system

Abstract

Provide a system. 【Solution means】Data input means, Data transmission means, Data reception means, Data preprocessing means, Database reference means, Generative artificial intelligence model application means, Advice formatting means, Advice transmission means, A system including these.

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 the chatbot's 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] Since small and medium-sized enterprises are overwhelmingly lacking in people, goods, money, and information compared to large enterprises, there is a current situation where it is difficult to receive appropriate advice and guidance in important business decisions such as business strategies, marketing, human resource management, and financing. For this reason, there is a demand for a system that utilizes the rich experience and know-how of famous business operators and provides optimal advice to business operators in real time.

Means for Solving the Problems

[0005] The present invention is a system including the following means.

[0006] 1. Data input means: providing an interface for a user to input industry type, business problems, and necessary support content.

[0007] 2. Data transmission and reception means: Provides network communication means for transmitting user input information to a server in structured data format and for the server to receive that data.

[0008] 3. Data preprocessing means: Imputing missing values ​​in the received data and normalizing the data.

[0009] 4. Database Reference Method: Extract past success stories, trend data, and competitor information related to the industry and business sector from the database.

[0010] 5. Method for applying generative artificial intelligence models: An artificial intelligence model is applied that generates optimal advice for input data based on the know-how of famous business leaders.

[0011] 6. Advice formatting method: Format the generated advice into a format that is easy for the user to understand.

[0012] 7. Advice transmission means: Formatted advice is sent to the user's terminal and displayed on the user interface.

[0013] These measures will enable small and medium-sized business owners to receive immediately useful advice and make quick and appropriate business decisions.

[0014] A "data entry method" is a means of providing an interface for users to input their industry, business challenges, and required support.

[0015] "Data transmission means" refers to a means for sending user input information to a server.

[0016] "Data receiving means" refers to the means by which a server receives data transmitted from a terminal.

[0017] "Data preprocessing means" refers to means for complementing missing values in received data and normalizing the data.

[0018] "Database reference means" refers to means for extracting past success cases, trend data, and competitive information related to industries and business types from a database.

[0019] "Generative AI model application means" refers to means for applying an AI model that generates optimal advice for input data based on the know-how of famous managers.

[0020] "Advice formatting means" refers to means for formatting the generated advice into a form that is easy for users to understand.

[0021] "Advice transmission means" refers to means for transmitting the formatted advice to the user's terminal and displaying it on the user interface.

Brief Description of Drawings

[0022] [[ID=:23]] [Figure 1] It is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] It is a conceptual diagram showing an example of the main functions of a data processing device and a smart device according to the first embodiment. [Figure 3] It is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] It is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8]This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0023] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0024] First, let's explain the terminology used in the following explanation.

[0025] 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).

[0026] 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.

[0027] 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.

[0028] 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).

[0029] 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."

[0030] [First Embodiment]

[0031] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0032] 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.

[0033] 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).

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0039] 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.

[0040] 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.

[0041] 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.

[0042] 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".

[0043] This invention provides a system for offering immediately useful advice to managers of small and medium-sized enterprises (SMEs) regarding the management challenges they face. This system includes the following main components:

[0044] System Configuration

[0045] 1. Data input means

[0046] Terminal: Provides an interface for users to input their industry, sector, business challenges, and required support. Examples include web forms and mobile applications.

[0047] 2. Data transmission means and receiving means

[0048] Terminal: Converts user-entered information into a structured data format (e.g., JSON format) and sends it to the server.

[0049] Server: Receives data sent from terminals and prepares it for analysis and processing.

[0050] 3. Data preprocessing means

[0051] Server: Performs operations on received data, such as imputing missing values ​​and normalizing the data. This includes processes such as completing incomplete entries and ensuring consistency.

[0052] 4. Database Reference Means

[0053] Server: Based on the entered industry and business type, it references relevant databases and extracts the necessary information. This includes past success stories, trend data, and competitor information.

[0054] 5. Means for applying generative artificial intelligence models

[0055] Server: Based on extracted and input data, a generative artificial intelligence (AI) model generates optimal advice. This AI model incorporates the know-how of renowned business leaders.

[0056] 6. Advice on cosmetic surgery methods

[0057] Server: Formats the generated advice into a user-friendly format. Specifically, it organizes the text, selects terminology, and adds supplementary explanations and examples as needed.

[0058] 7. Means of sending advice

[0059] Server: Sends formatted advice to the user's terminal and displays it in the user interface.

[0060] Terminal: Receives data for displaying advice from the server and displays it on the user interface.

[0061] Program processing

[0062] This system's program generates and provides optimal business advice based on information entered by the user, utilizing the expertise of renowned business leaders. The details of this process are explained below in natural language.

[0063] Specific example: Regarding the market launch of a new product

[0064] Let's take the example of a user seeking advice on launching a new product into the market.

[0065] 1. User input (on the device)

[0066] User: Enters the question, "We are considering launching a new product A into the market. What kind of marketing strategy should we adopt?" into the interface and presses the submit button.

[0067] 2. Data transmission and reception (terminal → server)

[0068] Terminal: Packages the user's question in JSON format and sends it to the server.

[0069] Server: Receives data in JSON format and prepares it for analysis.

[0070] 3. Data preprocessing (server)

[0071] Server: Performs data imputation and normalization to prepare the data for analysis.

[0072] 4. Accessing industry / sector databases (server)

[0073] Server: Extracts past success stories, competitor information, and marketing trends related to the market launch of new products from the database.

[0074] 5. Application of generative AI models (server)

[0075] Server: Based on input data and extracted data, a generative AI model generates strategic advice. For example, it might generate advice such as, "The target market for new product A is young people in their 20s and 30s. As an effective marketing method, utilize social media and influencer marketing and implement a campaign that emphasizes the product's features."

[0076] 6. Formatting advice (server)

[0077] Server: Formats the generated advice into natural-sounding text that is easy for the user to understand.

[0078] 7. Sending advice (Server → Terminal)

[0079] Server: Sends organized advice to the user's terminal.

[0080] Terminal: Receives advice and displays it in the user interface.

[0081] Specific example: Methods of fundraising

[0082] Let's take the example of a user seeking advice on how to raise funds.

[0083] 1. User input (on the device)

[0084] User: Enters the question, "We are considering raising funds for future business expansion, but what methods would be most suitable?" and presses the submit button.

[0085] 2. Data transmission and reception (terminal → server)

[0086] Terminal: Sends the user's question to the server in JSON format.

[0087] Server: Receives data in JSON format and prepares it for analysis.

[0088] 3. Data preprocessing (server)

[0089] Server: Imputes missing data and normalizes the data.

[0090] 4. Accessing industry / sector databases (server)

[0091] Server: Extracts data on past success stories and fundraising methods related to fundraising from the database.

[0092] 5. Application of generative AI models (server)

[0093] Server: Based on the input data and extracted data, a generative AI model generates advice suggesting the optimal fundraising method. For example, it might generate advice such as, "Direct investment from new investors is optimal. Crowdfunding is also an effective method."

[0094] 6. Formatting advice (server)

[0095] Server: Formats the generated advice into a user-friendly format.

[0096] 7. Sending advice (Server → Terminal)

[0097] Server: Sends formatted advice to the user's terminal.

[0098] Terminal: Receives advice and displays it in the user interface.

[0099] In this way, this system provides prompt and accurate advice to managers of small and medium-sized enterprises, supporting their business decisions.

[0100] The following describes the processing flow.

[0101] Step 1: Data Entry Method

[0102] User: Enter the industry, business challenges, and required support details into the terminal interface and press the send button.

[0103] Step 2: Data transmission means

[0104] Terminal: Converts user-entered information into JSON format and sends it to the server as a request.

[0105] Step 3: Data Reception Method

[0106] Server: Receives JSON data sent from terminals via API and prepares it for analysis.

[0107] Step 4: Data preprocessing means

[0108] Server: The server processes the received data, including imputing missing values ​​and normalizing the data, and converts it into a format that can be analyzed.

[0109] Step 5: Database Reference Method

[0110] Server: Based on the industry and business challenges entered by the user, the server extracts necessary information from a database containing past success stories, trend data, and competitor information using SQL queries and other methods.

[0111] Step 6: Generative Artificial Intelligence Model Application Method

[0112] Server: The extracted data and pre-processed user data are input into a generative AI model to generate optimal advice. This AI model incorporates the know-how of several well-known business leaders.

[0113] Step 7: Advice on cosmetic procedures

[0114] Server: Formats the generated advice into natural-sounding language that is easy for the user to understand. Specifically, it performs processes such as constructing sentences and replacing technical terms with more general expressions.

[0115] Step 8: Means of sending advice

[0116] Server: Returns formatted advice in structured data format to the user's terminal.

[0117] Step 9: Means of displaying advice

[0118] Terminal: Receives advice sent from the server and displays it in the user interface.

[0119] Step 10: User Verification Method

[0120] User: Review the advice displayed on the device and make business decisions based on it. If necessary, enter additional questions or provide feedback, then return to step 1.

[0121] (Example 1)

[0122] 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."

[0123] Providing timely and accurate advice to small and medium-sized business owners facing management challenges is crucial for many companies. However, existing systems have struggled to properly extract the necessary information and provide optimal advice. In particular, there has been a need for a system that seamlessly integrates multiple processes, such as data preprocessing, referencing industry- and sector-specific information, and generating advice using generative artificial intelligence models.

[0124] 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.

[0125] In this invention, the server includes means for the user to input information, means for converting the data into a structured data format and transmitting it, means for receiving the data in a structured data format, means for imputing missing values ​​and normalizing the data, means for referring to an industry / sector database and extracting relevant information, means for applying a generation AI model based on the extracted data and input data to generate advice, means for formatting the generated advice into a format that is easy for the user to understand, and means for transmitting and displaying the formatted advice. This makes it possible to provide quick and accurate management advice to managers of small and medium-sized enterprises.

[0126] "Means for users to input information" refers to a device or system that allows users to input their industry, business challenges, and required support through an interface.

[0127] "Means for converting data into a structured data format and transmitting it" refers to a device or software that converts user-inputted information into a structured data format such as JSON and transmits it to a server via a network.

[0128] "Means for receiving data in structured data format" refers to a device or system that receives transmitted data in structured data format (e.g., JSON format) over a network and prepares it for further processing.

[0129] "Means for imputing missing values ​​and normalizing data" refers to devices or software that imputate missing values ​​in received data and normalize the data to unify its format.

[0130] "Means for referencing industry / sector databases and extracting relevant information" refers to devices or software for searching databases that store data on existing industries and sectors and extracting the necessary information.

[0131] "Means for generating advice by applying a generative AI model" refers to a device or software that applies a generative artificial intelligence model using extracted data and user-input data to generate optimal business advice.

[0132] "Means for formatting generated advice into a user-friendly format" refers to a device or software that formats generated advice into text, avoids excessive use of technical jargon, and converts it into a format that is easy for the user to understand.

[0133] "Means for sending and displaying formatted advice" refers to a device or system for converting formatted advice back into a structured data format, sending it to a user's terminal, and displaying it on a user interface.

[0134] This invention is a system that provides rapid and accurate advice to small and medium-sized enterprise (SME) managers regarding the management challenges they face. This system generates and provides optimal management advice using a generative AI model based on information entered by the user. The details are described below.

[0135] Components and the hardware and software used

[0136] This system includes the following main components:

[0137] 1. Data input means

[0138] Terminal: An interface (e.g., a web form or mobile application) is provided for users to input their industry, sector, business challenges, and required support.

[0139] 2. Data transmission means and receiving means

[0140] Terminal: Converts user-entered information into a structured data format (e.g., JSON format) and sends it to the server.

[0141] Server: Receives data sent from terminals and prepares it for analysis and processing.

[0142] 3. Data preprocessing means

[0143] Server: This server processes the received data, including imputing missing values ​​and normalizing the data. A Python library such as Pandas is often used.

[0144] 4. Database Reference Means

[0145] Server: Based on the entered industry and business type, it references relevant databases (e.g., MySQL® or MongoDB) and extracts the necessary information.

[0146] 5. Means for applying generative artificial intelligence models

[0147] Server: Based on the extracted data and input data, it generates optimal advice using a generative artificial intelligence (AI) model (for example, a model using TENSORFLOW® or PyTorch).

[0148] 6. Advice on cosmetic surgery methods

[0149] Server: Formats the generated advice into a user-friendly format. Specifically, it uses libraries such as nltk to structure the text and select terminology, adding supplementary explanations and examples as needed.

[0150] 7. Means of sending advice

[0151] Server: Sends formatted advice to the user's terminal.

[0152] Terminal: Receives data for displaying advice from the server and displays it on the user interface.

[0153] Specific examples and prompt statements

[0154] Specific examples are given below.

[0155] Specific example 1: Regarding the market launch of a new product

[0156] 1. User input (on the device)

[0157] Example text input: The user enters the question "We are considering launching a new product A into the market, but what kind of marketing strategy should we adopt?" into the interface and presses the submit button.

[0158] 2. Data transmission and reception (terminal → server)

[0159] The terminal converts the input information into JSON format and sends it to the server using an HTTP POST request.

[0160] 3. Data preprocessing (server)

[0161] The server performs missing value imputation and normalization on the received data. It uses the Python Pandas library.

[0162] 4. Accessing industry / sector databases (server)

[0163] The server references relevant databases to extract past success stories and trend data related to the market launch of new products.

[0164] 5. Application of generative AI models (server)

[0165] The server uses a generative AI model to generate marketing strategy advice. For example, it might generate advice such as, "The target market for new product A is young people in their 20s and 30s. Utilize social media and influencer marketing to conduct a campaign that highlights the product's features."

[0166] 6. Formatting advice (server)

[0167] The server formats this advice into a format that is easy for the user to understand.

[0168] 7. Sending and displaying advice (Server → Terminal)

[0169] The server converts the formatted advice back into JSON format and sends it to the user's terminal via an HTTP response.

[0170] The device displays the received advice in the user interface.

[0171] Specific example 2: Methods of fundraising

[0172] 1. User input (on the device)

[0173] Example text input: The user enters the question "We are considering raising funds for future business expansion, but what methods would be most suitable?" into the interface and presses the submit button.

[0174] 2. Data transmission and reception (terminal → server)

[0175] The terminal converts this question into JSON format and sends it to the server.

[0176] 3. Data preprocessing (server)

[0177] The server preprocesses the received data to ensure consistency.

[0178] 4. Accessing industry / sector databases (server)

[0179] The server extracts past success stories and fundraising methods from a database.

[0180] 5. Application of generative AI models (server)

[0181] The server uses a generative AI model to generate advice on fundraising methods. For example, it might generate advice such as, "Direct investment from new investors is optimal. Crowdfunding is also an effective method."

[0182] 6. Formatting advice (server)

[0183] The generated advice is formatted into a user-friendly format.

[0184] 7. Sending and displaying advice (Server → Terminal)

[0185] The formatted advice is sent to the user's device and displayed.

[0186] In this way, this system provides prompt and accurate advice to managers of small and medium-sized enterprises, supporting their business decisions.

[0187] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0188] Step 1: User enters information

[0189] Operation: Users use their devices (PCs or smartphones) to input their industry, business type, management challenges, and required support through web forms or mobile applications. Specifically, they might input a question such as, "We are considering launching a new product A into the market, what kind of marketing strategy would be appropriate?"

[0190] Input: Text information entered by the user.

[0191] Output: A transmission command is generated at the terminal, and the process proceeds to the next step.

[0192] Step 2: Data transmission

[0193] Operation: The terminal converts the information entered by the user into JSON format. The converted data is sent to the server using an HTTP POST request.

[0194] Input: Text information entered by the user.

[0195] Output: Data in JSON format. This data will be sent to the server in the next step.

[0196] Step 3: Data Reception

[0197] Operation: The server receives JSON data sent from the terminal. It parses the HTTP request and extracts the JSON data.

[0198] Input: JSON formatted data sent from the device.

[0199] Output: Parsed JSON data. This data proceeds to the next preprocessing step.

[0200] Step 4: Data preprocessing

[0201] Operation: The server performs preprocessing on the received JSON data. Specifically, it imputes missing values ​​and normalizes the data. It uses the Python Pandas library to format the data.

[0202] Input: Parsed JSON data.

[0203] Output: Preprocessed data. The formatted data proceeds to the next reference step.

[0204] Step 5: Database Reference

[0205] Operation: The server references industry and business databases based on pre-processed data. These databases contain historical success stories, trend data, competitor information, and more. This reference is performed using SQL or NoSQL databases (e.g., MySQL, MongoDB).

[0206] Input: Pre-processed data.

[0207] Output: Relevant data extracted by reference. This data will be used to proceed to the next step in applying the AI ​​model.

[0208] Step 6: Applying the Generative AI Model

[0209] Operation: The server applies a generative AI model (for example, a model using TensorFlow or PyTorch) based on the extracted data and user input data. The generative AI model uses this data to generate optimal advice. For example, it might generate advice such as, "The target market for new product A is young people in their 20s and 30s. Utilize social media and influencer marketing to conduct a campaign that highlights the product's features."

[0210] Input: User input data and related data extracted from the database.

[0211] Output: Advice generated by the AI ​​model. This advice then proceeds to the formatting step.

[0212] Step 7: Refining the advice

[0213] Operation: The server formats the generated advice. Specifically, it uses natural language processing tools such as the NLTK library to format the advice into a user-friendly format. It avoids technical jargon and simplifies the text.

[0214] Input: Advice generated by a generative AI model.

[0215] Output: Formatted advice. This advice proceeds to the submission step.

[0216] Step 8: Sending and displaying advice

[0217] Operation: The server converts the formatted advice back into JSON format and sends it to the user's terminal as an HTTP response.

[0218] Input: Formatted advice.

[0219] Output: Advice converted back into JSON format. This advice is sent to and displayed on the user's device.

[0220] This will enable the system to provide quick and accurate management advice to the managers of small and medium-sized enterprises.

[0221] (Application Example 1)

[0222] 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."

[0223] In recent years, content creators have faced a wide range of challenges, particularly in finding the optimal strategy to capture audience interest. Therefore, there is a need for means to quickly grasp trends and develop effective content strategies. However, individual data collection and analysis by creators is time-consuming, laborious, and inefficient. To address this challenge, a system is needed that provides creators with immediate and accurate advice on the problems they face.

[0224] 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.

[0225] In this invention, the server includes data preprocessing means, database referencing means, and generative artificial intelligence model application means. This makes it possible to generate and provide rapid and accurate advice in response to a task input by a content creator.

[0226] "Data entry means" refers to an interface for users to input their industry, business challenges, required support, and content distribution challenges.

[0227] "Data transmission means" refers to network communication means that transmit data from a terminal to a server in a structured data format.

[0228] "Data receiving means" refers to network communication means by which a server receives structured data transmitted from a terminal.

[0229] "Data preprocessing means" refers to processes that perform data reconciliation, such as imputing missing values ​​or normalizing the received data.

[0230] "Database referencing means" refers to a method of referencing a database based on the entered industry and business type and extracting the necessary information.

[0231] "Method for applying a generative artificial intelligence model" refers to a method for generating optimal advice using a generative AI model based on extracted data and data input by the user.

[0232] "Advice formatting" refers to the process of formatting generated advice into a user-friendly format and adding specific examples and supplementary explanations.

[0233] "Advice transmission method" refers to a means of sending and displaying formatted advice on the user's device.

[0234] "Data reference means related to content distribution" refers to means of extracting data such as content distribution trends and past success stories and providing them to generative artificial intelligence models.

[0235] This invention realizes a system that provides immediately useful advice to content creators facing challenges. This system performs multi-stage data processing and utilizes generative AI models to generate optimal advice and provide it to the user.

[0236] Program Processing Overview

[0237] Hardware and software to be used

[0238] Hardware:

[0239] Devices: Smartphones, smart glasses, head-mounted displays, and robots

[0240] Server: Computer server used for data processing and running AI models.

[0241] software:

[0242] Programming language: Python

[0243] Framework: Flask (Web server), MySQL (database management), OpenAI (registered trademark), GPT-3 (registered trademark) (generative AI model)

[0244] Processing flow details

[0245] 1. Data entry means:

[0246] The user uses a device to input a question into the interface. For example, they might input the question, "What is the best strategy to capture the audience's interest?"

[0247] 2. Data transmission means:

[0248] The application on the device converts the user's input into a structured data format (JSON format) and sends it to the server via the network.

[0249] 3. Data reception means:

[0250] The server receives the data sent from the terminal and prepares it for analysis.

[0251] 4. Data preprocessing means:

[0252] The server performs data normalization and imputation of missing values ​​in the received data. This makes the data suitable for analysis.

[0253] 5. Database referencing methods:

[0254] The server extracts past success stories and trend data related to content delivery from a database. For example, it obtains information such as what content formats are preferred by viewers.

[0255] 6. Means for applying generative artificial intelligence models:

[0256] The server applies a generative AI model (OpenAI GPT-3) based on the received data and extracted trend data to generate advice.

[0257] For example, advice might be generated stating, "To capture viewers' interest, it is recommended to use currently popular short-form videos and add interactive elements."

[0258] 7. Advice on cosmetic procedures:

[0259] The server formats the generated advice into a user-friendly format and adds specific examples and supplementary explanations as needed.

[0260] 8. Means of sending advice:

[0261] The formatted advice is converted back into a structured data format and sent to the user's terminal via the network.

