System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving a business question from a user; means for pre-processing the question; means for inputting the pre-processed question into a generative AI model; means for generating an answer to the question based on literature and lecture content previously learned by the generative AI model; means for formatting the generated answer; and means for transmitting the formatted answer to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] The problem is that it is difficult for businesspeople to easily obtain the perspectives and advice of great business leaders. In fact, in many cases, getting direct advice from famous business leaders is either impossible or extremely limited. Another issue is that reading through the vast amount of material takes time and effort, making it difficult to obtain practical advice. This can lead to a lack of appropriate guidance for formulating business strategies and developing organizations. [Means for solving the problem]

[0005] The present invention provides a system that receives business-related questions from users, performs preprocessing to address the questions, and inputs the preprocessed questions into a generative AI model to generate answers based on previously studied literature and lectures by business leaders. The system then formats and sends the answers to the user. This allows users to easily obtain specific business advice from a business leader's perspective, which can be useful in formulating business strategies and developing organizations. Specifically, the system includes preprocessing techniques such as text cleaning and tokenization, a generative AI model using natural language processing techniques, and a means for formatting answers.

[0006] "User" refers to an entity that uses the system to enter business-related questions and receive answers.

[0007] A "question" is a specific business problem or matter of inquiry entered by a user.

[0008] "Preprocessing" refers to the process of receiving a question, performing necessary text cleaning and tokenization, and converting it into a format suitable for a generative AI model.

[0009] A "generative AI model" refers to an artificial intelligence model that generates answers to questions based on previously studied literature and lecture content.

[0010] "Literature" refers to management's books, papers, and other written materials.

[0011] "Speech content" refers to the content of lectures or speeches given by business managers.

[0012] "Answer" refers to the answer or advice generated by a generative AI model in response to a question.

[0013] "Formatting" is a process of correcting the grammar and expressions of the generated answer and converting it into a format that is easy for the user to understand.

[0014] "Sending" is the process of delivering the formatted response to the user's terminal.

[0015] "Text cleaning" is the process of removing unnecessary spaces and special characters from received text to make the data clean.

[0016] "Tokenization" is the process of breaking down incoming text into meaningful words and phrases.

[0017] "Natural language processing technology" refers to artificial intelligence technology for understanding and analyzing human language.

[0018] "Formatting" is the process of arranging answers into paragraphs, bullet points, or other formats. [Brief explanation of the drawings]

[0019] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9]1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0020] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0021] First, the terms used in the following description will be explained.

[0022] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0023] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0024] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0025] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0026] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0027] [First embodiment]

[0028] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0029] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0030] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0031] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0032] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0033] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0034] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0035] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0036] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0037] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0038] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0039] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0040] The present invention is a system that allows users to input business questions and receive answers, and the server utilizes a generative AI model to enable users to easily receive specific business advice. This system includes multiple processing steps, each of which is executed by the server, a terminal, and the user.

[0041] System Overview:

[0042] 1. User interface implementation:

[0043] The terminal provides an interface for the user to input a business question. The user interface includes a text box and a submit button.

[0044] 2. Details of the process listed on the server:

[0045] The server receives the question submitted by the user, performs preprocessing, and generates an answer using a generative AI model, which is then formatted and sent to the user.

[0046] A natural language description of what the program does:

[0047] 1. User inputs a question:

[0048] The user enters a business question into a text box on the terminal and presses the send button.

[0049] The terminal sends this input to the server.

[0050] Examples:

[0051] The user types in "What is your strategy for entering new markets?" and hits the submit button.

[0052] 2. Question preprocessing:

[0053] The server receives the question submitted by the user and performs text cleaning, for example removing extra spaces and special characters.

[0054] The server tokenizes the question, breaking it down into meaningful words and phrases.

[0055] Examples:

[0056] Take the question, "What is your strategy for entering a new market?" and break it down into keywords such as "new market," "entry," and "strategy."

[0057] 3. Input to the generative AI model:

[0058] The server inputs the preprocessed questions into the generative AI model.

[0059] The generative AI model generates appropriate answers to questions based on pre-trained literature and speeches by business leaders.

[0060] Examples:

[0061] The server passes the question "What is your strategy for entering a new market?" to the generative AI model, which generates an answer such as "When entering a new market, it is important to first conduct thorough market research and find points of differentiation through competitive analysis."

[0062] 4. Formatting the answer:

[0063] The server formats the generated answer, ensuring grammar and formatting.

[0064] If necessary, format your answers by dividing them into paragraphs or bullet points.

[0065] Examples:

[0066] The server checks the generated answer, "When entering a new market, it is important to first conduct thorough market research and find points of differentiation through competitive analysis," for grammar and formats it into a paragraph.

[0067] 5. Submitting and Viewing Your Answers:

[0068] The server sends the formatted response to the user's terminal.

[0069] The terminal displays the received answer on the user's screen.

[0070] Examples:

[0071] The server then sends the formatted response to the user's device as, "When entering a new market, it is important to first conduct thorough market research and find points of differentiation through competitive analysis," and the device displays it.

[0072] Through this series of steps, users can easily obtain specific business advice based on the wisdom of well-known business leaders, which can be used to help formulate business strategies and develop their organizations.

[0073] The processing flow will be explained below.

[0074] Step 1:

[0075] The user enters a question

[0076] The user enters a business question into the input form on the terminal and presses the send button. The terminal prepares to send the question to the server.

[0077] Step 2:

[0078] The device sends a question to the server

[0079] The terminal transmits the question entered by the user to the server as data, which includes the content of the user's question.

[0080] Step 3:

[0081] The server receives the query

[0082] The server receives the query data sent from the terminal and checks the integrity of the data.

[0083] Step 4:

[0084] The server preprocesses the query

[0085] The server performs text cleaning on the received question, removing unnecessary spaces and special characters to make the question clean.

[0086] The server also tokenizes the question, breaking it down into meaningful words and phrases.

[0087] Step 5:

[0088] The server inputs questions into the generative AI model

[0089] The server inputs the preprocessed questions into a generative AI model, which generates appropriate answers based on pre-trained literature and speeches by business leaders.

[0090] Step 6:

[0091] Generative AI models generate answers

[0092] The generative AI model generates specific advice from the manager's perspective based on the input question, and the generated answers are temporarily stored on the server.

[0093] Step 7:

[0094] The server formats the generated answer

[0095] The server formats the answers received from the generative AI model, checking grammar and expression to make them easier for the user to understand.

[0096] Step 8:

[0097] The server formats the response

[0098] If necessary, the server will format the answer into paragraphs or bullet points, which makes the answer more readable.

[0099] Step 9:

[0100] The server sends the formatted response to the device.

[0101] The server sends the formatted response to the user's terminal.

[0102] Step 10:

[0103] The device receives and displays the answer

[0104] The terminal receives the response sent from the server and displays it on the user's screen, allowing the user to check specific business advice.

[0105] Examples:

[0106] The user types "What is your strategy for entering a new market?" into the device and presses the send button. The device sends this question to the server, which receives it. The server preprocesses the question and inputs it into the generative AI model. The generative AI model generates the answer "When entering a new market, it is important to first conduct thorough market research and identify points of differentiation through competitive analysis." The server formats this answer and sends it to the device. The device receives the answer and displays it on the user's screen.

[0107] Example 1

[0108] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0109] Currently, there are limited systems that provide fast and specific answers to business questions, making it difficult for many users to obtain appropriate business advice. In addition, existing systems often do not automate the preprocessing of questions or the formatting of answers, requiring manual intervention. Furthermore, the quality of the generated answers is inconsistent, making it difficult for users to obtain reliable information. A system that addresses these issues is needed.

[0110] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0111] In this invention, the server includes a means for receiving business questions from users at a terminal, a means for preprocessing the questions at the server, and a means for inputting the preprocessed questions into a generative AI model at the server. This allows users to quickly obtain appropriate business advice. Furthermore, by automating preprocessing including text cleaning and tokenization, semantic analysis of questions can be performed accurately and efficiently. The generative AI model generates high-quality answers based on pre-trained data, and by formatting the answers, it is possible to provide consistent information. This allows users to quickly receive reliable business advice, which can be useful for planning business strategies and developing organizations.

[0112] A "user" is a person or organization that utilizes the system to enter business questions and receive answers.

[0113] A "terminal" is a hardware device used by a user to input questions and receive answers from a server. Examples include computers and smartphones.

[0114] A "server" is a computer system whose role is to process questions entered by users, generate answers using generative AI models, format them, and then send them to the user's device.

[0115] "Question preprocessing" is the process of cleaning up the text data submitted by the user and converting it into an analyzable format using methods such as tokenization.

[0116] "Text cleaning" is the process of removing extra spaces and special characters from questions entered by users.

[0117] "Tokenization" is the process of breaking down text data into words and phrases and converting it into a form that is easier to analyze.

[0118] A "generative AI model" is an artificial intelligence model that learns large amounts of data in advance and generates appropriate answers for input text.

[0119] "Answer formatting" is the process of checking the generated answers for grammar and arranging them into paragraphs, bullet points, or other formats.

[0120] "Data" is information processed within the system, including questions entered by users and answers generated by the server.

[0121] This invention is a system that allows users to input business questions and receive answers. The server utilizes a generative AI model, allowing users to easily receive specific business advice. This system uses the following hardware and software:

[0122] Hardware and software used

[0123] Server: A computer system that processes user questions and generates answers, using cloud services as needed.

[0124] Device: A device on which a user enters questions and receives answers. Examples include computers, tablets, and smartphones.

[0125] Generative AI models: Artificial intelligence models that have been trained on large amounts of data in advance (e.g., OpenAI GPT-3). They are accessible through APIs.

[0126] System Configuration

[0127] The system consists of the following main components:

[0128] 1. User Interface:

[0129] The terminal provides an interface that includes a text box for the user to enter a question and a submit button.

[0130] 2. Receiving and Preprocessing Questions:

[0131] The server receives the questions sent from the terminal and performs text cleaning and tokenization. Text cleaning includes removing extra spaces and special characters, and tokenization uses libraries such as NLTK and SpaCy.

[0132] 3. Use of generative AI models:

[0133] The server inputs the preprocessed questions into the generative AI model, which generates answers based on pre-trained management literature and data.

[0134] 4. Formatting the answer:

[0135] The answers output by the generative AI model are formatted on the server, checking for grammar and formatting as needed, which includes formatting into paragraphs and bullet points using regular expressions and HTML tags.

[0136] 5. Submitting and Viewing Answers:

[0137] The server sends the formatted answer to the terminal, which then displays the answer on the user's screen.

[0138] Specific operation example

[0139] User enters and submits question:

[0140] The user types in "What is your strategy for entering new markets?" and hits the submit button.

[0141] Question preprocessing:

[0142] The server receives the question, cleans it of extra spaces and special characters, and tokenizes it into words like "new market," "entry," and "strategy."

[0143] Use of generative AI models:

[0144] The server inputs the preprocessed data into a generative AI model and generates answers such as, "When entering a new market, it is important to first conduct thorough market research and find points of differentiation through competitive analysis."

[0145] Formatting the answer:

[0146] The server then formats the answer grammatically and summarizes it in a paragraph: "When entering a new market, it's important to first conduct thorough market research and identify points of differentiation through competitive analysis."

[0147] Submit and view your answers:

[0148] The server sends the formatted response to the terminal, which displays it on the user's screen.

[0149] Prompt Sentence Examples

[0150] "What are the main steps in developing a marketing strategy?"

[0151] "What makes a startup successful?"

[0152] "What points should I pay attention to when introducing a new product to the market?"

[0153] In this way, the system helps users get quick, specific and reliable business advice.

[0154] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0155] Step 1:

[0156] The user enters a business question into a text box on the device and presses the submit button, providing a prompt such as "What is your strategy for entering new markets?"

[0157] The device generates an HTTP POST request to send this input to the server, and sends the question to the server. Specifically, the input text is included in the request body and sent to the specified endpoint.

[0158] Step 2:

[0159] The server receives the question sent from the device, which includes as input an HTTP POST request from the device.

[0160] The server first performs text cleaning. Specifically, it uses regular expressions to remove extra spaces and special characters. As a result of the data processing, it outputs the question in a clean format: "What is your strategy for entering a new market?"

[0161] Step 3:

[0162] The server then tokenizes the question, including the cleaned text as input.

[0163] Specifically, it uses libraries such as the Natural Language Toolkit (NLTK) and SpaCy to break down the question into words and phrases, such as "new market," "entry," and "strategy." This results in tokenized data being output.

[0164] Step 4:

[0165] The server inputs the preprocessed question into the generative AI model, which includes tokenized text data as input.

[0166] Specifically, the API of a generative AI model (e.g., OpenAI GPT-3) is called, and the tokenized data is passed as an argument. The generative AI model generates an answer to the question based on the data it has learned in advance. The API response then outputs text such as, "When entering a new market, it is important to first conduct thorough market research and identify points of differentiation through competitive analysis."

[0167] Step 5:

[0168] The server formats the generated answer, which includes the text output from the generative AI model as input.

[0169] Specifically, it uses a grammar checking engine (e.g., Grammarly API) and regular expressions and HTML tags to format text into paragraphs and bullet points. For example, it outputs a paragraph-formatted text such as "When entering a new market, it is important to first conduct thorough market research and identify points of differentiation through competitive analysis."

[0170] Step 6:

[0171] The server sends the formatted answer to the user's terminal, including the formatted answer text as input.

[0172] Specifically, it generates an HTTP response and sends it to the terminal, including the response data. The terminal then displays the received response on the user's screen. For example, a formatted response such as "When entering a new market, it is important to first conduct thorough market research and identify points of differentiation through competitive analysis" is displayed on the screen.

[0173] Through this series of steps, users can quickly obtain specific and reliable business advice.

[0174] (Application example 1)

[0175] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0176] Currently, there are limited ways to receive appropriate advice immediately in response to specific business questions, making it difficult for users to easily obtain reliable information. Additionally, there is no system that allows users to resolve questions that arise on the spot while viewing business content, which reduces the user's learning efficiency.

[0177] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0178] In this invention, the server includes means for receiving a business-related question from a user, means for preprocessing the question, means for inputting the preprocessed question into a generative AI model, means for generating an answer to the question based on materials previously learned by the generative AI model, means for formatting the generated answer, means for sending the formatted answer to the user, means for inputting a question in real time while viewing business-related content on a smartphone and receiving an immediate answer, and means for receiving questions about videos or articles displayed on the smartphone, thereby enabling a user to immediately receive specific and appropriate business advice while viewing business content.

[0179] The "means for receiving business-related questions from users" refers to an interface that allows users to input specific business-related questions and have them received by the system.

[0180] The "means for preprocessing the question" is a process for analyzing the question received from the user and performing data cleaning, tokenization, etc.

[0181] "Means for inputting the preprocessed question into the generative AI model" refers to a method for inputting a question that has undergone preprocessing into the generative AI model.

[0182] "Means for generating answers to questions based on materials previously learned by the generative AI model" refers to a method for creating answers to questions using a generative AI model based on management-related literature and data previously learned.

[0183] The "means for formatting the generated answer" is a process for adjusting the grammar and format of the generated answer to make it easier to read.

[0184] The "means for transmitting the formatted answer to the user" is a communication method for providing the user with the answer that has been formatted.

[0185] "A means for viewing business-related content on a smartphone while inputting questions in real time and receiving instant answers" is a system that allows users to view videos or articles that are useful for business on their smartphones, input questions that arise while viewing them on the spot, and receive instant answers.

[0186] The "means for receiving questions about videos or articles displayed on the smartphone" refers to a function that allows a user to input questions about the content of business-related videos or articles while watching them on a smartphone, and the system receives those questions.

[0187] This invention provides a system that allows users to input business-related questions via smartphone and receive specific business advice in real time. Specific embodiments of this system will be described in detail below.

[0188] This system is implemented using a smartphone application. While users are watching business-related videos or articles within the application, they are provided with an interface where they can input questions. When users input a question, the smartphone sends it to the server.

[0189] The server then receives this question and performs text cleaning and tokenization. Text cleaning removes extra spaces and special characters. Tokenization breaks the question down into meaningful words and phrases, pre-processing the question and converting it into a format suitable for generative AI models.

[0190] After preprocessing, the question is input to a generative AI model by the server. The specific generative AI model used is OpenAI GPT. This model has been trained in advance on management-related literature and case studies, and is able to generate appropriate business advice in response to the user's question.

[0191] Once the generative AI model generates an answer based on the question, the server formats the answer grammatically and makes it easier for the user to understand, for example by dividing the answer into paragraphs or bullet points.

[0192] The formatted answers are then sent back to the smartphone, where the user can check them in real time on the application. This continuous interaction allows users to view business content while instantly resolving any questions that arise.

[0193] As a concrete example, suppose a user types, "Please tell me your strategy for entering a new market." The server passes this question to a generative AI model, which generates an answer such as, "When entering a new market, it is important to first conduct thorough market research and identify points of differentiation through competitive analysis." The server formats this answer and sends it to the smartphone. The user can view the answer on the app.

[0194] Examples of prompts that can be used include:

[0195] User Question: What is your strategy for entering new markets?

[0196] Business advice:

[0197] As can be seen from this example, this system provides users with a means to easily obtain specific business advice, greatly improving learning efficiency.

[0198] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0199] Step 1:

[0200] Question input from the user

[0201] The user enters a business question into a text box on the smartphone application and presses the submit button. The input data is the question text entered by the user, and this input is sent to the server.

[0202] Step 2:

[0203] Question Preprocessing

[0204] The server receives the question submitted by the user and performs text cleaning and tokenization. Specifically, it removes extra spaces and special characters and breaks the question into tokens. This process generates preprocessed question data. The input is the user's raw text question, and the output is the cleaned and tokenized text data.

[0205] Step 3:

[0206] Input to generative AI models

[0207] The server inputs the preprocessed question into a generative AI model (e.g., OpenAI's GPT). The input data is the preprocessed question text, and the generative AI model generates an answer to the question based on the pre-trained material. The output is the generated answer text.

[0208] Step 4:

[0209] Formatting answers

[0210] The server formats the answer text returned by the generative AI model. Specifically, it checks the grammar of the sentences, standardizes the format, and formats the answer by dividing it into paragraphs and bullet points. The input is the raw text answer from the generative AI model, and the output is the formatted text answer.

[0211] Step 5:

[0212] Submitting and viewing responses

[0213] The server sends the formatted answer back to the user's smartphone application, which displays the received answer on its screen. The input is the formatted text answer, and the output is a visual form on the user's smartphone display.

[0214] Step 6:

[0215] Entering questions while watching business-related content

[0216] Users can directly input questions while watching business-related videos or articles on a smartphone application. This question is also sent to the server as in step 1, and subsequent processing is executed. The input is a text question asked while watching the video or article, and the output is a display of the answer according to the flow up to step 5.

[0217] Through the above processing steps, the system realizing the present invention enables a user to obtain specific business advice in real time while viewing business content.

[0218] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0219] The present invention is a system that allows users to input business questions and obtain answers, and combines an emotion engine to provide advice that recognizes and takes into account the user's emotions. This system includes multiple processing steps, each of which is executed by a server, a terminal, and a user.

[0220] System Overview:

[0221] 1. User interface implementation:

[0222] The terminal provides an interface for the user to input a business question. The user interface includes a text box and a submit button.

[0223] 2. Details of the process listed on the server:

[0224] The server receives the question sent by the user, performs preprocessing, recognizes the user's emotions using an emotion engine, and generates an answer using a generative AI model. The generated answer is formatted and sent to the user.

[0225] A natural language description of what the program does:

[0226] 1. User inputs a question:

[0227] The user enters a business question into a text box on the terminal and presses the send button.

[0228] The terminal sends this input to the server.

[0229] Examples:

[0230] The user types in "What is your strategy for entering new markets?" and hits the submit button.

[0231] 2. Question preprocessing:

[0232] The server receives the question submitted by the user and performs text cleaning, removing unnecessary spaces and special characters to make the question clean.

[0233] The server tokenizes the question, breaking it down into meaningful words and phrases.

[0234] Examples:

[0235] Take the question, "What is your strategy for entering a new market?" and break it down into keywords such as "new market," "entry," and "strategy."

[0236] 3. Emotion recognition:

[0237] The server uses an emotion engine to recognize the user's emotion from the question sentence.

[0238] The emotion engine classifies emotions into categories such as positive, negative, and neutral.

[0239] Examples:

[0240] By analyzing the question, the emotion engine recognizes that the user is feeling anxious about the phrase "strategy for entering new markets."

[0241] 4. Input to the generative AI model:

[0242] The server inputs the preprocessed questions and emotional information from the emotion engine into the generative AI model.

[0243] The generative AI model generates appropriate answers to questions based on pre-trained literature and speech content from business executives, and also takes into consideration the user's emotions.

[0244] Examples:

[0245] For anxious users, the generative AI model generates a reassuring response such as, "Don't worry, when entering a new market, it's important to first conduct thorough market research and find points of differentiation through competitive analysis."

[0246] 5. Formatting your answer:

[0247] The server formats the generated answer, checking grammar and expression to make it easier for the user to understand.

[0248] Examples:

[0249] The server checks the generated answer, "Don't worry, when entering a new market, it's important to first conduct thorough market research and find your points of differentiation through competitive analysis," for grammar and formats it into a paragraph.

[0250] 6. Formatting your answers:

[0251] If necessary, the server will format the answer into paragraphs or bullet points, which makes the answer more readable.

[0252] 7. Submitting and Viewing Your Answers:

[0253] The server sends the formatted response to the user's terminal.

[0254] The terminal displays the received answer on the user's screen.

[0255] Examples:

[0256] The server then sends the formatted response to the user's device as, "Don't worry, when entering a new market, it's important to first conduct thorough market research and find points of differentiation through competitive analysis," and the device displays it.

[0257] This series of steps allows users to easily obtain specific business advice that takes into account the perspectives and feelings of well-known business leaders, which can be useful in formulating business strategies and developing organizations.

[0258] The processing flow will be explained below.

[0259] Step 1:

[0260] The user enters a question

[0261] The user enters a business question into a text box on the terminal and presses the send button. The terminal prepares the question as data to be sent to the server.

[0262] Step 2:

[0263] The device sends a question to the server

[0264] The terminal sends the user's input question to the server as a data packet, which contains the entire question.

[0265] Step 3:

[0266] The server receives the query

[0267] The server receives the query data sent from the terminal and verifies the integrity of the data. In this step, it checks whether any inappropriate data is included.

[0268] Step 4:

[0269] Server performs text cleaning

[0270] The server performs text cleaning on the received question, removing unnecessary spaces and special characters to make the question clean.

[0271] Step 5:

[0272] The server performs tokenization

[0273] The server tokenizes the question, breaking down the sentence into meaningful words and phrases, converting it into a format that is easy to feed into a generative AI model.

[0274] Step 6:

[0275] The server uses an emotion engine to recognize emotions.

[0276] The server inputs the question text into an emotion engine to recognize the user's emotion, which then classifies it into emotion categories such as positive, negative, and neutral.

[0277] Step 7:

[0278] The server inputs emotional information into the generative AI model

[0279] The server inputs the preprocessed question and the emotional information recognized by the emotion engine into the generative AI model, and the interaction of this data helps generate appropriate answers.

[0280] Step 8:

[0281] Generative AI models generate answers

[0282] The generative AI model generates optimal answers to questions based on pre-trained literature and lecture content, taking into account emotion recognition results.

[0283] Step 9:

[0284] The server formats the generated answer

[0285] The server formats the generated answers, correcting grammatical errors and making them more user-friendly.

[0286] Step 10:

[0287] The server formats the response

[0288] If necessary, the server will format the answer into paragraphs or bullet points, which will make the answer more readable and organized.

[0289] Step 11:

[0290] The server sends the formatted response to the device.

[0291] The server sends the formed and formatted response to the user's terminal.

[0292] Step 12:

[0293] The device receives and displays the answer

[0294] The terminal receives the response sent from the server and displays it on the user's screen, allowing the user to confirm specific business advice.

[0295] Examples:

[0296] The user types "What is your strategy for entering a new market?" into the device and presses the send button. The device sends this question to the server, which receives it. The server performs text cleaning and tokenization, and recognizes the user's emotions using an emotion engine. The server inputs the question and emotion information into a generative AI model, which then generates the answer, "Don't worry, when entering a new market, it's important to first conduct thorough market research and find points of differentiation through competitive analysis." The server then formats this answer and sends it to the device. The device receives the answer and displays it on the user's screen.

[0297] Example 2

[0298] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0299] Existing business advice systems do not provide answers that take the user's emotions into account, making it difficult to respond appropriately to the user's concerns and questions. Furthermore, simply generating answers based on literature or lecture content can sometimes lack consideration for the user's specific emotions and situation. There is a need for a system that can solve this problem and provide users with more personalized, emotion-sensitive, and appropriate business advice.

[0300] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0301] In this invention, the server includes means for receiving a business-related question from a user, means for preprocessing the question, means for inputting the preprocessed question into a sentiment analysis engine and recognizing the user's sentiment, means for inputting the sentiment information and the preprocessed question into a generative AI model, means for the generative AI model to generate an answer that takes into account the sentiment for the question based on materials it has previously learned, means for formatting the generated answer, and means for sending the formatted answer to the user, thereby enabling the provision of personalized business advice that takes into account the user's sentiment.

[0302] A "user" is an individual or entity that enters a business inquiry and uses the system.

[0303] "Business Questions" are specific inquiries or consultations related to business strategy, marketing, and management.

[0304] The "means for preprocessing questions" is a process for analyzing received questions and performing text cleaning and tokenization.

[0305] "Preprocessed questions" are question data that have been text cleaned and tokenized, and are formatted so that they can be analyzed.

[0306] An "emotion analysis engine" is a technology that analyzes and recognizes a user's emotions from input text and classifies them into emotional categories.

[0307] The "means for recognizing emotions" is a process of recognizing the user's emotions from preprocessed questions using an emotion analysis engine.

[0308] "Emotion information" refers to the emotion category (e.g., positive, negative, neutral) classified by the emotion analysis engine.

[0309] A "generative AI model" is an artificial intelligence model that generates appropriate answers to input data based on previously learned materials.

[0310] "Means for inputting to the generative AI model" refers to the process of providing preprocessed questions and emotion information to the generative AI model.

[0311] "Means of generation" refers to the process by which a generative AI model generates an appropriate answer based on input data.

[0312] The "formatting means" is a process of checking the grammar of the generated answer and making it into a format that is easy for the user to understand.

[0313] "Means for sending" refers to the process of sending the formatted answer to the user's terminal via communication.

[0314] The "system" is a set of devices and software that executes a series of processes to provide appropriate answers that take emotions into account to users' business-related questions.

[0315] The present invention is a system that allows users to input business-related questions and receive answers, and combines an emotion analysis engine to provide advice that recognizes and takes into account the user's emotions. This system is mainly processed using a server and terminals.

[0316] System configuration and specific details:

[0317] 1. User interface implementation:

[0318] The terminal provides an interface for users to input business-related questions. The interface includes a text box and a submit button. Through this interface, users can easily input their questions and proceed to the next processing step.

[0319] 2. Question preprocessing:

[0320] The server receives questions sent by users. The received questions are first text-cleaned to remove unnecessary spaces and special characters. Next, the server tokenizes the questions and breaks them down into meaningful words and phrases. Specifically, the question "What is your strategy for entering new markets?" is broken down into keywords such as "new market," "entry," and "strategy."

[0321] 3. Emotion recognition:

[0322] The server passes the preprocessed question to a sentiment analysis engine, which recognizes the user's emotions from the question and classifies them into sentiment categories such as positive, negative, and neutral. For example, the question "Please tell us your strategy for entering a new market" identifies the user's feelings of anxiety.

[0323] 4. Input to the generative AI model:

[0324] The server inputs the preprocessed question and emotion information into the generative AI model. Based on the pre-trained data, the generative AI model generates an appropriate answer to the question while also taking the user's emotions into consideration. For example, the generative AI model might generate the answer, "Don't worry, when entering a new market, it's important to first conduct thorough market research and identify points of differentiation through competitive analysis."

[0325] 5. Formatting your answer:

[0326] The generated answer is checked for grammar and expressions on the server, and is then formatted into a paragraph format that is easy for the user to understand.

[0327] 6. Formatting your answers:

[0328] If desired, the server can format the answer as paragraphs or bullet points, which makes the answer more readable.

[0329] 7. Submitting and Viewing Your Answers:

[0330] The formatted response is sent from the server to the user's device, which then displays the response on the user's screen. Specifically, the server sends the formatted response as "Don't worry, when entering a new market, it's important to first conduct thorough market research and find points of differentiation through competitive analysis," and the device displays it.

[0331] This system allows users to easily obtain specific business advice that takes into account the perspectives and sentiments of prominent business leaders, helping them to develop business strategies and develop their organizations. The system can be used on a variety of hardware, including servers, desktop computers, laptops, and mobile devices. The software used includes a generative AI model, a sentiment analysis engine, and a tokenization tool.

[0332] Examples:

[0333] When a user types "What is your strategy for entering a new market?" into a text box on their device and presses the send button, the server receives the question. The server then performs text cleaning and tokenization, and uses a sentiment analysis engine to identify the user's concerns. The generative AI model then generates an answer: "Don't worry, when entering a new market, it's important to first conduct thorough market research and identify points of differentiation through competitive analysis." The answer is then formatted by the server and sent to the user. The user can view this answer on their device.

[0334] Example prompt sentence:

[0335] "What is your strategy for entering new markets?"

[0336] This series of steps allows users to receive appropriate advice that takes their emotions into consideration.

[0337] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0338] Step 1:

[0339] A user enters a business question.

[0340] The user enters a question in the text box on the terminal and presses the send button.

[0341] Input: A business question from the user (e.g., "What is your strategy for entering new markets?")

[0342] Output: The entered question is sent from the terminal to the server. Specifically, the terminal constructs an HTTP request and sends the question data to the server.

[0343] Step 2:

[0344] The server receives the query and performs pre-processing.

[0345] The server performs text cleaning on the received question, removing unnecessary spaces and special characters.

[0346] Input: A question sent from the device (e.g., "What is your strategy for entering new markets?")

[0347] Output: Cleaned text (e.g., "What is your strategy for entering new markets?" with unnecessary spaces removed)

[0348] Specifically, the server uses regular expressions and other text processing techniques to remove unnecessary parts.

[0349] Step 3:

[0350] The server tokenizes the question.

[0351] Break down the cleaned question into words and phrases.

[0352] Input: Cleaned text (e.g., "What is your strategy for entering new markets?")

[0353] Output: Tokenized keywords (e.g., "new market," "entry," "strategy")

[0354] Specifically, the server applies a tokenization algorithm to break down the question into meaningful words and phrases.

[0355] Step 4:

[0356] The server uses an emotion analysis engine to recognize the user's emotions.

[0357] The server passes the preprocessed questions to a sentiment analysis engine for sentiment analysis.

[0358] Input: Tokenized keywords (e.g., "new market," "entry," "strategy")

[0359] Output: Emotional information (e.g., anxiety)

[0360] Specifically, the server inputs these keywords into an emotion analysis engine to obtain emotion categories such as positive, negative, and neutral.

[0361] Step 5:

[0362] The server inputs the preprocessed question and sentiment information into the generative AI model.

[0363] The generative AI model generates an answer.

[0364] Input: Tokenized keywords and sentiment information (e.g., "new market," "entry," "strategy," sentiment: anxiety)

[0365] Output: Generated answer (e.g., "Don't worry, when entering a new market, it's important to first conduct thorough market research and identify your points of differentiation through competitive analysis.")

[0366] Specifically, the server provides this data to the generative AI model and waits for it to generate an answer.

[0367] Step 6:

[0368] The server formats the generated answer.

[0369] The generated answers are checked for grammar and expression and formatted to make them easier to understand.

[0370] Input: Generated answer (e.g., "Don't worry, when entering a new market, it's important to first conduct thorough market research and identify your points of differentiation through competitive analysis.")

[0371] Output: Formatted answer (e.g. "Don't worry, when entering a new market, it's important to first conduct thorough market research and identify your points of differentiation through competitive analysis." formatted into a paragraph)

[0372] Specifically, the server performs a grammar check on the generated response and makes appropriate corrections.

[0373] Step 7:

[0374] The server sends the formatted response to the user's terminal.

[0375] The terminal displays.

[0376] Input: Formatted answer (e.g., "Don't worry, when entering a new market, it's important to first conduct thorough market research and identify your points of differentiation through competitive analysis.")

[0377] Output: The answer displayed on the user's screen

[0378] Specifically, the server sends the formatted answer as an HTTP response, and the terminal displays the received answer in the text area.

[0379] (Application example 2)

[0380] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0381] In modern manufacturing, it is difficult to obtain prompt and appropriate answers to real-time questions about factory operation and management. Furthermore, there is a lack of technology that provides advice that takes into account the emotions of workers and managers. As a result, efficiency on the shop floor and psychological stress among workers are negatively affected. The present invention aims to solve these problems by providing a system that enables factory workers and managers to ask questions in real time and receive answers that recognize their emotions.

[0382] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving an inquiry from a user, means for preprocessing the inquiry, means for inputting the preprocessed inquiry into a generative AI model, means for generating a response to the inquiry based on documents previously learned by the generative AI model, means for formatting the generated response, means for sending the formatted response to the user, and means including an emotion recognition engine for recognizing the user's emotions, the emotion recognition engine linking the user's emotion information to the generative AI model. This makes it possible to provide quick and appropriate answers to questions regarding factory operation and management and to provide advice that takes into consideration the emotions of workers.

[0383] "User" refers to the workers and managers who ask questions about factory operation and management to the system.

[0384] "Inquiry" means information that represents a question or request regarding factory operations and management.

[0385] "Preprocessing" refers to a series of steps that clean up queries and convert them into a format that is easy for generative AI models to interpret.

[0386] "Text cleaning" is the process of removing unnecessary spaces and special characters to make the text clean.

[0387] "Tokenization" is the process of breaking down text into meaningful words and phrases.

[0388] A "generative AI model" is an artificial intelligence model that generates appropriate responses to inquiries based on pre-trained data.

[0389] "Documents" refers to materials and records containing information relating to factory operations and management.

[0390] "Response" refers to the answer generated by a generative AI model in response to a query.

[0391] An "emotion recognition engine" is a technology that analyzes a user's emotions and classifies them into emotional categories such as positive, negative, and neutral.

[0392] "Integrating" refers to sharing the emotional information analyzed by the emotion recognition engine with the generative AI model and reflecting it in the response.

[0393] This invention provides a system that asks questions about factory operation and management and provides appropriate answers that recognize emotions. This system consists of a server, a user operation terminal, an emotion recognition engine, and a generative AI model.

[0394] First, the user (factory worker or manager) enters a question about factory operation and management into a text box on the terminal and presses the send button. This question is then sent from the terminal to the server. The terminal can be a regular PC, tablet, or smartphone.

[0395] The server receives the question submitted by the user and first performs text cleaning, removing unnecessary spaces and special characters to make the question clean. This step is part of preprocessing to enable accurate and efficient processing. Next, the server performs tokenization, breaking the question down into meaningful words and phrases.

[0396] After the question preprocessing is complete, the server uses an emotion recognition engine to analyze the preprocessed question and obtain the emotional information the user is feeling. This emotional information is classified into emotion categories such as positive, negative, and neutral. For example, if the question is "How can we operate a new assembly line efficiently?", the emotion recognition engine will analyze whether the user is feeling anxious or worried.

[0397] The server then inputs the preprocessed question and emotional information into a generative AI model. The generative AI model generates an appropriate answer based on document data related to factory operation and management that it has previously learned. In doing so, it also takes into account emotional information and generates an answer that takes the user's emotions into consideration. The generative AI model used here can be a large-scale language model such as GPT-3.

[0398] The generated answer is then formatted by the server, for example by checking grammar and adjusting paragraph structure. Finally, the formatted answer is sent from the server to the user's device and displayed on the device screen, allowing the user to receive specific advice that takes their feelings into consideration.

[0399] For example, if a worker types "Tell me about quality control" into a terminal and sends it, the server will clean and tokenize the question, and the emotion recognition engine will analyze the anxiety expressed in the question. The generative AI model will generate an answer such as "Quality control is important. Don't worry, please use the checklist to review your daily processes," which will then be formatted by the server and sent back to the user.

[0400] An example of a prompt might be, "How can we run our new assembly line efficiently? The workers are feeling impatient."

[0401] This system allows users to get appropriate answers to questions about factory operation and management in real time, improving work efficiency and reducing psychological stress.

[0402] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0403] Step 1:

[0404] The user enters a question about factory operation and management in the text box on the terminal and presses the send button.

[0405] Input: A user-typed question (e.g., "How can we run our new assembly line efficiently?")

[0406] Output: Question data sent from the terminal to the server

[0407] Step 2:

[0408] The server receives the questions sent by the user and performs text cleaning.

[0409] Input: Question data submitted by the user (e.g., "How can we run our new assembly line efficiently?")

[0410] Data processing: Remove unnecessary spaces and special characters to make the text clean.

[0411] Output: Cleaned question data (e.g., "How can we run our new assembly line efficiently?")

[0412] Step 3:

[0413] The server tokenizes the cleaned question, breaking it down into meaningful words and phrases.

[0414] Input: Cleaned question data

[0415] Data operations: Breaking down the question into words and phrases

[0416] Output: Tokenized question data (e.g., "New," "Assembly Line," "Efficient," "Operation," "What should I do?")

[0417] Step 4:

[0418] The emotion recognition engine analyzes the tokenized question data and reconnects with the user's emotions.

[0419] Input: Tokenized question data

[0420] Data Computing: Sentiment Analysis to Identify User Emotions

[0421] Output: Emotional information (e.g., impatience)

[0422] Step 5:

[0423] The server inputs the preprocessed question and emotion information into the generative AI model.

[0424] Input: Preprocessed question data and sentiment information

[0425] Data Computation: Generative AI models generate responses based on pre-trained data

[0426] Output: Generated response data (e.g., "Please proceed slowly. To operate a new assembly line efficiently, it is important to perform proper initial setup and regular maintenance.")

[0427] Step 6:

[0428] The server formats the generated answer.

[0429] Input: Generated response data

[0430] Data processing: checking grammar and adjusting paragraph structure

[0431] Output: Formatted response data (e.g., "Please proceed slowly. To operate a new assembly line efficiently, it is important to perform proper initial setup and regular maintenance.")

[0432] Step 7:

[0433] The server sends the formatted response to the user's terminal.

[0434] Input: Formatted answer data

[0435] Output: Answer data sent to the terminal (e.g., "Please proceed slowly. To operate a new assembly line efficiently, it is important to perform proper initial setup and regular maintenance.")

[0436] Step 8:

[0437] The terminal displays the received answer to the user.

[0438] Input: Formatted answer data sent from the server

[0439] Output: The answer displayed on the user's device (e.g., "Please proceed slowly. Proper initial setup and regular maintenance are essential to running your new assembly line efficiently.")

[0440] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0441] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0442] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0443] [Second embodiment]

[0444] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0445] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0446] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0447] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0448] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0449] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0450] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0451] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0452] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0453] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0454] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0455] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0456] The present invention is a system that allows users to input business questions and receive answers, and the server utilizes a generative AI model to enable users to easily receive specific business advice. This system includes multiple processing steps, each of which is executed by the server, a terminal, and the user.

[0457] System Overview:

[0458] 1. User interface implementation:

[0459] The terminal provides an interface for the user to input a business question. The user interface includes a text box and a submit button.

[0460] 2. Details of the process listed on the server:

[0461] The server receives the question submitted by the user, performs preprocessing, and generates an answer using a generative AI model, which is then formatted and sent to the user.

[0462] A natural language description of what the program does:

[0463] 1. User inputs a question:

[0464] The user enters a business question into a text box on the terminal and presses the send button.

[0465] The terminal sends this input to the server.

[0466] Examples:

[0467] The user types in "What is your strategy for entering new markets?" and hits the submit button.

[0468] 2. Question preprocessing:

[0469] The server receives the question submitted by the user and performs text cleaning, for example removing extra spaces and special characters.

[0470] The server tokenizes the question, breaking it down into meaningful words and phrases.

[0471] Examples:

[0472] Take the question, "What is your strategy for entering a new market?" and break it down into keywords such as "new market," "entry," and "strategy."

[0473] 3. Input to the generative AI model:

[0474] The server inputs the preprocessed questions into the generative AI model.

[0475] The generative AI model generates appropriate answers to questions based on pre-trained literature and speeches by business leaders.

[0476] Examples:

[0477] The server passes the question "What is your strategy for entering a new market?" to the generative AI model, which generates an answer such as "When entering a new market, it is important to first conduct thorough market research and find points of differentiation through competitive analysis."

[0478] 4. Formatting the answer:

[0479] The server formats the generated answer, ensuring grammar and formatting.

[0480] If necessary, format your answers by dividing them into paragraphs or bullet points.

[0481] Examples:

[0482] The server checks the generated answer, "When entering a new market, it is important to first conduct thorough market research and find points of differentiation through competitive analysis," for grammar and formats it into a paragraph.

[0483] 5. Submitting and Viewing Your Answers:

[0484] The server sends the formatted response to the user's terminal.

[0485] The terminal displays the received answer on the user's screen.

[0486] Examples:

[0487] The server then sends the formatted response to the user's device as, "When entering a new market, it is important to first conduct thorough market research and find points of differentiation through competitive analysis," and the device displays it.

[0488] Through this series of steps, users can easily obtain specific business advice based on the wisdom of well-known business leaders, which can be used to help formulate business strategies and develop their organizations.

[0489] The processing flow will be explained below.

[0490] Step 1:

[0491] The user enters a question

[0492] The user enters a business question into the input form on the terminal and presses the send button. The terminal prepares to send the question to the server.

[0493] Step 2:

[0494] The device sends a question to the server

[0495] The terminal transmits the question entered by the user to the server as data, which includes the content of the user's question.

[0496] Step 3:

[0497] The server receives the query

[0498] The server receives the query data sent from the terminal and checks the integrity of the data.

[0499] Step 4:

[0500] The server preprocesses the query

[0501] The server performs text cleaning on the received question, removing unnecessary spaces and special characters to make the question clean.

[0502] The server also tokenizes the question, breaking it down into meaningful words and phrases.

[0503] Step 5:

[0504] The server inputs questions into the generative AI model

[0505] The server inputs the preprocessed questions into a generative AI model, which generates appropriate answers based on pre-trained literature and speeches by business leaders.

[0506] Step 6:

[0507] Generative AI models generate answers

[0508] The generative AI model generates specific advice from the manager's perspective based on the input question, and the generated answers are temporarily stored on the server.

[0509] Step 7:

[0510] The server formats the generated answer

[0511] The server formats the answers received from the generative AI model, checking grammar and expression to make them easier for the user to understand.

[0512] Step 8:

[0513] The server formats the response

[0514] If necessary, the server will format the answer into paragraphs or bullet points, which makes the answer more readable.

[0515] Step 9:

[0516] The server sends the formatted response to the device.

[0517] The server sends the formatted response to the user's terminal.

[0518] Step 10:

[0519] The device receives and displays the answer

[0520] The terminal receives the response sent from the server and displays it on the user's screen, allowing the user to check specific business advice.

[0521] Examples:

[0522] The user types "What is your strategy for entering a new market?" into the device and presses the send button. The device sends this question to the server, which receives it. The server preprocesses the question and inputs it into the generative AI model. The generative AI model generates the answer "When entering a new market, it is important to first conduct thorough market research and identify points of differentiation through competitive analysis." The server formats this answer and sends it to the device. The device receives the answer and displays it on the user's screen.

[0523] Example 1

[0524] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0525] Currently, there are limited systems that provide fast and specific answers to business questions, making it difficult for many users to obtain appropriate business advice. In addition, existing systems often do not automate the preprocessing of questions or the formatting of answers, requiring manual intervention. Furthermore, the quality of the generated answers is inconsistent, making it difficult for users to obtain reliable information. A system that addresses these issues is needed.

[0526] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0527] In this invention, the server includes a means for receiving business questions from users at a terminal, a means for preprocessing the questions at the server, and a means for inputting the preprocessed questions into a generative AI model at the server. This allows users to quickly obtain appropriate business advice. Furthermore, by automating preprocessing including text cleaning and tokenization, semantic analysis of questions can be performed accurately and efficiently. The generative AI model generates high-quality answers based on pre-trained data, and by formatting the answers, it is possible to provide consistent information. This allows users to quickly receive reliable business advice, which can be useful for planning business strategies and developing organizations.

[0528] A "user" is a person or organization that utilizes the system to enter business questions and receive answers.

[0529] A "terminal" is a hardware device used by a user to input questions and receive answers from a server. Examples include computers and smartphones.

[0530] A "server" is a computer system whose role is to process questions entered by users, generate answers using generative AI models, format them, and then send them to the user's device.

[0531] "Question preprocessing" is the process of cleaning up the text data submitted by the user and converting it into an analyzable format using methods such as tokenization.

[0532] "Text cleaning" is the process of removing extra spaces and special characters from questions entered by users.

[0533] "Tokenization" is the process of breaking down text data into words and phrases and converting it into a form that is easier to analyze.

[0534] A "generative AI model" is an artificial intelligence model that learns large amounts of data in advance and generates appropriate answers for input text.

[0535] "Answer formatting" is the process of checking the generated answers for grammar and arranging them into paragraphs, bullet points, or other formats.

[0536] "Data" is information processed within the system, including questions entered by users and answers generated by the server.

[0537] This invention is a system that allows users to input business questions and receive answers. The server utilizes a generative AI model, allowing users to easily receive specific business advice. This system uses the following hardware and software:

[0538] Hardware and software used

[0539] Server: A computer system that processes user questions and generates answers, using cloud services as needed.

[0540] Device: A device on which a user enters questions and receives answers. Examples include computers, tablets, and smartphones.

[0541] Generative AI models: Artificial intelligence models that have been trained on large amounts of data in advance (e.g., OpenAI GPT-3). They are accessible through APIs.

[0542] System Configuration

[0543] The system consists of the following main components:

[0544] 1. User Interface:

[0545] The terminal provides an interface that includes a text box for the user to enter a question and a submit button.

[0546] 2. Receiving and Preprocessing Questions:

[0547] The server receives the questions sent from the terminal and performs text cleaning and tokenization. Text cleaning includes removing extra spaces and special characters, and tokenization uses libraries such as NLTK and SpaCy.

[0548] 3. Use of generative AI models:

[0549] The server inputs the preprocessed questions into the generative AI model, which generates answers based on pre-trained management literature and data.

[0550] 4. Formatting the answer:

[0551] The answers output by the generative AI model are formatted on the server, checking for grammar and formatting as needed, which includes formatting into paragraphs and bullet points using regular expressions and HTML tags.

[0552] 5. Submitting and Viewing Answers:

[0553] The server sends the formatted answer to the terminal, which then displays the answer on the user's screen.

[0554] Specific operation example

[0555] User enters and submits question:

[0556] The user types in "What is your strategy for entering new markets?" and hits the submit button.

[0557] Question preprocessing:

[0558] The server receives the question, cleans it of extra spaces and special characters, and tokenizes it into words like "new market," "entry," and "strategy."

[0559] Use of generative AI models:

[0560] The server inputs the preprocessed data into a generative AI model and generates answers such as, "When entering a new market, it is important to first conduct thorough market research and find points of differentiation through competitive analysis."

[0561] Formatting the answer:

[0562] The server then formats the answer grammatically and summarizes it in a paragraph: "When entering a new market, it's important to first conduct thorough market research and identify points of differentiation through competitive analysis."

[0563] Submit and view your answers:

[0564] The server sends the formatted response to the terminal, which displays it on the user's screen.

[0565] Prompt Sentence Examples

[0566] "What are the main steps in developing a marketing strategy?"

[0567] "What makes a startup successful?"

[0568] "What points should I pay attention to when introducing a new product to the market?"

[0569] In this way, the system helps users get quick, specific and reliable business advice.

[0570] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0571] Step 1:

[0572] The user enters a business question into a text box on the device and presses the submit button, providing a prompt such as "What is your strategy for entering new markets?"

[0573] The device generates an HTTP POST request to send this input to the server, and sends the question to the server. Specifically, the input text is included in the request body and sent to the specified endpoint.

[0574] Step 2:

[0575] The server receives the question sent from the device, which includes as input an HTTP POST request from the device.

[0576] The server first performs text cleaning. Specifically, it uses regular expressions to remove extra spaces and special characters. As a result of the data processing, it outputs the question in a clean format: "What is your strategy for entering a new market?"

[0577] Step 3:

[0578] The server then tokenizes the question, including the cleaned text as input.

[0579] Specifically, it uses libraries such as the Natural Language Toolkit (NLTK) and SpaCy to break down the question into words and phrases, such as "new market," "entry," and "strategy." This results in tokenized data being output.

[0580] Step 4:

[0581] The server inputs the preprocessed question into the generative AI model, which includes tokenized text data as input.

[0582] Specifically, the API of a generative AI model (e.g., OpenAI GPT-3) is called, and the tokenized data is passed as an argument. The generative AI model generates an answer to the question based on the data it has learned in advance. The API response then outputs text such as, "When entering a new market, it is important to first conduct thorough market research and identify points of differentiation through competitive analysis."

[0583] Step 5:

[0584] The server formats the generated answer, which includes the text output from the generative AI model as input.

[0585] Specifically, it uses a grammar checking engine (e.g., Grammarly API) and regular expressions and HTML tags to format text into paragraphs and bullet points. For example, it outputs a paragraph-formatted text such as "When entering a new market, it is important to first conduct thorough market research and identify points of differentiation through competitive analysis."

[0586] Step 6:

[0587] The server sends the formatted answer to the user's terminal, including the formatted answer text as input.

[0588] Specifically, it generates an HTTP response and sends it to the terminal, including the response data. The terminal then displays the received response on the user's screen. For example, a formatted response such as "When entering a new market, it is important to first conduct thorough market research and identify points of differentiation through competitive analysis" is displayed on the screen.

[0589] Through this series of steps, users can quickly obtain specific and reliable business advice.

[0590] (Application example 1)

[0591] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0592] Currently, there are limited ways to receive appropriate advice immediately in response to specific business questions, making it difficult for users to easily obtain reliable information. