1691] The server generates a specific action plan based on the business model. This involves creating steps and a timeline that best fit the business model, and listing specific tasks for each step. For example, a step-by-step plan such as "Month 1: Basic platform design, Month 2: Start content creation, Month 3: Start beta testing" is provided.

[1692] 8. Collecting Emotional Data with an Emotion Engine

[1693] The device collects the user's emotional data. The emotion engine analyzes the user's emotions based on facial recognition, voice analysis, and the tone of the input content, and generates emotional data. For example, if the user is excited or depressed while typing, that emotional data is collected in real time.

[1694] 9. Emotion Data Analysis

[1695] The server analyzes the collected emotional data to determine the user's emotional state, which is then used to generate business models and prioritize action plans.

[1696] 10. Emotional Adjustment

[1697] The server adjusts business models and action plans based on emotional data, for example, presenting simplified plans and motivational suggestions if the user is feeling stressed.

[1698] 11. User Feedback

[1699] The terminal displays the business model, stakeholders, development time, success stories and failure stories, and specific action plans received from the server to the user, and also reflects adjustments based on the analysis results of the emotion engine.

[1700] Specific examples

[1701] Below are some specific examples of how this system can be used.

[1702] 1. A user types, "I want to run an online learning platform."

[1703] 2. The device sends the input in JSON format to the server.

[1704] 3. The server uses an NLP library to extract the keyword "online learning platform" and intent.

[1705] 4. The server uses the generative AI model to generate a "subscription model."

[1706] 5. The stakeholders who require a server are the "educational content creator" and the "IT infrastructure manager," and the development time is calculated to be "6 months."

[1707] 6. The server extracts success stories of famous online learning platforms and failure stories of similar services in the past.

[1708] 7. The server generates a specific action plan and presents it as follows: "Month 1: Basic design of the platform, Month 2: Start content creation, Month 3: Start beta testing."

[1709] 8. The device collects emotional data as the user types and detects when the user is nervous.

[1710] 9. The server analyzes the emotional data and suggests additional support to help the user relax.

[1711] 10. The device displays all information to the user and also makes adjustments based on emotional data.

[1712] In this way, the system starts with user input, goes through various data analysis and generation processes, and then provides an optimal business model and feasible plan. It also incorporates an emotion engine to flexibly adjust according to the user's emotional state.

[1713] The processing flow will be explained below.

[1714] Step 1:

[1715] Users input data about the business they want to run. For example, they can enter a specific business idea into the input form, such as "I want to run an online learning platform."

[1716] Step 2:

[1717] The terminal sends the business direction data entered by the user to the server, usually via an API, packaging the input in JSON format.

[1718] Step 3:

[1719] The server parses the incoming data using natural language processing (NLP) libraries, tokenizing the text and performing morphological analysis to extract key keywords and intent.

[1720] Step 4:

[1721] The server generates the optimal business model based on the extracted keywords and intent. This generation process uses a generative AI model to select the most suitable business model from a number of options. For example, if the user enters "online learning platform," the server will suggest a "subscription model."

[1722] Step 5:

[1723] The server calculates the required stakeholders and development time based on the generated business model. This data is obtained by querying an internal database. As an example, the stakeholders "educational content creator" and "IT infrastructure manager" are presented, and the development time is "6 months."

[1724] Step 6:

[1725] The server extracts past success stories and failure stories from a database. To do this, it searches the database of success stories and failure stories to collect information that users can use as reference. For example, a success story might be presented as a "success story of a famous online learning platform," and a failure story might be presented as a "reason for the failure of a similar service in the past."

[1726] Step 7:

[1727] The server generates a specific action plan based on the business model. This involves creating steps and a timeline that best fit the business model, and listing specific tasks for each step. For example, a step-by-step plan such as "Month 1: Basic platform design, Month 2: Start content creation, Month 3: Start beta testing" is provided.

[1728] Step 8:

[1729] The device collects the user's emotional data. The emotion engine analyzes the user's emotions based on facial recognition, voice analysis, and the tone of the input content, and generates emotional data. For example, if the user is excited or depressed while typing, that emotional data is collected in real time.

[1730] Step 9:

[1731] The server analyzes the collected emotional data to determine the user's emotional state, which is then used to generate business models and prioritize action plans.

[1732] Step 10:

[1733] The server adjusts business models and action plans based on emotional data, for example, presenting simplified plans and motivational suggestions if the user is feeling stressed.