[0262] Examples of specific cases and prompt statements

[0263] If the user enters the following question into the interface:

[0264] "What is the best strategy to capture the viewers' interest?"

[0265] Example of generated prompt text

[0266] "Please provide the best strategies for content creators to capture audience interest. He / She is currently producing new content for a video streaming service."

[0267] In this way, creators can be quickly provided with optimal advice based on generative AI models, enabling them to efficiently develop content strategies.

[0268] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0269] Step 1:

[0270] The user uses a device to input a question into the interface. For example, they might input the question, "What is the best strategy to capture the audience's interest?" The input question is then converted into a structured data format (JSON format) by the data input method. In this step, the input is the text entered by the user, and the output is data in JSON format.

[0271] Step 2:

[0272] The application on the device sends JSON-formatted data to the server over the network. The data transmission method fulfills this role. The data sent is the task information entered by the user, and the output is JSON-formatted data that is input to the server.

[0273] Step 3:

[0274] The server uses a data receiving means to receive JSON-formatted data sent from the terminal. Since the received data is not suitable for analysis as is, the server uses a data preprocessing means to preprocess the data. The input for this step is the received JSON-formatted data, and the output is the preprocessed data.

[0275] Step 4:

[0276] The server performs data preprocessing, including imputing missing values ​​and normalizing the data. This ensures data consistency and prepares it for analysis. The input for this step is unprocessed JSON data, and the output is normalized data. As a concrete example of its operation, fields with missing values ​​are imputed with historical data or the mean.

[0277] Step 5:

[0278] The server uses database lookup mechanisms based on normalized data to extract relevant information from the database. Examples include data on content delivery trends and past success stories. The input for this step is normalized user data, and the output is reference data extracted from the database. A concrete example of its operation is executing an SQL query to retrieve the necessary data.

[0279] Step 6:

[0280] By means of the generative AI model application means, the server inputs user data and reference data into the generative AI model (OpenAI GPT-3) to generate advice. The input of this step is user data and reference data, and the output is the generated advice. Specifically, a prompt sentence is generated and input into the GPT-3 model.

[0281] Step 7:

[0282] The server uses advice formatting means to format the generated advice into a form that is easy for the user to understand. Specific examples and supplementary explanations are added as necessary. The input of this step is the generated advice, and the output is the formatted advice. As a specific operation example, the grammar correction tool is used to tidy up the text.

[0283] Step 8:

[0284] The server uses advice transmission means to convert the formatted advice into JSON format and transmit it to the user's terminal via the network. The advice is received by the terminal and displayed on the user interface. The input of this step is the formatted advice, and the output is the advice displayed on the user's terminal.

[0285] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform specific processing using the user's emotion.

[0286] The present invention is a system for providing immediately useful advice for the management problems faced by the operators of small and medium-sized enterprises, and by combining an emotion engine for recognizing the user's emotion, it provides more appropriate advice. This system includes the following main components.

[0287] System Configuration

[0288] 1. Data Input Means

[0289] Terminal: Provides an interface for users to input their industry, sector, business challenges, and required support. Examples include web forms and mobile applications.

[0290] 2. Data transmission means and receiving means

[0291] Terminal: Converts user-entered information into JSON format and sends it to the server as a request.

[0292] Server: Receives data sent from terminals and prepares it for analysis and processing.

[0293] 3. Data preprocessing means

[0294] Server: Performs operations on received data, such as imputing missing values ​​and normalizing the data. This includes processes such as completing incomplete entries and ensuring consistency.

[0295] 4. Database Reference Means

[0296] Server: Based on the entered industry and business type, it references relevant databases and extracts the necessary information. This includes past success stories, trend data, and competitor information.

[0297] 5. Means for applying generative artificial intelligence models

[0298] Server: Based on extracted and input data, a generative artificial intelligence (AI) model generates optimal advice. This AI model incorporates the know-how of renowned business leaders.

[0299] 6. Emotional Engine

[0300] Terminal: Analyzes user emotions based on user input and behavior while using the interface, and sends the results to the server.

[0301] Server: Analyze the received emotion data and use it for advice generation and formatting.

[0302] 7. Advice formatting means

[0303] Server: Format the generated advice into a form that is easy for the user to understand. Specifically, perform the composition of the text and the selection of terms, and adjust the tone and content of the text according to the results of the emotion engine.

[0304] 8. Advice transmission means

[0305] Server: Transmit the formatted advice to the user's terminal and display it on the user interface.

[0306] Terminal: Receive the data for displaying the advice received from the server and display it on the user interface.

[0307] Program processing

[0308] The program of this system generates optimal management advice by utilizing the know-how of famous managers based on the information input by the user, and provides more personalized feedback using an emotion engine. The details of the processing will be explained in natural language below.

[0309] Specific example: Regarding the market launch of a new product

[0310] <The Take the case where a user requests advice on the market launch of a new product.

[0311] 1. Data input means (terminal)

[0312] User: Enter "I am considering launching a new product A, but what marketing strategy should I adopt?" into the interface and press the send button.

[0313] It should be noted that there seems to be an error in the original text where "<The " is present. It should probably be something like " ". This has been left as is in the translation to maintain consistency with the provided text.2. Data transmission and reception means (terminal → server)

[0314] Terminal: Packages the user's question in JSON format and sends it to the server.

[0315] Server: Receives data in JSON format and prepares it for analysis.

[0316] Terminal: Analyzes user input and actions using an emotion engine and sends the results to the server as additional data.

[0317] 3. Data preprocessing (server)

[0318] Server: Performs data imputation and normalization to prepare the data for analysis.

[0319] 4. Accessing industry / sector databases (server)

[0320] Server: Extracts past success stories, competitor information, and marketing trends related to the market launch of new products from the database.

[0321] 5. Application of generative AI models (server)

[0322] Server: Based on input data, extracted data, and sentiment data from the sentiment engine, a generative AI model generates strategic advice. For example, it might generate advice such as, "The target market for new product A is young people in their 20s and 30s. As an effective marketing method, utilize social media and influencer marketing and implement a campaign that emphasizes the product's features."

[0323] 6. Formatting advice (server)

[0324] Server: Formats the generated advice into natural-sounding text that is easy for users to understand. Specifically, it adjusts the tone and content of the text based on the results from the sentiment engine to make it more acceptable to users.

[0325] 7. Sending advice (Server → Terminal)

[0326] Server: Sends neatly formatted advice to the user's terminal.

[0327] Terminal: Receives advice and displays it in the user interface.

[0328] Specific example: Methods of fundraising

[0329] Let's take the example of a user seeking advice on how to raise funds.

[0330] 1. Data input means (terminal)

[0331] The user types, "We are considering raising funds to expand our business in the future, but what methods would be most suitable?" and presses the submit button.

[0332] 2. Data transmission and reception means (terminal → server)

[0333] Terminal: Sends the user's question to the server in JSON format.

[0334] Server: Receives data in JSON format and prepares for analysis.

[0335] Terminal: Analyzes user input and actions using an emotion engine, and sends the resulting emotion data to the server.

[0336] 3. Data preprocessing (server)

[0337] Server: It fills in missing data in the received data and normalizes the data.

[0338] 4. Accessing industry / sector databases (server)

[0339] Server: Extracts data on past success stories and fundraising methods related to fundraising from the database.

[0340] 5. Application of generative AI models (server)

[0341] Server: Based on input data, extracted data, and sentiment data from the sentiment engine, a generative AI model generates advice suggesting the optimal fundraising method. For example, it might generate advice such as, "Direct investment from new investors is optimal. Crowdfunding is also an effective method."

[0342] 6. Formatting advice (server)

[0343] Server: Formats the generated advice into a user-friendly format. Based on emotional data from the emotion engine, it emphasizes an encouraging tone if the user is relaxed, and adjusts the language to be calm and reassuring if the user is stressed.

[0344] 7. Sending advice (Server → Terminal)

[0345] Server: Sends formatted advice to the user's terminal.

[0346] Terminal: Receives advice and displays it in the user interface.

[0347] In this way, this system, which incorporates an emotion engine, provides managers of small and medium-sized enterprises with prompt, accurate, and personalized advice, supporting them in making better business decisions.

[0348] The following describes the processing flow.

[0349] Step 1: Data Entry Method

[0350] The user enters their industry, business challenges, and required support details into the terminal interface and presses the submit button.

[0351] Step 2: Data transmission means

[0352] The terminal converts the information entered by the user into JSON format and sends it to the server as a request.

[0353] Step 3: Data Reception Method

[0354] The server receives JSON data sent from the terminal via the API and prepares it for analysis.

[0355] Step 4: Acquiring emotional data using an emotion engine

[0356] The device analyzes the user's input and actions using an emotion engine to determine the user's emotional state. The results are then sent to the server as additional data.

[0357] Step 5: Data preprocessing means

[0358] The server performs data reconciliation, including imputing missing values ​​and normalizing the received data, and converts it into a parseable format.

[0359] Step 6: Database Reference Method

[0360] Based on the industry and business challenges entered by the user, the server extracts necessary information from a database containing past success stories, trend data, and competitor information using SQL queries and other methods.

[0361] Step 7: Method for applying generative artificial intelligence models

[0362] The server inputs extracted data, pre-processed user data, and emotional data obtained from the emotion engine into a generative AI model to generate optimal advice. This AI model incorporates the know-how of famous business leaders.

[0363] Step 8: Advice on cosmetic procedures

[0364] The server formats the generated advice into natural-sounding text that is easy for the user to understand. Specifically, it adjusts the tone and content of the text based on the user's emotional data obtained from the emotion engine, making it a format that is easily accepted by the user.

[0365] Step 9: Means of sending advice

[0366] The server returns formatted advice to the user's terminal in structured data format.

[0367] Step 10: Advice Display Method

[0368] The terminal receives advice sent from the server and displays it in the user interface.

[0369] Step 11: User Verification Method

[0370] The user reviews the advice displayed on the device and makes business decisions based on it. They then enter additional questions or provide feedback as needed, and return to step 1.

[0371] (Example 2)

[0372] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0373] Small and medium-sized enterprise (SME) managers face a wide range of management challenges, requiring prompt and accurate advice. However, many managers are overwhelmed with daily operations, limiting their time and resources for acquiring specialized knowledge. Furthermore, general management advice often fails to adequately address individual emotions and circumstances, making it difficult to provide optimal advice. In contrast, current systems often provide uniform advice that does not consider the user's feelings, making it difficult to provide personalized feedback tailored to the unique circumstances of SMEs.

[0374] 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.

[0375] In this invention, the server includes data preprocessing means, database referencing means, generative artificial intelligence model application means, sentiment analysis means, advice shaping means, and advice transmission means. This makes it possible to provide managers of small and medium-sized enterprises with optimal management advice that utilizes the insights of famous business leaders based on the input industry and management challenges, as well as to realize personalized feedback that responds to the user's emotions.

[0376] A "data input device" is a device that provides an interface for users to input their industry, business challenges, and required support.

[0377] A "data transmission means" is a device that converts information entered by a user into a structured data format and transmits it to a server via a network.

[0378] A "data receiving means" is a device that receives information transmitted in a structured data format and provides it to a server for analysis.

[0379] A "data preprocessing device" is a device that performs data preprocessing, such as imputing missing values ​​and normalizing the received data, to prepare it for analysis.

[0380] A "database referencing device" is a device that extracts information from a database related to the industry and business challenges entered by the user.

[0381] A "generative artificial intelligence model application means" is a device that applies a generative artificial intelligence model to input data and information extracted from a database to generate optimal management advice.

[0382] An "emotion analysis device" is a device that analyzes the user's input and actions to generate user emotion data.

[0383] An "advice formatting tool" is a device that formats generated advice into natural-sounding text that is easy for users to understand, and adjusts the tone and expression based on emotional data.

[0384] An "advice transmission means" is a device that sends formatted advice to the user's terminal and displays it on the user interface.

[0385] This invention is a system that provides rapid and accurate advice to small and medium-sized business owners facing various management challenges. By combining this system with an emotion analysis means that recognizes the user's emotions, it can provide more appropriate and personalized advice.

[0386] System configuration:

[0387] 1. Data input means

[0388] The device provides the user with an interface for inputting their industry, business challenges, and required support. Specifically, this interface utilizes web forms or mobile applications.

[0389] 2. Data transmission means and receiving means

[0390] The terminal converts the information entered by the user into JSON format and sends it to the server as a request. The server parses the received data and prepares it for processing.

[0391] 3. Data preprocessing means

[0392] The server performs data imputation and normalization of the data it receives. The server uses algorithms and heuristics to complete incomplete entries and ensure consistency.

[0393] 4. Database Reference Means

[0394] The server references relevant databases based on the entered industry and business type, and extracts the necessary information. Specifically, this includes past success stories, trend data, and competitor information.

[0395] 5. Means for applying generative artificial intelligence models

[0396] The server generates optimal advice using a generative artificial intelligence model based on extracted data and input data. This model incorporates the know-how of famous business leaders.

[0397] 6. Emotion analysis method

[0398] The terminal analyzes the user's input and behavior while using the interface, generates sentiment data, and sends it to the server. The server analyzes the received sentiment data and uses it to generate and format advice.

[0399] 7. Advice on cosmetic surgery methods

[0400] The server formats the generated advice into a user-friendly format by structuring the text and selecting terminology. It also adjusts the tone and content of the text based on sentiment analysis results to provide more acceptable advice.

[0401] 8. Means of sending advice

[0402] The server sends formatted advice to the user's terminal, and the terminal displays the received advice in the user interface.

[0403] Specific example:

[0404] Regarding the market launch of new products:

[0405] When a user seeks advice regarding the market launch of a new product:

[0406] User: "We are considering launching a new product A into the market. What kind of marketing strategy should we adopt?" (Types this and submits.)

[0407] The terminal converts the input content into JSON format and sends it to the server. The server preprocesses the received data and extracts past success stories and competitor information for marketing strategies by referring to relevant databases.

[0408] The server uses a generative artificial intelligence model to generate specific advice such as, "The target market for new product A is young people in their 20s and 30s. As an effective marketing method, utilize social media and influencer marketing and conduct a campaign that emphasizes the product's features."

[0409] Based on the results of the emotion analysis, the server formats the generated advice to suit the user's tone and content and sends it to the terminal.

[0410] The device displays the received advice to the user.

[0411] Regarding fundraising methods:

[0412] If a user seeks advice on how to raise funds:

[0413] User: "We are considering raising funds for future business expansion. What methods would be most suitable?" (Types this and submits.)

[0414] The terminal converts the input content into JSON format and sends it to the server. The server preprocesses the received data and extracts past successful fundraising cases and fundraising methods by referring to relevant databases.

[0415] The server uses a generative artificial intelligence model to generate specific advice such as, "Direct investment from new investors is optimal. Crowdfunding is also an effective method."

[0416] Based on the results of the emotion analysis, the server formats the generated advice to suit the user's tone and content and sends it to the terminal.

[0417] The device displays the received advice to the user.

[0418] In this way, the system of the present invention, by using a generative artificial intelligence model and emotion analysis means, can provide managers of small and medium-sized enterprises with rapid, accurate, and personalized management advice.

[0419] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0420] Step 1:

[0421] The user enters their industry, business challenges, and required support. The user then enters specific questions in text format into the interface and presses the submit button. This allows for the collection of raw data from the user.

[0422] Step 2:

[0423] The terminal converts user input into JSON format and sends it to the server. This structures the data, allowing for efficient transfer over the network.

[0424] Step 3:

[0425] The server receives data in JSON format. The server stores the received data in memory for analysis and prepares for the next processing step.

[0426] Step 4:

[0427] To perform sentiment analysis, the device includes the user's input and actions in its sentiment analysis engine for analysis, and sends the results to the server as additional data in JSON format. This allows the user's sentiment data to be extracted.

[0428] Step 5:

[0429] The server performs data normalization and imputation of missing values ​​in the data it receives. The server fills in any erroneous or missing data, arranging it into a consistent format. For example, it might use statistical methods or algorithms to fill in incomplete entries.

[0430] Step 6:

[0431] The server references relevant databases based on the entered industry and business type, and extracts the necessary information. The server uses SQL queries to retrieve past success stories, marketing trends, and other information from the databases.

[0432] Step 7:

[0433] Based on the extracted data, user input data, and sentiment data, the server uses a generative artificial intelligence model to generate optimal advice. For example, it might generate specific strategic advice such as, "For the market launch of new product A, we recommend using social media and influencer marketing."

[0434] Step 8:

[0435] The server formats the generated advice into a user-friendly format. Based on the sentiment analysis results, it adjusts the tone and content of the text to suit the user.

[0436] Step 9:

[0437] The server sends the formatted advice to the user's terminal. The formatted advice is packaged as display data and transferred.

[0438] Step 10:

[0439] The device displays advice received from the server on the user interface. Users can access specific and personalized advice through the device.

[0440] In this way, the system provides small and medium-sized business owners with fast, accurate, and personalized business advice.

[0441] (Application Example 2)

[0442] 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 device 14 will be referred to as the "terminal."

[0443] Virtual store owners lack access to timely and accurate advice on the various challenges they face in online business. Furthermore, there is a lack of systems that provide personalized advice that takes into account the emotional state of the business owner. As a result, the quality of business decisions suffers, and they often fail to adopt appropriate business strategies.

[0444] 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.

[0445] In this invention, the server includes data preprocessing means, generative artificial intelligence model application means, and sentiment analysis engine means. This makes it possible to analyze user input data and sentiment data, generate optimal business advice, and provide it in an easy-to-understand format.

[0446] A "data entry means" is a device or system that provides an interface for users to input their industry, business challenges, and required support.

[0447] A "data transmission means" refers to a function or system for sending information entered by a user to a server in a structured data format.

[0448] "Data receiving means" refers to the functions and systems that allow a server to receive information sent by a user and prepare it for analysis.

[0449] "Data preprocessing means" refers to functions or systems that perform data normalization, such as imputing missing values, on received data.

[0450] A "database referencing means" is a function or system that extracts necessary information from relevant databases based on the input information.

[0451] A "generative artificial intelligence model application method" refers to a function or system that applies an artificial intelligence model that generates optimal advice based on extracted data and input data.

[0452] An "emotion analysis engine" is a function or system that analyzes a user's emotions based on their input and actions, and sends the results to a server.

[0453] An "advice formatting tool" is a function or system that formats generated advice into a format that is easy for the user to understand.

[0454] An "advice transmission method" refers to a function or system for sending formatted advice to a user's device.

[0455] This invention is a system that provides immediately useful advice to virtual store operators facing various management challenges. The system includes data input means, data transmission means, data reception means, data preprocessing means, database referencing means, generative artificial intelligence model application means, sentiment analysis engine, advice formatting means, and advice transmission means. As an example, this invention is implemented as follows.

[0456] System program

[0457] The server first receives data from the data input means, including the user's industry, business challenges, and required support, via the data transmission means. The received data is normalized and missing values ​​are imputed by the data preprocessing means. Then, relevant information is extracted by the database referencing means, and optimal advice is generated by the generative artificial intelligence model application means. Furthermore, the sentiment analysis engine analyzes the user's sentiment data, and the advice is refined based on that sentiment data. Finally, the advice is displayed on the user's terminal via the advice transmission means.

[0458] Processing details

[0459] The server uses software implemented in programming languages ​​such as Python and Java (registered trademark) to exchange data in JSON format. Received data is preprocessed using libraries such as Pandas and NumPy. SQL and NoSQL databases (e.g., MySQL and MongoDB) are used as databases. Generative artificial intelligence models utilize machine learning libraries such as TensorFlow and PyTorch. The sentiment analysis engine uses natural language processing APIs and proprietary sentiment analysis algorithms.

[0460] Specific example

[0461] For example, if a user is seeking advice on promoting a new fashion item, they can enter a question like this: "What are some ways to promote a new fashion item?" The server analyzes this and generates advice such as, "Target women in their 20s and 30s, and implement video marketing using Instagram and TikTok. Influencer collaborations are also effective."

[0462] Example of a prompt

[0463] "Please advise on how to promote new fashion items. I would also like to know more about the target market."

[0464] In this way, the system can provide virtual store managers with personalized business advice that takes emotions into account, thereby improving the quality of their business decisions.

[0465] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0466] Step 1:

[0467] Users input their industry, business challenges, and required support through the terminal's interface. This input data includes specific industry information, concrete problems, and the type of support needed.

[0468] Step 2:

[0469] The terminal converts the information entered by the user into JSON format and sends it to the server via a data transmission method. The input data at this time is the user's input, and the output data is request data in JSON format.

[0470] Step 3:

[0471] The server receives JSON-formatted data from the terminal using a data receiving device. This data includes information such as industry, business challenges, and support details, and serves as preparation data for analysis.

[0472] Step 4:

[0473] The server normalizes the received data using data preprocessing. Specifically, it performs actions such as imputing missing values ​​and standardizing the data format. In this process, the input data is the received data, and the output data is the preprocessed data.

[0474] Step 5:

[0475] The server extracts necessary information from relevant databases through a database referencing mechanism. This extracted information includes past success stories and trend data. The input data for this process is pre-processed data, and the output data is the result of the referencing.

[0476] Step 6:

[0477] The server generates optimal advice based on the data extracted using a generative artificial intelligence model application method and the input data. A machine learning model is used for this advice generation. The input data for processing is the reference result data, and the output data is the generated advice.

