system

Abstract

We provide the system. [Solution] A means to obtain the business details entered by the user as text data, A means of sending the acquired text data to the server, A means by which the server analyzes text data and extracts keywords, A means of searching for and obtaining related information based on extracted keywords, A means of organizing the acquired information and sending it to the terminal, A means by which the terminal displays organized information to the user, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In conventional startup support, those who want to start a business had to search for and organize a vast amount of information by themselves. This work required a great deal of time and effort and was a major burden in the initial stage of starting a business. Also, it was difficult to efficiently grasp the necessary skills and matters to be prepared, and appropriate preparations might not be made in some cases. Therefore, there is a demand for a system that automatically proposes the necessary skills and preparation matters just by inputting what the user wants to start a business with.

Means for Solving the Problems

[0005] This invention relates to a system that includes means for acquiring user-entered business startup details as text data and transmitting the acquired text data to a server. The server is equipped with means for analyzing the text data to extract keywords and for searching and acquiring related information based on the extracted keywords. Furthermore, the system includes means for organizing the acquired information and transmitting it to a terminal, and means for the terminal to display the organized information to the user, thereby enabling the user to easily grasp the necessary skills and preparations for starting a business. The server is equipped with a function to extract keywords from text data using natural language processing technology and a function to organize related information into categories of "necessary skills" and "preparations." This allows the user to efficiently proceed with preparations for starting a business.

[0006] A "user" is someone who uses the system to input information for starting a business and receives the results.

[0007] A "terminal" is a device used by a user to input business details and display information sent from a server. Specifically, this includes computers, smartphones, and tablets.

[0008] A "server" is a computer system that receives text data sent by users, analyzes that data, searches for necessary information, and retrieves it.

[0009] "Text data" refers to text information about the business that the user has entered.

[0010] "Analysis" refers to the process by which a server understands text data and extracts keywords based on its content.

[0011] "Keyword extraction" is the process by which a server extracts meaningful and important words and phrases from text data.

[0012] "Natural language processing technology" refers to the techniques that enable computers to understand and analyze human language. This includes tokenization, morphological analysis, syntactic analysis, and other methods.

[0013] "Searching" refers to the process by which a server finds relevant information from databases or external APIs based on extracted keywords.

[0014] "Related information" refers to data about the skills and preparations a user needs to start a business.

[0015] "Organization" refers to the process of compiling acquired related information into a format or category that is easy for the user to understand.

[0016] "Display" refers to the process by which a device visually provides users with organized information.

[0017] "Required skills" refer to the specific knowledge and techniques that one should acquire when starting a business.

[0018] "Preparation items" refer to the specific tasks and procedures that need to be carried out in preparation for starting a business.

[0019] "System" refers to the entire information processing device that executes a series of processes in the present invention and provides necessary information to the user. [Brief explanation of the drawing]

[0020] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of the data processing device and smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4]It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0021] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described according to the accompanying drawings.

[0022] First, the language used in the following description will be explained.

[0023] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), and APU (Accelerated Processing Unit).

[0024] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0025] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0026] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0027] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0028] [First Embodiment]

[0029] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0030] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0031] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0032] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0033] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0034] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0035] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0036] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0037] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0038] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0039] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0040] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0041] This invention is a system that allows users to input the details of the business they wish to start, and then automatically suggests the necessary skills and preparations based on that input. A specific embodiment of this system is described below.

[0042] System Overview

[0043] This system consists of three main components: the user, the terminal, and the server. The user inputs their business details through the terminal and sends the data from the terminal to the server. The server analyzes the transmitted data, searches for and retrieves relevant information, and sends it back to the terminal. The terminal displays the received information to the user.

[0044] Processing of the entire program

[0045] The user enters

[0046] The user enters specific details about their business into the input field on their device. For example, they might enter, "I want to start an online programming school." This input is stored on the device as text data.

[0047] Sending text data to the server

[0048] The terminal sends the text data entered by the user to the server. Specifically, it sends the data to the server using an HTTP POST request. This request contains the text of the entered business details.

[0049] Server-based analysis and keyword extraction

[0050] The server analyzes the received text data and extracts keywords from its content. Using natural language processing technology, it tokenizes the text and picks out important keywords. For example, keywords such as "online," "course," and "programming school" might be extracted.

[0051] Searching for and retrieving related information

[0052] The server uses internal databases and external APIs to search for relevant information based on the extracted keywords. Specifically, it retrieves technical documents, marketing materials, and relevant best practices related to the keywords.

[0053] Organizing and sending information

[0054] The server organizes the retrieved information into categories such as "Required Skills" and "Preparation Items." For example, it might be formatted as "Required Skills: Web development, course material creation" and "Preparation Items: Online platform selection, course material creation." The organized information is then sent to the terminal.

[0055] Displaying information on the device

[0056] The terminal receives organized information sent from the server and displays it to the user. Based on the displayed information, the user can acquire the necessary skills and perform the required preparations.

[0057] Specific example

[0058] For example, let's consider the case where a user enters "I want to start a sustainable fashion brand." In this case, the following process takes place.

[0059] 1. The user enters the details of the business they want to start.

[0060] 2. The terminal receives this input as text data and sends it to the server.

[0061] 3. The server extracts keywords such as "sustainable," "fashion brand," and "entrepreneurship."

[0062] 4. The server searches for information related to materials, fashion design, and brand building based on these keywords.

[0063] 5. Organize the information acquired by the server into "Required Skills" and "Preparation Items."

[0064] 6. The device displays organized information to the user.

[0065] This system allows users to easily obtain the necessary information and efficiently proceed with their startup preparations. The above describes the specific form of implementing this system invention.

[0066] The following describes the processing flow.

[0067] Step 1:

[0068] The user enters details of their business in the input field on their device. For example, they might enter, "I want to start an online programming school."

[0069] Step 2:

[0070] The terminal acquires the user's input as text data. This text data is temporarily stored in memory for subsequent processing.

[0071] Step 3:

[0072] The device sends the acquired text data to the server. Typically, an HTTP POST request is used, sending the text data as the payload.

[0073] Step 4:

[0074] The server receives an HTTP request. Text data is extracted from the request payload and prepared for analysis.

[0075] Step 5:

[0076] The server analyzes the text data using natural language processing technology. Specifically, it tokenizes the text and performs morphological analysis to extract important keywords. For example, keywords such as "online," "course," and "programming school" are extracted.

[0077] Step 6:

[0078] The server uses extracted keywords to search for relevant information from internal databases and external APIs. Database queries and API requests are executed to retrieve the relevant information.

[0079] Step 7:

[0080] The information acquired by the server is categorized into "Required Skills" and "Preparation Items." For example, it might be organized in the format of "Required Skills: Web development, course material creation" and "Preparation Items: Online platform selection, course material creation."

[0081] Step 8:

[0082] The server sends organized information to the terminal as an HTTP response. The data included in this response is formatted in a clear and easy-to-read format for the user.

[0083] Step 9:

[0084] The terminal receives an HTTP response from the server. It parses the response data and prepares it for display on the screen.

[0085] Step 10:

[0086] The device displays organized information to the user. The user can then review the displayed "required skills" and "preparations" and proceed with their startup preparations.

[0087] (Example 1)

[0088] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0089] Conventional startup support systems have difficulty suggesting appropriate skills and preparations based on the user's input of specific business details. Furthermore, the information is often poorly organized and displayed, making it time-consuming for users to find the information they actually need. Therefore, this invention aims to automatically suggest necessary skills and preparations based on the user's input of business details, and to efficiently organize and display the information.

[0090] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0091] In this invention, the server includes means for extracting keywords from text data using natural language processing technology, means for searching for related information using an internal database or external API based on the extracted keywords, and means for organizing the acquired related information into categories of "necessary skills" and "preparation items." This enables users to efficiently acquire and implement the information necessary for starting a business.

[0092] A "user" is an individual or corporation who uses this system to input details about their business and wants to know what skills and preparations are necessary.

[0093] "Text data" refers to the written information of the business startup entered by the user, which is then analyzed by the system.

[0094] A "device" refers to a device used by a user to input details about their business, and includes personal computers, smartphones, tablets, and other similar devices.

[0095] A "server" is a central processing unit that receives text data sent from a terminal, analyzes it, searches for, retrieves, and organizes the necessary information, and then sends it back to the terminal.

[0096] "Natural language processing technology" refers to techniques for analyzing text data and extracting keywords from its content, and includes methods such as morphological analysis and tokenization.

[0097] "Keywords" are important words and phrases extracted from the text data of the business details entered by the user.

[0098] An "internal database" is a data storage system used to store information managed by a server.

[0099] An "external API" is an application programming interface that a server uses to retrieve information from other services.

[0100] "Related information" refers to information such as skills and preparations necessary for starting a business, technical documents, and marketing materials, which are searched and retrieved based on keywords.

[0101] A "category" is a classification used to organize acquired related information, and includes "required skills" and "preparation items."

[0102] "Skills" refer to the abilities and knowledge required for a user to start a business.

[0103] "Preparation items" refer to the procedures and tasks necessary to realize the startup.

[0104] This invention is a system that allows users to input what they want to start as a business, and then automatically suggests the necessary skills and preparations based on that input. This system consists of three main components: the user, the terminal, and the server.

[0105] The user enters details of their business.

[0106] The user enters details of their business idea into the input field on the device. For example, they might enter, "I want to start an online programming school." This input is stored on the device as text data.

[0107] The device sends text data to the server.

[0108] The terminal uses an HTTP POST request to send the text data entered by the user to the server. This request contains the text data entered by the user.

[0109] The server analyzes the text data and extracts keywords.

[0110] The server analyzes the received text data and extracts important keywords using natural language processing techniques. This technique utilizes libraries for morphological analysis and tokenization (e.g., SpaCy). The extracted keywords are then used by the system to search for relevant information.

[0111] The server searches for and retrieves relevant information.

[0112] The server searches and retrieves relevant information using internal databases and external APIs based on the extracted keywords. External APIs may include the Google® Books API and other information provision services. This allows for the collection of specific information that the user needs (e.g., technical documentation, marketing materials).

[0113] The server organizes the information and sends it to the terminal.

[0114] The server organizes the acquired information into categories such as "required skills" and "preparation items." This organized information is then sent to the terminal and displayed to the user. This allows the user to efficiently grasp applicable skills and the preparation items they need to implement.

[0115] Specific example

[0116] For example, let's consider the case where a user enters "I want to start a sustainable fashion brand." In this case, the following process takes place.

[0117] 1. The user enters details of their business.

[0118] 2. The terminal receives this input as text data and sends it to the server.

[0119] 3. The server extracts keywords such as "sustainable," "fashion brand," and "entrepreneurship."

[0120] 4. The server searches for information related to materials, fashion design, and brand building based on these keywords.

[0121] 5. Organize the information acquired by the server into "Required Skills" and "Preparation Items."

[0122] 6. The device displays organized information to the user.

[0123] Examples of prompts to input into a generative AI model

[0124] Examples of prompt statements are shown below, and a system is created based on these to provide information that meets the user's needs:

[0125] Design a system that, upon receiving the input "I want to start a sustainable fashion brand," will suggest the necessary skills and preparations. This system should encompass a series of processes, from the user inputting their business plan to server-side data analysis, retrieval of relevant information, and organization and display of that information. Clearly specify the technologies and APIs to be used.

[0126] This system allows users to efficiently acquire the information necessary for preparing to start a business and put it into practice.

[0127] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0128] Step 1: The user enters details of their business.

[0129] The user enters details of their business into an input field on the terminal. This input data is sent to the terminal in text format. Specifically, the user might enter a sentence such as "I want to start a sustainable fashion brand." The input data is in text format, and the terminal temporarily stores this text data.

[0130] Input: "I want to start a sustainable fashion brand."

[0131] Output: Text data

[0132] Step 2: The device sends text data to the server.

[0133] The device uses an HTTP POST request to send the text data entered by the user to the server. Specifically, the device sends the text data to the appropriate API endpoint. The server then receives the text data of the business details entered by the user.

[0134] Input: Text data entered by the user

[0135] Output: HTTP POST request to the server

[0136] Step 3: The server receives the text data and performs analysis.

[0137] The server receives text data sent from the terminal. After receiving the data, it analyzes it using natural language processing techniques to extract key keywords. Specifically, it uses a natural language processing library (e.g., SpaCy) to tokenize the text and identify important keywords. Through this process, keywords such as "sustainable," "fashion brand," and "startup" are extracted from the text "I want to start a sustainable fashion brand."

[0138] Input: Text data sent to the server

[0139] Output: Extracted keyword list

[0140] Step 4: The server searches for and retrieves relevant information.

[0141] The server uses internal databases and external APIs to search for and retrieve relevant information based on the extracted keywords. Specifically, the server calls the Google Books API and other information-providing services to collect the corresponding information. This retrieves materials and documents related to the keywords "sustainable," "fashion brands," and "entrepreneurship."

[0142] Input: Extracted keyword list

[0143] Output: List of related information

[0144] Step 5: The server organizes the information and sends it to the terminal.

[0145] The server organizes the acquired relevant information into categories such as "Required Skills" and "Preparation Items." Specifically, the server classifies the information appropriately and sends it to the terminal as data in JSON format. For example, the information might be organized in the format of "Required Skills: Web Development, Fashion Design" and "Preparation Items: Brand Building, Marketing Plan."

[0146] Input: List of related information

[0147] Output: Organized information (JSON format)

[0148] Step 6: The device displays organized information to the user.

[0149] The terminal receives organized information sent from the server and displays it to the user. Specifically, it uses front-end technologies (such as HTML and JavaScript) to visually present the information to the user. This allows the user to check the necessary skills and preparations and plan the next steps.

[0150] Input: Organized information (JSON format)

[0151] Output: Information displayed to the user

[0152] The above outlines the specific processing steps of this system.

[0153] (Application Example 1)

[0154] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0155] For users planning to start a business, efficiently understanding the necessary skills and preparations and quickly proceeding with those preparations based on that understanding is extremely important. However, the process of researching, collecting, and organizing relevant information from scratch is time-consuming, laborious, and inefficient. This invention aims to provide a system that allows users to easily obtain the information necessary to efficiently proceed with their business startup preparations.

[0156] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0157] In this invention, the server includes means for acquiring a business plan entered by a user as text data, means for transmitting the acquired text data to a data processing device, means for the data processing device to analyze the text data and extract keywords, means for searching for and acquiring related information based on the extracted keywords, means for organizing the acquired information and transmitting it to a display device, and means for the display device to display the organized information to the user. This enables the user to efficiently grasp the necessary technologies and preparation items and to proceed with preparations quickly.

[0158] A "user" is primarily an individual or corporation who is planning to start a business and wants to learn about the skills and preparations required for it.

[0159] A "business plan" is a document that details the steps and skills required to start a specific business.

[0160] "Text data" refers to information stored in digital format based on content entered by the user as part of their business plan.

[0161] A "data processing device" is a computer system that functions as a server, analyzes received text data, extracts necessary keywords, and searches for and retrieves related information.

[0162] A "display device" is an electronic device used to visually display information transmitted from a data processing device to a user.

[0163] "Natural language processing technology" refers to a series of technologies that enable computers to understand, analyze, and generate human language.

[0164] "Keywords" are important terms that represent the main theme of a business plan, extracted from text data.

[0165] "Related information" refers to information necessary for starting a business, such as technical documents, skill sets, and marketing materials, which are searched based on the extracted keywords.

[0166] "Required technologies" refer to specific technologies and skills that users need to acquire based on their business plan.

[0167] "Preparation items" refer to the specific tasks and procedures that users must complete in advance to start their business.

[0168] This invention is a system in which a user inputs a business plan, and based on that, the system automatically suggests the necessary technologies and preparation items. This system consists of four main components: the user, a terminal, a data processing device (server), and a display device. Specific embodiments of this system are described below.

[0169] System Overview

[0170] The user enters their business plan into a terminal and sends the data from the terminal to a data processing unit. The data processing unit analyzes the transmitted data, searches for and retrieves relevant information, and sends it back to the terminal. The terminal displays the received information to the user. The user then acquires the necessary skills based on the displayed information and starts the business by executing the necessary preparation items.

[0171] Hardware and software to be used

[0172] Device: Smartphone, tablet, or PC

[0173] Data processing unit (server): High-performance server, specifically a server built with programming languages ​​such as Python, or a web framework such as Flask.

[0174] Natural language processing technologies: NLTK library, etc.

[0175] Display device: Smartphone or tablet screen, or head-mounted display

[0176] Explanation of data processing

[0177] Retrieving and sending text data:

[0178] The user enters a specific business plan into the terminal's input field. For example, they might enter, "I want to open a virtual art gallery using VR." This input is stored as text data on the terminal. The terminal then sends the text data entered by the user to a data processing device.

[0179] Text data analysis and keyword extraction:

[0180] The data processing device analyzes the received text data and extracts keywords from its content. Natural language processing techniques are used for specific keyword extraction. For example, the NLTK library is used to tokenize the text and pick out important keywords. In the example above, keywords such as "VR," "virtual," "art gallery," and "opening a business" are extracted.

[0181] Searching for and retrieving related information:

[0182] The data processing device uses internal databases and external APIs to search for relevant information based on the extracted keywords. Specifically, it retrieves technical documents, marketing materials, and relevant best practices related to the keywords. For example, information on "VR technology," "art curation," and "gallery design" might be searched.

[0183] Organizing and sending information:

[0184] The data processing device organizes the retrieved information into categories such as "required technologies" and "preparation items." For example, it might be formatted as "required technologies: 3D modeling, art curation" and "preparation items: artist recruitment, virtual gallery design."

[0185] Displaying information on the device:

[0186] The terminal receives organized information sent from the server and displays it to the user. Based on the displayed information, the user can efficiently acquire skills and perform preparatory tasks.

[0187] Examples of specific cases and prompt statements

[0188] For example, let's consider a case where a user enters "I want to start a programming school that offers online courses."

[0189] Extracted keywords: "online," "course," "programming school"

[0190] Related information:

[0191] Required skills: Web development, course material creation, online platform setup.

[0192] Preparation items: Curriculum design, platform selection, marketing strategy development.

[0193] Examples of prompts to input into a generative AI model:

[0194] "Based on a business plan to 'start an online programming school,' please propose the necessary technologies and preparation items."

[0195] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0196] Step 1:

[0197] The user enters the business plan.

[0198] The user enters a specific business plan into the input field on the device. For example, they might enter, "I want to open a virtual art gallery using VR." This input is stored on the device as text data.

[0199] Input: Text data "I want to open a virtual art gallery using VR"

[0200] Output: Text data is stored on the terminal.

[0201] Step 2:

[0202] Send text data to a data processing device.

[0203] The terminal sends the text data entered by the user to the data processing unit. This process uses an HTTP POST request.

[0204] Input: Text data entered by the user

[0205] Output: Text data is sent to the data processing unit.

[0206] Step 3:

[0207] Text data analysis and keyword extraction

[0208] The data processing device analyzes the received text data and extracts keywords from its content. Specifically, it uses the NLTK library to tokenize the text and pick out important keywords.