[1734] Step 11:

[1735] The terminal displays the business model, stakeholders, development time, success stories and failure stories, and specific action plans received from the server to the user, and also reflects adjustments based on the analysis results of the emotion engine.

[1736] This concrete flow allows users to obtain a comprehensive business model and a concrete implementation plan based on their business idea. Furthermore, by incorporating an emotion engine, the system can flexibly adjust according to the user's emotional state, providing more personalized recommendations.

[1737] Example 2

[1738] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1739] While conventional business model generation systems were capable of generating models based on the user's business direction, they lacked the ability to make optimal adjustments based on the user's emotional state. Furthermore, they lacked the ability to comprehensively provide the stakeholders and workload involved in the generated business model, as well as past successes and failures, and to present a feasible implementation plan for the user. This can lead to users feeling anxious about implementing the generated business model and lowering their motivation.

[1740] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1741] In this invention, the server includes means for receiving business direction data input by a user, means for analyzing the received business direction data and extracting main keywords and intentions, means for generating an appropriate business model based on the extracted keywords and intentions, means for collecting emotional data based on the generated business model, and means for analyzing the collected emotional data and adjusting the business model and action plan based on the emotional state of the user. This makes it possible to adjust the optimal business model according to the emotional state of the user, eliminating problems such as anxiety and decreased motivation, and providing a more feasible and effective business model.

[1742] "Business direction data" is information including the business objectives, goals, strategies, and specific ideas proposed by the user.

[1743] "Keywords" are particularly important words or phrases in business direction data, and are terms that play an important role in generating and analyzing business models.

[1744] "Intent" refers to the purpose or goal that the user wants to convey in the business direction data, as well as the thinking behind it.

[1745] A "business model" is a specific summary of the value of the products or services offered, revenue structure, customer base, and management methods.

[1746] "Emotional data" is information that indicates the emotional state of the user, and is data collected through facial recognition, voice analysis, tone analysis of input content, and the like.

[1747] "Stakeholders" refers to the people and organizations necessary to execute and maintain the business model, such as creators of educational content and IT infrastructure managers.

[1748] "Work volume" refers to the labor and time required to execute a business model, such as development man-hours.

[1749] "Success stories" are specific examples or data showing that similar business models or strategies have been successful in the past.

[1750] "Failure cases" are specific examples or data of similar business models or strategies that have failed in the past.

[1751] An "action plan" is a plan that includes specific steps and a timeline for realizing a business model, as well as tasks for each step.

[1752] A "generative AI model" is an algorithm or system that uses artificial intelligence technology to generate business models, and outputs an appropriate business model by inputting, for example, a prompt statement.

[1753] MODE FOR CARRYING OUT THE INVENTION

[1754] This invention relates to a system that allows users to input their business direction, generates an optimal business model based on that, and provides information on the people involved, workload, success stories, failure stories, and specific implementation plans.Furthermore, this invention combines an emotion engine that recognizes the user's emotions, and automatically adjusts the business model and proposal content based on the user's emotion data.

[1755] Hardware and software used

[1756] The system of the present invention uses the following hardware and software:

[1757] Server: Analyzes data, generates business models, analyzes sentiment data. The server hosts the database and AI models.

[1758] Terminal: A device where the user can provide input and receive feedback from the system. It is equipped with an emotion engine to collect user emotion data.

[1759] Natural Language Processing (NLP) libraries, such as NLTK or spaCy, to analyze user-entered text.

[1760] Generative AI model: For example, use OpenAI's GPT-3 to generate an appropriate business model.

[1761] Database: A database for storing data such as information on stakeholders, success stories, and failure stories.

[1762] System processing overview

[1763] 1. User input: The user inputs the business direction from the device. For example, the user inputs a specific idea such as "I want to operate an online learning platform."

[1764] 2. Sending input data: The terminal packages the business direction data entered by the user in JSON format and sends it to the server via API.

[1765] 3. Data Analysis: The server uses a natural language processing library to analyze the incoming data, tokenizing it and performing morphological analysis to extract key keywords and intent.

[1766] 4. Business model generation: The server uses the extracted keywords and intent to generate an optimal business model using a generative AI model. For example, if the input is "online learning platform," the server will suggest a "subscription model."