[0478] Step 7:

[0479] The terminal analyzes the user's actions and input using an emotion analysis engine and sends the results to the server. In this process, the input data is the emotion analysis result, and the output data is also the emotion analysis result.

[0480] Step 8:

[0481] The server integrates emotional data and formats the generated advice based on the results of the emotion analysis engine. During formatting, it adjusts the tone and structure of the text. The input data consists of the generated advice and emotional data, while the output data is the formatted advice that reflects the emotions.

[0482] Step 9:

[0483] The server sends the formatted advice to the user's terminal using a data transmission method. In this process, the input data is the formatted advice, and the output data is the transmitted advice.

[0484] Step 10:

[0485] The terminal receives advice sent from the server and displays it on the user interface. In this process, the input data is the advice from the server, and the output data is the advice in a display format that the user can see.

[0486] 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.

[0487] 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.

[0488] 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.

[0489] [Second Embodiment]

[0490] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0491] 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.

[0492] 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).

[0493] 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.

[0494] 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.

[0495] 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).

[0496] 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.

[0497] 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.

[0498] 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.

[0499] 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.

[0500] 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.

[0501] 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".

[0502] This invention provides a system for offering immediately useful advice to managers of small and medium-sized enterprises (SMEs) regarding the management challenges they face. This system includes the following main components:

[0503] System Configuration

[0504] 1. Data input means

[0505] Terminal: Provides an interface for users to input their industry, sector, business challenges, and required support. Examples include web forms and mobile applications.

[0506] 2. Data transmission means and receiving means

[0507] Terminal: Converts user-entered information into a structured data format (e.g., JSON format) and sends it to the server.

[0508] Server: Receives data sent from terminals and prepares it for analysis and processing.

[0509] 3. Data preprocessing means

[0510] Server: Performs operations on received data, such as imputing missing values ​​and normalizing the data. This includes processes such as completing incomplete entries and ensuring consistency.

[0511] 4. Database Reference Means

[0512] Server: Based on the entered industry and business type, it references relevant databases and extracts the necessary information. This includes past success stories, trend data, and competitor information.

[0513] 5. Means for applying generative artificial intelligence models

[0514] Server: Based on extracted and input data, a generative artificial intelligence (AI) model generates optimal advice. This AI model incorporates the know-how of renowned business leaders.

[0515] 6. Advice on cosmetic surgery methods

[0516] Server: Formats the generated advice into a user-friendly format. Specifically, it organizes the text, selects terminology, and adds supplementary explanations and examples as needed.

[0517] 7. Means of sending advice

[0518] Server: Sends formatted advice to the user's terminal and displays it in the user interface.

[0519] Terminal: Receives data for displaying advice from the server and displays it on the user interface.

[0520] Program processing

[0521] This system's program generates and provides optimal business advice based on information entered by the user, utilizing the expertise of renowned business leaders. The details of this process are explained below in natural language.

[0522] Specific example: Regarding the market launch of a new product

[0523] Let's take the example of a user seeking advice on launching a new product into the market.

[0524] 1. User input (on the device)

[0525] User: Enters the question, "We are considering launching a new product A into the market. What kind of marketing strategy should we adopt?" into the interface and presses the submit button.

[0526] 2. Data transmission and reception (terminal → server)

[0527] Terminal: Packages the user's question in JSON format and sends it to the server.

[0528] Server: Receives data in JSON format and prepares it for analysis.

[0529] 3. Data preprocessing (server)

[0530] Server: Performs data imputation and normalization to prepare the data for analysis.

[0531] 4. Accessing industry / sector databases (server)

[0532] Server: Extracts past success stories, competitor information, and marketing trends related to the market launch of new products from the database.

[0533] 5. Application of generative AI models (server)

[0534] Server: Based on input data and extracted data, a generative AI model generates strategic advice. For example, it might generate advice such as, "The target market for new product A is young people in their 20s and 30s. As an effective marketing method, utilize social media and influencer marketing and implement a campaign that emphasizes the product's features."

[0535] 6. Formatting advice (server)

[0536] Server: Formats the generated advice into natural-sounding text that is easy for the user to understand.

[0537] 7. Sending advice (Server → Terminal)

[0538] Server: Sends organized advice to the user's terminal.

[0539] Terminal: Receives advice and displays it in the user interface.

[0540] Specific example: Methods of fundraising

[0541] Let's take the example of a user seeking advice on how to raise funds.

[0542] 1. User input (on the device)

[0543] User: Enters the question, "We are considering raising funds for future business expansion, but what methods would be most suitable?" and presses the submit button.

[0544] 2. Data transmission and reception (terminal → server)

[0545] Terminal: Sends the user's question to the server in JSON format.

[0546] Server: Receives data in JSON format and prepares it for analysis.

[0547] 3. Data preprocessing (server)

[0548] Server: Imputes missing data and normalizes the data.

[0549] 4. Accessing industry / sector databases (server)

[0550] Server: Extracts data on past success stories and fundraising methods related to fundraising from the database.

[0551] 5. Application of generative AI models (server)

[0552] Server: Based on the input data and extracted data, a generative AI model generates advice suggesting the optimal fundraising method. For example, it might generate advice such as, "Direct investment from new investors is optimal. Crowdfunding is also an effective method."

[0553] 6. Formatting advice (server)

[0554] Server: Formats the generated advice into a user-friendly format.

[0555] 7. Sending advice (Server → Terminal)

[0556] Server: Sends formatted advice to the user's terminal.

[0557] Terminal: Receives advice and displays it in the user interface.

[0558] In this way, this system provides prompt and accurate advice to managers of small and medium-sized enterprises, supporting their business decisions.

[0559] The following describes the processing flow.

[0560] Step 1: Data Entry Method

[0561] User: Enter the industry, business challenges, and required support details into the terminal interface and press the send button.

[0562] Step 2: Data transmission means

[0563] Terminal: Converts user-entered information into JSON format and sends it to the server as a request.

[0564] Step 3: Data Reception Method

[0565] Server: Receives JSON data sent from terminals via API and prepares it for analysis.

[0566] Step 4: Data preprocessing means

[0567] Server: The server processes the received data, including imputing missing values ​​and normalizing the data, and converts it into a format that can be analyzed.

[0568] Step 5: Database Reference Method

[0569] Server: Based on the industry and business challenges entered by the user, the server extracts necessary information from a database containing past success stories, trend data, and competitor information using SQL queries and other methods.

[0570] Step 6: Generative Artificial Intelligence Model Application Method

[0571] Server: The extracted data and pre-processed user data are input into a generative AI model to generate optimal advice. This AI model incorporates the know-how of several well-known business leaders.

[0572] Step 7: Advice on cosmetic procedures

[0573] Server: Formats the generated advice into natural-sounding language that is easy for the user to understand. Specifically, it performs processes such as constructing sentences and replacing technical terms with more general expressions.

[0574] Step 8: Means of sending advice

[0575] Server: Returns formatted advice in structured data format to the user's terminal.

[0576] Step 9: Means of displaying advice

[0577] Terminal: Receives advice sent from the server and displays it in the user interface.

[0578] Step 10: User Verification Method

[0579] User: Review the advice displayed on the device and make business decisions based on it. If necessary, enter additional questions or provide feedback, then return to step 1.

[0580] (Example 1)

[0581] 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".

[0582] Providing timely and accurate advice to small and medium-sized business owners facing management challenges is crucial for many companies. However, existing systems have struggled to properly extract the necessary information and provide optimal advice. In particular, there has been a need for a system that seamlessly integrates multiple processes, such as data preprocessing, referencing industry- and sector-specific information, and generating advice using generative artificial intelligence models.

[0583] 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.

[0584] In this invention, the server includes means for the user to input information, means for converting the data into a structured data format and transmitting it, means for receiving the data in a structured data format, means for imputing missing values ​​and normalizing the data, means for referring to an industry / sector database and extracting relevant information, means for applying a generation AI model based on the extracted data and input data to generate advice, means for formatting the generated advice into a format that is easy for the user to understand, and means for transmitting and displaying the formatted advice. This makes it possible to provide quick and accurate management advice to managers of small and medium-sized enterprises.

[0585] "Means for users to input information" refers to a device or system that allows users to input their industry, business challenges, and required support through an interface.

[0586] "Means for converting data into a structured data format and transmitting it" refers to a device or software that converts user-inputted information into a structured data format such as JSON and transmits it to a server via a network.

[0587] "Means for receiving data in structured data format" refers to a device or system that receives transmitted data in structured data format (e.g., JSON format) over a network and prepares it for further processing.

[0588] "Means for imputing missing values ​​and normalizing data" refers to devices or software that imputate missing values ​​in received data and normalize the data to unify its format.

[0589] "Means for referencing industry / sector databases and extracting relevant information" refers to devices or software for searching databases that store data on existing industries and sectors and extracting the necessary information.

[0590] "Means for generating advice by applying a generative AI model" refers to a device or software that applies a generative artificial intelligence model using extracted data and user-input data to generate optimal business advice.

[0591] "Means for formatting generated advice into a user-friendly format" refers to a device or software that formats generated advice into text, avoids excessive use of technical jargon, and converts it into a format that is easy for the user to understand.

[0592] "Means for sending and displaying formatted advice" refers to a device or system for converting formatted advice back into a structured data format, sending it to a user's terminal, and displaying it on a user interface.

[0593] This invention is a system that provides rapid and accurate advice to small and medium-sized enterprise (SME) managers regarding the management challenges they face. This system generates and provides optimal management advice using a generative AI model based on information entered by the user. The details are described below.

[0594] Components and the hardware and software used

[0595] This system includes the following main components:

[0596] 1. Data input means

[0597] Terminal: An interface (e.g., a web form or mobile application) is provided for users to input their industry, sector, business challenges, and required support.

[0598] 2. Data transmission means and receiving means

[0599] Terminal: Converts user-entered information into a structured data format (e.g., JSON format) and sends it to the server.

[0600] Server: Receives data sent from terminals and prepares it for analysis and processing.

[0601] 3. Data preprocessing means

[0602] Server: This server processes the received data, including imputing missing values ​​and normalizing the data. A Python library such as Pandas is often used.

[0603] 4. Database Reference Means

[0604] Server: Based on the entered industry and business type, it references relevant databases (e.g., MySQL or MongoDB) and extracts the necessary information.

[0605] 5. Means for applying generative artificial intelligence models

[0606] Server: Based on the extracted data and input data, it generates optimal advice using a generative artificial intelligence (AI) model (for example, a model using TensorFlow or PyTorch).

[0607] 6. Advice on cosmetic surgery methods

[0608] Server: Formats the generated advice into a user-friendly format. Specifically, it uses libraries such as nltk to structure the text and select terminology, adding supplementary explanations and examples as needed.

[0609] 7. Means of sending advice

[0610] Server: Sends formatted advice to the user's terminal.

[0611] Terminal: Receives data for displaying advice from the server and displays it on the user interface.

[0612] Specific examples and prompt statements

[0613] Specific examples are given below.

[0614] Specific example 1: Regarding the market launch of a new product

[0615] 1. User input (on the device)

[0616] Example text input: The user enters the question "We are considering launching a new product A into the market, but what kind of marketing strategy should we adopt?" into the interface and presses the submit button.

[0617] 2. Data transmission and reception (terminal → server)

[0618] The terminal converts the input information into JSON format and sends it to the server using an HTTP POST request.

[0619] 3. Data preprocessing (server)

[0620] The server performs missing value imputation and normalization on the received data. It uses the Python Pandas library.

[0621] 4. Accessing industry / sector databases (server)

[0622] The server references relevant databases to extract past success stories and trend data related to the market launch of new products.

[0623] 5. Application of generative AI models (server)

[0624] The server uses a generative AI model to generate marketing strategy advice. For example, it might generate advice such as, "The target market for new product A is young people in their 20s and 30s. Utilize social media and influencer marketing to conduct a campaign that highlights the product's features."

[0625] 6. Formatting advice (server)

[0626] The server formats this advice into a format that is easy for the user to understand.

[0627] 7. Sending and displaying advice (Server → Terminal)

[0628] The server converts the formatted advice back into JSON format and sends it to the user's terminal via an HTTP response.

[0629] The device displays the received advice in the user interface.

[0630] Specific example 2: Methods of fundraising

[0631] 1. User input (on the device)

[0632] Example text input: The user enters the question "We are considering raising funds for future business expansion, but what methods would be most suitable?" into the interface and presses the submit button.

[0633] 2. Data transmission and reception (terminal → server)

[0634] The terminal converts this question into JSON format and sends it to the server.

[0635] 3. Data preprocessing (server)

[0636] The server preprocesses the received data to ensure consistency.

[0637] 4. Accessing industry / sector databases (server)

[0638] The server extracts past success stories and fundraising methods from a database.

[0639] 5. Application of generative AI models (server)

[0640] The server uses a generative AI model to generate advice on fundraising methods. For example, it might generate advice such as, "Direct investment from new investors is optimal. Crowdfunding is also an effective method."

[0641] 6. Formatting advice (server)

[0642] The generated advice is formatted into a user-friendly format.

[0643] 7. Sending and displaying advice (Server → Terminal)

[0644] The formatted advice is sent to the user's device and displayed.

[0645] In this way, this system provides prompt and accurate advice to managers of small and medium-sized enterprises, supporting their business decisions.

[0646] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0647] Step 1: User enters information

[0648] Operation: Users use their devices (PCs or smartphones) to input their industry, business type, management challenges, and required support through web forms or mobile applications. Specifically, they might input a question such as, "We are considering launching a new product A into the market, what kind of marketing strategy would be appropriate?"

[0649] Input: Text information entered by the user.

[0650] Output: A transmission command is generated at the terminal, and the process proceeds to the next step.

[0651] Step 2: Data transmission

[0652] Operation: The terminal converts the information entered by the user into JSON format. The converted data is sent to the server using an HTTP POST request.

[0653] Input: Text information entered by the user.

[0654] Output: Data in JSON format. This data will be sent to the server in the next step.

[0655] Step 3: Data Reception

[0656] Operation: The server receives JSON data sent from the terminal. It parses the HTTP request and extracts the JSON data.

[0657] Input: JSON formatted data sent from the device.

[0658] Output: Parsed JSON data. This data proceeds to the next preprocessing step.

[0659] Step 4: Data preprocessing

[0660] Operation: The server performs preprocessing on the received JSON data. Specifically, it imputes missing values ​​and normalizes the data. It uses the Python Pandas library to format the data.

[0661] Input: Parsed JSON data.

[0662] Output: Preprocessed data. The formatted data proceeds to the next reference step.

[0663] Step 5: Database Reference

[0664] Operation: The server references industry and business databases based on pre-processed data. These databases contain historical success stories, trend data, competitor information, and more. This reference is performed using SQL or NoSQL databases (e.g., MySQL, MongoDB).

[0665] Input: Pre-processed data.

[0666] Output: Relevant data extracted by reference. This data will be used to proceed to the next step in applying the AI ​​model.

[0667] Step 6: Applying the Generative AI Model

[0668] Operation: The server applies a generative AI model (for example, a model using TensorFlow or PyTorch) based on the extracted data and user input data. The generative AI model uses this data to generate optimal advice. For example, it might generate advice such as, "The target market for new product A is young people in their 20s and 30s. Utilize social media and influencer marketing to conduct a campaign that highlights the product's features."

[0669] Input: User input data and related data extracted from the database.

[0670] Output: Advice generated by the AI ​​model. This advice then proceeds to the formatting step.

[0671] Step 7: Refining the advice

[0672] Operation: The server formats the generated advice. Specifically, it uses natural language processing tools such as the NLTK library to format the advice into a user-friendly format. It avoids technical jargon and simplifies the text.

[0673] Input: Advice generated by a generative AI model.

[0674] Output: Formatted advice. This advice proceeds to the submission step.

[0675] Step 8: Sending and displaying advice

[0676] Operation: The server converts the formatted advice back into JSON format and sends it to the user's terminal as an HTTP response.

[0677] Input: Formatted advice.

[0678] Output: Advice converted back into JSON format. This advice is sent to and displayed on the user's device.

[0679] This will enable the system to provide quick and accurate management advice to the managers of small and medium-sized enterprises.

[0680] (Application Example 1)

[0681] 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."

[0682] In recent years, content creators have faced a wide range of challenges, particularly in finding the optimal strategy to capture audience interest. Therefore, there is a need for means to quickly grasp trends and develop effective content strategies. However, individual data collection and analysis by creators is time-consuming, laborious, and inefficient. To address this challenge, a system is needed that provides creators with immediate and accurate advice on the problems they face.

[0683] 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.

[0684] In this invention, the server includes data preprocessing means, database referencing means, and generative artificial intelligence model application means. This makes it possible to generate and provide rapid and accurate advice in response to a task input by a content creator.

[0685] "Data entry means" refers to an interface for users to input their industry, business challenges, required support, and content distribution challenges.

[0686] "Data transmission means" refers to network communication means that transmit data from a terminal to a server in a structured data format.

[0687] "Data receiving means" refers to network communication means by which a server receives structured data transmitted from a terminal.

[0688] "Data preprocessing means" refers to processes that perform data reconciliation, such as imputing missing values ​​or normalizing the received data.

[0689] "Database referencing means" refers to a method of referencing a database based on the entered industry and business type and extracting the necessary information.

[0690] "Method for applying a generative artificial intelligence model" refers to a method for generating optimal advice using a generative AI model based on extracted data and data input by the user.

[0691] "Advice formatting" refers to the process of formatting generated advice into a user-friendly format and adding specific examples and supplementary explanations.

[0692] "Advice transmission method" refers to a means of sending and displaying formatted advice on the user's device.

[0693] "Data reference means related to content distribution" refers to means of extracting data such as content distribution trends and past success stories and providing them to generative artificial intelligence models.

[0694] This invention realizes a system that provides immediately useful advice to content creators facing challenges. This system performs multi-stage data processing and utilizes generative AI models to generate optimal advice and provide it to the user.

[0695] Program Processing Overview

[0696] Hardware and software to be used

[0697] Hardware:

[0698] Devices: Smartphones, smart glasses, head-mounted displays, and robots

[0699] Server: Computer server used for data processing and running AI models.

[0700] software:

[0701] Programming language: Python

[0702] Framework: Flask (Web server), MySQL (database management), OpenAI GPT-3 (generative AI model)

[0703] Processing flow details

[0704] 1. Data entry means:

[0705] The user uses a device to input a question into the interface. For example, they might input the question, "What is the best strategy to capture the audience's interest?"

[0706] 2. Data transmission means:

[0707] The application on the device converts the user's input into a structured data format (JSON format) and sends it to the server via the network.

[0708] 3. Data reception means:

[0709] The server receives the data sent from the terminal and prepares it for analysis.

[0710] 4. Data preprocessing means:

[0711] The server performs data normalization and imputation of missing values ​​in the received data. This makes the data suitable for analysis.

[0712] 5. Database referencing methods:

[0713] The server extracts past success stories and trend data related to content delivery from a database. For example, it obtains information such as what content formats are preferred by viewers.

[0714] 6. Means for applying generative artificial intelligence models:

[0715] The server applies a generative AI model (OpenAI GPT-3) based on the received data and extracted trend data to generate advice.

[0716] For example, advice might be generated stating, "To capture viewers' interest, it is recommended to use currently popular short-form videos and add interactive elements."

[0717] 7. Advice on cosmetic procedures:

[0718] The server formats the generated advice into a user-friendly format and adds specific examples and supplementary explanations as needed.

[0719] 8. Means of sending advice:

[0720] The formatted advice is converted back into a structured data format and sent to the user's terminal via the network.

[0721] Examples of specific cases and prompt statements

[0722] If the user enters the following question into the interface:

[0723] "What is the best strategy to capture the viewers' interest?"

[0724] Example of generated prompt text

[0725] "Please provide the best strategies for content creators to capture audience interest. He / She is currently producing new content for a video streaming service."

[0726] In this way, creators can be quickly provided with optimal advice based on generative AI models, enabling them to efficiently develop content strategies.

[0727] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0728] Step 1:

[0729] The user uses a device to input a question into the interface. For example, they might input the question, "What is the best strategy to capture the audience's interest?" The input question is then converted into a structured data format (JSON format) by the data input method. In this step, the input is the text entered by the user, and the output is data in JSON format.

[0730] Step 2:

[0731] The application on the device sends JSON-formatted data to the server over the network. The data transmission method fulfills this role. The data sent is the task information entered by the user, and the output is JSON-formatted data that is input to the server.

[0732] Step 3:

[0733] The server uses a data receiving means to receive JSON-formatted data sent from the terminal. Since the received data is not suitable for analysis as is, the server uses a data preprocessing means to preprocess the data. The input for this step is the received JSON-formatted data, and the output is the preprocessed data.

[0734] Step 4:

[0735] The server performs data preprocessing, including imputing missing values ​​and normalizing the data. This ensures data consistency and prepares it for analysis. The input for this step is unprocessed JSON data, and the output is normalized data. As a concrete example of its operation, fields with missing values ​​are imputed with historical data or the mean.

[0736] Step 5:

[0737] The server uses database lookup mechanisms based on normalized data to extract relevant information from the database. Examples include data on content delivery trends and past success stories. The input for this step is normalized user data, and the output is reference data extracted from the database. A concrete example of its operation is executing an SQL query to retrieve the necessary data.

[0738] Step 6:

[0739] The generative artificial intelligence model application means allows the server to input user data and reference data into a generative artificial intelligence model (OpenAI GPT-3) to generate advice. The input for this step is user data and reference data, and the output is the generated advice. Specifically, prompt sentences are generated and input into the GPT-3 model.