[0209] Input: Submitted text data

[0210] Data processing: Tokenization and keyword extraction using the NLTK library.

[0211] Output: Extracted keywords (e.g., "VR", "virtual", "art gallery", "business opening")

[0212] Step 4:

[0213] Searching for and retrieving related information

[0214] The data processing unit uses internal databases and external APIs to search for relevant information based on the extracted keywords. For example, it retrieves technical documents, marketing materials, and relevant best practices related to the keywords.

[0215] Input: Extracted keywords

[0216] Data processing: Internal database searches and use of external APIs.

[0217] Output: Related information (e.g., "VR technology," "art curation," "gallery design")

[0218] Step 5:

[0219] Organizing and sending information

[0220] The data processing unit organizes the retrieved information into categories such as "required technologies" and "preparation items." For example, it might be formatted as "required technologies: 3D modeling, art curation" and "preparation items: artist recruitment, virtual gallery design." The organized information is then sent to the terminal.

[0221] Input: Retrieved related information

[0222] Data processing: Categorization of information ("Required skills," "Preparation items")

[0223] Output: The organized information is sent to the terminal.

[0224] Step 6:

[0225] Displaying information on the device

[0226] The terminal receives organized information sent from the server and displays it to the user. Based on the displayed information, the user can efficiently acquire skills and perform preparatory tasks.

[0227] Input: Organized information

[0228] Output: Information displayed to the user (e.g., "Required skills: 3D modeling, art curation", "Preparation items: Artist recruitment, virtual gallery design")

[0229] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0230] This invention combines a system that automatically suggests necessary skills and preparations based on the user's input of their desired business venture with an emotion engine that recognizes the user's emotions. Specific embodiments of this system are described below.

[0231] System Overview

[0232] This system consists of four main components: the user, the terminal, the server, and the emotion engine. The user inputs their business details through the terminal and sends the data from the terminal to the server. The server analyzes the transmitted data, searches for and retrieves relevant information, and sends it back to the terminal. Furthermore, the emotion engine analyzes the user's emotions from the text data and adjusts the suggestions to provide even more personalized feedback.

[0233] Program processing

[0234] The user enters

[0235] The user enters specific details about their business into the input field on their device. For example, they might enter, "I want to start an online programming school." This input is stored on the device as text data.

[0236] Sending text data to the server

[0237] The terminal sends the text data entered by the user to the server. Specifically, it sends the data to the server using an HTTP POST request. This request contains the text of the entered business details.

[0238] Server-based analysis and keyword extraction

[0239] The server analyzes the received text data and extracts keywords from its content. Using natural language processing technology, it tokenizes the text and picks out important keywords. For example, keywords such as "online," "course," and "programming school" might be extracted.

[0240] Emotional analysis using an emotion engine

[0241] The server passes the text data to the emotion engine, which analyzes the user's emotions. The emotion engine uses the text data and the user's past input history to recognize emotions. For example, emotions such as "excited" or "anxious" may be identified.

[0242] Searching for and retrieving related information

[0243] The server searches for relevant information using internal databases and external APIs based on extracted keywords and the analysis results of the sentiment engine. It executes database queries and API requests to retrieve relevant information.

[0244] Organizing and sending information

[0245] The server organizes the retrieved information into categories such as "Required Skills" and "Preparation Items," and further adjusts the suggestions based on the analysis results of the emotion engine. For example, it might be formatted as "Required Skills: Web development, course material creation" and "Preparation Items: Online platform selection, course material creation." The organized information is then sent to the terminal.

[0246] Displaying information on the device

[0247] The terminal receives organized information sent from the server and displays it to the user. Based on the displayed information, the user can acquire the necessary skills and perform the required preparations. Based on the analysis results of the emotion engine, flexible responses are made that are tailored to the user's current emotions.

[0248] Specific example

[0249] For example, let's consider the case where a user enters "I want to start a sustainable fashion brand." In this case, the following process takes place.

[0250] 1. The user enters the details of the business they want to start.

[0251] 2. The terminal receives this input as text data and sends it to the server.

[0252] 3. The server extracts keywords such as "sustainable," "fashion brand," and "entrepreneurship."

[0253] 4. The server passes text data to the emotion engine, which analyzes the user's emotions. Emotions such as "excited" are recognized.

[0254] 5. The server searches for information related to relevant materials, fashion design, and brand building based on these keywords and sentiment information.

[0255] 6. Organize the information acquired by the server into "Required Skills" and "Preparation Items," and make emotionally-based suggestions.

[0256] 7. The device displays organized information to the user.

[0257] This system allows users to easily obtain necessary information and efficiently prepare for starting a business by receiving emotion-based feedback. The above is a specific form of implementing this system invention that combines an emotion engine.

[0258] The following describes the processing flow.

[0259] Step 1:

[0260] The user enters specific details of their business into the input field on their device. For example, they might enter, "I want to start an online programming school."

[0261] Step 2:

[0262] The terminal retrieves the user's input as text data. This text data is stored in a variable for subsequent processing.

[0263] Step 3:

[0264] The terminal sends the acquired text data to the server using an HTTP POST request. The request payload contains the text of the business details entered by the user.

[0265] Step 4:

[0266] The server receives an HTTP request and extracts text data from the payload. The extracted text data is then prepared for analysis.

[0267] Step 5:

[0268] The server uses natural language processing technology to analyze text data, performing tokenization and morphological analysis. This extracts important keywords such as "online," "course," and "programming school."

[0269] Step 6:

[0270] The server extracts keywords and passes them to the emotion engine to analyze the user's emotions. Emotions are identified using text data and the user's past input history. For example, emotions such as "excited" or "anxious" may be recognized.

[0271] Step 7:

[0272] The server uses internal databases and external APIs to search for relevant information based on keyword and sentiment engine analysis results. It executes database queries and API requests to retrieve materials, teaching materials, and marketing strategies related to establishing a programming school.

[0273] Step 8:

[0274] The server categorizes the relevant information it acquires into "Required Skills" and "Preparation Items." For example, it might be organized in the format of "Required Skills: Web development, course material creation" and "Preparation Items: Online platform selection, course material creation." Furthermore, the recommendations are adjusted based on the analysis results of the emotion engine. For example, users who are feeling anxious will be provided with more detailed explanations and additional support resources.

[0275] Step 9:

[0276] The server sends organized information to the terminal as an HTTP response. This response contains information presented to the user in a clear and easy-to-read format.

[0277] Step 10:

[0278] The terminal receives an HTTP response from the server and analyzes the response data. Preparation is made to display the result of the analysis on the screen.

[0279] Step 11:

[0280] The terminal displays the organized information to the user. For example, lists such as "Skills required for starting a business: Web development, teaching material creation" and "Items to be prepared: Selection of an online platform, creation of teaching materials" are displayed. Also, advice and additional information based on emotions are displayed.

[0281] Through this series of processes, the user can efficiently obtain the necessary information and receive support tailored to their emotions.

[0282] (Example 2)

[0283] Next, Example 2 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart device 14 is referred to as the "terminal".

[0284] In a conventional business startup support system, only information is provided based on the business startup content input by the user, and feedback considering the user's emotional state cannot be given. Therefore, appropriate advice tailored to the user's feelings cannot be provided, and efficient business startup preparation is difficult. The present invention aims to solve this problem, analyze the user's emotions, and provide more personalized support.

[0285] The specific processing by the specific processing unit 290 of the data processing device 12 in Example 2 is realized by the following respective means.

[0286] In this invention, the server includes means for acquiring the business content input by the user as text data, means for transmitting the acquired text data to the server, means for the server to analyze the text data and extract keywords, means for searching for and acquiring relevant information based on the extracted keywords, means for organizing the acquired information and transmitting it to the terminal, means for the terminal to display the organized information to the user, means for analyzing the user's emotions using an emotion analysis engine, and means for adjusting the search results and proposed content based on the emotion analysis results. Thereby, it becomes possible to provide necessary information and advice while considering the user's emotions.

[0287] A "user" is an individual or a corporation that inputs business content using the system and receives necessary information and advice.

[0288] A "terminal" is a device that the user inputs with, and includes devices such as computers, smartphones, tablets, etc.

[0289] A "server" is a central processing unit that receives, analyzes, and provides text data transmitted from the user.

[0290] "Text data" is the character information of the business content input by the user via the terminal.

[0291] "Natural language processing technology" is a technology for a computer to understand and analyze human language, and includes text tokenization, keyword extraction, grammar analysis, etc.

[0292] A "keyword" is an important word or phrase extracted from text data and is used for searching and analyzing relevant information.

[0293] An "emotion analysis engine" is software or an algorithm for analyzing the emotional state based on the user's input text.

[0294] "Organized information" refers to related information that has been categorized by the server and is provided to the user in an easy-to-understand format.

[0295] "Required skills" refer to the knowledge and abilities that users must acquire in order to succeed in starting a business.

[0296] "Preparation items" refer to the tasks and arrangements that users need to make before starting their entrepreneurial activities.

[0297] "Related information" refers to information that the server searches and retrieves based on extracted keywords and sentiment analysis results, and that is useful for the user's business venture.

[0298] "Means for adjusting the proposed content" refers to a function that changes or modifies the content of advice and information provided to the user based on the sentiment analysis results.

[0299] This invention combines a system that automatically suggests necessary skills and preparations based on the user's input of the business they wish to start, with an emotion analysis engine that recognizes the user's emotions. This system consists of four main components: the user, the terminal, the server, and the emotion analysis engine.

[0300] The user enters details of their business through a device. This device can be a common device such as a computer, smartphone, or tablet. The information entered by the user is stored as text data on the device and sent to the server via an HTTP POST request.

[0301] The server analyzes the received text data and extracts keywords using natural language processing techniques. Keyword extraction is performed using libraries such as Python's NLTK and spaCy. Next, the server uses a sentiment analysis engine to analyze the user's emotions from the input text data. Sentiment analysis utilizes technologies such as IBM Watson® Natural Language Understanding and Google Cloud Natural Language API.

[0302] Based on the analysis results, the server searches for and retrieves relevant information. This search utilizes ElasticSearch® or external APIs. For example, if you want to start a sustainable fashion brand, you can provide information on fashion design and environmentally friendly materials.

[0303] The server organizes the acquired information into categories such as "required skills" and "preparation items," and adjusts the suggestions based on the sentiment analysis results. This process provides flexible feedback that is tailored to the user's current emotions. The organized information is then sent back to the terminal via an HTTP POST request.

[0304] The terminal receives organized information sent from the server and displays it in the user interface. Based on the provided information, the user can develop a concrete plan, acquire the necessary skills, and carry out the necessary preparations.

[0305] Examples of specific prompt messages include the following:

[0306] "Please specify what kind of business you want to start. For example, you could write something like, 'I want to start an online programming school.'"

[0307] With this system, users can easily obtain the necessary information and further efficiently prepare for starting a business by receiving emotion-based feedback. The above is the specific form for implementing the invention.

[0308] The flow of the specific process in Example 2 will be described using FIG. 13.

[0309] The processing flow of the program of this system

[0310] Step 1:

[0311] The user inputs the content they want to start a business with.

[0312] [[ID=ID=19]] The user specifically inputs the content they want to start a business with into the input field of the terminal. As an example of the input data, there is "want to start an online programming school that can be attended". The input content is stored in the terminal as text data.

[0313] (Input) Text data of the business start-up content input by the user.

[0314] (Output) Text data stored in the terminal.

[0315] Step 2:

[0316] The terminal sends the text data to the server.

[0317] The terminal collects the user's input data and sends it to the server using an HTTP POST request. For the specific operation, the JavaScript fetch API is often used.

[0318] (Input) Text data stored in the terminal.

[0319] (Output) Text data sent by the HTTP POST request.

[0320] Step

[0321] The server parses the text data.

[0322] The server analyzes the received text data using natural language processing libraries (such as Python's NLTK or spaCy). The text is tokenized, and grammatical analysis is performed.

[0323] (Input) Text data sent in an HTTP POST request.

[0324] (Output) Analyzed tokenized data.

[0325] Step 4:

[0326] The server extracts the keywords.

[0327] The server extracts key keywords from the text data analysis results. Specifically, it extracts nouns and verbs and picks out the most important words.

[0328] (Input) Analyzed tokenized data.

[0329] (Output) Extracted main keywords.

[0330] Step 5:

[0331] The server analyzes emotions using an emotion analysis engine.

[0332] The server sends tokenized text data to an emotion analysis engine (for example, IBM Watson Natural Language Understanding) to analyze the user's emotions.

[0333] (Input) Analyzed tokenized data.

[0334] (Output) Analyzed user sentiment data.

[0335] Step 6:

[0336] The server searches for and retrieves relevant information.

[0337] The server searches for and retrieves relevant information from internal databases and external APIs based on extracted keywords and sentiment analysis results. Elasticsearch and other APIs are frequently used.

[0338] (Input) Extracted key keywords and user sentiment data.

[0339] (Output) Related information obtained.

[0340] Step 7:

[0341] The server organizes and adjusts the information.

[0342] The server organizes the acquired information into categories such as "required skills" and "preparation items." Furthermore, it adjusts the proposed content based on the results of sentiment analysis.

[0343] (Input) Related information obtained.

[0344] (Output) Organized and adjusted information.

[0345] Step 8:

[0346] The server sends the organized information to the terminal.

[0347] The server sends organized information to the terminal using an HTTP POST request. The data is often sent in JSON format.

[0348] (Input) Organized and adjusted information.

[0349] (Output) Organizational information sent via HTTP POST request.

[0350] Step 9:

[0351] The device displays information to the user.

[0352] The terminal displays information received from the server in the user interface. Specifically, it uses HTML and JavaScript to insert data into the DOM.

[0353] (Input) Organizational information sent in an HTTP POST request.

[0354] (Output) Information displayed to the user.

[0355] (Application Example 2)

[0356] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0357] In autonomous vehicles, a challenge is to respond to the driver's emotions and circumstances, such as fatigue and anxiety, while driving, and to provide safe and personalized driving assistance. In particular, in special driving environments such as long-distance driving or adverse weather conditions, it is important to recognize the driver's emotions and circumstances in real time and provide appropriate advice and information.

[0358] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring the driving situation and feelings entered by the user as text data, means for transmitting the acquired text data and real-time data to the server, means for the server to analyze the text data and real-time data and extract keywords and emotions, means for searching for and acquiring related information based on the extracted keywords and emotions, means for organizing the acquired information and transmitting it to the terminal, and means for the terminal to display the organized information to the driver. This makes it possible to provide appropriate advice and information in real time according to the driver's emotions and situation.

[0359] "User" refers to an individual or legal entity that uses the system.

[0360] "Driving status" refers to information that represents the vehicle's current driving condition and environmental conditions.

[0361] "Feelings" refers to the emotions and moods that the user is experiencing.

[0362] "Text data" refers to the character information entered by the user.

[0363] "Real-time data" refers to instantaneous data acquired from vehicle sensors and cameras.

[0364] A "server" refers to a computing device that processes, analyzes, searches, and organizes incoming data.

[0365] "Means of acquisition" refers to the methods and technologies used to collect and capture data.

[0366] "Means of transmission" refers to methods and technologies for sending collected data to other devices.

[0367] "Means of analysis" refers to methods and techniques for analyzing input data and extracting useful information.

[0368] "Keywords" refer to important words or phrases extracted from text data.

[0369] "Emotions" refers to information that indicates the user's mood or psychological state.

[0370] "Means of searching" refers to methods and techniques for finding relevant information.

[0371] "Means of organization" refers to methods and techniques for categorizing and organizing acquired information.

[0372] A "terminal" refers to a device used by a user to input information and receive results.

[0373] "Means of display" refers to methods and technologies for visually conveying analysis results and information to users.

[0374] System Overview

[0375] This system is a driver assistance system that provides appropriate advice and information based on the driving conditions and feelings entered by the driver. The system mainly consists of the following elements:

[0376] 1. User: The driver that uses the system.

[0377] 2. Device: Smartphone or head-mounted display.

[0378] 3. Server: A computing device that processes, analyzes, and organizes data to obtain necessary information.

[0379] 4. Real-time data: Immediate data obtained from vehicle sensors and cameras.

[0380] Hardware and software to use

[0381] Hardware: Smartphones, head-mounted displays, in-vehicle cameras, GPS, speed sensors, etc.

[0382] Software: Natural language processing libraries (e.g., Google NLP API), sentiment analysis engines (e.g., IBM Watson), databases (e.g., MySQL®, PostgreSQL), cloud services (e.g., AWS®, Google Cloud).

[0383] Specific implementations of the system

[0384] 1. Data Acquisition

[0385] Users input their feelings and experiences while driving into their smartphones or head-mounted displays. This data includes, for example, text such as "I'm worried about fatigue during long-distance driving," or data converted from voice to text.

[0386] In addition, it acquires real-time data from in-vehicle cameras and various sensors.

[0387] 2. Data transmission

[0388] The terminal sends the acquired text data and real-time data to the server using an HTTP POST request.

[0389] 3. Data Analysis

[0390] The server tokenizes the received data using a natural language processing library and recognizes the user's emotions using an emotion analysis engine.

[0391] In addition, real-time data analysis is performed in parallel to evaluate the operating conditions.

[0392] 4. Information retrieval and acquisition

[0393] Based on the analysis results, the server uses internal databases and external APIs to search for and retrieve relevant information (such as rest stops and driving advice).

[0394] 5. Organizing and transmitting information

[0395] Organize the acquired information by categorizing it (e.g., driving advice, rest stops).

[0396] Based on the sentiment analysis results, personalized suggestions are sent to the device.

[0397] 6. Display of Information

[0398] The terminal displays received information to the driver, providing advice and warnings while driving. This allows the driver to receive appropriate support in real time, enabling safer driving.

[0399] Specific example

[0400] For example, let's consider the case where a driver enters "I'm concerned about fatigue during long-distance driving."

[0401] 1. The driver voice-inputs into their smartphone that they are concerned about fatigue during long-distance driving.

[0402] 2. The smartphone sends the data as text to the cloud server.

[0403] 3. The server uses natural language processing and an emotion engine to analyze keywords such as "fatigue" and "long-distance driving," as well as emotions (feelings of fatigue).

[0404] 4. The server then searches for nearby rest areas and driving safety tips based on this information.

[0405] 5. The results are summarized, and the smartphone displays "We recommend taking a break at the next service area."

[0406] Example of a prompt

[0407] "Please describe your current driving situation and feelings. Please be as specific as possible. Examples: 'I'm worried about fatigue during long-distance driving,' 'I'm anxious about driving in the rain,' 'I feel sleepy when driving at night.'"

[0408] This allows drivers to receive appropriate instructions in real time, enabling them to continue driving with peace of mind.

[0409] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0410] Step 1:

[0411] The user inputs their driving situation and feelings. For example, the user might voice-input "I'm worried about fatigue during long-distance driving" into their smartphone. This input is then converted into text data.

[0412] Step 2:

[0413] The terminal sends acquired text data and real-time data to the server via an HTTP POST request. The entered text data and real-time data from vehicle sensors (speed, location information, etc.) are sent to the server.

[0414] Step 3:

[0415] The server tokenizes the received text data using a natural language processing library (e.g., Google NLP API) and extracts important keywords. The input data is analyzed, and keywords such as "fatigue" and "long-distance driving" are extracted.

[0416] Step 4:

[0417] The server passes extracted keywords and real-time data to an emotion analysis engine (e.g., IBM Watson) to analyze the user's emotions. For example, "fatigue" might be recognized. The input is keywords and real-time data, and the output is information about the user's emotions.

[0418] Step 5:

[0419] The server searches for relevant information using internal databases and external APIs based on extracted keywords and sentiment information. For example, nearby rest stops and driving tips might be retrieved from the database.

[0420] Step 6:

[0421] The server organizes the information it acquires into categories such as "driving advice" and "rest stops," and creates suggestions based on emotional information. The input is raw data from search results, and the output is categorized suggestion information.

[0422] Step 7:

[0423] The server sends organized information to the terminal. For example, advice such as "We recommend taking a break at the next service area" is sent to the terminal.

[0424] Step 8:

[0425] The device displays the received information to the user. The user sees a message on their smartphone saying, "We recommend taking a break at the next service area." This display allows the user to receive specific advice in real time to continue driving safely.

[0426] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0427] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0428] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0429] [Second Embodiment]

[0430] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0431] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0432] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0433] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0434] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0435] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0436] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0437] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0438] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0439] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0440] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0441] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0442] This invention is a system that allows users to input what they want to start as a business, and then automatically suggests the necessary skills and preparations based on that input. A specific embodiment of this system is described below.

[0443] System Overview

[0444] This system consists of three main components: the user, the terminal, and the server. The user inputs their business details through the terminal and sends the data from the terminal to the server. The server analyzes the transmitted data, searches for and retrieves relevant information, and sends it back to the terminal. The terminal displays the received information to the user.

[0445] Processing of the entire program

[0446] The user enters

[0447] The user enters specific details about their business into the input field on their device. For example, they might enter, "I want to start an online programming school." This input is stored on the device as text data.

[0448] Sending text data to the server

[0449] The terminal sends the text data entered by the user to the server. Specifically, it sends the data to the server using an HTTP POST request. This request contains the text of the entered business details.

[0450] Server-based analysis and keyword extraction

[0451] The server analyzes the received text data and extracts keywords from its content. Using natural language processing technology, it tokenizes the text and picks out important keywords. For example, keywords such as "online," "course," and "programming school" might be extracted.

[0452] Searching for and retrieving related information

[0453] The server uses internal databases and external APIs to search for relevant information based on the extracted keywords. Specifically, it retrieves technical documents, marketing materials, and relevant best practices related to the keywords.

[0454] Organizing and sending information

[0455] The server organizes the retrieved information into categories such as "Required Skills" and "Preparation Items." For example, it might be formatted as "Required Skills: Web development, course material creation" and "Preparation Items: Online platform selection, course material creation." The organized information is then sent to the terminal.

[0456] Displaying information on the device

[0457] The terminal receives organized information sent from the server and displays it to the user. Based on the displayed information, the user can acquire the necessary skills and perform the required preparations.

[0458] Specific example

[0459] For example, let's consider the case where a user enters "I want to start a sustainable fashion brand." In this case, the following process takes place.

[0460] 1. The user enters the details of the business they want to start.

[0461] 2. The terminal receives this input as text data and sends it to the server.

[0462] 3. The server extracts keywords such as "sustainable," "fashion brand," and "entrepreneurship."

[0463] 4. The server searches for information related to materials, fashion design, and brand building based on these keywords.

[0464] 5. Organize the information acquired by the server into "Required Skills" and "Preparation Items."

[0465] 6. The device displays organized information to the user.

[0466] This system allows users to easily obtain the necessary information and efficiently proceed with their startup preparations. The above describes the specific form of implementing this system invention.

[0467] The following describes the processing flow.

[0468] Step 1:

[0469] The user enters details of their business in the input field on their device. For example, they might enter, "I want to start an online programming school."

[0470] Step 2:

[0471] The terminal acquires the user's input as text data. This text data is temporarily stored in memory for subsequent processing.

[0472] Step 3:

[0473] The device sends the acquired text data to the server. Typically, an HTTP POST request is used, sending the text data as the payload.

[0474] Step 4:

[0475] The server receives an HTTP request. Text data is extracted from the request payload and prepared for analysis.

[0476] Step 5:

[0477] The server analyzes the text data using natural language processing technology. Specifically, it tokenizes the text and performs morphological analysis to extract important keywords. For example, keywords such as "online," "course," and "programming school" are extracted.

[0478] Step 6:

[0479] The server uses extracted keywords to search for relevant information from internal databases and external APIs. Database queries and API requests are executed to retrieve the relevant information.

[0480] Step 7:

[0481] The information acquired by the server is categorized into "Required Skills" and "Preparation Items." For example, it might be organized in the format of "Required Skills: Web development, course material creation" and "Preparation Items: Online platform selection, course material creation."

[0482] Step 8:

[0483] The server sends organized information to the terminal as an HTTP response. The data included in this response is formatted in a clear and easy-to-read format for the user.

[0484] Step 9:

[0485] The terminal receives an HTTP response from the server. It parses the response data and prepares it for display on the screen.

[0486] Step 10:

[0487] The device displays organized information to the user. The user can then review the displayed "required skills" and "preparations" and proceed with their startup preparations.

[0488] (Example 1)

[0489] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0490] Conventional startup support systems have difficulty suggesting appropriate skills and preparations based on the user's input of specific business details. Furthermore, the information is often poorly organized and displayed, making it time-consuming for users to find the information they actually need. Therefore, this invention aims to automatically suggest necessary skills and preparations based on the user's input of business details, and to efficiently organize and display the information.

[0491] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0492] In this invention, the server includes means for extracting keywords from text data using natural language processing technology, means for searching for related information using an internal database or external API based on the extracted keywords, and means for organizing the acquired related information into categories of "necessary skills" and "preparation items." This enables users to efficiently acquire and implement the information necessary for starting a business.

[0493] A "user" is an individual or corporation who uses this system to input details about their business and wants to know what skills and preparations are necessary.

[0494] "Text data" refers to the written information of the business startup entered by the user, which is then analyzed by the system.

[0495] A "device" refers to a device used by a user to input details about their business, and includes personal computers, smartphones, tablets, and other similar devices.

[0496] A "server" is a central processing unit that receives text data sent from a terminal, analyzes it, searches for, retrieves, and organizes the necessary information, and then sends it back to the terminal.

[0497] "Natural language processing technology" refers to techniques for analyzing text data and extracting keywords from its content, and includes methods such as morphological analysis and tokenization.

[0498] "Keywords" are important words and phrases extracted from the text data of the business details entered by the user.

[0499] An "internal database" is a data storage system used to store information managed by a server.

[0500] An "external API" is an application programming interface that a server uses to retrieve information from other services.

[0501] "Related information" refers to information such as skills and preparations necessary for starting a business, technical documents, and marketing materials, which are searched and retrieved based on keywords.

[0502] A "category" is a classification used to organize acquired related information, and includes "required skills" and "preparation items."

[0503] "Skills" refer to the abilities and knowledge required for a user to start a business.

[0504] "Preparation items" refer to the procedures and tasks necessary to realize the startup.

[0505] This invention is a system that allows users to input what they want to start as a business, and then automatically suggests the necessary skills and preparations based on that input. This system consists of three main components: the user, the terminal, and the server.

[0506] The user enters details of their business.

[0507] The user enters details of their business idea into the input field on the device. For example, they might enter, "I want to start an online programming school." This input is stored on the device as text data.

[0508] The device sends text data to the server.

[0509] The terminal uses an HTTP POST request to send the text data entered by the user to the server. This request contains the text data entered by the user.

[0510] The server analyzes the text data and extracts keywords.

[0511] The server analyzes the received text data and extracts important keywords using natural language processing techniques. This technique utilizes libraries for morphological analysis and tokenization (e.g., SpaCy). The extracted keywords are then used by the system to search for relevant information.

[0512] The server searches for and retrieves relevant information.

[0513] The server searches and retrieves relevant information using internal databases and external APIs based on the extracted keywords. External APIs may include the Google Books API and other information provision services. This allows for the collection of specific information that the user needs (e.g., technical documentation, marketing materials).

[0514] The server organizes the information and sends it to the terminal.

[0515] The server organizes the acquired information into categories such as "required skills" and "preparation items." This organized information is then sent to the terminal and displayed to the user. This allows the user to efficiently grasp applicable skills and the preparation items they need to implement.

[0516] Specific example

[0517] For example, let's consider the case where a user enters "I want to start a sustainable fashion brand." In this case, the following process takes place.

[0518] 1. The user enters details of their business.

[0519] 2. The terminal receives this input as text data and sends it to the server.

[0520] 3. The server extracts keywords such as "sustainable," "fashion brand," and "entrepreneurship."

[0521] 4. The server searches for information related to materials, fashion design, and brand building based on these keywords.

[0522] 5. Organize the information acquired by the server into "Required Skills" and "Preparation Items."

[0523] 6. The device displays organized information to the user.

[0524] Examples of prompts to input into a generative AI model

[0525] Examples of prompt statements are shown below, and a system is created based on these to provide information that meets the user's needs:

[0526] Design a system that, upon receiving the input "I want to start a sustainable fashion brand," will suggest the necessary skills and preparations. This system should encompass a series of processes, from the user inputting their business plan to server-side data analysis, retrieval of relevant information, and organization and display of that information. Clearly specify the technologies and APIs to be used.

[0527] This system allows users to efficiently acquire the information necessary for preparing to start a business and put it into practice.

[0528] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0529] Step 1: The user enters details of their business.

[0530] The user enters details of their business into an input field on the terminal. This input data is sent to the terminal in text format. Specifically, the user might enter a sentence such as "I want to start a sustainable fashion brand." The input data is in text format, and the terminal temporarily stores this text data.

[0531] Input: "I want to start a sustainable fashion brand."

[0532] Output: Text data

[0533] Step 2: The device sends text data to the server.

[0534] The device uses an HTTP POST request to send the text data entered by the user to the server. Specifically, the device sends the text data to the appropriate API endpoint. The server then receives the text data of the business details entered by the user.

[0535] Input: Text data entered by the user

[0536] Output: HTTP POST request to the server

[0537] Step 3: The server receives the text data and performs analysis.

[0538] The server receives text data sent from the terminal. After receiving the data, it analyzes it using natural language processing techniques to extract key keywords. Specifically, it uses a natural language processing library (e.g., SpaCy) to tokenize the text and identify important keywords. Through this process, keywords such as "sustainable," "fashion brand," and "startup" are extracted from the text "I want to start a sustainable fashion brand."

[0539] Input: Text data sent to the server

[0540] Output: Extracted keyword list

[0541] Step 4: The server searches for and retrieves relevant information.

[0542] The server uses internal databases and external APIs to search for and retrieve relevant information based on the extracted keywords. Specifically, the server calls the Google Books API and other information-providing services to collect the corresponding information. This retrieves materials and documents related to the keywords "sustainable," "fashion brands," and "entrepreneurship."

[0543] Input: Extracted keyword list

[0544] Output: List of related information

[0545] Step 5: The server organizes the information and sends it to the terminal.

[0546] The server organizes the acquired relevant information into categories such as "Required Skills" and "Preparation Items." Specifically, the server classifies the information appropriately and sends it to the terminal as data in JSON format. For example, the information might be organized in the format of "Required Skills: Web Development, Fashion Design" and "Preparation Items: Brand Building, Marketing Plan."

[0547] Input: List of related information

[0548] Output: Organized information (JSON format)

[0549] Step 6: The device displays organized information to the user.

[0550] The terminal receives organized information sent from the server and displays it to the user. Specifically, it uses front-end technologies (such as HTML and JavaScript) to visually present the information to the user. This allows the user to check the necessary skills and preparations and plan the next steps.

[0551] Input: Organized information (JSON format)

[0552] Output: Information displayed to the user

[0553] The above outlines the specific processing steps of this system.

[0554] (Application Example 1)

[0555] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0556] For users planning to start a business, efficiently understanding the necessary skills and preparations and quickly proceeding with those preparations based on that understanding is extremely important. However, the process of researching, collecting, and organizing relevant information from scratch is time-consuming, laborious, and inefficient. This invention aims to provide a system that allows users to easily obtain the information necessary to efficiently proceed with their business startup preparations.

[0557] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0558] In this invention, the server includes means for acquiring a business plan entered by a user as text data, means for transmitting the acquired text data to a data processing device, means for the data processing device to analyze the text data and extract keywords, means for searching for and acquiring related information based on the extracted keywords, means for organizing the acquired information and transmitting it to a display device, and means for the display device to display the organized information to the user. This enables the user to efficiently grasp the necessary technologies and preparation items and to proceed with preparations quickly.

[0559] A "user" is primarily an individual or corporation who is planning to start a business and wants to learn about the skills and preparations required for it.

[0560] A "business plan" is a document that details the steps and skills required to start a specific business.

[0561] "Text data" refers to information stored in digital format based on content entered by the user as part of their business plan.

[0562] A "data processing device" is a computer system that functions as a server, analyzes received text data, extracts necessary keywords, and searches for and retrieves related information.

[0563] A "display device" is an electronic device used to visually display information transmitted from a data processing device to a user.

[0564] "Natural language processing technology" refers to a set of technologies that enable computers to understand, analyze, and generate human language.

[0565] "Keywords" are important terms that represent the main theme of a business plan, extracted from text data.

[0566] "Related information" refers to information necessary for starting a business, such as technical documents, skill sets, and marketing materials, which are searched based on the extracted keywords.

[0567] "Required technologies" refer to specific technologies and skills that users need to acquire based on their business plan.

[0568] "Preparation items" refer to the specific tasks and procedures that users must complete in advance to start their business.

[0569] This invention is a system in which a user inputs a business plan, and based on that, the system automatically suggests the necessary technologies and preparation items. This system consists of four main components: the user, a terminal, a data processing device (server), and a display device. Specific embodiments of this system are described below.

[0570] System Overview

[0571] The user enters their business plan into a terminal and sends the data from the terminal to a data processing unit. The data processing unit analyzes the transmitted data, searches for and retrieves relevant information, and sends it back to the terminal. The terminal displays the received information to the user. The user then acquires the necessary skills based on the displayed information and starts the business by executing the necessary preparation items.

[0572] Hardware and software to be used

[0573] Device: Smartphone, tablet, or PC

[0574] Data processing unit (server): High-performance server, specifically a server built with programming languages ​​such as Python, or a web framework such as Flask.

[0575] Natural language processing technologies: NLTK library, etc.

[0576] Display device: Smartphone or tablet screen, or head-mounted display

[0577] Explanation of data processing

[0578] Retrieving and sending text data:

[0579] The user enters a specific business plan into the terminal's input field. For example, they might enter, "I want to open a virtual art gallery using VR." This input is stored as text data on the terminal. The terminal then sends the text data entered by the user to a data processing device.

[0580] Text data analysis and keyword extraction:

[0581] The data processing device analyzes the received text data and extracts keywords from its content. Natural language processing techniques are used for specific keyword extraction. For example, the NLTK library is used to tokenize the text and pick out important keywords. In the example above, keywords such as "VR," "virtual," "art gallery," and "opening a business" are extracted.

[0582] Searching for and retrieving related information:

[0583] The data processing device uses internal databases and external APIs to search for relevant information based on the extracted keywords. Specifically, it retrieves technical documents, marketing materials, and relevant best practices related to the keywords. For example, information on "VR technology," "art curation," and "gallery design" might be searched.

[0584] Organizing and sending information:

[0585] The data processing device organizes the retrieved information into categories such as "required technologies" and "preparation items." For example, it might be formatted as "required technologies: 3D modeling, art curation" and "preparation items: artist recruitment, virtual gallery design."

[0586] Displaying information on the device:

[0587] The terminal receives organized information sent from the server and displays it to the user. Based on the displayed information, the user can efficiently acquire skills and perform preparatory tasks.

[0588] Examples of specific cases and prompt statements

[0589] For example, let's consider a case where a user enters "I want to start a programming school that offers online courses."

[0590] Extracted keywords: "online," "course," "programming school"

[0591] Related information:

[0592] Required skills: Web development, course material creation, online platform setup.

[0593] Preparation items: Curriculum design, platform selection, marketing strategy development.

[0594] Examples of prompts to input into a generative AI model:

[0595] "Based on a business plan to 'start an online programming school,' please propose the necessary technologies and preparations."

[0596] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0597] Step 1:

[0598] The user enters the business plan.

[0599] The user enters a specific business plan into the input field on the device. For example, they might enter, "I want to open a virtual art gallery using VR." This input is stored on the device as text data.

[0600] Input: Text data "I want to open a virtual art gallery using VR"

[0601] Output: Text data is stored on the terminal.

[0602] Step 2:

[0603] Send text data to a data processing device.

[0604] The terminal sends the text data entered by the user to the data processing unit. This process uses an HTTP POST request.

[0605] Input: Text data entered by the user

[0606] Output: Text data is sent to the data processing unit.

[0607] Step 3:

[0608] Text data analysis and keyword extraction

[0609] The data processing device analyzes the received text data and extracts keywords from its content. Specifically, it uses the NLTK library to tokenize the text and pick out important keywords.

[0610] Input: Submitted text data

[0611] Data processing: Tokenization and keyword extraction using the NLTK library.

[0612] Output: Extracted keywords (e.g., "VR", "virtual", "art gallery", "business opening")

[0613] Step 4:

[0614] Searching for and retrieving related information

[0615] The data processing unit uses internal databases and external APIs to search for relevant information based on the extracted keywords. For example, it retrieves technical documents, marketing materials, and relevant best practices related to the keywords.

[0616] Input: Extracted keywords

[0617] Data processing: Internal database searches and use of external APIs.

[0618] Output: Related information (e.g., "VR technology," "art curation," "gallery design")

[0619] Step 5:

[0620] Organizing and sending information

[0621] The data processing unit organizes the retrieved information into categories such as "required technologies" and "preparation items." For example, it might be formatted as "required technologies: 3D modeling, art curation" and "preparation items: artist recruitment, virtual gallery design." The organized information is then sent to the terminal.

[0622] Input: Retrieved related information

[0623] Data processing: Categorization of information ("Required skills," "Preparation items")

[0624] Output: The organized information is sent to the terminal.

[0625] Step 6:

[0626] Displaying information on the device

[0627] The terminal receives organized information sent from the server and displays it to the user. Based on the displayed information, the user can efficiently acquire skills and perform preparatory tasks.

[0628] Input: Organized information

[0629] Output: Information displayed to the user (e.g., "Required skills: 3D modeling, art curation", "Preparation items: Artist recruitment, virtual gallery design")

[0630] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0631] This invention combines a system that automatically suggests necessary skills and preparations based on the user's input of their desired business venture with an emotion engine that recognizes the user's emotions. Specific embodiments of this system are described below.