[1767] 5. Calculation of stakeholders and workload: Based on the generated business model, the server retrieves and calculates the necessary stakeholders (e.g., educational content creator, IT infrastructure administrator) and workload (e.g., 6 months) from its internal database.

[1768] 6. Presentation of success and failure cases: The server extracts relevant success and failure cases from the database. For example, it displays "success stories of famous online learning platforms" as success cases and "reasons for failure of similar services in the past" as failure cases.

[1769] 7. Generate a detailed execution plan: The server generates an execution plan with specific steps and a timeline. For example, a step-by-step plan such as "Month 1: Platform basic design, Month 2: Content creation begins, Month 3: Beta testing begins" is provided.

[1770] 8. Emotional data collection and analysis: The device collects the user's emotional data and sends it to the server. The server analyzes the collected emotional data and determines the user's emotional state.

[1771] 9. Emotional Adjustment: The server adjusts business models and execution plans based on emotional data. For example, if a user is feeling stressed, it will provide simplified plans and motivational suggestions.

[1772] Specific examples

[1773] Here are some concrete examples of how this system can be used:

[1774] 1. A user types, "I want to run an online learning platform."

[1775] 2. The device sends the input in JSON format to the server.

[1776] 3. The server uses a natural language processing library to extract the keyword "online learning platform" and the intent.

[1777] 4. The server uses the generative AI model to generate a "subscription model."

[1778] 5. The parties who will need a server are the "educational content creator" and the "IT infrastructure manager," and the amount of work is calculated to be "6 months."

[1779] 6. The server extracts success stories of famous online learning platforms and failure stories of similar services in the past.

[1780] 7. The server generates a specific execution plan and presents it as follows: "Month 1: Basic design of the platform, Month 2: Start content creation, Month 3: Start beta testing."

[1781] 8. The device collects emotional data as the user types and detects when the user is nervous.

[1782] 9. The server analyzes the emotional data and suggests additional support to help the user relax.

[1783] 10. The device displays all information to the user and also makes adjustments based on emotional data.

[1784] Prompt Sentence Examples

[1785] Below are some example prompts to input to a generative AI model:

[1786] A user has entered, "I want to run an online learning platform." Please generate the optimal business model based on this direction. Also, please calculate the necessary stakeholders and the amount of work, provide examples of past successes and failures, and even provide a concrete implementation plan.

[1787] In this way, the system of the present invention starts with user input, goes through various data analysis and generation processes, and provides an optimal business model and a feasible plan. Furthermore, by incorporating an emotion engine, it can make flexible adjustments according to the user's emotional state.

[1788] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1789] Step 1:

[1790] Accepting user input

[1791] The user enters business direction data into an input form on the device. For example, they enter a specific idea such as "I want to operate an online learning platform." The input data is temporarily stored in the device's memory and used for the next processing step.

[1792] Input: Business direction data (e.g., "I want to run an online learning platform")

[1793] Output: Business direction data stored in the device's memory

[1794] Step 2:

[1795] Sending input data

[1796] The terminal packages the business direction data entered by the user in JSON format and sends it to the server using an HTTP POST request to the specified API endpoint, where the data is stored in the server's receive buffer.

[1797] Input: User orientation data (data stored in device memory)

[1798] Output: JSON format data stored in the server's receive buffer

[1799] Step 3:

[1800] Data analysis

[1801] The server unpacks the received JSON data and parses it using a natural language processing (NLP) library (e.g., NLTK or spaCy) to tokenize the text, perform morphological analysis, and extract key keywords and intent.

[1802] Input: JSON format data stored in the server's receive buffer

[1803] Output: Key keywords and user intent (stored in server memory)

[1804] Step 4:

[1805] Business model generation

[1806] The server generates an optimal business model using a generative AI model (e.g., OpenAI's GPT-3) based on the extracted keywords and intent. The extracted intent and keywords are input into the generative AI model as prompt statements, and the model outputs an optimal business model, such as a "subscription model."

[1807] Input: Key keywords and user intent (stored in server memory)

[1808] Output: Generated business model (e.g. "Subscription model")

[1809] Step 5:

[1810] Calculating stakeholders and effort

[1811] Based on the generated business model, the server retrieves the necessary stakeholders (e.g., educational content creator, IT infrastructure manager) and the amount of work (e.g., 6 months) from an internal database and calculates them. The calculation is performed by querying the existing project database.

[1812] Input: Generated business model (stored in server memory)

[1813] Output: The stakeholders and effort required (e.g., "Educational content creator, 6 months")

[1814] Step 6:

[1815] Presentation of success and failure cases

[1816] The server searches the internal database and extracts success stories and failure stories related to the business model. Specific examples include a success story, "The success story of a famous online learning platform," and a failure story, "The reasons for the failure of similar services in the past."

[1817] Input: Generated business model (stored in server memory)

[1818] Output: Success stories and failure stories (e.g., "Success stories of famous online learning platforms")

[1819] Step 7:

[1820] Generate a concrete execution plan

[1821] The server creates specific steps and a timeline based on the business model. This plan has a step-by-step structure, listing specific tasks and the duration of each step. For example, "Month 1: Platform basic design, Month 2: Content creation begins, Month 3: Beta testing begins."

[1822] Input: Generated business model (stored in server memory)

[1823] Output: A concrete action plan (e.g., "Month 1: Basic design of the platform, Month 2: Start creating content")

[1824] Step 8:

[1825] Emotion data collection and analysis

[1826] The device uses facial recognition, voice analysis, and tone analysis of input content to collect user emotional data, thereby capturing the user's emotional state (e.g., nervousness, excitement, etc.) in real time and sending the data in JSON format to the server.

[1827] Input: User's facial expressions, voice, and input (device sensor data)

[1828] Output: Emotion data (sent to server in JSON format)

[1829] Step 9:

[1830] Emotional Data Analysis

[1831] The server analyzes the collected emotional data to determine the user's emotional state. It processes the data using an emotion analysis algorithm to identify the user's emotional state (e.g., nervous, stressed, relaxed).

[1832] Input: Emotion data (stored on the server in JSON format)

[1833] Output: User's emotional state (e.g., "tense," "relaxed")

[1834] Step 10:

[1835] Emotional adjustment

[1836] The server adjusts business models and action plans based on the analyzed emotional data, for example, presenting simplified plans and motivational suggestions if the user is feeling stressed.

[1837] Input: User's emotional state (stored in server memory)

[1838] Output: A tailored business model and implementation plan (e.g., a "simplified plan")

[1839] Step 11:

[1840] User feedback

[1841] The device receives information from the server and displays it to the user, including the business model, stakeholders, workload, success stories, failure stories, and specific implementation plans, along with adjustments based on emotion data.

[1842] Input: Adjusted business model and execution plan (data sent from the server)

[1843] Output: Information displayed to the user (displayed on the device display)

[1844] As mentioned above, the program processing of this system is carried out through a series of steps, starting with user input, data analysis, business model generation, and adjustments based on emotion data. This process makes it possible to provide a more specific and feasible business model.

[1845] (Application example 2)

[1846] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1847] Conventional business model generation systems provide models and action plans based on business direction data entered by users, but they are unable to consider the user's emotional state. As a result, there are problems such as users feeling stressed or being unable to make appropriate proposals based on their emotional state. Furthermore, when it comes to immediately applying improvement ideas on the factory floor, it is difficult to provide appropriate plans that take emotions into account in real time.

[1848] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1849] In this invention, the server includes means for receiving business direction data input by a user, means for analyzing the received business direction data and extracting main keywords and intentions, means for generating an appropriate business model based on the extracted keywords and intentions, means for generating a specific action plan based on the business model, means for collecting and analyzing user emotion data, and means for adjusting the business model and action plan based on the emotion data. This not only appropriately reflects the user's input, but also makes it possible to provide a real-time improvement action plan that is adjusted according to the user's emotional state.

[1850] "Business direction data" is information about the business direction the user is aiming for, the processes they want to improve, their goals, etc.

[1851] "Key Keywords" are important words and phrases extracted from business direction data.

[1852] "Intent" refers to the user's purpose or goal that is inferred based on the business direction data entered by the user.

[1853] A "business model" is an optimal business structure and revenue mechanism proposed based on business direction data.

[1854] An "action plan" is a specific action plan and timeline for realizing a business model.

[1855] "Emotional data" refers to information that indicates a user's emotional state and is collected through facial recognition, voice analysis, tone of input, etc.

[1856] An "emotion engine" is a system or algorithm that collects and analyzes a user's emotional data and determines their emotional state.

[1857] "Stakeholders" are those interested in implementing the business model and action plan.

[1858] "Development man-hours" refers to the working hours and labor required to realize a business model or action plan.

[1859] A "success story" is a specific example of a similar business model or action plan that was successful in the past.

[1860] "Failure cases" are specific examples of similar business models or action plans that have failed in the past.

[1861] The system realizing this invention includes various means for receiving and analyzing business direction data input by users, which not only generates an appropriate business model and a specific action plan, but also makes adjustments taking into account the emotional state of the user.

[1862] Hardware and software used

[1863] Hardware: smart glasses, server, camera, microphone.

[1864] Software: NLP libraries (e.g. spaCy), generative AI models (e.g. OpenAI GPT), databases (e.g. PostgreSQL), sentiment analysis libraries (e.g. Affectiva).

[1865] System Operation Overview

[1866] User Input

[1867] Users input the business process they want to improve through the smart glasses using voice or text, which is first captured by the smart glasses and then sent to the server in JSON format.

[1868] Data analysis and business model generation

[1869] The server uses an NLP library (e.g., spaCy) to tokenize the received data and extract key keywords and intent, as shown in the example below.

[1870] Example prompt sentence:

[1871] "We want to improve the production efficiency of our line. Which step is causing the bottleneck in our current setup?"

[1872] Next, a generative AI model (e.g., OpenAI GPT) is used to generate optimal business models and action plans based on the extracted keywords and intent.

[1873] Stakeholders and development effort calculations

[1874] Based on the generated business model, the required stakeholders and development effort are retrieved from a database (e.g., PostgreSQL) and calculated.

[1875] Extraction of success and failure cases

[1876] Past examples of successful and unsuccessful similar business models are extracted from a database and presented to the user.

[1877] Generate and view an action plan

[1878] Generate specific action plans based on your business model, including timelines for completing each task by when.

[1879] Emotion data collection and analysis

[1880] The smart glasses use a built-in camera and microphone to collect and analyze the user's emotional data, which is then analyzed in real time using an emotion analysis library (e.g., Affectiva) to determine the user's emotional state.

[1881] Emotional adjustment

[1882] Based on the collected emotional data, the generated business model and action plan can be adjusted accordingly. For example, if the user is feeling stressed, a simplified plan or an encouraging message can be provided.

[1883] User feedback

[1884] Finally, the adjusted business model, action plan, stakeholders, development time, success stories, and failure stories are displayed on the user's smart glasses.

[1885] Through the above process, starting from user input and going through various data analysis and generation processes, it becomes possible to provide optimal business models and action plans. In addition, by combining it with an emotion engine, it is possible to flexibly adjust according to the user's emotional state.

[1886] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1887] Step 1:

[1888] Users can input the business process they want to improve through voice or text input via smart glasses, which is captured by the device, converted into JSON format, and sent to the server.

[1889] Input: User voice or text input

[1890] Output: JSON format data

[1891] Step 2:

[1892] The server parses the received JSON-formatted business direction data using a natural language processing (NLP) library (e.g., spaCy), tokenizing the data and extracting key keywords and intent.

[1893] Input: Business direction data in JSON format

[1894] Output: Primary keywords and intent

[1895] Step 3:

[1896] Based on the extracted keywords and intent, the server uses a generative AI model (e.g., OpenAI GPT) to generate an optimal business model. This generation process takes into account the conditions and options necessary for business success.

[1897] Input: Extracted keywords and intent

[1898] Output: Generated business model

[1899] Step 4:

[1900] Based on the generated business model, the server retrieves and calculates the required stakeholders and development effort from an internal database (e.g., PostgreSQL).

[1901] Input: Generated business model

[1902] Output: Required stakeholders and development effort

[1903] Step 5:

[1904] The server extracts past success stories and failure stories from a related database and collects information that users can use as reference.

[1905] Input: Generated business model

[1906] Output: Success stories and failure stories

[1907] Step 6:

[1908] The server generates a specific action plan based on the business model and related information (stakeholders, development time, success stories, failure stories). The action plan includes specific tasks and timelines for each step.

[1909] Input: Business model, stakeholders, development time, success stories, failure stories

[1910] Output: A concrete action plan

[1911] Step 7:

[1912] The device collects the user's emotional data using the camera and microphone built into the smart glasses, which are then used to collect emotions through facial recognition and voice analysis. This data is then sent to a server in real time.

[1913] Input: User's video and audio data

[1914] Output: Real-time emotion data

[1915] Step 8:

[1916] The server analyzes the collected emotional data using an emotion analysis library (e.g., Affectiva) to determine the user's emotional state.

[1917] Input: Real-time emotion data

[1918] Output: Parsed emotion data

[1919] Step 9:

[1920] The server then adjusts the generated business model and action plan based on the analyzed emotional data, for example, providing a simplified plan or encouraging message if the user is feeling stressed.

[1921] Input: Analyzed emotion data and action plan

[1922] Output: A tailored business model and action plan

[1923] Step 10:

[1924] The device compiles information on the business model, stakeholders, development time, success stories, failure stories, specific action plans, and adjusted content, and displays it on the smart glasses.

[1925] Input: Adjusted business model, stakeholders, development time, success stories, failure stories, concrete action plan

[1926] Output: Display feedback to the user

[1927] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1928] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1929] 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 the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1930] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1931] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1932] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1933] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1934] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1935] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1936] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1937] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1938] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1939] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1940] 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.

[1941] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1942] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1943] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1944] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1945] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1946] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1947] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1948] The following is further disclosed regarding the above embodiment.

[1949] (Claim 1)

[1950] means for receiving business direction data input by a user;

[1951] a means for parsing the received business direction data and extracting key keywords and intent;

[1952] The system includes a means for generating an appropriate business model based on the extracted keywords and intent.

[1953] (Claim 2)

[1954] A method to calculate the required stakeholders and development man-hours based on the business model;

[1955] A means of extracting past success stories and failure stories from a database,

[1956] 10. The system of claim 1, further comprising: means for generating a specific action plan tailored to said business model.

[1957] (Claim 3)

[1958] 10. The system according to claim 1, further comprising means for comprehensively displaying the generated business model, stakeholders, development effort, success stories, and failure stories.

[1959] "Example 1"

[1960] (Claim 1)

[1961] means for receiving business direction data input by a user;

[1962] a means for parsing the received business direction data and extracting key keywords and intent;

[1963] A means for utilizing a generative AI model to generate an appropriate business model based on the extracted keywords and intent; and

[1964] A means for calculating the required stakeholders and development man-hours based on the generated business model;

[1965] A means of extracting past success stories and failure stories from a database,

[1966] A means for generating a specific action plan tailored to the business model;

[1967] A means to comprehensively display the generated business model, stakeholders, development effort, success stories, and failure stories;

[1968] A means for converting user input data into JSON format and sending it to the server;

[1969] means for analyzing the received data using a natural language processing library;

[1970] A system including:

[1971] (Claim 2)

[1972] A means for calculating the required stakeholders and development man-hours based on the generated business model;

[1973] A means of extracting past success stories and failure stories from a database,

[1974] A means for generating a specific action plan tailored to the business model;

[1975] Includes a comprehensive view of the generated business models, stakeholders, development efforts, success stories, and failure stories.

[1976] 10. The system of claim 1.

[1977] (Claim 3)

[1978] Includes a comprehensive view of the generated business models, stakeholders, development efforts, success stories, and failure stories.

[1979] 10. The system of claim 1.

[1980] "Application Example 1"

[1981] (Claim 1)

[1982] means for receiving business direction data input by a user;

[1983] a means for parsing the received business direction data and extracting key keywords and intent;

[1984] A means for generating an appropriate business model based on the extracted keywords and intent;

[1985] A method for calculating the required stakeholders and development man-hours based on the generated business model;

[1986] A means of extracting past success stories and failure stories,

[1987] A means to generate a specific action plan tailored to your business model,

[1988] A system that includes a means to comprehensively display the generated business model, stakeholders, development effort, and success and failure cases.

[1989] (Claim 2)

[1990] The system according to claim 1, wherein the system is an application installed on a smartphone, and includes means for generating solutions based on a user's input of business directions for an online shopping site, in particular.

[1991] (Claim 3)

[1992] The system of claim 2 uses the system of claim 1 to generate and display a business model, stakeholders, de...

Claims

1. means for receiving business direction data input by a user; a means for parsing the received business direction data and extracting key keywords and intent; The system includes a means for generating an appropriate business model based on the extracted keywords and intent.

2. A method to calculate the required stakeholders and development time based on the business model, A means of extracting past success stories and failure stories from a database, The system of claim 1 , further comprising: means for generating a specific action plan tailored to the business model.

3. The system according to claim 1, further comprising means for comprehensively displaying the generated business model, stakeholders, development man-hours, success cases, and failure cases.

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