[0740] Step 7:

[0741] The server uses an advice formatting tool to reshape the generated advice into a format that is easy for the user to understand. Specific examples and supplementary explanations are added as needed. The input for this step is the generated advice, and the output is the formatted advice. A concrete example of this operation is using a grammar correction tool to refine the text.

[0742] Step 8:

[0743] The server uses an advice transmission method to convert the formatted advice into JSON format and send it to the user's terminal over the network. The terminal receives the advice and displays it in the user interface. The input for this step is the formatted advice, and the output is the advice displayed on the user's terminal.

[0744] 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.

[0745] This invention is a system for providing immediately useful advice to managers of small and medium-sized enterprises (SMEs) regarding management challenges they face. By combining this with an emotion engine that recognizes the user's emotions, it provides more appropriate advice. This system includes the following main components:

[0746] System Configuration

[0747] 1. Data input means

[0748] Terminal: Provides an interface for users to input their industry, sector, business challenges, and required support. Examples include web forms and mobile applications.

[0749] 2. Data transmission means and receiving means

[0750] Terminal: Converts user-entered information into JSON format and sends it to the server as a request.

[0751] Server: Receives data sent from terminals and prepares it for analysis and processing.

[0752] 3. Data preprocessing means

[0753] Server: Performs operations on received data, such as imputing missing values ​​and normalizing the data. This includes processes such as completing incomplete entries and ensuring consistency.

[0754] 4. Database Reference Means

[0755] Server: Based on the entered industry and business type, it references relevant databases and extracts the necessary information. This includes past success stories, trend data, and competitor information.

[0756] 5. Means for applying generative artificial intelligence models

[0757] Server: Based on extracted and input data, a generative artificial intelligence (AI) model generates optimal advice. This AI model incorporates the know-how of renowned business leaders.

[0758] 6. Emotional Engine

[0759] Terminal: Analyzes user emotions based on user input and behavior while using the interface, and sends the results to the server.

[0760] Server: Analyzes received emotional data and uses it to generate and format advice.

[0761] 7. Advice on cosmetic surgery methods

[0762] Server: Formats the generated advice into a user-friendly format. Specifically, it handles sentence structure and terminology selection, and adjusts the tone and content of the text based on the results of the sentiment engine.

[0763] 8. Means of sending advice

[0764] Server: Sends formatted advice to the user's terminal and displays it in the user interface.

[0765] Terminal: Receives data for displaying advice from the server and displays it on the user interface.

[0766] Program processing

[0767] This system's program generates optimal business advice based on user input, leveraging the expertise of renowned business leaders, and provides more personalized feedback using an emotion engine. The details of this process are explained below in natural language.

[0768] Specific example: Regarding the market launch of a new product

[0769] Let's take the example of a user seeking advice on launching a new product into the market.

[0770] 1. Data input means (terminal)

[0771] The user enters the following into the interface: "We are considering launching a new product A into the market. What kind of marketing strategy should we adopt?" and presses the submit button.

[0772] 2. Data transmission and reception means (terminal → server)

[0773] Terminal: Packages the user's question in JSON format and sends it to the server.

[0774] Server: Receives data in JSON format and prepares it for analysis.

[0775] Terminal: Analyzes user input and actions using an emotion engine and sends the results to the server as additional data.

[0776] 3. Data preprocessing (server)

[0777] Server: Performs data imputation and normalization to prepare the data for analysis.

[0778] 4. Accessing industry / sector databases (server)

[0779] Server: Extracts past success stories, competitor information, and marketing trends related to the market launch of new products from the database.

[0780] 5. Application of generative AI models (server)

[0781] Server: Based on input data, extracted data, and sentiment data from the sentiment engine, a generative AI model generates strategic advice. For example, it might generate advice such as, "The target market for new product A is young people in their 20s and 30s. As an effective marketing method, utilize social media and influencer marketing and implement a campaign that emphasizes the product's features."

[0782] 6. Formatting advice (server)

[0783] Server: Formats the generated advice into natural-sounding text that is easy for users to understand. Specifically, it adjusts the tone and content of the text based on the results from the sentiment engine to make it more acceptable to users.

[0784] 7. Sending advice (Server → Terminal)

[0785] Server: Sends neatly formatted advice to the user's terminal.

[0786] Terminal: Receives advice and displays it in the user interface.

[0787] Specific example: Methods of fundraising

[0788] Let's take the example of a user seeking advice on how to raise funds.

[0789] 1. Data input means (terminal)

[0790] The user types, "We are considering raising funds to expand our business in the future, but what methods would be most suitable?" and presses the submit button.

[0791] 2. Data transmission and reception means (terminal → server)

[0792] Terminal: Sends the user's question to the server in JSON format.

[0793] Server: Receives data in JSON format and prepares for analysis.

[0794] Terminal: Analyzes user input and actions using an emotion engine, and sends the resulting emotion data to the server.

[0795] 3. Data preprocessing (server)

[0796] Server: It fills in missing data in the received data and normalizes the data.

[0797] 4. Accessing industry / sector databases (server)

[0798] Server: Extracts data on past success stories and fundraising methods related to fundraising from the database.

[0799] 5. Application of generative AI models (server)

[0800] Server: Based on input data, extracted data, and sentiment data from the sentiment engine, a generative AI model generates advice suggesting the optimal fundraising method. For example, it might generate advice such as, "Direct investment from new investors is optimal. Crowdfunding is also an effective method."

[0801] 6. Formatting advice (server)

[0802] Server: Formats the generated advice into a user-friendly format. Based on emotional data from the emotion engine, it emphasizes an encouraging tone if the user is relaxed, and adjusts the language to be calm and reassuring if the user is stressed.

[0803] 7. Sending advice (Server → Terminal)

[0804] Server: Sends formatted advice to the user's terminal.

[0805] Terminal: Receives advice and displays it in the user interface.

[0806] In this way, this system, which incorporates an emotion engine, provides managers of small and medium-sized enterprises with prompt, accurate, and personalized advice, supporting them in making better business decisions.

[0807] The following describes the processing flow.

[0808] Step 1: Data Entry Method

[0809] The user enters their industry, business challenges, and required support details into the terminal interface and presses the submit button.

[0810] Step 2: Data transmission means

[0811] The terminal converts the information entered by the user into JSON format and sends it to the server as a request.

[0812] Step 3: Data Reception Method

[0813] The server receives JSON data sent from the terminal via the API and prepares it for analysis.

[0814] Step 4: Acquiring emotional data using an emotion engine

[0815] The device analyzes the user's input and actions using an emotion engine to determine the user's emotional state. The results are then sent to the server as additional data.

[0816] Step 5: Data preprocessing means

[0817] The server performs data reconciliation, including imputing missing values ​​and normalizing the received data, and converts it into a parseable format.

[0818] Step 6: Database Reference Method

[0819] Based on the industry and business challenges entered by the user, the server extracts necessary information from a database containing past success stories, trend data, and competitor information using SQL queries and other methods.

[0820] Step 7: Method for applying generative artificial intelligence models

[0821] The server inputs extracted data, pre-processed user data, and emotional data obtained from the emotion engine into a generative AI model to generate optimal advice. This AI model incorporates the know-how of famous business leaders.

[0822] Step 8: Advice on cosmetic procedures

[0823] The server formats the generated advice into natural-sounding text that is easy for the user to understand. Specifically, it adjusts the tone and content of the text based on the user's emotional data obtained from the emotion engine, making it a format that is easily accepted by the user.

[0824] Step 9: Means of sending advice

[0825] The server returns formatted advice to the user's terminal in structured data format.

[0826] Step 10: Advice Display Method

[0827] The terminal receives advice sent from the server and displays it in the user interface.

[0828] Step 11: User Verification Method

[0829] The user reviews the advice displayed on the device and makes business decisions based on it. They then enter additional questions or provide feedback as needed, and return to step 1.

[0830] (Example 2)

[0831] 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".

[0832] Small and medium-sized enterprise (SME) managers face a wide range of management challenges, requiring prompt and accurate advice. However, many managers are overwhelmed with daily operations, limiting their time and resources for acquiring specialized knowledge. Furthermore, general management advice often fails to adequately address individual emotions and circumstances, making it difficult to provide optimal advice. In contrast, current systems often provide uniform advice that does not consider the user's feelings, making it difficult to provide personalized feedback tailored to the unique circumstances of SMEs.

[0833] 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.

[0834] In this invention, the server includes data preprocessing means, database referencing means, generative artificial intelligence model application means, sentiment analysis means, advice shaping means, and advice transmission means. This makes it possible to provide managers of small and medium-sized enterprises with optimal management advice that utilizes the insights of famous business leaders based on the input industry and management challenges, as well as to realize personalized feedback that responds to the user's emotions.

[0835] A "data input device" is a device that provides an interface for users to input their industry, business challenges, and required support.

[0836] A "data transmission means" is a device that converts information entered by a user into a structured data format and transmits it to a server via a network.

[0837] A "data receiving means" is a device that receives information transmitted in a structured data format and provides it to a server for analysis.

[0838] A "data preprocessing device" is a device that performs data preprocessing, such as imputing missing values ​​and normalizing the received data, to prepare it for analysis.

[0839] A "database referencing device" is a device that extracts information from a database related to the industry and business challenges entered by the user.

[0840] A "generative artificial intelligence model application means" is a device that applies a generative artificial intelligence model to input data and information extracted from a database to generate optimal management advice.

[0841] An "emotion analysis device" is a device that analyzes the user's input and actions to generate user emotion data.

[0842] An "advice formatting tool" is a device that formats generated advice into natural-sounding text that is easy for users to understand, and adjusts the tone and expression based on emotional data.

[0843] An "advice transmission means" is a device that sends formatted advice to the user's terminal and displays it on the user interface.

[0844] This invention is a system that provides rapid and accurate advice to small and medium-sized business owners facing various management challenges. By combining this system with an emotion analysis means that recognizes the user's emotions, it can provide more appropriate and personalized advice.

[0845] System configuration:

[0846] 1. Data input means

[0847] The device provides the user with an interface for inputting their industry, business challenges, and required support. Specifically, this interface utilizes web forms or mobile applications.

[0848] 2. Data transmission means and receiving means

[0849] The terminal converts the information entered by the user into JSON format and sends it to the server as a request. The server parses the received data and prepares it for processing.

[0850] 3. Data preprocessing means

[0851] The server performs data imputation and normalization of the data it receives. The server uses algorithms and heuristics to complete incomplete entries and ensure consistency.

[0852] 4. Database Reference Means

[0853] The server references relevant databases based on the entered industry and business type, and extracts the necessary information. Specifically, this includes past success stories, trend data, and competitor information.

[0854] 5. Means for applying generative artificial intelligence models

[0855] The server generates optimal advice using a generative artificial intelligence model based on extracted data and input data. This model incorporates the know-how of famous business leaders.

[0856] 6. Emotion analysis method

[0857] The terminal analyzes the user's input and behavior while using the interface, generates sentiment data, and sends it to the server. The server analyzes the received sentiment data and uses it to generate and format advice.

[0858] 7. Advice on cosmetic surgery methods

[0859] The server formats the generated advice into a user-friendly format by structuring the text and selecting terminology. It also adjusts the tone and content of the text based on sentiment analysis results to provide more acceptable advice.

[0860] 8. Means of sending advice

[0861] The server sends formatted advice to the user's terminal, and the terminal displays the received advice in the user interface.

[0862] Specific example:

[0863] Regarding the market launch of new products:

[0864] When a user seeks advice regarding the market launch of a new product:

[0865] User: "We are considering launching a new product A into the market. What kind of marketing strategy should we adopt?" (Types this and submits.)

[0866] The terminal converts the input content into JSON format and sends it to the server. The server preprocesses the received data and extracts past success stories and competitor information for marketing strategies by referring to relevant databases.

[0867] The server uses a generative artificial intelligence model to generate specific advice such as, "The target market for new product A is young people in their 20s and 30s. As an effective marketing method, utilize social media and influencer marketing and conduct a campaign that emphasizes the product's features."

[0868] Based on the results of the emotion analysis, the server formats the generated advice to suit the user's tone and content and sends it to the terminal.

[0869] The device displays the received advice to the user.

[0870] Regarding fundraising methods:

[0871] If a user seeks advice on how to raise funds:

[0872] User: "We are considering raising funds for future business expansion. What methods would be most suitable?" (Types this and submits.)

[0873] The terminal converts the input content into JSON format and sends it to the server. The server preprocesses the received data and extracts past successful fundraising cases and fundraising methods by referring to relevant databases.

[0874] The server uses a generative artificial intelligence model to generate specific advice such as, "Direct investment from new investors is optimal. Crowdfunding is also an effective method."

[0875] Based on the results of the emotion analysis, the server formats the generated advice to suit the user's tone and content and sends it to the terminal.

[0876] The device displays the received advice to the user.

[0877] In this way, the system of the present invention, by using a generative artificial intelligence model and emotion analysis means, can provide managers of small and medium-sized enterprises with rapid, accurate, and personalized management advice.

[0878] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0879] Step 1:

[0880] The user enters their industry, business challenges, and required support. The user then enters specific questions in text format into the interface and presses the submit button. This allows for the collection of raw data from the user.

[0881] Step 2:

[0882] The terminal converts user input into JSON format and sends it to the server. This structures the data, allowing for efficient transfer over the network.

[0883] Step 3:

[0884] The server receives data in JSON format. The server stores the received data in memory for analysis and prepares for the next processing step.

[0885] Step 4:

[0886] To perform sentiment analysis, the device includes the user's input and actions in its sentiment analysis engine for analysis, and sends the results to the server as additional data in JSON format. This allows the user's sentiment data to be extracted.

[0887] Step 5:

[0888] The server performs data normalization and imputation of missing values ​​in the data it receives. The server fills in any erroneous or missing data, arranging it into a consistent format. For example, it might use statistical methods or algorithms to fill in incomplete entries.

[0889] Step 6:

[0890] The server references relevant databases based on the entered industry and business type, and extracts the necessary information. The server uses SQL queries to retrieve past success stories, marketing trends, and other information from the databases.

[0891] Step 7:

[0892] Based on the extracted data, user input data, and sentiment data, the server uses a generative artificial intelligence model to generate optimal advice. For example, it might generate specific strategic advice such as, "For the market launch of new product A, we recommend using social media and influencer marketing."

[0893] Step 8:

[0894] The server formats the generated advice into a user-friendly format. Based on the sentiment analysis results, it adjusts the tone and content of the text to suit the user.

[0895] Step 9:

[0896] The server sends the formatted advice to the user's terminal. The formatted advice is packaged as display data and transferred.

[0897] Step 10:

[0898] The device displays advice received from the server on the user interface. Users can access specific and personalized advice through the device.

[0899] In this way, the system provides small and medium-sized business owners with fast, accurate, and personalized business advice.

[0900] (Application Example 2)

[0901] 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."

[0902] Virtual store owners lack access to timely and accurate advice on the various challenges they face in online business. Furthermore, there is a lack of systems that provide personalized advice that takes into account the emotional state of the business owner. As a result, the quality of business decisions suffers, and they often fail to adopt appropriate business strategies.

[0903] 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.

[0904] In this invention, the server includes data preprocessing means, generative artificial intelligence model application means, and sentiment analysis engine means. This makes it possible to analyze user input data and sentiment data, generate optimal business advice, and provide it in an easy-to-understand format.

[0905] A "data entry means" is a device or system that provides an interface for users to input their industry, business challenges, and required support.

[0906] A "data transmission means" refers to a function or system for sending information entered by a user to a server in a structured data format.

[0907] "Data receiving means" refers to the functions and systems that allow a server to receive information sent by a user and prepare it for analysis.

[0908] "Data preprocessing means" refers to functions or systems that perform data normalization, such as imputing missing values, on received data.

[0909] A "database referencing means" is a function or system that extracts necessary information from relevant databases based on the input information.

[0910] A "generative artificial intelligence model application method" refers to a function or system that applies an artificial intelligence model that generates optimal advice based on extracted data and input data.

[0911] An "emotion analysis engine" is a function or system that analyzes a user's emotions based on their input and actions, and sends the results to a server.

[0912] An "advice formatting tool" is a function or system that formats generated advice into a format that is easy for the user to understand.

[0913] An "advice transmission method" refers to a function or system for sending formatted advice to a user's device.

[0914] This invention is a system that provides immediately useful advice to virtual store operators facing various management challenges. The system includes data input means, data transmission means, data reception means, data preprocessing means, database referencing means, generative artificial intelligence model application means, sentiment analysis engine, advice formatting means, and advice transmission means. As an example, this invention is implemented as follows.

[0915] System program

[0916] The server first receives data from the data input means, including the user's industry, business challenges, and required support, via the data transmission means. The received data is normalized and missing values ​​are imputed by the data preprocessing means. Then, relevant information is extracted by the database referencing means, and optimal advice is generated by the generative artificial intelligence model application means. Furthermore, the sentiment analysis engine analyzes the user's sentiment data, and the advice is refined based on that sentiment data. Finally, the advice is displayed on the user's terminal via the advice transmission means.

[0917] Processing details

[0918] The server uses software implemented in programming languages ​​such as Python and Java to exchange data in JSON format. Received data is preprocessed using libraries such as Pandas and NumPy. SQL and NoSQL databases (e.g., MySQL and MongoDB) are used as databases. Generative artificial intelligence models utilize machine learning libraries such as TensorFlow and PyTorch. The sentiment analysis engine uses natural language processing APIs and proprietary sentiment analysis algorithms.

[0919] Specific example

[0920] For example, if a user is seeking advice on promoting a new fashion item, they can enter a question like this: "What are some ways to promote a new fashion item?" The server analyzes this and generates advice such as, "Target women in their 20s and 30s, and implement video marketing using Instagram and TikTok. Influencer collaborations are also effective."

[0921] Example of a prompt

[0922] "Please advise on how to promote new fashion items. I would also like to know more about the target market."

[0923] In this way, the system can provide virtual store managers with personalized business advice that takes emotions into account, thereby improving the quality of their business decisions.

[0924] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0925] Step 1:

[0926] Users input their industry, business challenges, and required support through the terminal's interface. This input data includes specific industry information, concrete problems, and the type of support needed.

[0927] Step 2:

[0928] The terminal converts the information entered by the user into JSON format and sends it to the server via a data transmission method. The input data at this time is the user's input, and the output data is request data in JSON format.

[0929] Step 3:

[0930] The server receives JSON-formatted data from the terminal using a data receiving device. This data includes information such as industry, business challenges, and support details, and serves as preparation data for analysis.

[0931] Step 4:

[0932] The server normalizes the received data using data preprocessing. Specifically, it performs actions such as imputing missing values ​​and standardizing the data format. In this process, the input data is the received data, and the output data is the preprocessed data.

[0933] Step 5:

[0934] The server extracts necessary information from relevant databases through a database referencing mechanism. This extracted information includes past success stories and trend data. The input data for this process is pre-processed data, and the output data is the result of the referencing.

[0935] Step 6:

[0936] The server generates optimal advice based on the data extracted using a generative artificial intelligence model application method and the input data. A machine learning model is used for this advice generation. The input data for processing is the reference result data, and the output data is the generated advice.

[0937] Step 7:

[0938] The terminal analyzes the user's actions and input using an emotion analysis engine and sends the results to the server. In this process, the input data is the emotion analysis result, and the output data is also the emotion analysis result.

[0939] Step 8:

[0940] The server integrates emotional data and formats the generated advice based on the results of the emotion analysis engine. During formatting, it adjusts the tone and structure of the text. The input data consists of the generated advice and emotional data, while the output data is the formatted advice that reflects the emotions.

[0941] Step 9:

[0942] The server sends the formatted advice to the user's terminal using a data transmission method. In this process, the input data is the formatted advice, and the output data is the transmitted advice.

[0943] Step 10:

[0944] The terminal receives advice sent from the server and displays it on the user interface. In this process, the input data is the advice from the server, and the output data is the advice in a display format that the user can see.

[0945] 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.

[0946] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0947] 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.

[0948] [Third Embodiment]

[0949] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0950] 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.

[0951] 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).

[0952] 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.

[0953] 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.

[0954] 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).

[0955] 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.

[0956] 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.

[0957] 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.

[0958] 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.

[0959] 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.

[0960] 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".

[0961] This invention provides a system for offering immediately useful advice to managers of small and medium-sized enterprises (SMEs) regarding the management challenges they face. This system includes the following main components:

[0962] System Configuration

[0963] 1. Data input means

[0964] Terminal: Provides an interface for users to input their industry, sector, business challenges, and required support. Examples include web forms and mobile applications.

[0965] 2. Data transmission means and receiving means

[0966] Terminal: Converts user-entered information into a structured data format (e.g., JSON format) and sends it to the server.

[0967] Server: Receives data sent from terminals and prepares it for analysis and processing.

[0968] 3. Data preprocessing means

[0969] Server: Performs operations on received data, such as imputing missing values ​​and normalizing the data. This includes processes such as completing incomplete entries and ensuring consistency.

[0970] 4. Database Reference Means

[0971] Server: Based on the entered industry and business type, it references relevant databases and extracts the necessary information. This includes past success stories, trend data, and competitor information.

[0972] 5. Means for applying generative artificial intelligence models

[0973] Server: Based on extracted and input data, a generative artificial intelligence (AI) model generates optimal advice. This AI model incorporates the know-how of renowned business leaders.

[0974] 6. Advice on cosmetic surgery methods

[0975] Server: Formats the generated advice into a user-friendly format. Specifically, it organizes the text, selects terminology, and adds supplementary explanations and examples as needed.

[0976] 7. Means of sending advice

[0977] Server: Sends formatted advice to the user's terminal and displays it in the user interface.

[0978] Terminal: Receives data for displaying advice from the server and displays it on the user interface.

[0979] Program processing

[0980] This system's program generates and provides optimal business advice based on information entered by the user, utilizing the expertise of renowned business leaders. The details of this process are explained below in natural language.

[0981] Specific example: Regarding the market launch of a new product

[0982] Let's take the example of a user seeking advice on launching a new product into the market.

[0983] 1. User input (on the device)

[0984] User: Enters the question, "We are considering launching a new product A into the market. What kind of marketing strategy should we adopt?" into the interface and presses the submit button.

[0985] 2. Data transmission and reception (terminal → server)

[0986] Terminal: Packages the user's question in JSON format and sends it to the server.

[0987] Server: Receives data in JSON format and prepares it for analysis.

[0988] 3. Data preprocessing (server)

[0989] Server: Performs data imputation and normalization to prepare the data for analysis.

[0990] 4. Accessing industry / sector databases (server)

[0991] Server: Extracts past success stories, competitor information, and marketing trends related to the market launch of new products from the database.

[0992] 5. Application of generative AI models (server)

[0993] Server: Based on input data and extracted data, a generative AI model generates strategic advice. For example, it might generate advice such as, "The target market for new product A is young people in their 20s and 30s. As an effective marketing method, utilize social media and influencer marketing and implement a campaign that emphasizes the product's features."

[0994] 6. Formatting advice (server)

[0995] Server: Formats the generated advice into natural-sounding text that is easy for the user to understand.

[0996] 7. Sending advice (Server → Terminal)

[0997] Server: Sends organized advice to the user's terminal.

[0998] Terminal: Receives advice and displays it in the user interface.

[0999] Specific example: Methods of fundraising

[1000] Let's take the example of a user seeking advice on how to raise funds.

[1001] 1. User input (on the device)

[1002] User: Enters the question, "We are considering raising funds for future business expansion, but what methods would be most suitable?" and presses the submit button.

[1003] 2. Data transmission and reception (terminal → server)

[1004] Terminal: Sends the user's question to the server in JSON format.

[1005] Server: Receives data in JSON format and prepares it for analysis.

[1006] 3. Data preprocessing (server)

[1007] Server: Imputes missing data and normalizes the data.

[1008] 4. Accessing industry / sector databases (server)

[1009] Server: Extracts data on past success stories and fundraising methods related to fundraising from the database.

[1010] 5. Application of generative AI models (server)

[1011] Server: Based on the input data and extracted data, a generative AI model generates advice suggesting the optimal fundraising method. For example, it might generate advice such as, "Direct investment from new investors is optimal. Crowdfunding is also an effective method."

[1012] 6. Formatting advice (server)

[1013] Server: Formats the generated advice into a user-friendly format.

[1014] 7. Sending advice (Server → Terminal)

[1015] Server: Sends formatted advice to the user's terminal.

[1016] Terminal: Receives advice and displays it in the user interface.

[1017] In this way, this system provides prompt and accurate advice to managers of small and medium-sized enterprises, supporting their business decisions.

[1018] The following describes the processing flow.

[1019] Step 1: Data Entry Method

[1020] User: Enter the industry, business challenges, and required support details into the terminal interface and press the send button.

[1021] Step 2: Data transmission means

[1022] Terminal: Converts user-entered information into JSON format and sends it to the server as a request.

[1023] Step 3: Data Reception Method

[1024] Server: Receives JSON data sent from terminals via API and prepares it for analysis.

[1025] Step 4: Data preprocessing means

[1026] Server: The server processes the received data, including imputing missing values ​​and normalizing the data, and converts it into a format that can be analyzed.

[1027] Step 5: Database Reference Method

[1028] Server: Based on the industry and business challenges entered by the user, the server extracts necessary information from a database containing past success stories, trend data, and competitor information using SQL queries and other methods.

[1029] Step 6: Generative Artificial Intelligence Model Application Method

[1030] Server: The extracted data and pre-processed user data are input into a generative AI model to generate optimal advice. This AI model incorporates the know-how of several well-known business leaders.

[1031] Step 7: Advice on cosmetic procedures

[1032] Server: Formats the generated advice into natural-sounding language that is easy for the user to understand. Specifically, it performs processes such as constructing sentences and replacing technical terms with more general expressions.

[1033] Step 8: Means of sending advice

[1034] Server: Returns formatted advice in structured data format to the user's terminal.

[1035] Step 9: Means of displaying advice

[1036] Terminal: Receives advice sent from the server and displays it in the user interface.

[1037] Step 10: User Verification Method

[1038] User: Review the advice displayed on the device and make business decisions based on it. If necessary, enter additional questions or provide feedback, then return to step 1.

[1039] (Example 1)

[1040] 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."

[1041] Providing timely and accurate advice to small and medium-sized business owners facing management challenges is crucial for many companies. However, existing systems have struggled to properly extract the necessary information and provide optimal advice. In particular, there has been a need for a system that seamlessly integrates multiple processes, such as data preprocessing, referencing industry- and sector-specific information, and generating advice using generative artificial intelligence models.

[1042] 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.

[1043] In this invention, the server includes means for the user to input information, means for converting the data into a structured data format and transmitting it, means for receiving the data in a structured data format, means for imputing missing values ​​and normalizing the data, means for referring to an industry / sector database and extracting relevant information, means for applying a generation AI model based on the extracted data and input data to generate advice, means for formatting the generated advice into a format that is easy for the user to understand, and means for transmitting and displaying the formatted advice. This makes it possible to provide quick and accurate management advice to managers of small and medium-sized enterprises.

[1044] "Means for users to input information" refers to a device or system that allows users to input their industry, business challenges, and required support through an interface.

[1045] "Means for converting data into a structured data format and transmitting it" refers to a device or software that converts user-inputted information into a structured data format such as JSON and transmits it to a server via a network.

[1046] "Means for receiving data in structured data format" refers to a device or system that receives transmitted data in structured data format (e.g., JSON format) over a network and prepares it for further processing.

[1047] "Means for imputing missing values ​​and normalizing data" refers to devices or software that imputate missing values ​​in received data and normalize the data to unify its format.

[1048] "Means for referencing industry / sector databases and extracting relevant information" refers to devices or software for searching databases that store data on existing industries and sectors and extracting the necessary information.

[1049] "Means for generating advice by applying a generative AI model" refers to a device or software that applies a generative artificial intelligence model using extracted data and user-input data to generate optimal business advice.

[1050] "Means for formatting generated advice into a user-friendly format" refers to a device or software that formats generated advice into text, avoids excessive use of technical jargon, and converts it into a format that is easy for the user to understand.

[1051] "Means for sending and displaying formatted advice" refers to a device or system for converting formatted advice back into a structured data format, sending it to a user's terminal, and displaying it on a user interface.

[1052] This invention is a system that provides rapid and accurate advice to small and medium-sized enterprise (SME) managers regarding the management challenges they face. This system generates and provides optimal management advice using a generative AI model based on information entered by the user. The details are described below.

[1053] Components and the hardware and software used

[1054] This system includes the following main components:

[1055] 1. Data input means

[1056] Terminal: An interface (e.g., a web form or mobile application) is provided for users to input their industry, sector, business challenges, and required support.

[1057] 2. Data transmission means and receiving means

[1058] Terminal: Converts user-entered information into a structured data format (e.g., JSON format) and sends it to the server.

[1059] Server: Receives data sent from terminals and prepares it for analysis and processing.

[1060] 3. Data preprocessing means

[1061] Server: This server processes the received data, including imputing missing values ​​and normalizing the data. A Python library such as Pandas is often used.

[1062] 4. Database Reference Means

[1063] Server: Based on the entered industry and business type, it references relevant databases (e.g., MySQL or MongoDB) and extracts the necessary information.

[1064] 5. Means for applying generative artificial intelligence models

[1065] Server: Based on the extracted data and input data, it generates optimal advice using a generative artificial intelligence (AI) model (for example, a model using TensorFlow or PyTorch).

[1066] 6. Advice on cosmetic surgery methods

[1067] Server: Formats the generated advice into a user-friendly format. Specifically, it uses libraries such as nltk to structure the text and select terminology, adding supplementary explanations and examples as needed.

[1068] 7. Means of sending advice

[1069] Server: Sends formatted advice to the user's terminal.

[1070] Terminal: Receives data for displaying advice from the server and displays it on the user interface.

[1071] Specific examples and prompt statements

[1072] Specific examples are given below.

[1073] Specific example 1: Regarding the market launch of a new product

[1074] 1. User input (on the device)

[1075] Example text input: The user enters the question "We are considering launching a new product A into the market, but what kind of marketing strategy should we adopt?" into the interface and presses the submit button.

[1076] 2. Data transmission and reception (terminal → server)

[1077] The terminal converts the input information into JSON format and sends it to the server using an HTTP POST request.

[1078] 3. Data preprocessing (server)

[1079] The server performs missing value imputation and normalization on the received data. It uses the Python Pandas library.

[1080] 4. Accessing industry / sector databases (server)

[1081] The server references relevant databases to extract past success stories and trend data related to the market launch of new products.

[1082] 5. Application of generative AI models (server)

[1083] The server uses a generative AI model to generate marketing strategy advice. For example, it might generate advice such as, "The target market for new product A is young people in their 20s and 30s. Utilize social media and influencer marketing to conduct a campaign that highlights the product's features."

[1084] 6. Formatting advice (server)

[1085] The server formats this advice into a format that is easy for the user to understand.

[1086] 7. Sending and displaying advice (Server → Terminal)

[1087] The server converts the formatted advice back into JSON format and sends it to the user's terminal via an HTTP response.

[1088] The device displays the received advice in the user interface.

[1089] Specific example 2: Methods of fundraising

[1090] 1. User input (on the device)

[1091] Example text input: The user enters the question "We are considering raising funds for future business expansion, but what methods would be most suitable?" into the interface and presses the submit button.

[1092] 2. Data transmission and reception (terminal → server)

[1093] The terminal converts this question into JSON format and sends it to the server.

[1094] 3. Data preprocessing (server)

[1095] The server preprocesses the received data to ensure consistency.

[1096] 4. Accessing industry / sector databases (server)

[1097] The server extracts past success stories and fundraising methods from a database.

[1098] 5. Application of generative AI models (server)

[1099] The server uses a generative AI model to generate advice on fundraising methods. For example, it might generate advice such as, "Direct investment from new investors is optimal. Crowdfunding is also an effective method."

[1100] 6. Formatting advice (server)

[1101] The generated advice is formatted into a user-friendly format.

[1102] 7. Sending and displaying advice (Server → Terminal)

[1103] The formatted advice is sent to the user's device and displayed.

[1104] In this way, this system provides prompt and accurate advice to managers of small and medium-sized enterprises, supporting their business decisions.

[1105] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1106] Step 1: User enters information

[1107] Operation: Users use their devices (PCs or smartphones) to input their industry, business type, management challenges, and required support through web forms or mobile applications. Specifically, they might input a question such as, "We are considering launching a new product A into the market, what kind of marketing strategy would be appropriate?"

[1108] Input: Text information entered by the user.

[1109] Output: A transmission command is generated at the terminal, and the process proceeds to the next step.

[1110] Step 2: Data transmission

[1111] Operation: The terminal converts the information entered by the user into JSON format. The converted data is sent to the server using an HTTP POST request.

[1112] Input: Text information entered by the user.

[1113] Output: Data in JSON format. This data will be sent to the server in the next step.

[1114] Step 3: Data Reception

[1115] Operation: The server receives JSON data sent from the terminal. It parses the HTTP request and extracts the JSON data.

[1116] Input: JSON formatted data sent from the device.

[1117] Output: Parsed JSON data. This data proceeds to the next preprocessing step.

[1118] Step 4: Data preprocessing

[1119] Operation: The server performs preprocessing on the received JSON data. Specifically, it imputes missing values ​​and normalizes the data. It uses the Python Pandas library to format the data.

[1120] Input: Parsed JSON data.

[1121] Output: Preprocessed data. The formatted data proceeds to the next reference step.

[1122] Step 5: Database Reference

[1123] Operation: The server references industry and business databases based on pre-processed data. These databases contain historical success stories, trend data, competitor information, and more. This reference is performed using SQL or NoSQL databases (e.g., MySQL, MongoDB).

[1124] Input: Pre-processed data.

[1125] Output: Relevant data extracted by reference. This data will be used to proceed to the next step in applying the AI ​​model.

[1126] Step 6: Applying the Generative AI Model

[1127] Operation: The server applies a generative AI model (for example, a model using TensorFlow or PyTorch) based on the extracted data and user input data. The generative AI model uses this data to generate optimal advice. For example, it might generate advice such as, "The target market for new product A is young people in their 20s and 30s. Utilize social media and influencer marketing to conduct a campaign that highlights the product's features."

[1128] Input: User input data and related data extracted from the database.

[1129] Output: Advice generated by the AI ​​model. This advice then proceeds to the formatting step.

[1130] Step 7: Refining the advice

[1131] Operation: The server formats the generated advice. Specifically, it uses natural language processing tools such as the NLTK library to format the advice into a user-friendly format. It avoids technical jargon and simplifies the text.

[1132] Input: Advice generated by a generative AI model.

[1133] Output: Formatted advice. This advice proceeds to the submission step.

[1134] Step 8: Sending and displaying advice

[1135] Operation: The server converts the formatted advice back into JSON format and sends it to the user's terminal as an HTTP response.

[1136] Input: Formatted advice.

[1137] Output: Advice converted back into JSON format. This advice is sent to and displayed on the user's device.

[1138] This will enable the system to provide quick and accurate management advice to the managers of small and medium-sized enterprises.

[1139] (Application Example 1)

[1140] 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."

[1141] In recent years, content creators have faced a wide range of challenges, particularly in finding the optimal strategy to capture audience interest. Therefore, there is a need for means to quickly grasp trends and develop effective content strategies. However, individual data collection and analysis by creators is time-consuming, laborious, and inefficient. To address this challenge, a system is needed that provides creators with immediate and accurate advice on the problems they face.

[1142] 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.

[1143] In this invention, the server includes data preprocessing means, database referencing means, and generative artificial intelligence model application means. This makes it possible to generate and provide rapid and accurate advice in response to a task input by a content creator.

[1144] "Data entry means" refers to an interface for users to input their industry, business challenges, required support, and content distribution challenges.

[1145] "Data transmission means" refers to network communication means that transmit data from a terminal to a server in a structured data format.

[1146] "Data receiving means" refers to network communication means by which a server receives structured data transmitted from a terminal.

[1147] "Data preprocessing means" refers to processes that perform data reconciliation, such as imputing missing values ​​or normalizing the received data.

[1148] "Database referencing means" refers to a method of referencing a database based on the entered industry and business type and extracting the necessary information.

[1149] "Method for applying a generative artificial intelligence model" refers to a method for generating optimal advice using a generative AI model based on extracted data and data input by the user.

[1150] "Advice formatting" refers to the process of formatting generated advice into a user-friendly format and adding specific examples and supplementary explanations.

[1151] "Advice transmission method" refers to a means of sending and displaying formatted advice on the user's device.

[1152] "Data reference means related to content distribution" refers to means of extracting data such as content distribution trends and past success stories and providing them to generative artificial intelligence models.

[1153] This invention realizes a system that provides immediately useful advice to content creators facing challenges. This system performs multi-stage data processing and utilizes generative AI models to generate optimal advice and provide it to the user.

[1154] Program Processing Overview

[1155] Hardware and software to be used

[1156] Hardware:

[1157] Devices: Smartphones, smart glasses, head-mounted displays, and robots

[1158] Server: Computer server used for data processing and running AI models.

[1159] software:

[1160] Programming language: Python

[1161] Framework: Flask (Web server), MySQL (database management), OpenAI GPT-3 (generative AI model)

[1162] Processing flow details

[1163] 1. Data entry means:

[1164] The user uses a device to input a question into the interface. For example, they might input the question, "What is the best strategy to capture the audience's interest?"

[1165] 2. Data transmission means:

[1166] The application on the device converts the user's input into a structured data format (JSON format) and sends it to the server via the network.

[1167] 3. Data reception means:

[1168] The server receives the data sent from the terminal and prepares it for analysis.

[1169] 4. Data preprocessing means:

[1170] The server performs data normalization and imputation of missing values ​​in the received data. This makes the data suitable for analysis.

[1171] 5. Database referencing methods:

[1172] The server extracts past success stories and trend data related to content delivery from a database. For example, it obtains information such as what content formats are preferred by viewers.

[1173] 6. Means for applying generative artificial intelligence models:

[1174] The server applies a generative AI model (OpenAI GPT-3) based on the received data and extracted trend data to generate advice.

[1175] For example, advice might be generated stating, "To capture viewers' interest, it is recommended to use currently popular short-form videos and add interactive elements."

[1176] 7. Advice on cosmetic procedures:

[1177] The server formats the generated advice into a user-friendly format and adds specific examples and supplementary explanations as needed.

[1178] 8. Means of sending advice:

[1179] The formatted advice is converted back into a structured data format and sent to the user's terminal via the network.

[1180] Examples of specific cases and prompt statements

[1181] If the user enters the following question into the interface:

[1182] "What is the best strategy to capture the viewers' interest?"

[1183] Example of generated prompt text

[1184] "Please provide the best strategies for content creators to capture audience interest. He / She is currently producing new content for a video streaming service."

[1185] In this way, creators can be quickly provided with optimal advice based on generative AI models, enabling them to efficiently develop content strategies.

[1186] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1187] Step 1:

[1188] The user uses a device to input a question into the interface. For example, they might input the question, "What is the best strategy to capture the audience's interest?" The input question is then converted into a structured data format (JSON format) by the data input method. In this step, the input is the text entered by the user, and the output is data in JSON format.

[1189] Step 2:

[1190] The application on the device sends JSON-formatted data to the server over the network. The data transmission method fulfills this role. The data sent is the task information entered by the user, and the output is JSON-formatted data that is input to the server.

[1191] Step 3:

[1192] The server uses a data receiving means to receive JSON-formatted data sent from the terminal. Since the received data is not suitable for analysis as is, the server uses a data preprocessing means to preprocess the data. The input for this step is the received JSON-formatted data, and the output is the preprocessed data.

[1193] Step 4:

[1194] The server performs data preprocessing, including imputing missing values ​​and normalizing the data. This ensures data consistency and prepares it for analysis. The input for this step is unprocessed JSON data, and the output is normalized data. As a concrete example of its operation, fields with missing values ​​are imputed with historical data or the mean.

[1195] Step 5:

[1196] The server uses database lookup mechanisms based on normalized data to extract relevant information from the database. Examples include data on content delivery trends and past success stories. The input for this step is normalized user data, and the output is reference data extracted from the database. A concrete example of its operation is executing an SQL query to retrieve the necessary data.

[1197] Step 6:

[1198] The generative artificial intelligence model application means allows the server to input user data and reference data into a generative artificial intelligence model (OpenAI GPT-3) to generate advice. The input for this step is user data and reference data, and the output is the generated advice. Specifically, prompt sentences are generated and input into the GPT-3 model.

[1199] Step 7:

[1200] The server uses an advice formatting tool to reshape the generated advice into a format that is easy for the user to understand. Specific examples and supplementary explanations are added as needed. The input for this step is the generated advice, and the output is the formatted advice. A concrete example of this operation is using a grammar correction tool to refine the text.

[1201] Step 8:

[1202] The server uses an advice transmission method to convert the formatted advice into JSON format and send it to the user's terminal over the network. The terminal receives the advice and displays it in the user interface. The input for this step is the formatted advice, and the output is the advice displayed on the user's terminal.

[1203] 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.

[1204] This invention is a system for providing immediately useful advice to managers of small and medium-sized enterprises (SMEs) regarding management challenges they face. By combining this with an emotion engine that recognizes the user's emotions, it provides more appropriate advice. This system includes the following main components:

[1205] System Configuration

[1206] 1. Data input means

[1207] Terminal: Provides an interface for users to input their industry, sector, business challenges, and required support. Examples include web forms and mobile applications.

[1208] 2. Data transmission means and receiving means

[1209] Terminal: Converts user-entered information into JSON format and sends it to the server as a request.

[1210] Server: Receives data sent from terminals and prepares it for analysis and processing.

[1211] 3. Data preprocessing means

[1212] Server: Performs operations on received data, such as imputing missing values ​​and normalizing the data. This includes processes such as completing incomplete entries and ensuring consistency.

[1213] 4. Database Reference Means

[1214] Server: Based on the entered industry and business type, it references relevant databases and extracts the necessary information. This includes past success stories, trend data, and competitor information.

[1215] 5. Means for applying generative artificial intelligence models

[1216] Server: Based on extracted and input data, a generative artificial intelligence (AI) model generates optimal advice. This AI model incorporates the know-how of renowned business leaders.

[1217] 6. Emotional Engine

[1218] Terminal: Analyzes user emotions based on user input and behavior while using the interface, and sends the results to the server.

[1219] Server: Analyzes received emotional data and uses it to generate and format advice.

[1220] 7. Advice on cosmetic surgery methods

[1221] Server: Formats the generated advice into a user-friendly format. Specifically, it handles sentence structure and terminology selection, and adjusts the tone and content of the text based on the results of the sentiment engine.

[1222] 8. Means of sending advice

[1223] Server: Sends formatted advice to the user's terminal and displays it in the user interface.

[1224] Terminal: Receives data for displaying advice from the server and displays it on the user interface.

[1225] Program processing

[1226] This system's program generates optimal business advice based on user input, leveraging the expertise of renowned business leaders, and provides more personalized feedback using an emotion engine. The details of this process are explained below in natural language.

[1227] Specific example: Regarding the market launch of a new product

[1228] Let's take the example of a user seeking advice on launching a new product into the market.

[1229] 1. Data input means (terminal)

[1230] The user enters the following into the interface: "We are considering launching a new product A into the market. What kind of marketing strategy should we adopt?" and presses the submit button.

[1231] 2. Data transmission and reception means (terminal → server)

[1232] Terminal: Packages the user's question in JSON format and sends it to the server.

[1233] Server: Receives data in JSON format and prepares it for analysis.

[1234] Terminal: Analyzes user input and actions using an emotion engine and sends the results to the server as additional data.

[1235] 3. Data preprocessing (server)

[1236] Server: Performs data imputation and normalization to prepare the data for analysis.

[1237] 4. Accessing industry / sector databases (server)

[1238] Server: Extracts past success stories, competitor information, and marketing trends related to the market launch of new products from the database.

[1239] 5. Application of generative AI models (server)

[1240] Server: Based on input data, extracted data, and sentiment data from the sentiment engine, a generative AI model generates strategic advice. For example, it might generate advice such as, "The target market for new product A is young people in their 20s and 30s. As an effective marketing method, utilize social media and influencer marketing and implement a campaign that emphasizes the product's features."

[1241] 6. Formatting advice (server)

[1242] Server: Formats the generated advice into natural-sounding text that is easy for users to understand. Specifically, it adjusts the tone and content of the text based on the results from the sentiment engine to make it more acceptable to users.

[1243] 7. Sending advice (Server → Terminal)

[1244] Server: Sends neatly formatted advice to the user's terminal.

[1245] Terminal: Receives advice and displays it in the user interface.

[1246] Specific example: Methods of fundraising

[1247] Let's take the example of a user seeking advice on how to raise funds.

[1248] 1. Data input means (terminal)

[1249] The user types, "We are considering raising funds to expand our business in the future, but what methods would be most suitable?" and presses the submit button.

[1250] 2. Data transmission and reception means (terminal → server)

[1251] Terminal: Sends the user's question to the server in JSON format.

[1252] Server: Receives data in JSON format and prepares for analysis.

[1253] Terminal: Analyzes user input and actions using an emotion engine, and sends the resulting emotion data to the server.

[1254] 3. Data preprocessing (server)

[1255] Server: It fills in missing data in the received data and normalizes the data.

[1256] 4. Accessing industry / sector databases (server)

[1257] Server: Extracts data on past success stories and fundraising methods related to fundraising from the database.

[1258] 5. Application of generative AI models (server)

[1259] Server: Based on input data, extracted data, and sentiment data from the sentiment engine, a generative AI model generates advice suggesting the optimal fundraising method. For example, it might generate advice such as, "Direct investment from new investors is optimal. Crowdfunding is also an effective method."

[1260] 6. Formatting advice (server)

[1261] Server: Formats the generated advice into a user-friendly format. Based on emotional data from the emotion engine, it emphasizes an encouraging tone if the user is relaxed, and adjusts the language to be calm and reassuring if the user is stressed.

[1262] 7. Sending advice (Server → Terminal)

[1263] Server: Sends formatted advice to the user's terminal.

[1264] Terminal: Receives advice and displays it in the user interface.

[1265] In this way, this system, which incorporates an emotion engine, provides managers of small and medium-sized enterprises with prompt, accurate, and personalized advice, supporting them in making better business decisions.

[1266] The following describes the processing flow.

[1267] Step 1: Data Entry Method

[1268] The user enters their industry, business challenges, and required support details into the terminal interface and presses the submit button.

[1269] Step 2: Data transmission means

[1270] The terminal converts the information entered by the user into JSON format and sends it to the server as a request.

[1271] Step 3: Data Reception Method

[1272] The server receives JSON data sent from the terminal via the API and prepares it for analysis.

[1273] Step 4: Acquiring emotional data using an emotion engine

[1274] The device analyzes the user's input and actions using an emotion engine to determine the user's emotional state. The results are then sent to the server as additional data.

[1275] Step 5: Data preprocessing means

[1276] The server performs data reconciliation, including imputing missing values ​​and normalizing the received data, and converts it into a parseable format.

[1277] Step 6: Database Reference Method

[1278] Based on the industry and business challenges entered by the user, the server extracts necessary information from a database containing past success stories, trend data, and competitor information using SQL queries and other methods.

[1279] Step 7: Method for applying generative artificial intelligence models

[1280] The server inputs extracted data, pre-processed user data, and emotional data obtained from the emotion engine into a generative AI model to generate optimal advice. This AI model incorporates the know-how of famous business leaders.

[1281] Step 8: Advice on cosmetic procedures

[1282] The server formats the generated advice into natural-sounding text that is easy for the user to understand. Specifically, it adjusts the tone and content of the text based on the user's emotional data obtained from the emotion engine, making it a format that is easily accepted by the user.

[1283] Step 9: Means of sending advice

[1284] The server returns formatted advice to the user's terminal in structured data format.

[1285] Step 10: Advice Display Method

[1286] The terminal receives advice sent from the server and displays it in the user interface.

[1287] Step 11: User Verification Method

[1288] The user reviews the advice displayed on the device and makes business decisions based on it. They then enter additional questions or provide feedback as needed, and return to step 1.

[1289] (Example 2)

[1290] 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."

[1291] Small and medium-sized enterprise (SME) managers face a wide range of management challenges, requiring prompt and accurate advice. However, many managers are overwhelmed with daily operations, limiting their time and resources for acquiring specialized knowledge. Furthermore, general management advice often fails to adequately address individual emotions and circumstances, making it difficult to provide optimal advice. In contrast, current systems often provide uniform advice that does not consider the user's feelings, making it difficult to provide personalized feedback tailored to the unique circumstances of SMEs.

[1292] 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.

[1293] In this invention, the server includes data preprocessing means, database referencing means, generative artificial intelligence model application means, sentiment analysis means, advice shaping means, and advice transmission means. This makes it possible to provide managers of small and medium-sized enterprises with optimal management advice that utilizes the insights of famous business leaders based on the input industry and management challenges, as well as to realize personalized feedback that responds to the user's emotions.

[1294] A "data input device" is a device that provides an interface for users to input their industry, business challenges, and required support.

[1295] A "data transmission means" is a device that converts information entered by a user into a structured data format and transmits it to a server via a network.

[1296] A "data receiving means" is a device that receives information transmitted in a structured data format and provides it to a server for analysis.

[1297] A "data preprocessing device" is a device that performs data preprocessing, such as imputing missing values ​​and normalizing the received data, to prepare it for analysis.

[1298] A "database referencing device" is a device that extracts information from a database related to the industry and business challenges entered by the user.

[1299] A "generative artificial intelligence model application means" is a device that applies a generative artificial intelligence model to input data and information extracted from a database to generate optimal management advice.

[1300] An "emotion analysis device" is a device that analyzes the user's input and actions to generate user emotion data.

[1301] An "advice formatting tool" is a device that formats generated advice into natural-sounding text that is easy for users to understand, and adjusts the tone and expression based on emotional data.

[1302] An "advice transmission means" is a device that sends formatted advice to the user's terminal and displays it on the user interface.

[1303] This invention is a system that provides rapid and accurate advice to small and medium-sized business owners facing various management challenges. By combining this system with an emotion analysis means that recognizes the user's emotions, it can provide more appropriate and personalized advice.

[1304] System configuration:

[1305] 1. Data input means

[1306] The device provides the user with an interface for inputting their industry, business challenges, and required support. Specifically, this interface utilizes web forms or mobile applications.

[1307] 2. Data transmission means and receiving means

[1308] The terminal converts the information entered by the user into JSON format and sends it to the server as a request. The server parses the received data and prepares it for processing.

[1309] 3. Data preprocessing means

[1310] The server performs data imputation and normalization of the data it receives. The server uses algorithms and heuristics to complete incomplete entries and ensure consistency.

[1311] 4. Database Reference Means

[1312] The server references relevant databases based on the entered industry and business type, and extracts the necessary information. Specifically, this includes past success stories, trend data, and competitor information.

[1313] 5. Means for applying generative artificial intelligence models

[1314] The server generates optimal advice using a generative artificial intelligence model based on extracted data and input data. This model incorporates the know-how of famous business leaders.

[1315] 6. Emotion analysis method

[1316] The terminal analyzes the user's input and behavior while using the interface, generates sentiment data, and sends it to the server. The server analyzes the received sentiment data and uses it to generate and format advice.

[1317] 7. Advice on cosmetic surgery methods

[1318] The server formats the generated advice into a user-friendly format by structuring the text and selecting terminology. It also adjusts the tone and content of the text based on sentiment analysis results to provide more acceptable advice.

[1319] 8. Means of sending advice

[1320] The server sends formatted advice to the user's terminal, and the terminal displays the received advice in the user interface.

[1321] Specific example:

[1322] Regarding the market launch of new products:

[1323] When a user seeks advice regarding the market launch of a new product:

[1324] User: "We are considering launching a new product A into the market. What kind of marketing strategy should we adopt?" (Types this and submits.)

[1325] The terminal converts the input content into JSON format and sends it to the server. The server preprocesses the received data and extracts past success stories and competitor information for marketing strategies by referring to relevant databases.

[1326] The server uses a generative artificial intelligence model to generate specific advice such as, "The target market for new product A is young people in their 20s and 30s. As an effective marketing method, utilize social media and influencer marketing and conduct a campaign that emphasizes the product's features."

[1327] Based on the results of the emotion analysis, the server formats the generated advice to suit the user's tone and content and sends it to the terminal.

[1328] The device displays the received advice to the user.

[1329] Regarding fundraising methods:

[1330] If a user seeks advice on how to raise funds:

[1331] User: "We are considering raising funds for future business expansion. What methods would be most suitable?" (Types this and submits.)

[1332] The terminal converts the input content into JSON format and sends it to the server. The server preprocesses the received data and extracts past successful fundraising cases and fundraising methods by referring to relevant databases.

[1333] The server uses a generative artificial intelligence model to generate specific advice such as, "Direct investment from new investors is optimal. Crowdfunding is also an effective method."

[1334] Based on the results of the emotion analysis, the server formats the generated advice to suit the user's tone and content and sends it to the terminal.

[1335] The device displays the received advice to the user.

[1336] In this way, the system of the present invention, by using a generative artificial intelligence model and emotion analysis means, can provide managers of small and medium-sized enterprises with rapid, accurate, and personalized management advice.

[1337] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1338] Step 1:

[1339] The user enters their industry, business challenges, and required support. The user then enters specific questions in text format into the interface and presses the submit button. This allows for the collection of raw data from the user.

[1340] Step 2:

[1341] The terminal converts user input into JSON format and sends it to the server. This structures the data, allowing for efficient transfer over the network.

[1342] Step 3:

[1343] The server receives data in JSON format. The server stores the received data in memory for analysis and prepares for the next processing step.

[1344] Step 4:

[1345] To perform sentiment analysis, the device includes the user's input and actions in its sentiment analysis engine for analysis, and sends the results to the server as additional data in JSON format. This allows the user's sentiment data to be extracted.

[1346] Step 5:

[1347] The server performs data normalization and imputation of missing values ​​in the data it receives. The server fills in any erroneous or missing data, arranging it into a consistent format. For example, it might use statistical methods or algorithms to fill in incomplete entries.

[1348] Step 6:

[1349] The server references relevant databases based on the entered industry and business type, and extracts the necessary information. The server uses SQL queries to retrieve past success stories, marketing trends, and other information from the databases.

[1350] Step 7:

[1351] Based on the extracted data, user input data, and sentiment data, the server uses a generative artificial intelligence model to generate optimal advice. For example, it might generate specific strategic advice such as, "For the market launch of new product A, we recommend using social media and influencer marketing."

[1352] Step 8:

[1353] The server formats the generated advice into a user-friendly format. Based on the sentiment analysis results, it adjusts the tone and content of the text to suit the user.

[1354] Step 9:

[1355] The server sends the formatted advice to the user's terminal. The formatted advice is packaged as display data and transferred.

[1356] Step 10:

[1357] The device displays advice received from the server on the user interface. Users can access specific and personalized advice through the device.

[1358] In this way, the system provides small and medium-sized business owners with fast, accurate, and personalized business advice.

[1359] (Application Example 2)

[1360] 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."

[1361] Virtual store owners lack access to timely and accurate advice on the various challenges they face in online business. Furthermore, there is a lack of systems that provide personalized advice that takes into account the emotional state of the business owner. As a result, the quality of business decisions suffers, and they often fail to adopt appropriate business strategies.

[1362] 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.

[1363] In this invention, the server includes data preprocessing means, generative artificial intelligence model application means, and sentiment analysis engine means. This makes it possible to analyze user input data and sentiment data, generate optimal business advice, and provide it in an easy-to-understand format.

[1364] A "data entry means" is a device or system that provides an interface for users to input their industry, business challenges, and required support.

[1365] A "data transmission means" refers to a function or system for sending information entered by a user to a server in a structured data format.

[1366] "Data receiving means" refers to the functions and systems that allow a server to receive information sent by a user and prepare it for analysis.

[1367] "Data preprocessing means" refers to functions or systems that perform data normalization, such as imputing missing values, on received data.

[1368] A "database referencing means" is a function or system that extracts necessary information from relevant databases based on the input information.

[1369] A "generative artificial intelligence model application method" refers to a function or system that applies an artificial intelligence model that generates optimal advice based on extracted data and input data.

[1370] An "emotion analysis engine" is a function or system that analyzes a user's emotions based on their input and actions, and sends the results to a server.

[1371] An "advice formatting tool" is a function or system that formats generated advice into a format that is easy for the user to understand.

[1372] An "advice transmission method" refers to a function or system for sending formatted advice to a user's device.

[1373] This invention is a system that provides immediately useful advice to virtual store operators facing various management challenges. The system includes data input means, data transmission means, data reception means, data preprocessing means, database referencing means, generative artificial intelligence model application means, sentiment analysis engine, advice formatting means, and advice transmission means. As an example, this invention is implemented as follows.

[1374] System program

[1375] The server first receives data from the data input means, including the user's industry, business challenges, and required support, via the data transmission means. The received data is normalized and missing values ​​are imputed by the data preprocessing means. Then, relevant information is extracted by the database referencing means, and optimal advice is generated by the generative artificial intelligence model application means. Furthermore, the sentiment analysis engine analyzes the user's sentiment data, and the advice is refined based on that sentiment data. Finally, the advice is displayed on the user's terminal via the advice transmission means.

[1376] Processing details

[1377] The server uses software implemented in programming languages ​​such as Python and Java to exchange data in JSON format. Received data is preprocessed using libraries such as Pandas and NumPy. SQL and NoSQL databases (e.g., MySQL and MongoDB) are used as databases. Generative artificial intelligence models utilize machine learning libraries such as TensorFlow and PyTorch. The sentiment analysis engine uses natural language processing APIs and proprietary sentiment analysis algorithms.

[1378] Specific example

[1379] For example, if a user is seeking advice on promoting a new fashion item, they can enter a question like this: "What are some ways to promote a new fashion item?" The server analyzes this and generates advice such as, "Target women in their 20s and 30s, and implement video marketing using Instagram and TikTok. Influencer collaborations are also effective."

[1380] Example of a prompt

[1381] "Please advise on how to promote new fashion items. I would also like to know more about the target market."

[1382] In this way, the system can provide virtual store managers with personalized business advice that takes emotions into account, thereby improving the quality of their business decisions.

[1383] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1384] Step 1:

[1385] Users input their industry, business challenges, and required support through the terminal's interface. This input data includes specific industry information, concrete problems, and the type of support needed.

[1386] Step 2:

[1387] The terminal converts the information entered by the user into JSON format and sends it to the server via a data transmission method. The input data at this time is the user's input, and the output data is request data in JSON format.

[1388] Step 3:

[1389] The server receives JSON-formatted data from the terminal using a data receiving device. This data includes information such as industry, business challenges, and support details, and serves as preparation data for analysis.

[1390] Step 4:

[1391] The server normalizes the received data using data preprocessing. Specifically, it performs actions such as imputing missing values ​​and standardizing the data format. In this process, the input data is the received data, and the output data is the preprocessed data.

[1392] Step 5:

[1393] The server extracts necessary information from relevant databases through a database referencing mechanism. This extracted information includes past success stories and trend data. The input data for this process is pre-processed data, and the output data is the result of the referencing.

[1394] Step 6:

[1395] The server generates optimal advice based on the data extracted using a generative artificial intelligence model application method and the input data. A machine learning model is used for this advice generation. The input data for processing is the reference result data, and the output data is the generated advice.

[1396] Step 7:

[1397] The terminal analyzes the user's actions and input using an emotion analysis engine and sends the results to the server. In this process, the input data is the emotion analysis result, and the output data is also the emotion analysis result.

[1398] Step 8:

[1399] The server integrates emotional data and formats the generated advice based on the results of the emotion analysis engine. During formatting, it adjusts the tone and structure of the text. The input data consists of the generated advice and emotional data, while the output data is the formatted advice that reflects the emotions.

[1400] Step 9:

[1401] The server sends the formatted advice to the user's terminal using a data transmission method. In this process, the input data is the formatted advice, and the output data is the transmitted advice.

[1402] Step 10:

[1403] The terminal receives advice sent from the server and displays it on the user interface. In this process, the input data is the advice from the server, and the output data is the advice in a display format that the user can see.

[1404] 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.

[1405] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1406] 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.

[1407] [Fourth Embodiment]

[1408] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[1409] 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.

[1410] 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).

[1411] 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.

[1412] 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.

[1413] 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).

[1414] 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.

[1415] 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.

[1416] 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.

[1417] 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.

[1418] 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.

[1419] 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.

[1420] 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".

[1421] This invention provides a system for offering immediately useful advice to managers of small and medium-sized enterprises (SMEs) regarding the management challenges they face. This system includes the following main components:

[1422] System Configuration

[1423] 1. Data input means

[1424] Terminal: Provides an interface for users to input their industry, sector, business challenges, and required support. Examples include web forms and mobile applications.

[1425] 2. Data transmission means and receiving means

[1426] Terminal: Converts user-entered information into a structured data format (e.g., JSON format) and sends it to the server.

[1427] Server: Receives data sent from terminals and prepares it for analysis and processing.

[1428] 3. Data preprocessing means

[1429] Server: Performs operations on received data, such as imputing missing values ​​and normalizing the data. This includes processes such as completing incomplete entries and ensuring consistency.

[1430] 4. Database Reference Means

[1431] Server: Based on the entered industry and business type, it references relevant databases and extracts the necessary information. This includes past success stories, trend data, and competitor information.

[1432] 5. Means for applying generative artificial intelligence models

[1433] Server: Based on extracted and input data, a generative artificial intelligence (AI) model generates optimal advice. This AI model incorporates the know-how of renowned business leaders.

[1434] 6. Advice on cosmetic surgery methods

[1435] Server: Formats the generated advice into a user-friendly format. Specifically, it organizes the text, selects terminology, and adds supplementary explanations and examples as needed.

[1436] 7. Means of sending advice

[1437] Server: Sends formatted advice to the user's terminal and displays it in the user interface.

[1438] Terminal: Receives data for displaying advice from the server and displays it on the user interface.

[1439] Program processing

[1440] This system's program generates and provides optimal business advice based on information entered by the user, utilizing the expertise of renowned business leaders. The details of this process are explained below in natural language.

[1441] Specific example: Regarding the market launch of a new product

[1442] Let's take the example of a user seeking advice on launching a new product into the market.

[1443] 1. User input (on the device)

[1444] User: Enters the question, "We are considering launching a new product A into the market. What kind of marketing strategy should we adopt?" into the interface and presses the submit button.

[1445] 2. Data transmission and reception (terminal → server)

[1446] Terminal: Packages the user's question in JSON format and sends it to the server.

[1447] Server: Receives data in JSON format and prepares it for analysis.

[1448] 3. Data preprocessing (server)

[1449] Server: Performs data imputation and normalization to prepare the data for analysis.

[1450] 4. Accessing industry / sector databases (server)

[1451] Server: Extracts past success stories, competitor information, and marketing trends related to the market launch of new products from the database.

[1452] 5. Application of generative AI models (server)

[1453] Server: Based on input data and extracted data, a generative AI model generates strategic advice. For example, it might generate advice such as, "The target market for new product A is young people in their 20s and 30s. As an effective marketing method, utilize social media and influencer marketing and implement a campaign that emphasizes the product's features."

[1454] 6. Formatting advice (server)

[1455] Server: Formats the generated advice into natural-sounding text that is easy for the user to understand.

[1456] 7. Sending advice (Server → Terminal)

[1457] Server: Sends organized advice to the user's terminal.

[1458] Terminal: Receives advice and displays it in the user interface.

[1459] Specific example: Methods of fundraising

[1460] Let's take the example of a user seeking advice on how to raise funds.

[1461] 1. User input (on the device)

[1462] User: Enters the question, "We are considering raising funds for future business expansion, but what methods would be most suitable?" and presses the submit button.

[1463] 2. Data transmission and reception (terminal → server)

[1464] Terminal: Sends the user's question to the server in JSON format.

[1465] Server: Receives data in JSON format and prepares it for analysis.

[1466] 3. Data preprocessing (server)

[1467] Server: Imputes missing data and normalizes the data.

[1468] 4. Accessing industry / sector databases (server)

[1469] Server: Extracts data on past success stories and fundraising methods related to fundraising from the database.

[1470] 5. Application of generative AI models (server)

[1471] Server: Based on the input data and extracted data, a generative AI model generates advice suggesting the optimal fundraising method. For example, it might generate advice such as, "Direct investment from new investors is optimal. Crowdfunding is also an effective method."

[1472] 6. Formatting advice (server)

[1473] Server: Formats the generated advice into a user-friendly format.

[1474] 7. Sending advice (Server → Terminal)

[1475] Server: Sends formatted advice to the user's terminal.

[1476] Terminal: Receives advice and displays it in the user interface.

[1477] In this way, this system provides prompt and accurate advice to managers of small and medium-sized enterprises, supporting their business decisions.

[1478] The following describes the processing flow.

[1479] Step 1: Data Entry Method

[1480] User: Enter the industry, business challenges, and required support details into the terminal interface and press the send button.

[1481] Step 2: Data transmission means

[1482] Terminal: Converts user-entered information into JSON format and sends it to the server as a request.

[1483] Step 3: Data Reception Method

[1484] Server: Receives JSON data sent from terminals via API and prepares it for analysis.

[1485] Step 4: Data preprocessing means

[1486] Server: The server processes the received data, including imputing missing values ​​and normalizing the data, and converts it into a format that can be analyzed.

[1487] Step 5: Database Reference Method

[1488] Server: Based on the industry and business challenges entered by the user, the server extracts necessary information from a database containing past success stories, trend data, and competitor information using SQL queries and other methods.

[1489] Step 6: Generative Artificial Intelligence Model Application Method

[1490] Server: The extracted data and pre-processed user data are input into a generative AI model to generate optimal advice. This AI model incorporates the know-how of several well-known business leaders.

[1491] Step 7: Advice on cosmetic procedures

[1492] Server: Formats the generated advice into natural-sounding language that is easy for the user to understand. Specifically, it performs processes such as constructing sentences and replacing technical terms with more general expressions.

[1493] Step 8: Means of sending advice

[1494] Server: Returns formatted advice in structured data format to the user's terminal.

[1495] Step 9: Means of displaying advice

[1496] Terminal: Receives advice sent from the server and displays it in the user interface.

[1497] Step 10: User Verification Method

[1498] User: Review the advice displayed on the device and make business decisions based on it. If necessary, enter additional questions or provide feedback, then return to step 1.

[1499] (Example 1)

[1500] 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".

[1501] Providing timely and accurate advice to small and medium-sized business owners facing management challenges is crucial for many companies. However, existing systems have struggled to properly extract the necessary information and provide optimal advice. In particular, there has been a need for a system that seamlessly integrates multiple processes, such as data preprocessing, referencing industry- and sector-specific information, and generating advice using generative artificial intelligence models.

[1502] 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.

[1503] In this invention, the server includes means for the user to input information, means for converting the data into a structured data format and transmitting it, means for receiving the data in a structured data format, means for imputing missing values ​​and normalizing the data, means for referring to an industry / sector database and extracting relevant information, means for applying a generation AI model based on the extracted data and input data to generate advice, means for formatting the generated advice into a format that is easy for the user to understand, and means for transmitting and displaying the formatted advice. This makes it possible to provide quick and accurate management advice to managers of small and medium-sized enterprises.

[1504] "Means for users to input information" refers to a device or system that allows users to input their industry, business challenges, and required support through an interface.

[1505] "Means for converting data into a structured data format and transmitting it" refers to a device or software that converts user-inputted information into a structured data format such as JSON and transmits it to a server via a network.

[1506] "Means for receiving data in structured data format" refers to a device or system that receives transmitted data in structured data format (e.g., JSON format) over a network and prepares it for further processing.

[1507] "Means for imputing missing values ​​and normalizing data" refers to devices or software that imputate missing values ​​in received data and normalize the data to unify its format.

[1508] "Means for referencing industry / sector databases and extracting relevant information" refers to devices or software for searching databases that store data on existing industries and sectors and extracting the necessary information.

[1509] "Means for generating advice by applying a generative AI model" refers to a device or software that applies a generative artificial intelligence model using extracted data and user-input data to generate optimal business advice.

[1510] "Means for formatting generated advice into a user-friendly format" refers to a device or software that formats generated advice into text, avoids excessive use of technical jargon, and converts it into a format that is easy for the user to understand.

[1511] "Means for sending and displaying formatted advice" refers to a device or system for converting formatted advice back into a structured data format, sending it to a user's terminal, and displaying it on a user interface.

[1512] This invention is a system that provides rapid and accurate advice to small and medium-sized enterprise (SME) managers regarding the management challenges they face. This system generates and provides optimal management advice using a generative AI model based on information entered by the user. The details are described below.

[1513] Components and the hardware and software used

[1514] This system includes the following main components:

[1515] 1. Data input means

[1516] Terminal: An interface (e.g., a web form or mobile application) is provided for users to input their industry, sector, business challenges, and required support.

[1517] 2. Data transmission means and receiving means

[1518] Terminal: Converts user-entered information into a structured data format (e.g., JSON format) and sends it to the server.

[1519] Server: Receives data sent from terminals and prepares it for analysis and processing.

[1520] 3. Data preprocessing means

[1521] Server: This server processes the received data, including imputing missing values ​​and normalizing the data. A Python library such as Pandas is often used.

[1522] 4. Database Reference Means

[1523] Server: Based on the entered industry and business type, it references relevant databases (e.g., MySQL or MongoDB) and extracts the necessary information.

[1524] 5. Means for applying generative artificial intelligence models

[1525] Server: Based on the extracted data and input data, it generates optimal advice using a generative artificial intelligence (AI) model (for example, a model using TensorFlow or PyTorch).

[1526] 6. Advice on cosmetic surgery methods

[1527] Server: Formats the generated advice into a user-friendly format. Specifically, it uses libraries such as nltk to structure the text and select terminology, adding supplementary explanations and examples as needed.

[1528] 7. Means of sending advice

[1529] Server: Sends formatted advice to the user's terminal.

[1530] Terminal: Receives data for displaying advice from the server and displays it on the user interface.

[1531] Specific examples and prompt statements

[1532] Specific examples are given below.

[1533] Specific example 1: Regarding the market launch of a new product

[1534] 1. User input (on the device)

[1535] Example text input: The user enters the question "We are considering launching a new product A into the market, but what kind of marketing strategy should we adopt?" into the interface and presses the submit button.

[1536] 2. Data transmission and reception (terminal → server)

[1537] The terminal converts the input information into JSON format and sends it to the server using an HTTP POST request.

[1538] 3. Data preprocessing (server)

[1539] The server performs missing value imputation and normalization on the received data. It uses the Python Pandas library.

[1540] 4. Accessing industry / sector databases (server)

[1541] The server references relevant databases to extract past success stories and trend data related to the market launch of new products.

[1542] 5. Application of generative AI models (server)

[1543] The server uses a generative AI model to generate marketing strategy advice. For example, it might generate advice such as, "The target market for new product A is young people in their 20s and 30s. Utilize social media and influencer marketing to conduct a campaign that highlights the product's features."

[1544] 6. Formatting advice (server)

[1545] The server formats this advice into a format that is easy for the user to understand.

[1546] 7. Sending and displaying advice (Server → Terminal)

[1547] The server converts the formatted advice back into JSON format and sends it to the user's terminal via an HTTP response.

[1548] The device displays the received advice in the user interface.

[1549] Specific example 2: Methods of fundraising

[1550] 1. User input (on the device)

[1551] Example text input: The user enters the question "We are considering raising funds for future business expansion, but what methods would be most suitable?" into the interface and presses the submit button.

[1552] 2. Data transmission and reception (terminal → server)

[1553] The terminal converts this question into JSON format and sends it to the server.

[1554] 3. Data preprocessing (server)

[1555] The server preprocesses the received data to ensure consistency.

[1556] 4. Accessing industry / sector databases (server)

[1557] The server extracts past success stories and fundraising methods from a database.

[1558] 5. Application of generative AI models (server)

[1559] The server uses a generative AI model to generate advice on fundraising methods. For example, it might generate advice such as, "Direct investment from new investors is optimal. Crowdfunding is also an effective method."

[1560] 6. Formatting advice (server)

[1561] The generated advice is formatted into a user-friendly format.

[1562] 7. Sending and displaying advice (Server → Terminal)

[1563] The formatted advice is sent to the user's device and displayed.

[1564] In this way, this system provides prompt and accurate advice to managers of small and medium-sized enterprises, supporting their business decisions.

[1565] The flow of the specific processing in Example 1 will be explained using Figure 11.

[1566] Step 1: User enters information

[1567] Operation: Users use their devices (PCs or smartphones) to input their industry, business type, management challenges, and required support through web forms or mobile applications. Specifically, they might input a question such as, "We are considering launching a new product A into the market, what kind of marketing strategy would be appropriate?"

[1568] Input: Text information entered by the user.

[1569] Output: A transmission command is generated at the terminal, and the process proceeds to the next step.

[1570] Step 2: Data transmission

[1571] Operation: The terminal converts the information entered by the user into JSON format. The converted data is sent to the server using an HTTP POST request.

[1572] Input: Text information entered by the user.

[1573] Output: Data in JSON format. This data will be sent to the server in the next step.

[1574] Step 3: Data Reception

[1575] Operation: The server receives JSON data sent from the terminal. It parses the HTTP request and extracts the JSON data.

[1576] Input: JSON formatted data sent from the device.

[1577] Output: Parsed JSON data. This data proceeds to the next preprocessing step.

[1578] Step 4: Data preprocessing

[1579] Operation: The server performs preprocessing on the received JSON data. Specifically, it imputes missing values ​​and normalizes the data. It uses the Python Pandas library to format the data.

[1580] Input: Parsed JSON data.

[1581] Output: Preprocessed data. The formatted data proceeds to the next reference step.

[1582] Step 5: Database Reference

[1583] Operation: The server references industry and business databases based on pre-processed data. These databases contain historical success stories, trend data, competitor information, and more. This reference is performed using SQL or NoSQL databases (e.g., MySQL, MongoDB).

[1584] Input: Pre-processed data.

[1585] Output: Relevant data extracted by reference. This data will be used to proceed to the next step in applying the AI ​​model.

[1586] Step 6: Applying the Generative AI Model

[1587] Operation: The server applies a generative AI model (for example, a model using TensorFlow or PyTorch) based on the extracted data and user input data. The generative AI model uses this data to generate optimal advice. For example, it might generate advice such as, "The target market for new product A is young people in their 20s and 30s. Utilize social media and influencer marketing to conduct a campaign that highlights the product's features."

[1588] Input: User input data and related data extracted from the database.

[1589] Output: Advice generated by the AI ​​model. This advice then proceeds to the formatting step.

[1590] Step 7: Refining the advice

[1591] Operation: The server formats the generated advice. Specifically, it uses natural language processing tools such as the NLTK library to format the advice into a user-friendly format. It avoids technical jargon and simplifies the text.

[1592] Input: Advice generated by a generative AI model.

[1593] Output: Formatted advice. This advice proceeds to the submission step.

[1594] Step 8: Sending and displaying advice

[1595] Operation: The server converts the formatted advice back into JSON format and sends it to the user's terminal as an HTTP response.

[1596] Input: Formatted advice.

[1597] Output: Advice converted back into JSON format. This advice is sent to and displayed on the user's device.

[1598] This will enable the system to provide quick and accurate management advice to the managers of small and medium-sized enterprises.

[1599] (Application Example 1)

[1600] 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".

[1601] In recent years, content creators have faced a wide range of challenges, particularly in finding the optimal strategy to capture audience interest. Therefore, there is a need for means to quickly grasp trends and develop effective content strategies. However, individual data collection and analysis by creators is time-consuming, laborious, and inefficient. To address this challenge, a system is needed that provides creators with immediate and accurate advice on the problems they face.

[1602] 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.

[1603] In this invention, the server includes data preprocessing means, database referencing means, and generative artificial intelligence model application means. This makes it possible to generate and provide rapid and accurate advice in response to a task input by a content creator.

[1604] "Data entry means" refers to an interface for users to input their industry, business challenges, required support, and content distribution challenges.

[1605] "Data transmission means" refers to network communication means that transmit data from a terminal to a server in a structured data format.

[1606] "Data receiving means" refers to network communication means by which a server receives structured data transmitted from a terminal.

[1607] "Data preprocessing means" refers to processes that perform data reconciliation, such as imputing missing values ​​or normalizing the received data.

[1608] "Database referencing means" refers to a method of referencing a database based on the entered industry and business type and extracting the necessary information.

[1609] "Method for applying a generative artificial intelligence model" refers to a method for generating optimal advice using a generative AI model based on extracted data and data input by the user.

[1610] "Advice formatting" refers to the process of formatting generated advice into a user-friendly format and adding specific examples and supplementary explanations.

[1611] "Advice transmission method" refers to a means of sending and displaying formatted advice on the user's device.

[1612] "Data reference means related to content distribution" refers to means of extracting data such as content distribution trends and past success stories and providing them to generative artificial intelligence models.

[1613] This invention realizes a system that provides immediately useful advice to content creators facing challenges. This system performs multi-stage data processing and utilizes generative AI models to generate optimal advice and provide it to the user.

[1614] Program Processing Overview

[1615] Hardware and software to be used

[1616] Hardware:

[1617] Devices: Smartphones, smart glasses, head-mounted displays, and robots

[1618] Server: Computer server used for data processing and running AI models.

[1619] software:

[1620] Programming language: Python

[1621] Framework: Flask (Web server), MySQL (database management), OpenAI GPT-3 (generative AI model)

[1622] Processing flow details

[1623] 1. Data entry means:

[1624] The user uses a device to input a question into the interface. For example, they might input the question, "What is the best strategy to capture the audience's interest?"

[1625] 2. Data transmission means:

[1626] The application on the device converts the user's input into a structured data format (JSON format) and sends it to the server via the network.

[1627] 3. Data reception means:

[1628] The server receives the data sent from the terminal and prepares it for analysis.

[1629] 4. Data preprocessing means:

[1630] The server performs data normalization and imputation of missing values ​​in the received data. This makes the data suitable for analysis.

[1631] 5. Database referencing methods:

[1632] The server extracts past success stories and trend data related to content delivery from a database. For example, it obtains information such as what content formats are preferred by viewers.

[1633] 6. Means for applying generative artificial intelligence models:

[1634] The server applies a generative AI model (OpenAI GPT-3) based on the received data and extracted trend data to generate advice.

[1635] For example, advice might be generated stating, "To capture viewers' interest, it is recommended to use currently popular short-form videos and add interactive elements."

[1636] 7. Advice on cosmetic procedures:

[1637] The server formats the generated advice into a user-friendly format and adds specific examples and supplementary explanations as needed.

[1638] 8. Means of sending advice:

[1639] The formatted advice is converted back into a structured data format and sent to the user's terminal via the network.

[1640] Examples of specific cases and prompt statements

[1641] If the user enters the following question into the interface:

[1642] "What is the best strategy to capture the viewers' interest?"

[1643] Example of generated prompt text

[1644] "Please provide the best strategies for content creators to capture audience interest. He / She is currently producing new content for a video streaming service."

[1645] In this way, creators can be quickly provided with optimal advice based on generative AI models, enabling them to efficiently develop content strategies.

[1646] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[1647] Step 1:

[1648] The user uses a device to input a question into the interface. For example, they might input the question, "What is the best strategy to capture the audience's interest?" The input question is then converted into a structured data format (JSON format) by the data input method. In this step, the input is the text entered by the user, and the output is data in JSON format.

[1649] Step 2:

[1650] The application on the device sends JSON-formatted data to the server over the network. The data transmission method fulfills this role. The data sent is the task information entered by the user, and the output is JSON-formatted data that is input to the server.

[1651] Step 3:

[1652] The server uses a data receiving means to receive JSON-formatted data sent from the terminal. Since the received data is not suitable for analysis as is, the server uses a data preprocessing means to preprocess the data. The input for this step is the received JSON-formatted data, and the output is the preprocessed data.

[1653] Step 4:

[1654] The server performs data preprocessing, including imputing missing values ​​and normalizing the data. This ensures data consistency and prepares it for analysis. The input for this step is unprocessed JSON data, and the output is normalized data. As a concrete example of its operation, fields with missing values ​​are imputed with historical data or the mean.

[1655] Step 5:

[1656] The server uses database lookup mechanisms based on normalized data to extract relevant information from the database. Examples include data on content delivery trends and past success stories. The input for this step is normalized user data, and the output is reference data extracted from the database. A concrete example of its operation is executing an SQL query to retrieve the necessary data.

[1657] Step 6:

[1658] The generative artificial intelligence model application means allows the server to input user data and reference data into a generative artificial intelligence model (OpenAI GPT-3) to generate advice. The input for this step is user data and reference data, and the output is the generated advice. Specifically, prompt sentences are generated and input into the GPT-3 model.

[1659] Step 7:

[1660] The server uses an advice formatting tool to reshape the generated advice into a format that is easy for the user to understand. Specific examples and supplementary explanations are added as needed. The input for this step is the generated advice, and the output is the formatted advice. A concrete example of this operation is using a grammar correction tool to refine the text.

[1661] Step 8:

[1662] The server uses an advice transmission method to convert the formatted advice into JSON format and send it to the user's terminal over the network. The terminal receives the advice and displays it in the user interface. The input for this step is the formatted advice, and the output is the advice displayed on the user's terminal.

[1663] 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.

[1664] This invention is a system for providing immediately useful advice to managers of small and medium-sized enterprises (SMEs) regarding management challenges they face. By combining this with an emotion engine that recognizes the user's emotions, it provides more appropriate advice. This system includes the following main components:

[1665] System Configuration

[1666] 1. Data input means

[1667] Terminal: Provides an interface for users to input their industry, sector, business challenges, and required support. Examples include web forms and mobile applications.

[1668] 2. Data transmission means and receiving means

[1669] Terminal: Converts user-entered information into JSON format and sends it to the server as a request.

[1670] Server: Receives data sent from terminals and prepares it for analysis and processing.

[1671] 3. Data preprocessing means

[1672] Server: Performs operations on received data, such as imputing missing values ​​and normalizing the data. This includes processes such as completing incomplete entries and ensuring consistency.

[1673] 4. Database Reference Means

[1674] Server: Based on the entered industry and business type, it references relevant databases and extracts the necessary information. This includes past success stories, trend data, and competitor information.

[1675] 5. Means for applying generative artificial intelligence models

[1676] Server: Based on extracted and input data, a generative artificial intelligence (AI) model generates optimal advice. This AI model incorporates the know-how of renowned business leaders.

[1677] 6. Emotional Engine

[1678] Terminal: Analyzes user emotions based on user input and behavior while using the interface, and sends the results to the server.

[1679] Server: Analyzes received emotional data and uses it to generate and format advice.

[1680] 7. Advice on cosmetic surgery methods

[1681] Server: Formats the generated advice into a user-friendly format. Specifically, it handles sentence structure and terminology selection, and adjusts the tone and content of the text based on the results of the sentiment engine.

[1682] 8. Means of sending advice

[1683] Server: Sends formatted advice to the user's terminal and displays it in the user interface.

[1684] Terminal: Receives data for displaying advice from the server and displays it on the user interface.

[1685] Program processing

[1686] This system's program generates optimal business advice based on user input, leveraging the expertise of renowned business leaders, and provides more personalized feedback using an emotion engine. The details of this process are explained below in natural language.

[1687] Specific example: Regarding the market launch of a new product

[1688] Let's take the example of a user seeking advice on launching a new product into the market.

[1689] 1. Data input means (terminal)

[1690] The user enters the following into the interface: "We are considering launching a new product A into the market. What kind of marketing strategy should we adopt?" and presses the submit button.

[1691] 2. Data transmission and reception means (terminal → server)

[1692] Terminal: Packages the user's question in JSON format and sends it to the server.

[1693] Server: Receives data in JSON format and prepares it for analysis.

[1694] Terminal: Analyzes user input and actions using an emotion engine and sends the results to the server as additional data.

[1695] 3. Data preprocessing (server)

[1696] Server: Performs data imputation and normalization to prepare the data for analysis.

[1697] 4. Accessing industry / sector databases (server)

[1698] Server: Extracts past success stories, competitor information, and marketing trends related to the market launch of new products from the database.

[1699] 5. Application of generative AI models (server)

[1700] Server: Based on input data, extracted data, and sentiment data from the sentiment engine, a generative AI model generates strategic advice. For example, it might generate advice such as, "The target market for new product A is young people in their 20s and 30s. As an effective marketing method, utilize social media and influencer marketing and implement a campaign that emphasizes the product's features."

[1701] 6. Formatting advice (server)

[1702] Server: Formats the generated advice into natural-sounding text that is easy for users to understand. Specifically, it adjusts the tone and content of the text based on the results from the sentiment engine to make it more acceptable to users.

[1703] 7. Sending advice (Server → Terminal)

[1704] Server: Sends neatly formatted advice to the user's terminal.

[1705] Terminal: Receives advice and displays it in the user interface.

[1706] Specific example: Methods of fundraising

[1707] Let's take the example of a user seeking advice on how to raise funds.

[1708] 1. Data input means (terminal)

[1709] The user types, "We are considering raising funds to expand our business in the future, but what methods would be most suitable?" and presses the submit button.

[1710] 2. Data transmission and reception means (terminal → server)

[1711] Terminal: Sends the user's question to the server in JSON format.

[1712] Server: Receives data in JSON format and prepares for analysis.

[1713] Terminal: Analyzes user input and actions using an emotion engine, and sends the resulting emotion data to the server.

[1714] 3. Data preprocessing (server)

[1715] Server: It fills in missing data in the received data and normalizes the data.

[1716] 4. Accessing industry / sector databases (server)

[1717] Server: Extracts data on past success stories and fundraising methods related to fundraising from the database.

[1718] 5. Application of generative AI models (server)

[1719] Server: Based on input data, extracted data, and sentiment data from the sentiment engine, a generative AI model generates advice suggesting the optimal fundraising method. For example, it might generate advice such as, "Direct investment from new investors is optimal. Crowdfunding is also an effective method."

[1720] 6. Formatting advice (server)

[1721] Server: Formats the generated advice into a user-friendly format. Based on emotional data from the emotion engine, it emphasizes an encouraging tone if the user is relaxed, and adjusts the language to be calm and reassuring if the user is stressed.

[1722] 7. Sending advice (Server → Terminal)

[1723] Server: Sends formatted advice to the user's terminal.

[1724] Terminal: Receives advice and displays it in the user interface.

[1725] In this way, this system, which incorporates an emotion engine, provides managers of small and medium-sized enterprises with prompt, accurate, and personalized advice, supporting them in making better business decisions.

[1726] The following describes the processing flow.

[1727] Step 1: Data Entry Method

[1728] The user enters their industry, business challenges, and required support details into the terminal interface and presses the submit button.

[1729] Step 2: Data transmission means

[1730] The terminal converts the information entered by the user into JSON format and sends it to the server as a request.

[1731] Step 3: Data Reception Method

[1732] The server receives JSON data sent from the terminal via the API and prepares it for analysis.

[1733] Step 4: Acquiring emotional data using an emotion engine

[1734] The device analyzes the user's input and actions using an emotion engine to determine the user's emotional state. The results are then sent to the server as additional data.

[1735] Step 5: Data preprocessing means

[1736] The server performs data reconciliation, including imputing missing values ​​and normalizing the received data, and converts it into a parseable format.

[1737] Step 6: Database Reference Method

[1738] Based on the industry and business challenges entered by the user, the server extracts necessary information from a database containing past success stories, trend data, and competitor information using SQL queries and other methods.

[1739] Step 7: Method for applying generative artificial intelligence models

[1740] The server inputs extracted data, pre-processed user data, and emotional data obtained from the emotion engine into a generative AI model to generate optimal advice. This AI model incorporates the know-how of famous business leaders.

[1741] Step 8: Advice on cosmetic procedures

[1742] The server formats the generated advice into natural-sounding text that is easy for the user to understand. Specifically, it adjusts the tone and content of the text based on the user's emotional data obtained from the emotion engine, making it a format that is easily accepted by the user.

[1743] Step 9: Means of sending advice

[1744] The server returns formatted advice to the user's terminal in structured data format.

[1745] Step 10: Advice Display Method

[1746] The terminal receives advice sent from the server and displays it in the user interface.

[1747] Step 11: User Verification Method

[1748] The user reviews the advice displayed on the device and makes business decisions based on it. They then enter additional questions or provide feedback as needed, and return to step 1.

[1749] (Example 2)

[1750] 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".

[1751] Small and medium-sized enterprise (SME) managers face a wide range of management challenges, requiring prompt and accurate advice. However, many managers are overwhelmed with daily operations, limiting their time and resources for acquiring specialized knowledge. Furthermore, general management advice often fails to adequately address individual emotions and circumstances, making it difficult to provide optimal advice. In contrast, current systems often provide uniform advice that does not consider the user's feelings, making it difficult to provide personalized feedback tailored to the unique circumstances of SMEs.

[1752] 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.

[1753] In this invention, the server includes data preprocessing means, database referencing means, generative artificial intelligence model application means, sentiment analysis means, advice shaping means, and advice transmission means. This makes it possible to provide managers of small and medium-sized enterprises with optimal management advice that utilizes the insights of famous business leaders based on the input industry and management challenges, as well as to realize personalized feedback that responds to the user's emotions.

[1754] A "data input device" is a device that provides an interface for users to input their industry, business challenges, and required support.

[1755] A "data transmission means" is a device that converts information entered by a user into a structured data format and transmits it to a server via a network.

[1756] A "data receiving means" is a device that receives information transmitted in a structured data format and provides it to a server for analysis.

[1757] A "data preprocessing device" is a device that performs data preprocessing, such as imputing missing values ​​and normalizing the received data, to prepare it for analysis.

[1758] A "database referencing device" is a device that extracts information from a database related to the industry and business challenges entered by the user.

[1759] A "generative artificial intelligence model application means" is a device that applies a generative artificial intelligence model to input data and information extracted from a database to generate optimal management advice.

[1760] An "emotion analysis device" is a device that analyzes the user's input and actions to generate user emotion data.

[1761] An "advice formatting tool" is a device that formats generated advice into natural-sounding text that is easy for users to understand, and adjusts the tone and expression based on emotional data.

[1762] An "advice transmission means" is a device that sends formatted advice to the user's terminal and displays it on the user interface.

[1763] This invention is a system that provides rapid and accurate advice to small and medium-sized business owners facing various management challenges. By combining this system with an emotion analysis means that recognizes the user's emotions, it can provide more appropriate and personalized advice.

[1764] System configuration:

[1765] 1. Data input means

[1766] The device provides the user with an interface for inputting their industry, business challenges, and required support. Specifically, this interface utilizes web forms or mobile applications.

[1767] 2. Data transmission means and receiving means

[1768] The terminal converts the information entered by the user into JSON format and sends it to the server as a request. The server parses the received data and prepares it for processing.

[1769] 3. Data preprocessing means

[1770] The server performs data imputation and normalization of the data it receives. The server uses algorithms and heuristics to complete incomplete entries and ensure consistency.

[1771] 4. Database Reference Means

[1772] The server references relevant databases based on the entered industry and business type, and extracts the necessary information. Specifically, this includes past success stories, trend data, and competitor information.

[1773] 5. Means for applying generative artificial intelligence models

[1774] The server generates optimal advice using a generative artificial intelligence model based on extracted data and input data. This model incorporates the know-how of famous business leaders.

[1775] 6. Emotion analysis method

[1776] The terminal analyzes the user's input and behavior while using the interface, generates sentiment data, and sends it to the server. The server analyzes the received sentiment data and uses it to generate and format advice.

[1777] 7. Advice on cosmetic surgery methods

[1778] The server formats the generated advice into a user-friendly format by structuring the text and selecting terminology. It also adjusts the tone and content of the text based on sentiment analysis results to provide more acceptable advice.

[1779] 8. Means of sending advice

[1780] The server sends formatted advice to the user's terminal, and the terminal displays the received advice in the user interface.

[1781] Specific example:

[1782] Regarding the market launch of new products:

[1783] When a user seeks advice regarding the market launch of a new product:

[1784] User: "We are considering launching a new product A into the market. What kind of marketing strategy should we adopt?" (Types this and submits.)

[1785] The terminal converts the input content into JSON format and sends it to the server. The server preprocesses the received data and extracts past success stories and competitor information for marketing strategies by referring to relevant databases.

[1786] The server uses a generative artificial intelligence model to generate specific advice such as, "The target market for new product A is young people in their 20s and 30s. As an effective marketing method, utilize social media and influencer marketing and conduct a campaign that emphasizes the product's features."

[1787] Based on the results of the emotion analysis, the server formats the generated advice to suit the user's tone and content and sends it to the terminal.

[1788] The device displays the received advice to the user.

[1789] Regarding fundraising methods:

[1790] If a user seeks advice on how to raise funds:

[1791] User: "We are considering raising funds for future business expansion. What methods would be most suitable?" (Types this and submits.)

[1792] The terminal converts the input content into JSON format and sends it to the server. The server preprocesses the received data and extracts past successful fundraising cases and fundraising methods by referring to relevant databases.

[1793] The server uses a generative artificial intelligence model to generate specific advice such as, "Direct investment from new investors is optimal. Crowdfunding is also an effective method."

[1794] Based on the results of the emotion analysis, the server formats the generated advice to suit the user's tone and content and sends it to the terminal.

[1795] The device displays the received advice to the user.

[1796] In this way, the system of the present invention, by using a generative artificial intelligence model and emotion analysis means, can provide managers of small and medium-sized enterprises with rapid, accurate, and personalized management advice.

[1797] The flow of the specific processing in Example 2 will be explained using Figure 13.

[1798] Step 1:

[1799] The user enters their industry, business challenges, and required support. The user then enters specific questions in text format into the interface and presses the submit button. This allows for the collection of raw data from the user.

[1800] Step 2:

[1801] The terminal converts user input into JSON format and sends it to the server. This structures the data, allowing for efficient transfer over the network.

[1802] Step 3:

[1803] The server receives data in JSON format. The server stores the received data in memory for analysis and prepares for the next processing step.

[1804] Step 4:

[1805] To perform sentiment analysis, the device includes the user's input and actions in its sentiment analysis engine for analysis, and sends the results to the server as additional data in JSON format. This allows the user's sentiment data to be extracted.

[1806] Step 5:

[1807] The server performs data normalization and imputation of missing values ​​in the data it receives. The server fills in any erroneous or missing data, arranging it into a consistent format. For example, it might use statistical methods or algorithms to fill in incomplete entries.

[1808] Step 6:

[1809] The server references relevant databases based on the entered industry and business type, and extracts the necessary information. The server uses SQL queries to retrieve past success stories, marketing trends, and other information from the databases.

[1810] Step 7:

[1811] Based on the extracted data, user input data, and sentiment data, the server uses a generative artificial intelligence model to generate optimal advice. For example, it might generate specific strategic advice such as, "For the market launch of new product A, we recommend using social media and influencer marketing."

[1812] Step 8:

[1813] The server formats the generated advice into a user-friendly format. Based on the sentiment analysis results, it adjusts the tone and content of the text to suit the user.

[1814] Step 9:

[1815] The server sends the formatted advice to the user's terminal. The formatted advice is packaged as display data and transferred.

[1816] Step 10:

[1817] The device displays advice received from the server on the user interface. Users can access specific and personalized advice through the device.

[1818] In this way, the system provides small and medium-sized business owners with fast, accurate, and personalized business advice.

[1819] (Application Example 2)

[1820] 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".

[1821] Virtual store owners lack access to timely and accurate advice on the various challenges they face in online business. Furthermore, there is a lack of systems that provide personalized advice that takes into account the emotional state of the business owner. As a result, the quality of business decisions suffers, and they often fail to adopt appropriate business strategies.

[1822] 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.

[1823] In this invention, the server includes data preprocessing means, generative artificial intelligence model application means, and sentiment analysis engine means. This makes it possible to analyze user input data and sentiment data, generate optimal business advice, and provide it in an easy-to-understand format.

[1824] A "data entry means" is a device or system that provides an interface for users to input their industry, business challenges, and required support.

[1825] A "data transmission means" refers to a function or system for sending information entered by a user to a server in a structured data format.

[1826] "Data receiving means" refers to the functions and systems that allow a server to receive information sent by a user and prepare it for analysis.

[1827] "Data preprocessing means" refers to functions or systems that perform data normalization, such as imputing missing values, on received data.

[1828] A "database referencing means" is a function or system that extracts necessary information from relevant databases based on the input information.

[1829] A "generative artificial intelligence model application method" refers to a function or system that applies an artificial intelligence model that generates optimal advice based on extracted data and input data.

[1830] An "emotion analysis engine" is a function or system that analyzes a user's emotions based on their input and actions, and sends the results to a server.

[1831] An "advice formatting tool" is a function or system that formats generated advice into a format that is easy for the user to understand.

[1832] An "advice transmission method" refers to a function or system for sending formatted advice to a user's device.

[1833] This invention is a system that provides immediately useful advice to virtual store operators facing various management challenges. The system includes data input means, data transmission means, data reception means, data preprocessing means, database referencing means, generative artificial intelligence model application means, sentiment analysis engine, advice formatting means, and advice transmission means. As an example, this invention is implemented as follows.

[1834] System program

[1835] The server first receives data from the data input means, including the user's industry, business challenges, and required support, via the data transmission means. The received data is normalized and missing values ​​are imputed by the data preprocessing means. Then, relevant information is extracted by the database referencing means, and optimal advice is generated by the generative artificial intelligence model application means. Furthermore, the sentiment analysis engine analyzes the user's sentiment data, and the advice is refined based on that sentiment data. Finally, the advice is displayed on the user's terminal via the advice transmission means.

[1836] Processing details

[1837] The server uses software implemented in programming languages ​​such as Python and Java to exchange data in JSON format. Received data is preprocessed using libraries such as Pandas and NumPy. SQL and NoSQL databases (e.g., MySQL and MongoDB) are used as databases. Generative artificial intelligence models utilize machine learning libraries such as TensorFlow and PyTorch. The sentiment analysis engine uses natural language processing APIs and proprietary sentiment analysis algorithms.

[1838] Specific example

[1839] For example, if a user is seeking advice on promoting a new fashion item, they can enter a question like this: "What are some ways to promote a new fashion item?" The server analyzes this and generates advice such as, "Target women in their 20s and 30s, and implement video marketing using Instagram and TikTok. Influencer collaborations are also effective."

[1840] Example of a prompt

[1841] "Please advise on how to promote new fashion items. I would also like to know more about the target market."

[1842] In this way, the system can provide virtual store managers with personalized business advice that takes emotions into account, thereby improving the quality of their business decisions.

[1843] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[1844] Step 1:

[1845] Users input their industry, business challenges, and required support through the terminal's interface. This input data includes specific industry information, concrete problems, and the type of support needed.

[1846] Step 2:

[1847] The terminal converts the information entered by the user into JSON format and sends it to the server via a data transmission method. The input data at this time is the user's input, and the output data is request data in JSON format.

[1848] Step 3:

[1849] The server receives JSON-formatted data from the terminal using a data receiving device. This data includes information such as industry, business challenges, and support details, and serves as preparation data for analysis.

[1850] Step 4:

[1851] The server normalizes the received data using data preprocessing. Specifically, it performs actions such as imputing missing values ​​and standardizing the data format. In this process, the input data is the received data, and the output data is the preprocessed data.

[1852] Step 5:

[1853] The server extracts necessary information from relevant databases through a database referencing mechanism. This extracted information includes past success stories and trend data. The input data for this process is pre-processed data, and the output data is the result of the referencing.

[1854] Step 6:

[1855] The server generates optimal advice based on the data extracted using a generative artificial intelligence model application method and the input data. A machine learning model is used for this advice generation. The input data for processing is the reference result data, and the output data is the generated advice.

[1856] Step 7:

[1857] The terminal analyzes the user's actions and input using an emotion analysis engine and sends the results to the server. In this process, the input data is the emotion analysis result, and the output data is also the emotion analysis result.

[1858] Step 8:

[1859] The server integrates emotional data and formats the generated advice based on the results of the emotion analysis engine. During formatting, it adjusts the tone and structure of the text. The input data consists of the generated advice and emotional data, while the output data is the formatted advice that reflects the emotions.

[1860] Step 9:

[1861] The server sends the formatted advice to the user's terminal using a data transmission method. In this process, the input data is the formatted advice, and the output data is the transmitted advice.

[1862] Step 10:

[1863] The terminal receives advice sent from the server and displays it on the user interface. In this process, the input data is the advice from the server, and the output data is the advice in a display format that the user can see.

[1864] 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.

[1865] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1866] 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 robot 414.

[1867] 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.

[1868] 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.

[1869] 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.

[1870] 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.

[1871] 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.

[1872] 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."

[1873] 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.

[1874] 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.

[1875] 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.

[1876] 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.

[1877] 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.

[1878] 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.

[1879] 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.

[1880] 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.

[1881] 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.

[1882] 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.

[1883] 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.

[1884] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted as being incorporated by reference.

[1885] The following is further disclosed regarding the embodiments described above.

[1886] (Claim 1)

[1887] Data input means,

[1888] Data transmission means,

[1889] Data receiving means,

[1890] Data preprocessing means,

[1891] Database referencing means,

[1892] A means for applying a generative artificial intelligence model,

[1893] Advice on cosmetic surgery methods,

[1894] Means of sending advice,

[1895] A system that includes this.

[1896] (Claim 2)

[1897] The system according to claim 1, wherein the data input means includes an interface in which the user inputs the industry, management challenges, and required support.

[1898] (Claim 3)

[1899] The system according to claim 1, wherein the data transmission means and data reception means include network communication means for transmitting and receiving data in a structured data format.

[1900] (Claim 4)

[1901] The system according to claim 1, wherein the data preprocessing means includes means for imputing missing values ​​and normalizing data.

[1902] (Claim 5)

[1903] The system according to claim 1, wherein the database referencing means includes means for extracting past success stories, trend data, and competitor information from the database.

[1904] (Claim 6)

[1905] The system according to claim 1, wherein the means for applying a generative artificial intelligence model includes means for applying an artificial intelligence model that generates optimal advice based on the know-how of a well-known business leader.

[1906] (Claim 7)

[1907] The system according to claim 1, wherein the advice transmission means includes means for transmitting the generated advice to the user's terminal.

[1908]

[1909] "Example 1"

[1910] (Claim 1)

[1911] The means by which users input information,

[1912] A means of converting data into a structured data format and sending it,

[1913] A means of receiving data in a structured data format,

[1914] Methods for imputing missing values ​​and normalizing data,

[1915] A means of referencing industry and business databases and extracting relevant information,

[1916] A means of generating advice by applying a generated AI model based on extracted data and input data,

[1917] A means of formatting the generated advice into a user-friendly format,

[1918] A means of sending and displaying formatted advice,

[1919] A system that includes this.

[1920] (Claim 2)

[1921] The system according to claim 1, including an interface in which the user inputs their industry, business challenges, and required support.

[1922] (Claim 3)

[1923] The system according to claim 1, comprising network communication means for transmitting and receiving data in a structured data format.

[1924] "Application Example 1"

[1925] (Claim 1)

[1926] Data input means,

[1927] Data transmission means,

[1928] Data receiving means,

[1929] Data preprocessing means,

[1930] Database referencing means,

[1931] A means for applying a generative artificial intelligence model,

[1932] Advice on cosmetic surgery methods,

[1933] Means of sending advice,

[1934] A means for applying a generative artificial intelligence model, which includes a means for referencing data related to content distribution,

[1935] A system that includes this.

[1936] (Claim 2)

[1937] The system according to claim 1, wherein the data input means includes an interface in which the user inputs the industry, management challenges, required support, and content distribution challenges.

[1938] (Claim 3)

[1939] The system according to claim 1, wherein the data transmission means and data reception means include network communication means for transmitting and receiving data in structured data format, and further include data related to content distribution.

[1940] "Example 2 of combining an emotion engine"

[1941] (Claim 1)

[1942] Data input means,

[1943] Data transmission means,

[1944] Data receiving means,

[1945] Data preprocessing means,

[1946] Database referencing means,

[1947] A means for applying a generative artificial intelligence model,

[1948] Emotion analysis methods,

[1949] Advice on cosmetic surgery methods,

[1950] Means of sending advice,

[1951] A system that includes this.

[1952] (Claim 2)

[1953] The system according to claim 1, wherein the data input means includes an interface in which the user inputs the industry, management challenges, and required support.

[1954] (Claim 3)

[1955] The system according to claim 1, wherein the data transmission means and data reception means include network communication means for transmitting and receiving data in a structured data format.

[1956] "Application example 2 when combining with an emotional engine"

[1957] (Claim 1)

[1958] Data input means,

[1959] Data transmission means,

[1960] Data receiving means,

[1961] Data preprocessing means,

[1962] Database referencing means,

[1963] A means for applying a generative artificial intelligence model,

[1964] Emotion analysis engine,

[1965] Advice on cosmetic surgery methods,

[1966] Means of sending advice,

[1967] A system that includes this.

[1968] (Claim 2)

[1969] The system according to claim 1, wherein the data input means includes an interface in which the user inputs the industry, management challenges, and required support.

[1970] (Claim 3)

[1971] The system according to claim 1, wherein the data transmission means and data reception means include network communication means for transmitting and receiving data in a structured data format. [Explanation of Symbols]

[1972] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

[Claim 1] A data entry method in which users input their industry, business challenges, and required support, A data transmission means for transmitting data in a structured data format, A data receiving means for receiving data in a structured data format, A data preprocessing means for imputing missing values ​​and normalizing data, A database referencing method for extracting past success stories, trend data, and competitor information from the database, A means of applying a generative artificial intelligence model that generates optimal advice based on the know-how of famous business leaders, An advice formatting method that formats the generated advice into a format that is easy for the user to understand, An advice transmission means that sends the generated advice to the user's terminal, A system that includes this. s

Images