[0632] System Overview

[0633] This system consists of four main components: the user, the terminal, the server, and the emotion engine. The user inputs their business details through the terminal, and the terminal sends the data to the server. The server analyzes the transmitted data, searches for and retrieves relevant information, and sends it back to the terminal. Furthermore, the emotion engine analyzes the user's emotions from the text data and adjusts the suggestions to provide even more personalized feedback.

[0634] Program processing

[0635] The user enters

[0636] The user enters specific details about their business into the input field on their device. For example, they might enter, "I want to start an online programming school." This input is stored on the device as text data.

[0637] Sending text data to the server

[0638] The terminal sends the text data entered by the user to the server. Specifically, it sends the data to the server using an HTTP POST request. This request contains the text of the entered business details.

[0639] Server-based analysis and keyword extraction

[0640] The server analyzes the received text data and extracts keywords from its content. Using natural language processing technology, it tokenizes the text and picks out important keywords. For example, keywords such as "online," "course," and "programming school" might be extracted.

[0641] Emotional analysis using an emotion engine

[0642] The server passes the text data to the emotion engine, which analyzes the user's emotions. The emotion engine uses the text data and the user's past input history to recognize emotions. For example, emotions such as "excited" or "anxious" may be identified.

[0643] Searching for and retrieving related information

[0644] The server searches for relevant information using internal databases and external APIs based on extracted keywords and the analysis results of the sentiment engine. It executes database queries and API requests to retrieve relevant information.

[0645] Organizing and sending information

[0646] The server organizes the retrieved information into categories such as "Required Skills" and "Preparation Items," and further adjusts the suggestions based on the analysis results of the emotion engine. For example, it might be formatted as "Required Skills: Web development, course material creation" and "Preparation Items: Online platform selection, course material creation." The organized information is then sent to the terminal.

[0647] Displaying information on the device

[0648] The terminal receives organized information sent from the server and displays it to the user. Based on the displayed information, the user can acquire the necessary skills and perform the required preparations. Based on the analysis results of the emotion engine, flexible responses are made that are tailored to the user's current emotions.

[0649] Specific example

[0650] For example, let's consider the case where a user enters "I want to start a sustainable fashion brand." In this case, the following process takes place.

[0651] 1. The user enters the details of the business they want to start.

[0652] 2. The terminal receives this input as text data and sends it to the server.

[0653] 3. The server extracts keywords such as "sustainable," "fashion brand," and "entrepreneurship."

[0654] 4. The server passes text data to the emotion engine, which analyzes the user's emotions. Emotions such as "excited" are recognized.

[0655] 5. The server searches for information related to relevant materials, fashion design, and brand building based on these keywords and sentiment information.

[0656] 6. Organize the information acquired by the server into "Required Skills" and "Preparation Items," and make emotionally-based suggestions.

[0657] 7. The device displays organized information to the user.

[0658] This system allows users to easily obtain necessary information and efficiently prepare for starting a business by receiving emotion-based feedback. The above is a specific form of implementing this system invention that combines an emotion engine.

[0659] The following describes the processing flow.

[0660] Step 1:

[0661] The user enters specific details of their business into the input field on their device. For example, they might enter, "I want to start an online programming school."

[0662] Step 2:

[0663] The terminal retrieves the user's input as text data. This text data is stored in a variable for subsequent processing.

[0664] Step 3:

[0665] The terminal sends the acquired text data to the server using an HTTP POST request. The request payload contains the text of the business details entered by the user.

[0666] Step 4:

[0667] The server receives an HTTP request and extracts text data from the payload. The extracted text data is then prepared for analysis.

[0668] Step 5:

[0669] The server uses natural language processing technology to analyze text data, performing tokenization and morphological analysis. This extracts important keywords such as "online," "course," and "programming school."

[0670] Step 6:

[0671] The server extracts keywords and passes them to the emotion engine to analyze the user's emotions. Emotions are identified using text data and the user's past input history. For example, emotions such as "excited" or "anxious" may be recognized.

[0672] Step 7:

[0673] The server uses internal databases and external APIs to search for relevant information based on keyword and sentiment engine analysis results. It executes database queries and API requests to retrieve materials, teaching materials, and marketing strategies related to establishing a programming school.

[0674] Step 8:

[0675] The server categorizes the relevant information it acquires into "Required Skills" and "Preparation Items." For example, it might be organized in the format of "Required Skills: Web development, course material creation" and "Preparation Items: Online platform selection, course material creation." Furthermore, the recommendations are adjusted based on the analysis results of the emotion engine. For example, users who are feeling anxious will be provided with more detailed explanations and additional support resources.

[0676] Step 9:

[0677] The server sends organized information to the terminal as an HTTP response. This response contains information presented to the user in a clear and easy-to-read format.

[0678] Step 10:

[0679] The terminal receives an HTTP response from the server and parses the response data. It then prepares to display the results of the analysis on the screen.

[0680] Step 11:

[0681] The device displays organized information to the user. For example, it might show lists such as "Required skills for starting a business: Web development, course material creation" and "Items to prepare: Online platform selection, course material creation." It also displays emotion-based advice and additional information.

[0682] This series of processes allows users to efficiently obtain the information they need and receive emotionally tailored support.

[0683] (Example 2)

[0684] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0685] Conventional startup support systems only provide information based on the startup details entered by the user, and are unable to provide feedback that takes into account the user's emotional state. As a result, they cannot provide appropriate advice tailored to the user's feelings, making efficient startup preparation difficult. This invention aims to solve this problem by analyzing the user's emotions and providing more personalized support.

[0686] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0687] In this invention, the server includes means for acquiring the business details entered by the user as text data, means for transmitting the acquired text data to the server, means for the server to analyze the text data and extract keywords, means for searching for and acquiring related information based on the extracted keywords, means for organizing the acquired information and transmitting it to the terminal, means for the terminal to display the organized information to the user, means for analyzing the user's emotions using an emotion analysis engine, and means for adjusting the search results and suggested content based on the emotion analysis results. This makes it possible to provide necessary information and advice while taking the user's emotions into consideration.

[0688] A "user" is an individual or legal entity that uses the system to input details about their business and receive necessary information and advice.

[0689] A "terminal" is a device used by a user to input data, and includes devices such as computers, smartphones, and tablets.

[0690] A "server" is a central processing unit that receives text data sent by users, analyzes it, and provides information.

[0691] "Text data" refers to the written information of the business that the user enters via their device.

[0692] "Natural language processing technology" refers to techniques that enable computers to understand and analyze human language, and includes text tokenization, keyword extraction, and grammatical analysis.

[0693] "Keywords" are important words or phrases extracted from text data and are used for searching and analyzing related information.

[0694] An "emotion analysis engine" is software or an algorithm that analyzes a user's emotional state based on the text they input.

[0695] "Organized information" refers to related information that has been categorized by the server and is provided to the user in an easy-to-understand format.

[0696] "Required skills" refer to the knowledge and abilities that users must acquire in order to succeed in starting a business.

[0697] "Preparation items" refer to the tasks and arrangements that users need to make before starting their entrepreneurial activities.

[0698] "Related information" refers to information that the server searches and retrieves based on extracted keywords and sentiment analysis results, and that is useful for the user's business venture.

[0699] "Means for adjusting the proposed content" refers to a function that changes or modifies the content of advice and information provided to the user based on the sentiment analysis results.

[0700] This invention combines a system that automatically suggests necessary skills and preparations based on the user's input of the business they wish to start, with an emotion analysis engine that recognizes the user's emotions. This system consists of four main components: the user, the terminal, the server, and the emotion analysis engine.

[0701] The user enters details of their business through a device. This device can be a common device such as a computer, smartphone, or tablet. The information entered by the user is stored as text data on the device and sent to the server via an HTTP POST request.

[0702] The server analyzes the received text data and extracts keywords using natural language processing techniques. Keyword extraction is performed using libraries such as Python's NLTK and spaCy. Next, the server uses a sentiment analysis engine to analyze the user's emotions from the input text data. Sentiment analysis utilizes technologies such as IBM Watson Natural Language Understanding and Google Cloud Natural Language API.

[0703] Based on the analysis results, the server searches for and retrieves relevant information. This search utilizes Elasticsearch and external APIs. For example, if you want to start a sustainable fashion brand, you can provide information on fashion design and environmentally friendly materials.

[0704] The server organizes the acquired information into categories such as "required skills" and "preparation items," and adjusts the suggestions based on the sentiment analysis results. This process provides flexible feedback that is tailored to the user's current emotions. The organized information is then sent back to the terminal via an HTTP POST request.

[0705] The terminal receives organized information sent from the server and displays it in the user interface. Based on the provided information, the user can develop a concrete plan, acquire the necessary skills, and carry out the necessary preparations.

[0706] Examples of specific prompt messages include the following:

[0707] "Please specify what kind of business you want to start. For example, you could write something like, 'I want to start an online programming school.'"

[0708] This system allows users to easily obtain necessary information and receive emotion-based feedback, enabling them to efficiently prepare for starting a business. The above describes the specific form of implementing the invention.

[0709] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0710] The processing flow of this system's program

[0711] Step 1:

[0712] The user enters the details of the business they want to start.

[0713] The user enters specific details of the business they want to start into the input field on their device. An example of input data is, "I want to start an online programming school." The entered content is stored on the device as text data.

[0714] (Input) Text data of the business details entered by the user.

[0715] (Output) Text data stored on the terminal.

[0716] Step 2:

[0717] The terminal sends text data to the server.

[0718] The device collects user input data and sends it to the server using an HTTP POST request. This is often done using the JavaScript fetch API.

[0719] (Input) Text data stored on the terminal.

[0720] (Output) Text data sent in an HTTP POST request.

[0721] Step 3:

[0722] The server parses the text data.

[0723] The server analyzes the received text data using natural language processing libraries (such as Python's NLTK or spaCy). The text is tokenized, and grammatical analysis is performed.

[0724] (Input) Text data sent in an HTTP POST request.

[0725] (Output) Analyzed tokenized data.

[0726] Step 4:

[0727] The server extracts the keywords.

[0728] The server extracts key keywords from the text data analysis results. Specifically, it extracts nouns and verbs and picks out the most important words.

[0729] (Input) Analyzed tokenized data.

[0730] (Output) Extracted main keywords.

[0731] Step 5:

[0732] The server analyzes emotions using an emotion analysis engine.

[0733] The server sends tokenized text data to an emotion analysis engine (for example, IBM Watson Natural Language Understanding) to analyze the user's emotions.

[0734] (Input) Analyzed tokenized data.

[0735] (Output) Analyzed user sentiment data.

[0736] Step 6:

[0737] The server searches for and retrieves relevant information.

[0738] The server searches for and retrieves relevant information from internal databases and external APIs based on extracted keywords and sentiment analysis results. Elasticsearch and other APIs are frequently used.

[0739] (Input) Extracted key keywords and user sentiment data.

[0740] (Output) Related information obtained.

[0741] Step 7:

[0742] The server organizes and adjusts the information.

[0743] The server organizes the acquired information into categories such as "required skills" and "preparation items." Furthermore, it adjusts the proposed content based on the results of sentiment analysis.

[0744] (Input) Related information obtained.

[0745] (Output) Organized and adjusted information.

[0746] Step 8:

[0747] The server sends the organized information to the terminal.

[0748] The server sends organized information to the terminal using an HTTP POST request. The data is often sent in JSON format.

[0749] (Input) Organized and adjusted information.

[0750] (Output) Organizational information sent via HTTP POST request.

[0751] Step 9:

[0752] The device displays information to the user.

[0753] The terminal displays information received from the server in the user interface. Specifically, it uses HTML and JavaScript to insert data into the DOM.

[0754] (Input) Organizational information sent in an HTTP POST request.

[0755] (Output) Information displayed to the user.

[0756] (Application Example 2)

[0757] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0758] In autonomous vehicles, a challenge is to respond to the driver's emotions and circumstances, such as fatigue and anxiety, while driving, and to provide safe and personalized driving assistance. In particular, in special driving environments such as long-distance driving or adverse weather conditions, it is important to recognize the driver's emotions and circumstances in real time and provide appropriate advice and information.

[0759] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring the driving situation and feelings entered by the user as text data, means for transmitting the acquired text data and real-time data to the server, means for the server to analyze the text data and real-time data and extract keywords and emotions, means for searching for and acquiring related information based on the extracted keywords and emotions, means for organizing the acquired information and transmitting it to the terminal, and means for the terminal to display the organized information to the driver. This makes it possible to provide appropriate advice and information in real time according to the driver's emotions and situation.

[0760] "User" refers to an individual or legal entity that uses the system.

[0761] "Driving status" refers to information that represents the vehicle's current driving condition and environmental conditions.

[0762] "Feelings" refers to the emotions and moods that the user is experiencing.

[0763] "Text data" refers to the character information entered by the user.

[0764] "Real-time data" refers to instantaneous data acquired from vehicle sensors and cameras.

[0765] A "server" refers to a computing device that processes, analyzes, searches, and organizes incoming data.

[0766] "Means of acquisition" refers to the methods and technologies used to collect and capture data.

[0767] "Means of transmission" refers to methods and technologies for sending collected data to other devices.

[0768] "Means of analysis" refers to methods and techniques for analyzing input data and extracting useful information.

[0769] "Keywords" refer to important words or phrases extracted from text data.

[0770] "Emotions" refers to information that indicates the user's mood or psychological state.

[0771] "Means of searching" refers to methods and techniques for finding relevant information.

[0772] "Means of organization" refers to methods and techniques for categorizing and organizing acquired information.

[0773] A "terminal" refers to a device used by a user to input information and receive results.

[0774] "Means of display" refers to methods and technologies for visually conveying analysis results and information to users.

[0775] System Overview

[0776] This system is a driver assistance system that provides appropriate advice and information based on the driving conditions and feelings entered by the driver. The system mainly consists of the following elements:

[0777] 1. User: The driver that uses the system.

[0778] 2. Device: Smartphone or head-mounted display.

[0779] 3. Server: A computing device that processes, analyzes, and organizes data to obtain necessary information.

[0780] 4. Real-time data: Immediate data obtained from vehicle sensors and cameras.

[0781] Hardware and software to use

[0782] Hardware: Smartphones, head-mounted displays, in-vehicle cameras, GPS, speed sensors, etc.

[0783] Software: Natural language processing libraries (e.g., Google NLP API), sentiment analysis engines (e.g., IBM Watson), databases (e.g., MySQL, PostgreSQL), cloud services (e.g., AWS, Google Cloud).

[0784] Specific implementations of the system

[0785] 1. Data Acquisition

[0786] Users input their feelings and experiences while driving into their smartphones or head-mounted displays. This data includes text and voice recordings such as, "I'm worried about fatigue during long-distance driving."

[0787] In addition, it acquires real-time data from in-vehicle cameras and various sensors.

[0788] 2. Data transmission

[0789] The terminal sends the acquired text data and real-time data to the server using an HTTP POST request.

[0790] 3. Data Analysis

[0791] The server tokenizes the received data using a natural language processing library and recognizes the user's emotions using an emotion analysis engine.

[0792] In addition, real-time data analysis is performed in parallel to evaluate the operating conditions.

[0793] 4. Information retrieval and acquisition

[0794] Based on the analysis results, the server uses internal databases and external APIs to search for and retrieve relevant information (such as rest stops and driving advice).

[0795] 5. Organizing and transmitting information

[0796] Organize the acquired information by categorizing it (e.g., driving advice, rest stops).

[0797] Based on the sentiment analysis results, personalized suggestions are sent to the device.

[0798] 6. Display of Information

[0799] The terminal displays received information to the driver, providing advice and warnings while driving. This allows the driver to receive appropriate support in real time, enabling safer driving.

[0800] Specific example

[0801] For example, let's consider the case where a driver enters "I'm concerned about fatigue during long-distance driving."

[0802] 1. The driver voice-inputs into their smartphone that they are concerned about fatigue during long-distance driving.

[0803] 2. The smartphone sends the data as text to the cloud server.

[0804] 3. The server uses natural language processing and an emotion engine to analyze keywords such as "fatigue" and "long-distance driving," as well as emotions (feelings of fatigue).

[0805] 4. The server then searches for nearby rest areas and driving safety tips based on this information.

[0806] 5. The results are summarized, and the smartphone displays "We recommend taking a break at the next service area."

[0807] Example of a prompt

[0808] "Please describe your current driving situation and feelings. Please be as specific as possible. Examples: 'I'm worried about fatigue during long-distance driving,' 'I'm anxious about driving in the rain,' 'I feel sleepy when driving at night.'"

[0809] This allows drivers to receive appropriate instructions in real time, enabling them to continue driving with peace of mind.

[0810] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0811] Step 1:

[0812] The user inputs their driving situation and feelings. For example, the user might voice-input "I'm worried about fatigue during long-distance driving" into their smartphone. This input is then converted into text data.

[0813] Step 2:

[0814] The terminal sends acquired text data and real-time data to the server via an HTTP POST request. The entered text data and real-time data from vehicle sensors (speed, location information, etc.) are sent to the server.

[0815] Step 3:

[0816] The server tokenizes the received text data using a natural language processing library (e.g., Google NLP API) and extracts important keywords. The input data is analyzed, and keywords such as "fatigue" and "long-distance driving" are extracted.

[0817] Step 4:

[0818] The server passes extracted keywords and real-time data to an emotion analysis engine (e.g., IBM Watson) to analyze the user's emotions. For example, "fatigue" might be recognized. The input is keywords and real-time data, and the output is information about the user's emotions.

[0819] Step 5:

[0820] The server searches for relevant information using internal databases and external APIs based on extracted keywords and sentiment information. For example, nearby rest stops and driving tips might be retrieved from the database.

[0821] Step 6:

[0822] The server organizes the information it acquires into categories such as "driving advice" and "rest stops," and creates suggestions based on emotional information. The input is raw data from search results, and the output is categorized suggestion information.

[0823] Step 7:

[0824] The server sends organized information to the terminal. For example, advice such as "We recommend taking a break at the next service area" is sent to the terminal.

[0825] Step 8:

[0826] The device displays the received information to the user. The user sees a message on their smartphone saying, "We recommend taking a break at the next service area." This display allows the user to receive specific advice in real time to continue driving safely.

[0827] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0828] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0829] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0830] [Third Embodiment]

[0831] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0832] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0833] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0834] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0835] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0836] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0837] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0838] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0839] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0840] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0841] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0842] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0843] This invention is a system that allows users to input what they want to start as a business, and then automatically suggests the necessary skills and preparations based on that input. A specific embodiment of this system is described below.

[0844] System Overview

[0845] This system consists of three main components: the user, the terminal, and the server. The user inputs their business details through the terminal and sends the data from the terminal to the server. The server analyzes the transmitted data, searches for and retrieves relevant information, and sends it back to the terminal. The terminal displays the received information to the user.

[0846] Processing of the entire program

[0847] The user enters

[0848] The user enters specific details about their business into the input field on their device. For example, they might enter, "I want to start an online programming school." This input is stored on the device as text data.

[0849] Sending text data to the server

[0850] The terminal sends the text data entered by the user to the server. Specifically, it sends the data to the server using an HTTP POST request. This request contains the text of the entered business details.

[0851] Server-based analysis and keyword extraction

[0852] The server analyzes the received text data and extracts keywords from its content. Using natural language processing technology, it tokenizes the text and picks out important keywords. For example, keywords such as "online," "course," and "programming school" might be extracted.

[0853] Searching for and retrieving related information

[0854] The server uses internal databases and external APIs to search for relevant information based on the extracted keywords. Specifically, it retrieves technical documents, marketing materials, and relevant best practices related to the keywords.

[0855] Organizing and sending information

[0856] The server organizes the retrieved information into categories such as "Required Skills" and "Preparation Items." For example, it might be formatted as "Required Skills: Web development, course material creation" and "Preparation Items: Online platform selection, course material creation." The organized information is then sent to the terminal.

[0857] Displaying information on the device

[0858] The terminal receives organized information sent from the server and displays it to the user. Based on the displayed information, the user can acquire the necessary skills and perform the required preparations.

[0859] Specific example

[0860] For example, let's consider the case where a user enters "I want to start a sustainable fashion brand." In this case, the following process takes place.

[0861] 1. The user enters the details of the business they want to start.

[0862] 2. The terminal receives this input as text data and sends it to the server.

[0863] 3. The server extracts keywords such as "sustainable," "fashion brand," and "entrepreneurship."

[0864] 4. The server searches for information related to materials, fashion design, and brand building based on these keywords.

[0865] 5. Organize the information acquired by the server into "Required Skills" and "Preparation Items."

[0866] 6. The device displays organized information to the user.

[0867] This system allows users to easily obtain the necessary information and efficiently proceed with their startup preparations. The above describes the specific form of implementing this system invention.

[0868] The following describes the processing flow.

[0869] Step 1:

[0870] The user enters details of their business in the input field on their device. For example, they might enter, "I want to start an online programming school."

[0871] Step 2:

[0872] The terminal acquires the user's input as text data. This text data is temporarily stored in memory for subsequent processing.

[0873] Step 3:

[0874] The device sends the acquired text data to the server. Typically, an HTTP POST request is used, sending the text data as the payload.

[0875] Step 4:

[0876] The server receives an HTTP request. Text data is extracted from the request payload and prepared for analysis.

[0877] Step 5:

[0878] The server analyzes the text data using natural language processing technology. Specifically, it tokenizes the text and performs morphological analysis to extract important keywords. For example, keywords such as "online," "course," and "programming school" are extracted.

[0879] Step 6:

[0880] The server uses extracted keywords to search for relevant information from internal databases and external APIs. Database queries and API requests are executed to retrieve the relevant information.

[0881] Step 7:

[0882] The information acquired by the server is categorized into "Required Skills" and "Preparation Items." For example, it might be organized in the format of "Required Skills: Web development, course material creation" and "Preparation Items: Online platform selection, course material creation."

[0883] Step 8:

[0884] The server sends organized information to the terminal as an HTTP response. The data included in this response is formatted in a clear and easy-to-read format for the user.

[0885] Step 9:

[0886] The terminal receives an HTTP response from the server. It parses the response data and prepares it for display on the screen.

[0887] Step 10:

[0888] The device displays organized information to the user. The user can then review the displayed "required skills" and "preparations" and proceed with their startup preparations.

[0889] (Example 1)

[0890] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0891] Conventional startup support systems have difficulty suggesting appropriate skills and preparations based on the user's input of specific business details. Furthermore, the information is often poorly organized and displayed, making it time-consuming for users to find the information they actually need. Therefore, this invention aims to automatically suggest necessary skills and preparations based on the user's input of business details, and to efficiently organize and display the information.

[0892] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0893] In this invention, the server includes means for extracting keywords from text data using natural language processing technology, means for searching for related information using an internal database or external API based on the extracted keywords, and means for organizing the acquired related information into categories of "necessary skills" and "preparation items." This enables users to efficiently acquire and implement the information necessary for starting a business.

[0894] A "user" is an individual or corporation who uses this system to input details about their business and wants to know what skills and preparations are necessary.

[0895] "Text data" refers to the written information of the business startup entered by the user, which is then analyzed by the system.

[0896] A "device" refers to a device used by a user to input details about their business, and includes personal computers, smartphones, tablets, and other similar devices.

[0897] A "server" is a central processing unit that receives text data sent from a terminal, analyzes it, searches for, retrieves, and organizes the necessary information, and then sends it back to the terminal.

[0898] "Natural language processing technology" refers to techniques for analyzing text data and extracting keywords from its content, and includes methods such as morphological analysis and tokenization.

[0899] "Keywords" are important words and phrases extracted from the text data of the business details entered by the user.

[0900] An "internal database" is a data storage system used to store information managed by a server.

[0901] An "external API" is an application programming interface that a server uses to retrieve information from other services.

[0902] "Related information" refers to information such as skills and preparations necessary for starting a business, technical documents, and marketing materials, which are searched and retrieved based on keywords.

[0903] A "category" is a classification used to organize acquired related information, and includes "required skills" and "preparation items."

[0904] "Skills" refer to the abilities and knowledge required for a user to start a business.

[0905] "Preparation items" refer to the procedures and tasks necessary to realize the startup.

[0906] This invention is a system that allows users to input what they want to start as a business, and then automatically suggests the necessary skills and preparations based on that input. This system consists of three main components: the user, the terminal, and the server.

[0907] The user enters details of their business.

[0908] The user enters details of their business idea into the input field on the device. For example, they might enter, "I want to start an online programming school." This input is stored on the device as text data.

[0909] The device sends text data to the server.

[0910] The terminal uses an HTTP POST request to send the text data entered by the user to the server. This request contains the text data entered by the user.

[0911] The server analyzes the text data and extracts keywords.

[0912] The server analyzes the received text data and extracts important keywords using natural language processing techniques. This technique utilizes libraries for morphological analysis and tokenization (e.g., SpaCy). The extracted keywords are then used by the system to search for relevant information.

[0913] The server searches for and retrieves relevant information.

[0914] The server searches and retrieves relevant information using internal databases and external APIs based on the extracted keywords. External APIs may include the Google Books API and other information provision services. This allows for the collection of specific information that the user needs (e.g., technical documentation, marketing materials).

[0915] The server organizes the information and sends it to the terminal.

[0916] The server organizes the acquired information into categories such as "required skills" and "preparation items." This organized information is then sent to the terminal and displayed to the user. This allows the user to efficiently grasp applicable skills and the preparation items they need to implement.

[0917] Specific example

[0918] For example, let's consider the case where a user enters "I want to start a sustainable fashion brand." In this case, the following process takes place.

[0919] 1. The user enters details of their business.

[0920] 2. The terminal receives this input as text data and sends it to the server.

[0921] 3. The server extracts keywords such as "sustainable," "fashion brand," and "entrepreneurship."

[0922] 4. The server searches for information related to materials, fashion design, and brand building based on these keywords.

[0923] 5. Organize the information acquired by the server into "Required Skills" and "Preparation Items."

[0924] 6. The device displays organized information to the user.

[0925] Examples of prompts to input into a generative AI model

[0926] Examples of prompt statements are shown below, and a system is created based on these to provide information that meets the user's needs:

[0927] Design a system that, upon receiving the input "I want to start a sustainable fashion brand," will suggest the necessary skills and preparations. This system should encompass a series of processes, from the user inputting their business plan to server-side data analysis, retrieval of relevant information, and organization and display of that information. Clearly specify the technologies and APIs to be used.

[0928] This system allows users to efficiently acquire the information necessary for preparing to start a business and put it into practice.

[0929] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0930] Step 1: The user enters details of their business.

[0931] The user enters details of their business into an input field on the terminal. This input data is sent to the terminal in text format. Specifically, the user might enter a sentence such as "I want to start a sustainable fashion brand." The input data is in text format, and the terminal temporarily stores this text data.

[0932] Input: "I want to start a sustainable fashion brand."

[0933] Output: Text data

[0934] Step 2: The device sends text data to the server.

[0935] The device uses an HTTP POST request to send the text data entered by the user to the server. Specifically, the device sends the text data to the appropriate API endpoint. The server then receives the text data of the business details entered by the user.

[0936] Input: Text data entered by the user

[0937] Output: HTTP POST request to the server

[0938] Step 3: The server receives the text data and performs analysis.

[0939] The server receives text data sent from the terminal. After receiving the data, it analyzes it using natural language processing techniques to extract key keywords. Specifically, it uses a natural language processing library (e.g., SpaCy) to tokenize the text and identify important keywords. Through this process, keywords such as "sustainable," "fashion brand," and "startup" are extracted from the text "I want to start a sustainable fashion brand."

[0940] Input: Text data sent to the server

[0941] Output: Extracted keyword list

[0942] Step 4: The server searches for and retrieves relevant information.

[0943] The server uses internal databases and external APIs to search for and retrieve relevant information based on the extracted keywords. Specifically, the server calls the Google Books API and other information-providing services to collect the corresponding information. This retrieves materials and documents related to the keywords "sustainable," "fashion brands," and "entrepreneurship."

[0944] Input: Extracted keyword list

[0945] Output: List of related information

[0946] Step 5: The server organizes the information and sends it to the terminal.

[0947] The server organizes the acquired relevant information into categories such as "Required Skills" and "Preparation Items." Specifically, the server classifies the information appropriately and sends it to the terminal as data in JSON format. For example, the information might be organized in the format of "Required Skills: Web Development, Fashion Design" and "Preparation Items: Brand Building, Marketing Plan."

[0948] Input: List of related information

[0949] Output: Organized information (JSON format)

[0950] Step 6: The device displays organized information to the user.

[0951] The terminal receives organized information sent from the server and displays it to the user. Specifically, it uses front-end technologies (such as HTML and JavaScript) to visually present the information to the user. This allows the user to check the necessary skills and preparations and plan the next steps.

[0952] Input: Organized information (JSON format)

[0953] Output: Information displayed to the user

[0954] The above outlines the specific processing steps of this system.

[0955] (Application Example 1)

[0956] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0957] For users planning to start a business, efficiently understanding the necessary skills and preparations and quickly proceeding with those preparations based on that understanding is extremely important. However, the process of researching, collecting, and organizing relevant information from scratch is time-consuming, laborious, and inefficient. This invention aims to provide a system that allows users to easily obtain the information necessary to efficiently proceed with their business startup preparations.

[0958] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0959] In this invention, the server includes means for acquiring a business plan entered by a user as text data, means for transmitting the acquired text data to a data processing device, means for the data processing device to analyze the text data and extract keywords, means for searching for and acquiring related information based on the extracted keywords, means for organizing the acquired information and transmitting it to a display device, and means for the display device to display the organized information to the user. This enables the user to efficiently grasp the necessary technologies and preparation items and to proceed with preparations quickly.

[0960] A "user" is primarily an individual or corporation who is planning to start a business and wants to learn about the skills and preparations required for it.

[0961] A "business plan" is a document that details the steps and skills required to start a specific business.

[0962] "Text data" refers to information stored in digital format based on content entered by the user as part of their business plan.

[0963] A "data processing device" is a computer system that functions as a server, analyzes received text data, extracts necessary keywords, and searches for and retrieves related information.

[0964] A "display device" is an electronic device used to visually display information transmitted from a data processing device to a user.

[0965] "Natural language processing technology" refers to a set of technologies that enable computers to understand, analyze, and generate human language.

[0966] "Keywords" are important terms that represent the main theme of a business plan, extracted from text data.

[0967] "Related information" refers to information necessary for starting a business, such as technical documents, skill sets, and marketing materials, which are searched based on the extracted keywords.

[0968] "Required technologies" refer to specific technologies and skills that users need to acquire based on their business plan.

[0969] "Preparation items" refer to the specific tasks and procedures that users must complete in advance to start their business.

[0970] This invention is a system in which a user inputs a business plan, and based on that, the system automatically suggests the necessary technologies and preparation items. This system consists of four main components: the user, a terminal, a data processing device (server), and a display device. Specific embodiments of this system are described below.

[0971] System Overview

[0972] The user enters their business plan into a terminal and sends the data from the terminal to a data processing unit. The data processing unit analyzes the transmitted data, searches for and retrieves relevant information, and sends it back to the terminal. The terminal displays the received information to the user. The user then acquires the necessary skills based on the displayed information and starts the business by executing the necessary preparation items.

[0973] Hardware and software to be used

[0974] Device: Smartphone, tablet, or PC

[0975] Data processing unit (server): High-performance server, specifically a server built with programming languages ​​such as Python, or a web framework such as Flask.

[0976] Natural language processing technologies: NLTK library, etc.

[0977] Display device: Smartphone or tablet screen, or head-mounted display

[0978] Explanation of data processing

[0979] Retrieving and sending text data:

[0980] The user enters a specific business plan into the terminal's input field. For example, they might enter, "I want to open a virtual art gallery using VR." This input is stored as text data on the terminal. The terminal then sends the text data entered by the user to a data processing device.

[0981] Text data analysis and keyword extraction:

[0982] The data processing device analyzes the received text data and extracts keywords from its content. Natural language processing techniques are used for specific keyword extraction. For example, the NLTK library is used to tokenize the text and pick out important keywords. In the example above, keywords such as "VR," "virtual," "art gallery," and "opening a business" are extracted.

[0983] Searching for and retrieving related information:

[0984] The data processing device uses internal databases and external APIs to search for relevant information based on the extracted keywords. Specifically, it retrieves technical documents, marketing materials, and relevant best practices related to the keywords. For example, information on "VR technology," "art curation," and "gallery design" might be searched.

[0985] Organizing and sending information:

[0986] The data processing device organizes the retrieved information into categories such as "required technologies" and "preparation items." For example, it might be formatted as "required technologies: 3D modeling, art curation" and "preparation items: artist recruitment, virtual gallery design."

[0987] Displaying information on the device:

[0988] The terminal receives organized information sent from the server and displays it to the user. Based on the displayed information, the user can efficiently acquire skills and perform preparatory tasks.

[0989] Examples of specific cases and prompt statements

[0990] For example, let's consider a case where a user enters "I want to start a programming school that offers online courses."

[0991] Extracted keywords: "online," "course," "programming school"

[0992] Related information:

[0993] Required skills: Web development, course material creation, online platform setup.

[0994] Preparation items: Curriculum design, platform selection, marketing strategy development.

[0995] Examples of prompts to input into a generative AI model:

[0996] "Based on a business plan to 'start an online programming school,' please propose the necessary technologies and preparations."

[0997] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0998] Step 1:

[0999] The user enters the business plan.

[1000] The user enters a specific business plan into the input field on the device. For example, they might enter, "I want to open a virtual art gallery using VR." This input is stored on the device as text data.

[1001] Input: Text data "I want to open a virtual art gallery using VR"

[1002] Output: Text data is stored on the terminal.

[1003] Step 2:

[1004] Send text data to a data processing device.

[1005] The terminal sends the text data entered by the user to the data processing unit. This process uses an HTTP POST request.

[1006] Input: Text data entered by the user

[1007] Output: Text data is sent to the data processing unit.

[1008] Step 3:

[1009] Text data analysis and keyword extraction

[1010] The data processing device analyzes the received text data and extracts keywords from its content. Specifically, it uses the NLTK library to tokenize the text and pick out important keywords.

[1011] Input: Submitted text data

[1012] Data processing: Tokenization and keyword extraction using the NLTK library.

[1013] Output: Extracted keywords (e.g., "VR", "virtual", "art gallery", "business opening")

[1014] Step 4:

[1015] Searching for and retrieving related information

[1016] The data processing unit uses internal databases and external APIs to search for relevant information based on the extracted keywords. For example, it retrieves technical documents, marketing materials, and relevant best practices related to the keywords.

[1017] Input: Extracted keywords

[1018] Data processing: Internal database searches and use of external APIs.

[1019] Output: Related information (e.g., "VR technology," "art curation," "gallery design")

[1020] Step 5:

[1021] Organizing and sending information

[1022] The data processing unit organizes the retrieved information into categories such as "required technologies" and "preparation items." For example, it might be formatted as "required technologies: 3D modeling, art curation" and "preparation items: artist recruitment, virtual gallery design." The organized information is then sent to the terminal.

[1023] Input: Retrieved related information

[1024] Data processing: Categorization of information ("Required skills," "Preparation items")

[1025] Output: The organized information is sent to the terminal.

[1026] Step 6:

[1027] Displaying information on the device

[1028] The terminal receives organized information sent from the server and displays it to the user. Based on the displayed information, the user can efficiently acquire skills and perform preparatory tasks.

[1029] Input: Organized information

[1030] Output: Information displayed to the user (e.g., "Required skills: 3D modeling, art curation", "Preparation items: Artist recruitment, virtual gallery design")

[1031] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1032] This invention combines a system that automatically suggests necessary skills and preparations based on the user's input of their desired business venture with an emotion engine that recognizes the user's emotions. Specific embodiments of this system are described below.

[1033] System Overview

[1034] This system consists of four main components: the user, the terminal, the server, and the emotion engine. The user inputs their business details through the terminal, and the terminal sends the data to the server. The server analyzes the transmitted data, searches for and retrieves relevant information, and sends it back to the terminal. Furthermore, the emotion engine analyzes the user's emotions from the text data and adjusts the suggestions to provide even more personalized feedback.

[1035] Program processing

[1036] The user enters

[1037] The user enters specific details about their business into the input field on their device. For example, they might enter, "I want to start an online programming school." This input is stored on the device as text data.

[1038] Sending text data to the server

[1039] The terminal sends the text data entered by the user to the server. Specifically, it sends the data to the server using an HTTP POST request. This request contains the text of the entered business details.

[1040] Server-based analysis and keyword extraction

[1041] The server analyzes the received text data and extracts keywords from its content. Using natural language processing technology, it tokenizes the text and picks out important keywords. For example, keywords such as "online," "course," and "programming school" might be extracted.

[1042] Emotional analysis using an emotion engine

[1043] The server passes the text data to the emotion engine, which analyzes the user's emotions. The emotion engine uses the text data and the user's past input history to recognize emotions. For example, emotions such as "excited" or "anxious" may be identified.

[1044] Searching for and retrieving related information

[1045] The server searches for relevant information using internal databases and external APIs based on extracted keywords and the analysis results of the sentiment engine. It executes database queries and API requests to retrieve relevant information.

[1046] Organizing and sending information

[1047] The server organizes the retrieved information into categories such as "Required Skills" and "Preparation Items," and further adjusts the suggestions based on the analysis results of the emotion engine. For example, it might be formatted as "Required Skills: Web development, course material creation" and "Preparation Items: Online platform selection, course material creation." The organized information is then sent to the terminal.

[1048] Displaying information on the device

[1049] The terminal receives organized information sent from the server and displays it to the user. Based on the displayed information, the user can acquire the necessary skills and perform the required preparations. Based on the analysis results of the emotion engine, flexible responses are made that are tailored to the user's current emotions.

[1050] Specific example

[1051] For example, let's consider the case where a user enters "I want to start a sustainable fashion brand." In this case, the following process takes place.

[1052] 1. The user enters the details of the business they want to start.

[1053] 2. The terminal receives this input as text data and sends it to the server.

[1054] 3. The server extracts keywords such as "sustainable," "fashion brand," and "entrepreneurship."

[1055] 4. The server passes text data to the emotion engine, which analyzes the user's emotions. Emotions such as "excited" are recognized.

[1056] 5. The server searches for information related to relevant materials, fashion design, and brand building based on these keywords and sentiment information.

[1057] 6. Organize the information acquired by the server into "Required Skills" and "Preparation Items," and make emotionally-based suggestions.

[1058] 7. The device displays organized information to the user.

[1059] This system allows users to easily obtain necessary information and efficiently prepare for starting a business by receiving emotion-based feedback. The above is a specific form of implementing this system invention that combines an emotion engine.

[1060] The following describes the processing flow.

[1061] Step 1:

[1062] The user enters specific details of their business into the input field on their device. For example, they might enter, "I want to start an online programming school."

[1063] Step 2:

[1064] The terminal retrieves the user's input as text data. This text data is stored in a variable for subsequent processing.

[1065] Step 3:

[1066] The terminal sends the acquired text data to the server using an HTTP POST request. The request payload contains the text of the business details entered by the user.

[1067] Step 4:

[1068] The server receives an HTTP request and extracts text data from the payload. The extracted text data is then prepared for analysis.

[1069] Step 5:

[1070] The server uses natural language processing technology to analyze text data, performing tokenization and morphological analysis. This extracts important keywords such as "online," "course," and "programming school."

[1071] Step 6:

[1072] The server extracts keywords and passes them to the emotion engine to analyze the user's emotions. Emotions are identified using text data and the user's past input history. For example, emotions such as "excited" or "anxious" may be recognized.

[1073] Step 7:

[1074] The server uses internal databases and external APIs to search for relevant information based on keyword and sentiment engine analysis results. It executes database queries and API requests to retrieve materials, teaching materials, and marketing strategies related to establishing a programming school.

[1075] Step 8:

[1076] The server categorizes the relevant information it acquires into "Required Skills" and "Preparation Items." For example, it might be organized in the format of "Required Skills: Web development, course material creation" and "Preparation Items: Online platform selection, course material creation." Furthermore, the recommendations are adjusted based on the analysis results of the emotion engine. For example, users who are feeling anxious will be provided with more detailed explanations and additional support resources.

[1077] Step 9:

[1078] The server sends organized information to the terminal as an HTTP response. This response contains information presented to the user in a clear and easy-to-read format.

[1079] Step 10:

[1080] The terminal receives an HTTP response from the server and parses the response data. It then prepares to display the results of the analysis on the screen.

[1081] Step 11:

[1082] The device displays organized information to the user. For example, it might show lists such as "Required skills for starting a business: Web development, course material creation" and "Items to prepare: Online platform selection, course material creation." It also displays emotion-based advice and additional information.

[1083] This series of processes allows users to efficiently obtain the information they need and receive emotionally tailored support.

[1084] (Example 2)

[1085] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1086] Conventional startup support systems only provide information based on the startup details entered by the user, and are unable to provide feedback that takes into account the user's emotional state. As a result, they cannot provide appropriate advice tailored to the user's feelings, making efficient startup preparation difficult. This invention aims to solve this problem by analyzing the user's emotions and providing more personalized support.

[1087] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1088] In this invention, the server includes means for acquiring the business details entered by the user as text data, means for transmitting the acquired text data to the server, means for the server to analyze the text data and extract keywords, means for searching for and acquiring related information based on the extracted keywords, means for organizing the acquired information and transmitting it to the terminal, means for the terminal to display the organized information to the user, means for analyzing the user's emotions using an emotion analysis engine, and means for adjusting the search results and suggested content based on the emotion analysis results. This makes it possible to provide necessary information and advice while taking the user's emotions into consideration.

[1089] A "user" is an individual or legal entity that uses the system to input details about their business and receive necessary information and advice.

[1090] A "terminal" is a device used by a user to input data, and includes devices such as computers, smartphones, and tablets.

[1091] A "server" is a central processing unit that receives text data sent by users, analyzes it, and provides information.

[1092] "Text data" refers to the written information of the business that the user enters via their device.

[1093] "Natural language processing technology" refers to techniques that enable computers to understand and analyze human language, and includes text tokenization, keyword extraction, and grammatical analysis.

[1094] "Keywords" are important words or phrases extracted from text data and are used for searching and analyzing related information.

[1095] An "emotion analysis engine" is software or an algorithm that analyzes a user's emotional state based on the text they input.

[1096] "Organized information" refers to related information that has been categorized by the server and is provided to the user in an easy-to-understand format.

[1097] "Required skills" refer to the knowledge and abilities that users must acquire in order to succeed in starting a business.

[1098] "Preparation items" refer to the tasks and arrangements that users need to make before starting their entrepreneurial activities.

[1099] "Related information" refers to information that the server searches and retrieves based on extracted keywords and sentiment analysis results, and that is useful for the user's business venture.

[1100] "Means for adjusting the proposed content" refers to a function that changes or modifies the content of advice and information provided to the user based on the sentiment analysis results.

[1101] This invention combines a system that automatically suggests necessary skills and preparations based on the user's input of the business they wish to start, with an emotion analysis engine that recognizes the user's emotions. This system consists of four main components: the user, the terminal, the server, and the emotion analysis engine.

[1102] The user enters details of their business through a device. This device can be a common device such as a computer, smartphone, or tablet. The information entered by the user is stored as text data on the device and sent to the server via an HTTP POST request.

[1103] The server analyzes the received text data and extracts keywords using natural language processing techniques. Keyword extraction is performed using libraries such as Python's NLTK and spaCy. Next, the server uses a sentiment analysis engine to analyze the user's emotions from the input text data. Sentiment analysis utilizes technologies such as IBM Watson Natural Language Understanding and Google Cloud Natural Language API.

[1104] Based on the analysis results, the server searches for and retrieves relevant information. This search utilizes Elasticsearch and external APIs. For example, if you want to start a sustainable fashion brand, you can provide information on fashion design and environmentally friendly materials.

[1105] The server organizes the acquired information into categories such as "required skills" and "preparation items," and adjusts the suggestions based on the sentiment analysis results. This process provides flexible feedback that is tailored to the user's current emotions. The organized information is then sent back to the terminal via an HTTP POST request.

[1106] The terminal receives organized information sent from the server and displays it in the user interface. Based on the provided information, the user can develop a concrete plan, acquire the necessary skills, and carry out the necessary preparations.

[1107] Examples of specific prompt messages include the following:

[1108] "Please specify what kind of business you want to start. For example, you could write something like, 'I want to start an online programming school.'"

[1109] This system allows users to easily obtain necessary information and receive emotion-based feedback, enabling them to efficiently prepare for starting a business. The above describes the specific form of implementing the invention.

[1110] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1111] The processing flow of this system's program

[1112] Step 1:

[1113] The user enters the details of the business they want to start.

[1114] The user enters specific details of the business they want to start into the input field on their device. An example of input data is, "I want to start an online programming school." The entered content is stored on the device as text data.

[1115] (Input) Text data of the business details entered by the user.

[1116] (Output) Text data stored on the terminal.

[1117] Step 2:

[1118] The terminal sends text data to the server.

[1119] The device collects user input data and sends it to the server using an HTTP POST request. This is often done using the JavaScript fetch API.

[1120] (Input) Text data stored on the terminal.

[1121] (Output) Text data sent in an HTTP POST request.

[1122] Step 3:

[1123] The server parses the text data.

[1124] The server analyzes the received text data using natural language processing libraries (such as Python's NLTK or spaCy). The text is tokenized, and grammatical analysis is performed.

[1125] (Input) Text data sent in an HTTP POST request.

[1126] (Output) Analyzed tokenized data.

[1127] Step 4:

[1128] The server extracts the keywords.

[1129] The server extracts key keywords from the text data analysis results. Specifically, it extracts nouns and verbs and picks out the most important words.

[1130] (Input) Analyzed tokenized data.

[1131] (Output) Extracted main keywords.

[1132] Step 5:

[1133] The server analyzes emotions using an emotion analysis engine.

[1134] The server sends tokenized text data to an emotion analysis engine (for example, IBM Watson Natural Language Understanding) to analyze the user's emotions.

[1135] (Input) Analyzed tokenized data.

[1136] (Output) Analyzed user sentiment data.

[1137] Step 6:

[1138] The server searches for and retrieves relevant information.

[1139] The server searches for and retrieves relevant information from internal databases and external APIs based on extracted keywords and sentiment analysis results. Elasticsearch and other APIs are frequently used.

[1140] (Input) Extracted key keywords and user sentiment data.

[1141] (Output) Related information obtained.

[1142] Step 7:

[1143] The server organizes and adjusts the information.

[1144] The server organizes the acquired information into categories such as "required skills" and "preparation items." Furthermore, it adjusts the proposed content based on the results of sentiment analysis.

[1145] (Input) Related information obtained.

[1146] (Output) Organized and adjusted information.

[1147] Step 8:

[1148] The server sends the organized information to the terminal.

[1149] The server sends organized information to the terminal using an HTTP POST request. The data is often sent in JSON format.

[1150] (Input) Organized and adjusted information.

[1151] (Output) Organizational information sent via HTTP POST request.

[1152] Step 9:

[1153] The device displays information to the user.

[1154] The terminal displays information received from the server in the user interface. Specifically, it uses HTML and JavaScript to insert data into the DOM.

[1155] (Input) Organizational information sent in an HTTP POST request.

[1156] (Output) Information displayed to the user.

[1157] (Application Example 2)

[1158] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[1159] In autonomous vehicles, a challenge is to respond to the driver's emotions and circumstances, such as fatigue and anxiety, while driving, and to provide safe and personalized driving assistance. In particular, in special driving environments such as long-distance driving or adverse weather conditions, it is important to recognize the driver's emotions and circumstances in real time and provide appropriate advice and information.

[1160] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring the driving situation and feelings entered by the user as text data, means for transmitting the acquired text data and real-time data to the server, means for the server to analyze the text data and real-time data and extract keywords and emotions, means for searching for and acquiring related information based on the extracted keywords and emotions, means for organizing the acquired information and transmitting it to the terminal, and means for the terminal to display the organized information to the driver. This makes it possible to provide appropriate advice and information in real time according to the driver's emotions and situation.

[1161] "User" refers to an individual or legal entity that uses the system.

[1162] "Driving status" refers to information that represents the vehicle's current driving condition and environmental conditions.

[1163] "Feelings" refers to the emotions and moods that the user is experiencing.

[1164] "Text data" refers to the character information entered by the user.

[1165] "Real-time data" refers to instantaneous data acquired from vehicle sensors and cameras.

[1166] A "server" refers to a computing device that processes, analyzes, searches, and organizes incoming data.

[1167] "Means of acquisition" refers to the methods and technologies used to collect and capture data.

[1168] "Means of transmission" refers to methods and technologies for sending collected data to other devices.

[1169] "Means of analysis" refers to methods and techniques for analyzing input data and extracting useful information.

[1170] "Keywords" refer to important words or phrases extracted from text data.

[1171] "Emotions" refers to information that indicates the user's mood or psychological state.

[1172] "Means of searching" refers to methods and techniques for finding relevant information.

[1173] "Means of organization" refers to methods and techniques for categorizing and organizing acquired information.

[1174] A "terminal" refers to a device used by a user to input information and receive results.

[1175] "Means of display" refers to methods and technologies for visually conveying analysis results and information to users.

[1176] System Overview

[1177] This system is a driver assistance system that provides appropriate advice and information based on the driving conditions and feelings entered by the driver. The system mainly consists of the following elements:

[1178] 1. User: The driver that uses the system.

[1179] 2. Device: Smartphone or head-mounted display.

[1180] 3. Server: A computing device that processes, analyzes, and organizes data to obtain necessary information.

[1181] 4. Real-time data: Immediate data obtained from vehicle sensors and cameras.

[1182] Hardware and software to use

[1183] Hardware: Smartphones, head-mounted displays, in-vehicle cameras, GPS, speed sensors, etc.

[1184] Software: Natural language processing libraries (e.g., Google NLP API), sentiment analysis engines (e.g., IBM Watson), databases (e.g., MySQL, PostgreSQL), cloud services (e.g., AWS, Google Cloud).

[1185] Specific implementations of the system

[1186] 1. Data Acquisition

[1187] Users input their feelings and experiences while driving into their smartphones or head-mounted displays. This data includes text and voice recordings such as, "I'm worried about fatigue during long-distance driving."

[1188] In addition, it acquires real-time data from in-vehicle cameras and various sensors.

[1189] 2. Data transmission

[1190] The terminal sends the acquired text data and real-time data to the server using an HTTP POST request.

[1191] 3. Data Analysis

[1192] The server tokenizes the received data using a natural language processing library and recognizes the user's emotions using an emotion analysis engine.

[1193] In addition, real-time data analysis is performed in parallel to evaluate the operating conditions.

[1194] 4. Information retrieval and acquisition

[1195] Based on the analysis results, the server uses internal databases and external APIs to search for and retrieve relevant information (such as rest stops and driving advice).

[1196] 5. Organizing and transmitting information

[1197] Organize the acquired information by categorizing it (e.g., driving advice, rest stops).

[1198] Based on the sentiment analysis results, personalized suggestions are sent to the device.

[1199] 6. Display of Information

[1200] The terminal displays received information to the driver, providing advice and warnings while driving. This allows the driver to receive appropriate support in real time, enabling safer driving.

[1201] Specific example

[1202] For example, let's consider the case where a driver enters "I'm concerned about fatigue during long-distance driving."

[1203] 1. The driver voice-inputs into their smartphone that they are concerned about fatigue during long-distance driving.

[1204] 2. The smartphone sends the data as text to the cloud server.

[1205] 3. The server uses natural language processing and an emotion engine to analyze keywords such as "fatigue" and "long-distance driving," as well as emotions (feelings of fatigue).

[1206] 4. The server then searches for nearby rest areas and driving safety tips based on this information.

[1207] 5. The results are summarized, and the smartphone displays "We recommend taking a break at the next service area."

[1208] Example of a prompt

[1209] "Please describe your current driving situation and feelings. Please be as specific as possible. Examples: 'I'm worried about fatigue during long-distance driving,' 'I'm anxious about driving in the rain,' 'I feel sleepy when driving at night.'"

[1210] This allows drivers to receive appropriate instructions in real time, enabling them to continue driving with peace of mind.

[1211] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1212] Step 1:

[1213] The user inputs their driving situation and feelings. For example, the user might voice-input "I'm worried about fatigue during long-distance driving" into their smartphone. This input is then converted into text data.

[1214] Step 2:

[1215] The terminal sends acquired text data and real-time data to the server via an HTTP POST request. The entered text data and real-time data from vehicle sensors (speed, location information, etc.) are sent to the server.

[1216] Step 3:

[1217] The server tokenizes the received text data using a natural language processing library (e.g., Google NLP API) and extracts important keywords. The input data is analyzed, and keywords such as "fatigue" and "long-distance driving" are extracted.

[1218] Step 4:

[1219] The server passes extracted keywords and real-time data to an emotion analysis engine (e.g., IBM Watson) to analyze the user's emotions. For example, "fatigue" might be recognized. The input is keywords and real-time data, and the output is information about the user's emotions.

[1220] Step 5:

[1221] The server searches for relevant information using internal databases and external APIs based on extracted keywords and sentiment information. For example, nearby rest stops and driving tips might be retrieved from the database.

[1222] Step 6:

[1223] The server organizes the information it acquires into categories such as "driving advice" and "rest stops," and creates suggestions based on emotional information. The input is raw data from search results, and the output is categorized suggestion information.

[1224] Step 7:

[1225] The server sends organized information to the terminal. For example, advice such as "We recommend taking a break at the next service area" is sent to the terminal.

[1226] Step 8:

[1227] The device displays the received information to the user. The user sees a message on their smartphone saying, "We recommend taking a break at the next service area." This display allows the user to receive specific advice in real time to continue driving safely.

[1228] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1229] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1230] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[1231] [Fourth Embodiment]

[1232] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1233] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1234] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1235] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[1236] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[1237] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[1238] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[1239] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[1240] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[1241] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1242] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1243] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[1244] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1245] This invention is a system that allows users to input what they want to start as a business, and then automatically suggests the necessary skills and preparations based on that input. A specific embodiment of this system is described below.

[1246] System Overview

[1247] This system consists of three main components: the user, the terminal, and the server. The user inputs their business details through the terminal and sends the data from the terminal to the server. The server analyzes the transmitted data, searches for and retrieves relevant information, and sends it back to the terminal. The terminal displays the received information to the user.

[1248] Processing of the entire program

[1249] The user enters

[1250] The user enters specific details about their business into the input field on their device. For example, they might enter, "I want to start an online programming school." This input is stored on the device as text data.

[1251] Sending text data to the server

[1252] The terminal sends the text data entered by the user to the server. Specifically, it sends the data to the server using an HTTP POST request. This request contains the text of the entered business details.

[1253] Server-based analysis and keyword extraction

[1254] The server analyzes the received text data and extracts keywords from its content. Using natural language processing technology, it tokenizes the text and picks out important keywords. For example, keywords such as "online," "course," and "programming school" might be extracted.

[1255] Searching for and retrieving related information

[1256] The server uses internal databases and external APIs to search for relevant information based on the extracted keywords. Specifically, it retrieves technical documents, marketing materials, and relevant best practices related to the keywords.

[1257] Organizing and sending information

[1258] The server organizes the retrieved information into categories such as "Required Skills" and "Preparation Items." For example, it might be formatted as "Required Skills: Web development, course material creation" and "Preparation Items: Online platform selection, course material creation." The organized information is then sent to the terminal.

[1259] Displaying information on the device

[1260] The terminal receives organized information sent from the server and displays it to the user. Based on the displayed information, the user can acquire the necessary skills and perform the required preparations.

[1261] Specific example

[1262] For example, let's consider the case where a user enters "I want to start a sustainable fashion brand." In this case, the following process takes place.

[1263] 1. The user enters the details of the business they want to start.

[1264] 2. The terminal receives this input as text data and sends it to the server.

[1265] 3. The server extracts keywords such as "sustainable," "fashion brand," and "entrepreneurship."

[1266] 4. The server searches for information related to materials, fashion design, and brand building based on these keywords.

[1267] 5. Organize the information acquired by the server into "Required Skills" and "Preparation Items."

[1268] 6. The device displays organized information to the user.

[1269] This system allows users to easily obtain the necessary information and efficiently proceed with their startup preparations. The above describes the specific form of implementing this system invention.

[1270] The following describes the processing flow.

[1271] Step 1:

[1272] The user enters details of their business in the input field on their device. For example, they might enter, "I want to start an online programming school."

[1273] Step 2:

[1274] The terminal acquires the user's input as text data. This text data is temporarily stored in memory for subsequent processing.

[1275] Step 3:

[1276] The device sends the acquired text data to the server. Typically, an HTTP POST request is used, sending the text data as the payload.

[1277] Step 4:

[1278] The server receives an HTTP request. Text data is extracted from the request payload and prepared for analysis.

[1279] Step 5:

[1280] The server analyzes the text data using natural language processing technology. Specifically, it tokenizes the text and performs morphological analysis to extract important keywords. For example, keywords such as "online," "course," and "programming school" are extracted.

[1281] Step 6:

[1282] The server uses extracted keywords to search for relevant information from internal databases and external APIs. Database queries and API requests are executed to retrieve the relevant information.

[1283] Step 7:

[1284] The information acquired by the server is categorized into "Required Skills" and "Preparation Items." For example, it might be organized in the format of "Required Skills: Web development, course material creation" and "Preparation Items: Online platform selection, course material creation."

[1285] Step 8:

[1286] The server sends organized information to the terminal as an HTTP response. The data included in this response is formatted in a clear and easy-to-read format for the user.

[1287] Step 9:

[1288] The terminal receives an HTTP response from the server. It parses the response data and prepares it for display on the screen.

[1289] Step 10:

[1290] The device displays organized information to the user. The user can then review the displayed "required skills" and "preparations" and proceed with their startup preparations.

[1291] (Example 1)

[1292] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1293] Conventional startup support systems have difficulty suggesting appropriate skills and preparations based on the user's input of specific business details. Furthermore, the information is often poorly organized and displayed, making it time-consuming for users to find the information they actually need. Therefore, this invention aims to automatically suggest necessary skills and preparations based on the user's input of business details, and to efficiently organize and display the information.

[1294] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[1295] In this invention, the server includes means for extracting keywords from text data using natural language processing technology, means for searching for related information using an internal database or external API based on the extracted keywords, and means for organizing the acquired related information into categories of "necessary skills" and "preparation items." This enables users to efficiently acquire and implement the information necessary for starting a business.

[1296] A "user" is an individual or corporation who uses this system to input details about their business and wants to know what skills and preparations are necessary.

[1297] "Text data" refers to the written information of the business startup entered by the user, which is then analyzed by the system.

[1298] A "device" refers to a device used by a user to input details about their business, and includes personal computers, smartphones, tablets, and other similar devices.

[1299] A "server" is a central processing unit that receives text data sent from a terminal, analyzes it, searches for, retrieves, and organizes the necessary information, and then sends it back to the terminal.

[1300] "Natural language processing technology" refers to techniques for analyzing text data and extracting keywords from its content, and includes methods such as morphological analysis and tokenization.

[1301] "Keywords" are important words and phrases extracted from the text data of the business details entered by the user.

[1302] An "internal database" is a data storage system used to store information managed by a server.

[1303] An "external API" is an application programming interface that a server uses to retrieve information from other services.

[1304] "Related information" refers to information such as skills and preparations necessary for starting a business, technical documents, and marketing materials, which are searched and retrieved based on keywords.

[1305] A "category" is a classification used to organize acquired related information, and includes "required skills" and "preparation items."

[1306] "Skills" refer to the abilities and knowledge required for a user to start a business.

[1307] "Preparation items" refer to the procedures and tasks necessary to realize the startup.

[1308] This invention is a system that allows users to input what they want to start as a business, and then automatically suggests the necessary skills and preparations based on that input. This system consists of three main components: the user, the terminal, and the server.

[1309] The user enters details of their business.

[1310] The user enters details of their business idea into the input field on the device. For example, they might enter, "I want to start an online programming school." This input is stored on the device as text data.

[1311] The device sends text data to the server.

[1312] The terminal uses an HTTP POST request to send the text data entered by the user to the server. This request contains the text data entered by the user.

[1313] The server analyzes the text data and extracts keywords.

[1314] The server analyzes the received text data and extracts important keywords using natural language processing techniques. This technique utilizes libraries for morphological analysis and tokenization (e.g., SpaCy). The extracted keywords are then used by the system to search for relevant information.

[1315] The server searches for and retrieves relevant information.

[1316] The server searches and retrieves relevant information using internal databases and external APIs based on the extracted keywords. External APIs may include the Google Books API and other information provision services. This allows for the collection of specific information that the user needs (e.g., technical documentation, marketing materials).

[1317] The server organizes the information and sends it to the terminal.

[1318] The server organizes the acquired information into categories such as "required skills" and "preparation items." This organized information is then sent to the terminal and displayed to the user. This allows the user to efficiently grasp applicable skills and the preparation items they need to implement.

[1319] Specific example

[1320] For example, let's consider the case where a user enters "I want to start a sustainable fashion brand." In this case, the following process takes place.

[1321] 1. The user enters details of their business.

[1322] 2. The terminal receives this input as text data and sends it to the server.

[1323] 3. The server extracts keywords such as "sustainable," "fashion brand," and "entrepreneurship."

[1324] 4. The server searches for information related to materials, fashion design, and brand building based on these keywords.

[1325] 5. Organize the information acquired by the server into "Required Skills" and "Preparation Items."

[1326] 6. The device displays organized information to the user.

[1327] Examples of prompts to input into a generative AI model

[1328] Examples of prompt statements are shown below, and a system is created based on these to provide information that meets the user's needs:

[1329] Design a system that, upon receiving the input "I want to start a sustainable fashion brand," will suggest the necessary skills and preparations. This system should encompass a series of processes, from the user inputting their business plan to server-side data analysis, retrieval of relevant information, and organization and display of that information. Clearly specify the technologies and APIs to be used.

[1330] This system allows users to efficiently acquire the information necessary for preparing to start a business and put it into practice.

[1331] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1332] Step 1: The user enters details of their business.

[1333] The user enters details of their business into an input field on the terminal. This input data is sent to the terminal in text format. Specifically, the user might enter a sentence such as "I want to start a sustainable fashion brand." The input data is in text format, and the terminal temporarily stores this text data.

[1334] Input: "I want to start a sustainable fashion brand."

[1335] Output: Text data

[1336] Step 2: The device sends text data to the server.

[1337] The device uses an HTTP POST request to send the text data entered by the user to the server. Specifically, the device sends the text data to the appropriate API endpoint. The server then receives the text data of the business details entered by the user.

[1338] Input: Text data entered by the user

[1339] Output: HTTP POST request to the server

[1340] Step 3: The server receives the text data and performs analysis.

[1341] The server receives text data sent from the terminal. After receiving the data, it analyzes it using natural language processing techniques to extract key keywords. Specifically, it uses a natural language processing library (e.g., SpaCy) to tokenize the text and identify important keywords. Through this process, keywords such as "sustainable," "fashion brand," and "startup" are extracted from the text "I want to start a sustainable fashion brand."

[1342] Input: Text data sent to the server

[1343] Output: Extracted keyword list

[1344] Step 4: The server searches for and retrieves relevant information.

[1345] The server uses internal databases and external APIs to search for and retrieve relevant information based on the extracted keywords. Specifically, the server calls the Google Books API and other information-providing services to collect the corresponding information. This retrieves materials and documents related to the keywords "sustainable," "fashion brands," and "entrepreneurship."

[1346] Input: Extracted keyword list

[1347] Output: List of related information

[1348] Step 5: The server organizes the information and sends it to the terminal.

[1349] The server organizes the acquired relevant information into categories such as "Required Skills" and "Preparation Items." Specifically, the server classifies the information appropriately and sends it to the terminal as data in JSON format. For example, the information might be organized in the format of "Required Skills: Web Development, Fashion Design" and "Preparation Items: Brand Building, Marketing Plan."

[1350] Input: List of related information

[1351] Output: Organized information (JSON format)

[1352] Step 6: The device displays organized information to the user.

[1353] The terminal receives organized information sent from the server and displays it to the user. Specifically, it uses front-end technologies (such as HTML and JavaScript) to visually present the information to the user. This allows the user to check the necessary skills and preparations and plan the next steps.

[1354] Input: Organized information (JSON format)

[1355] Output: Information displayed to the user

[1356] The above outlines the specific processing steps of this system.

[1357] (Application Example 1)

[1358] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1359] For users planning to start a business, efficiently understanding the necessary skills and preparations and quickly proceeding with those preparations based on that understanding is extremely important. However, the process of researching, collecting, and organizing relevant information from scratch is time-consuming, laborious, and inefficient. This invention aims to provide a system that allows users to easily obtain the information necessary to efficiently proceed with their business startup preparations.

[1360] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[1361] In this invention, the server includes means for acquiring a business plan entered by a user as text data, means for transmitting the acquired text data to a data processing device, means for the data processing device to analyze the text data and extract keywords, means for searching for and acquiring related information based on the extracted keywords, means for organizing the acquired information and transmitting it to a display device, and means for the display device to display the organized information to the user. This enables the user to efficiently grasp the necessary technologies and preparation items and to proceed with preparations quickly.

[1362] A "user" is primarily an individual or corporation who is planning to start a business and wants to learn about the skills and preparations required for it.

[1363] A "business plan" is a document that details the steps and skills required to start a specific business.

[1364] "Text data" refers to information stored in digital format based on content entered by the user as part of their business plan.

[1365] A "data processing device" is a computer system that functions as a server, analyzes received text data, extracts necessary keywords, and searches for and retrieves related information.

[1366] A "display device" is an electronic device used to visually display information transmitted from a data processing device to a user.

[1367] "Natural language processing technology" refers to a set of technologies that enable computers to understand, analyze, and generate human language.

[1368] "Keywords" are important terms that represent the main theme of a business plan, extracted from text data.

[1369] "Related information" refers to information necessary for starting a business, such as technical documents, skill sets, and marketing materials, which are searched based on the extracted keywords.

[1370] "Required technologies" refer to specific technologies and skills that users need to acquire based on their business plan.

[1371] "Preparation items" refer to the specific tasks and procedures that users must complete in advance to start their business.

[1372] This invention is a system in which a user inputs a business plan, and based on that, the system automatically suggests the necessary technologies and preparation items. This system consists of four main components: the user, a terminal, a data processing device (server), and a display device. Specific embodiments of this system are described below.

[1373] System Overview

[1374] The user enters their business plan into a terminal and sends the data from the terminal to a data processing unit. The data processing unit analyzes the transmitted data, searches for and retrieves relevant information, and sends it back to the terminal. The terminal displays the received information to the user. The user then acquires the necessary skills based on the displayed information and starts the business by executing the necessary preparation items.

[1375] Hardware and software to be used

[1376] Device: Smartphone, tablet, or PC

[1377] Data processing unit (server): High-performance server, specifically a server built with programming languages ​​such as Python, or a web framework such as Flask.

[1378] Natural language processing technologies: NLTK library, etc.

[1379] Display device: Smartphone or tablet screen, or head-mounted display

[1380] Explanation of data processing

[1381] Retrieving and sending text data:

[1382] The user enters a specific business plan into the terminal's input field. For example, they might enter, "I want to open a virtual art gallery using VR." This input is stored as text data on the terminal. The terminal then sends the text data entered by the user to a data processing device.

[1383] Text data analysis and keyword extraction:

[1384] The data processing device analyzes the received text data and extracts keywords from its content. Natural language processing techniques are used for specific keyword extraction. For example, the NLTK library is used to tokenize the text and pick out important keywords. In the example above, keywords such as "VR," "virtual," "art gallery," and "opening a business" are extracted.

[1385] Searching for and retrieving related information:

[1386] The data processing device uses internal databases and external APIs to search for relevant information based on the extracted keywords. Specifically, it retrieves technical documents, marketing materials, and relevant best practices related to the keywords. For example, information on "VR technology," "art curation," and "gallery design" might be searched.

[1387] Organizing and sending information:

[1388] The data processing device organizes the retrieved information into categories such as "required technologies" and "preparation items." For example, it might be formatted as "required technologies: 3D modeling, art curation" and "preparation items: artist recruitment, virtual gallery design."

[1389] Displaying information on the device:

[1390] The terminal receives organized information sent from the server and displays it to the user. Based on the displayed information, the user can efficiently acquire skills and perform preparatory tasks.

[1391] Examples of specific cases and prompt statements

[1392] For example, let's consider a case where a user enters "I want to start a programming school that offers online courses."

[1393] Extracted keywords: "online," "course," "programming school"

[1394] Related information:

[1395] Required skills: Web development, course material creation, online platform setup.

[1396] Preparation items: Curriculum design, platform selection, marketing strategy development.

[1397] Examples of prompts to input into a generative AI model:

[1398] "Based on a business plan to 'start an online programming school,' please propose the necessary technologies and preparations."

[1399] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1400] Step 1:

[1401] The user enters the business plan.

[1402] The user enters a specific business plan into the input field on the device. For example, they might enter, "I want to open a virtual art gallery using VR." This input is stored on the device as text data.

[1403] Input: Text data "I want to open a virtual art gallery using VR"

[1404] Output: Text data is stored on the terminal.

[1405] Step 2:

[1406] Send text data to a data processing device.

[1407] The terminal sends the text data entered by the user to the data processing unit. This process uses an HTTP POST request.

[1408] Input: Text data entered by the user

[1409] Output: Text data is sent to the data processing unit.

[1410] Step 3:

[1411] Text data analysis and keyword extraction

[1412] The data processing device analyzes the received text data and extracts keywords from its content. Specifically, it uses the NLTK library to tokenize the text and pick out important keywords.

[1413] Input: Submitted text data

[1414] Data processing: Tokenization and keyword extraction using the NLTK library.

[1415] Output: Extracted keywords (e.g., "VR", "virtual", "art gallery", "business opening")

[1416] Step 4:

[1417] Searching for and retrieving related information

[1418] The data processing unit uses internal databases and external APIs to search for relevant information based on the extracted keywords. For example, it retrieves technical documents, marketing materials, and relevant best practices related to the keywords.

[1419] Input: Extracted keywords

[1420] Data processing: Internal database searches and use of external APIs.

[1421] Output: Related information (e.g., "VR technology," "art curation," "gallery design")

[1422] Step 5:

[1423] Organizing and sending information

[1424] The data processing unit organizes the retrieved information into categories such as "required technologies" and "preparation items." For example, it might be formatted as "required technologies: 3D modeling, art curation" and "preparation items: artist recruitment, virtual gallery design." The organized information is then sent to the terminal.

[1425] Input: Retrieved related information

[1426] Data processing: Categorization of information ("Required skills," "Preparation items")

[1427] Output: The organized information is sent to the terminal.

[1428] Step 6:

[1429] Displaying information on the device

[1430] The terminal receives organized information sent from the server and displays it to the user. Based on the displayed information, the user can efficiently acquire skills and perform preparatory tasks.

[1431] Input: Organized information

[1432] Output: Information displayed to the user (e.g., "Required skills: 3D modeling, art curation", "Preparation items: Artist recruitment, virtual gallery design")

[1433] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[1434] This invention combines a system that automatically suggests necessary skills and preparations based on the user's input of their desired business venture with an emotion engine that recognizes the user's emotions. Specific embodiments of this system are described below.

[1435] System Overview

[1436] This system consists of four main components: the user, the terminal, the server, and the emotion engine. The user inputs their business details through the terminal, and the terminal sends the data to the server. The server analyzes the transmitted data, searches for and retrieves relevant information, and sends it back to the terminal. Furthermore, the emotion engine analyzes the user's emotions from the text data and adjusts the suggestions to provide even more personalized feedback.

[1437] Program processing

[1438] The user enters

[1439] The user enters specific details about their business into the input field on their device. For example, they might enter, "I want to start an online programming school." This input is stored on the device as text data.

[1440] Sending text data to the server

[1441] The terminal sends the text data entered by the user to the server. Specifically, it sends the data to the server using an HTTP POST request. This request contains the text of the entered business details.

[1442] Server-based analysis and keyword extraction

[1443] The server analyzes the received text data and extracts keywords from its content. Using natural language processing technology, it tokenizes the text and picks out important keywords. For example, keywords such as "online," "course," and "programming school" might be extracted.

[1444] Emotional analysis using an emotion engine

[1445] The server passes the text data to the emotion engine, which analyzes the user's emotions. The emotion engine uses the text data and the user's past input history to recognize emotions. For example, emotions such as "excited" or "anxious" may be identified.

[1446] Searching for and retrieving related information

[1447] The server searches for relevant information using internal databases and external APIs based on extracted keywords and the analysis results of the sentiment engine. It executes database queries and API requests to retrieve relevant information.

[1448] Organizing and sending information

[1449] The server organizes the retrieved information into categories such as "Required Skills" and "Preparation Items," and further adjusts the suggestions based on the analysis results of the emotion engine. For example, it might be formatted as "Required Skills: Web development, course material creation" and "Preparation Items: Online platform selection, course material creation." The organized information is then sent to the terminal.

[1450] Displaying information on the device

[1451] The terminal receives organized information sent from the server and displays it to the user. Based on the displayed information, the user can acquire the necessary skills and perform the required preparations. Based on the analysis results of the emotion engine, flexible responses are made that are tailored to the user's current emotions.

[1452] Specific example

[1453] For example, let's consider the case where a user enters "I want to start a sustainable fashion brand." In this case, the following process takes place.

[1454] 1. The user enters the details of the business they want to start.

[1455] 2. The terminal receives this input as text data and sends it to the server.

[1456] 3. The server extracts keywords such as "sustainable," "fashion brand," and "entrepreneurship."

[1457] 4. The server passes text data to the emotion engine, which analyzes the user's emotions. Emotions such as "excited" are recognized.

[1458] 5. The server searches for information related to relevant materials, fashion design, and brand building based on these keywords and sentiment information.

[1459] 6. Organize the information acquired by the server into "Required Skills" and "Preparation Items," and make emotionally-based suggestions.

[1460] 7. The device displays organized information to the user.

[1461] This system allows users to easily obtain necessary information and efficiently prepare for starting a business by receiving emotion-based feedback. The above is a specific form of implementing this system invention that combines an emotion engine.

[1462] The following describes the processing flow.

[1463] Step 1:

[1464] The user enters specific details of their business into the input field on their device. For example, they might enter, "I want to start an online programming school."

[1465] Step 2:

[1466] The terminal retrieves the user's input as text data. This text data is stored in a variable for subsequent processing.

[1467] Step 3:

[1468] The terminal sends the acquired text data to the server using an HTTP POST request. The request payload contains the text of the business details entered by the user.

[1469] Step 4:

[1470] The server receives an HTTP request and extracts text data from the payload. The extracted text data is then prepared for analysis.

[1471] Step 5:

[1472] The server uses natural language processing technology to analyze text data, performing tokenization and morphological analysis. This extracts important keywords such as "online," "course," and "programming school."

[1473] Step 6:

[1474] The server extracts keywords and passes them to the emotion engine to analyze the user's emotions. Emotions are identified using text data and the user's past input history. For example, emotions such as "excited" or "anxious" may be recognized.

[1475] Step 7:

[1476] The server uses internal databases and external APIs to search for relevant information based on keyword and sentiment engine analysis results. It executes database queries and API requests to retrieve materials, teaching materials, and marketing strategies related to establishing a programming school.

[1477] Step 8:

[1478] The server categorizes the relevant information it acquires into "Required Skills" and "Preparation Items." For example, it might be organized in the format of "Required Skills: Web development, course material creation" and "Preparation Items: Online platform selection, course material creation." Furthermore, the recommendations are adjusted based on the analysis results of the emotion engine. For example, users who are feeling anxious will be provided with more detailed explanations and additional support resources.

[1479] Step 9:

[1480] The server sends organized information to the terminal as an HTTP response. This response contains information presented to the user in a clear and easy-to-read format.

[1481] Step 10:

[1482] The terminal receives an HTTP response from the server and parses the response data. It then prepares to display the results of the analysis on the screen.

[1483] Step 11:

[1484] The device displays organized information to the user. For example, it might show lists such as "Required skills for starting a business: Web development, course material creation" and "Items to prepare: Online platform selection, course material creation." It also displays emotion-based advice and additional information.

[1485] This series of processes allows users to efficiently obtain the information they need and receive emotionally tailored support.

[1486] (Example 2)

[1487] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1488] Conventional startup support systems only provide information based on the startup details entered by the user, and are unable to provide feedback that takes into account the user's emotional state. As a result, they cannot provide appropriate advice tailored to the user's feelings, making efficient startup preparation difficult. This invention aims to solve this problem by analyzing the user's emotions and providing more personalized support.

[1489] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[1490] In this invention, the server includes means for acquiring the business details entered by the user as text data, means for transmitting the acquired text data to the server, means for the server to analyze the text data and extract keywords, means for searching for and acquiring related information based on the extracted keywords, means for organizing the acquired information and transmitting it to the terminal, means for the terminal to display the organized information to the user, means for analyzing the user's emotions using an emotion analysis engine, and means for adjusting the search results and suggested content based on the emotion analysis results. This makes it possible to provide necessary information and advice while taking the user's emotions into consideration.

[1491] A "user" is an individual or legal entity that uses the system to input details about their business and receive necessary information and advice.

[1492] A "terminal" is a device used by a user to input data, and includes devices such as computers, smartphones, and tablets.

[1493] A "server" is a central processing unit that receives text data sent by users, analyzes it, and provides information.

[1494] "Text data" refers to the written information of the business that the user enters via their device.

[1495] "Natural language processing technology" refers to techniques that enable computers to understand and analyze human language, and includes text tokenization, keyword extraction, and grammatical analysis.

[1496] "Keywords" are important words or phrases extracted from text data and are used for searching and analyzing related information.

[1497] An "emotion analysis engine" is software or an algorithm that analyzes a user's emotional state based on the text they input.

[1498] "Organized information" refers to related information that has been categorized by the server and is provided to the user in an easy-to-understand format.

[1499] "Required skills" refer to the knowledge and abilities that users must acquire in order to succeed in starting a business.

[1500] "Preparation items" refer to the tasks and arrangements that users need to make before starting their entrepreneurial activities.

[1501] "Related information" refers to information that the server searches and retrieves based on extracted keywords and sentiment analysis results, and that is useful for the user's business venture.

[1502] "Means for adjusting the proposed content" refers to a function that changes or modifies the content of advice and information provided to the user based on the sentiment analysis results.

[1503] This invention combines a system that automatically suggests necessary skills and preparations based on the user's input of the business they wish to start, with an emotion analysis engine that recognizes the user's emotions. This system consists of four main components: the user, the terminal, the server, and the emotion analysis engine.

[1504] The user enters details of their business through a device. This device can be a common device such as a computer, smartphone, or tablet. The information entered by the user is stored as text data on the device and sent to the server via an HTTP POST request.

[1505] The server analyzes the received text data and extracts keywords using natural language processing techniques. Keyword extraction is performed using libraries such as Python's NLTK and spaCy. Next, the server uses a sentiment analysis engine to analyze the user's emotions from the input text data. Sentiment analysis utilizes technologies such as IBM Watson Natural Language Understanding and Google Cloud Natural Language API.

[1506] Based on the analysis results, the server searches for and retrieves relevant information. This search utilizes Elasticsearch and external APIs. For example, if you want to start a sustainable fashion brand, you can provide information on fashion design and environmentally friendly materials.

[1507] The server organizes the acquired information into categories such as "required skills" and "preparation items," and adjusts the suggestions based on the sentiment analysis results. This process provides flexible feedback that is tailored to the user's current emotions. The organized information is then sent back to the terminal via an HTTP POST request.

[1508] The terminal receives organized information sent from the server and displays it in the user interface. Based on the provided information, the user can develop a concrete plan, acquire the necessary skills, and carry out the necessary preparations.

[1509] Examples of specific prompt messages include the following:

[1510] "Please specify what kind of business you want to start. For example, you could write something like, 'I want to start an online programming school.'"

[1511] This system allows users to easily obtain necessary information and receive emotion-based feedback, enabling them to efficiently prepare for starting a business. The above describes the specific form of implementing the invention.

[1512] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1513] The processing flow of this system's program

[1514] Step 1:

[1515] The user enters the details of the business they want to start.

[1516] The user enters specific details of the business they want to start into the input field on their device. An example of input data is, "I want to start an online programming school." The entered content is stored on the device as text data.

[1517] (Input) Text data of the business details entered by the user.

[1518] (Output) Text data stored on the terminal.

[1519] Step 2:

[1520] The terminal sends text data to the server.

[1521] The device collects user input data and sends it to the server using an HTTP POST request. This is often done using the JavaScript fetch API.

[1522] (Input) Text data stored on the terminal.

[1523] (Output) Text data sent in an HTTP POST request.

[1524] Step 3:

[1525] The server parses the text data.

[1526] The server analyzes the received text data using natural language processing libraries (such as Python's NLTK or spaCy). The text is tokenized, and grammatical analysis is performed.

[1527] (Input) Text data sent in an HTTP POST request.

[1528] (Output) Analyzed tokenized data.

[1529] Step 4:

[1530] The server extracts the keywords.

[1531] The server extracts key keywords from the text data analysis results. Specifically, it extracts nouns and verbs and picks out the most important words.

[1532] (Input) Analyzed tokenized data.

[1533] (Output) Extracted main keywords.

[1534] Step 5:

[1535] The server analyzes emotions using an emotion analysis engine.

[1536] The server sends tokenized text data to an emotion analysis engine (for example, IBM Watson Natural Language Understanding) to analyze the user's emotions.

[1537] (Input) Analyzed tokenized data.

[1538] (Output) Analyzed user sentiment data.

[1539] Step 6:

[1540] The server searches for and retrieves relevant information.

[1541] The server searches for and retrieves relevant information from internal databases and external APIs based on extracted keywords and sentiment analysis results. Elasticsearch and other APIs are frequently used.

[1542] (Input) Extracted key keywords and user sentiment data.

[1543] (Output) Related information obtained.

[1544] Step 7:

[1545] The server organizes and adjusts the information.

[1546] The server organizes the acquired information into categories such as "required skills" and "preparation items." Furthermore, it adjusts the proposed content based on the results of sentiment analysis.

[1547] (Input) Related information obtained.

[1548] (Output) Organized and adjusted information.

[1549] Step 8:

[1550] The server sends the organized information to the terminal.

[1551] The server sends organized information to the terminal using an HTTP POST request. The data is often sent in JSON format.

[1552] (Input) Organized and adjusted information.

[1553] (Output) Organizational information sent via HTTP POST request.

[1554] Step 9:

[1555] The device displays information to the user.

[1556] The terminal displays information received from the server in the user interface. Specifically, it uses HTML and JavaScript to insert data into the DOM.

[1557] (Input) Organizational information sent in an HTTP POST request.

[1558] (Output) Information displayed to the user.

[1559] (Application Example 2)

[1560] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[1561] In autonomous vehicles, a challenge is to respond to the driver's emotions and circumstances, such as fatigue and anxiety, while driving, and to provide safe and personalized driving assistance. In particular, in special driving environments such as long-distance driving or adverse weather conditions, it is important to recognize the driver's emotions and circumstances in real time and provide appropriate advice and information.

[1562] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for acquiring the driving situation and feelings entered by the user as text data, means for transmitting the acquired text data and real-time data to the server, means for the server to analyze the text data and real-time data and extract keywords and emotions, means for searching for and acquiring related information based on the extracted keywords and emotions, means for organizing the acquired information and transmitting it to the terminal, and means for the terminal to display the organized information to the driver. This makes it possible to provide appropriate advice and information in real time according to the driver's emotions and situation.

[1563] "User" refers to an individual or legal entity that uses the system.

[1564] "Driving status" refers to information that represents the vehicle's current driving condition and environmental conditions.

[1565] "Feelings" refers to the emotions and moods that the user is experiencing.

[1566] "Text data" refers to the character information entered by the user.

[1567] "Real-time data" refers to instantaneous data acquired from vehicle sensors and cameras.

[1568] A "server" refers to a computing device that processes, analyzes, searches, and organizes incoming data.

[1569] "Means of acquisition" refers to the methods and technologies used to collect and capture data.

[1570] "Means of transmission" refers to methods and technologies for sending collected data to other devices.

[1571] "Means of analysis" refers to methods and techniques for analyzing input data and extracting useful information.

[1572] "Keywords" refer to important words or phrases extracted from text data.

[1573] "Emotions" refers to information that indicates the user's mood or psychological state.

[1574] "Means of searching" refers to methods and techniques for finding relevant information.

[1575] "Means of organization" refers to methods and techniques for categorizing and organizing acquired information.

[1576] A "terminal" refers to a device used by a user to input information and receive results.

[1577] "Means of display" refers to methods and technologies for visually conveying analysis results and information to users.

[1578] System Overview

[1579] This system is a driver assistance system that provides appropriate advice and information based on the driving conditions and feelings entered by the driver. The system mainly consists of the following elements:

[1580] 1. User: The driver that uses the system.

[1581] 2. Device: Smartphone or head-mounted display.

[1582] 3. Server: A computing device that processes, analyzes, and organizes data to obtain necessary information.

[1583] 4. Real-time data: Immediate data obtained from vehicle sensors and cameras.

[1584] Hardware and software to use

[1585] Hardware: Smartphones, head-mounted displays, in-vehicle cameras, GPS, speed sensors, etc.

[1586] Software: Natural language processing libraries (e.g., Google NLP API), sentiment analysis engines (e.g., IBM Watson), databases (e.g., MySQL, PostgreSQL), cloud services (e.g., AWS, Google Cloud).

[1587] Specific implementations of the system

[1588] 1. Data Acquisition

[1589] Users input their feelings and experiences while driving into their smartphones or head-mounted displays. This data includes text and voice recordings such as, "I'm worried about fatigue during long-distance driving."

[1590] In addition, it acquires real-time data from in-vehicle cameras and various sensors.

[1591] 2. Data transmission

[1592] The terminal sends the acquired text data and real-time data to the server using an HTTP POST request.

[1593] 3. Data Analysis

[1594] The server tokenizes the received data using a natural language processing library and recognizes the user's emotions using an emotion analysis engine.

[1595] In addition, real-time data analysis is performed in parallel to evaluate the operating conditions.

[1596] 4. Information retrieval and acquisition

[1597] Based on the analysis results, the server uses internal databases and external APIs to search for and retrieve relevant information (such as rest stops and driving advice).

[1598] 5. Organizing and transmitting information

[1599] Organize the acquired information by categorizing it (e.g., driving advice, rest stops).

[1600] Based on the sentiment analysis results, personalized suggestions are sent to the device.

[1601] 6. Display of Information

[1602] The terminal displays received information to the driver, providing advice and warnings while driving. This allows the driver to receive appropriate support in real time, enabling safer driving.

[1603] Specific example

[1604] For example, let's consider the case where a driver enters "I'm concerned about fatigue during long-distance driving."

[1605] 1. The driver voice-inputs into their smartphone that they are concerned about fatigue during long-distance driving.

[1606] 2. The smartphone sends the data as text to the cloud server.

[1607] 3. The server uses natural language processing and an emotion engine to analyze keywords such as "fatigue" and "long-distance driving," as well as emotions (feelings of fatigue).

[1608] 4. The server then searches for nearby rest areas and driving safety tips based on this information.

[1609] 5. The results are summarized, and the smartphone displays "We recommend taking a break at the next service area."

[1610] Example of a prompt

[1611] "Please describe your current driving situation and feelings. Please be as specific as possible. Examples: 'I'm worried about fatigue during long-distance driving,' 'I'm anxious about driving in the rain,' 'I feel sleepy when driving at night.'"

[1612] This allows drivers to receive appropriate instructions in real time, enabling them to continue driving with peace of mind.

[1613] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1614] Step 1:

[1615] The user inputs their driving situation and feelings. For example, the user might voice-input "I'm worried about fatigue during long-distance driving" into their smartphone. This input is then converted into text data.

[1616] Step 2:

[1617] The terminal sends acquired text data and real-time data to the server via an HTTP POST request. The entered text data and real-time data from vehicle sensors (speed, location information, etc.) are sent to the server.

[1618] Step 3:

[1619] The server tokenizes the received text data using a natural language processing library (e.g., Google NLP API) and extracts important keywords. The input data is analyzed, and keywords such as "fatigue" and "long-distance driving" are extracted.

[1620] Step 4:

[1621] The server passes extracted keywords and real-time data to an emotion analysis engine (e.g., IBM Watson) to analyze the user's emotions. For example, "fatigue" might be recognized. The input is keywords and real-time data, and the output is information about the user's emotions.

[1622] Step 5:

[1623] The server searches for relevant information using internal databases and external APIs based on extracted keywords and sentiment information. For example, nearby rest stops and driving tips might be retrieved from the database.

[1624] Step 6:

[1625] The server organizes the information it acquires into categories such as "driving advice" and "rest stops," and creates suggestions based on emotional information. The input is raw data from search results, and the output is categorized suggestion information.

[1626] Step 7:

[1627] The server sends organized information to the terminal. For example, advice such as "We recommend taking a break at the next service area" is sent to the terminal.

[1628] Step 8:

[1629] The device displays the received information to the user. The user sees a message on their smartphone saying, "We recommend taking a break at the next service area." This display allows the user to receive specific advice in real time to continue driving safely.

[1630] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[1631] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1632] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[1633] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1634] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[1635] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[1636] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[1637] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[1638] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[1639] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[1640] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[1641] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[1642] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[1643] 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.

[1644] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[1645] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[1646] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[1647] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[1648] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[1649] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[1650] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[1651] The following is further disclosed regarding the embodiments described above.

[1652] (Claim 1)

[1653] A means of obtaining the business details entered by the user as text data,

[1654] A means of sending the acquired text data to the server,

[1655] A means by which the server analyzes text data and extracts keywords,

[1656] A means of searching for and obtaining related information based on extracted keywords,

[1657] A means of organizing the acquired information and sending it to the terminal,

[1658] A means by which the terminal displays organized information to the user,

[1659] A system that includes this.

[1660] (Claim 2)

[1661] The system according to claim 1, further comprising means for the server to extract keywords from text data using natural language processing technology.

[1662] (Claim 3)

[1663] The system according to claim 1, further comprising means for organizing the relevant information acquired by the server into categories of "required skills" and "preparation items".

[1664] "Example 1"

[1665] (Claim 1)

[1666] A means of obtaining the business details entered by the user as text data,

[1667] A means of sending the acquired text data to the server,

[1668] A means by which the server analyzes text data and extracts keywords,

[1669] A means of searching for and obtaining related information based on extracted keywords,

[1670] A means of organizing the acquired information and sending it to the terminal,

[1671] A means by which the terminal displays organized information to the user,

[1672] A system that includes this.

[1673] (Claim 2)

[1674] A server uses natural language processing technology to extract keywords from text data,

[1675] A means of searching for related information using internal databases and external APIs based on extracted keywords,

[1676] The system according to claim 1, further comprising:

[1677] (Claim 3)

[1678] A method for organizing the relevant information acquired by the server into the categories of "Required Skills" and "Preparation Items,"

[1679] A means of appropriately displaying organized information to the user,

[1680] The system according to claim 1, further comprising:

[1681] "Application Example 1"

[1682] (Claim 1)

[1683] A means of obtaining the business plan entered by the user as text data,

[1684] A means for transmitting acquired text data to a data processing device,

[1685] A data processing device provides a means for analyzing text data and extracting keywords,

[1686] A means of searching for and obtaining related information based on extracted keywords,

[1687] A means for organizing the acquired information and transmitting it to a display device,

[1688] A means by which a display device shows organized information to the user,

[1689] A system that includes this.

[1690] (Claim 2)

[1691] The system according to claim 1, further comprising means for extracting keywords from text data using natural language processing technology for the data processing device.

[1692] (Claim 3)

[1693] The system according to claim 1, further comprising means for organizing relevant information acquired by the data processing device into categories of "necessary technology" and "preparation items".

[1694] "Example 2 of combining an emotion engine"

[1695] (Claim 1)

[1696] A means of obtaining the business details entered by the user as text data,

[1697] A means of sending the acquired text data to the server,

[1698] A means by which the server analyzes text data and extracts keywords,

[1699] A means of searching for and obtaining related information based on extracted keywords,

[1700] A means of organizing the acquired information and sending it to the terminal,

[1701] A means by which the terminal displays organized information to the user,

[1702] A means of analyzing user emotions using an emotion analysis engine,

[1703] A means of adjusting search results and suggestions based on sentiment analysis results,

[1704] A system that includes this.

[1705] (Claim 2)

[1706] The system according to claim 1, further comprising means for the server to extract keywords from text data using natural language processing technology.

[1707] (Claim 3)

[1708] The system according to claim 1, further comprising means for organizing the relevant information acquired by the server into categories of "required skills" and "preparation items".

[1709] "Application example 2 of combining emotional engines"

[1710] (Claim 1)

[1711] A means of acquiring user-entered driving conditions and feelings as text data,

[1712] A means for sending acquired text data and real-time data to a server,

[1713] The server analyzes text data and real-time data and extracts keywords and sentiments.

[1714] A means of searching for and obtaining relevant information based on extracted keywords and sentiments,

[1715] A means of organizing the acquired information and sending it to the terminal,

[1716] A means by which the terminal displays organized information to the driver,

[1717] A system that includes this.

[1718] (Claim 2)

[1719] The system according to claim 1, further comprising means for a server to extract keywords and sentiments from text data and real-time data using natural language processing technology and sentiment analysis technology.

[1720] (Claim 3)

[1721] The system according to claim 1, further comprising means for categorizing and organizing relevant information acquired by the server and making emotion-based suggestions. [Explanation of symbols]

[1722] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of obtaining the business details entered by the user as text data, A means of sending the acquired text data to the server, A means by which the server analyzes text data and extracts keywords, A means of searching for and obtaining related information based on extracted keywords, A means of organizing the acquired information and sending it to the terminal, A means by which the terminal displays organized information to the user, A system that includes this.

2. The system according to claim 1, further comprising means for the server to extract keywords from text data using natural language processing technology.

3. The system according to claim 1, further comprising means for organizing the relevant information acquired by the server into the categories of "required skills" and "preparation items".

Images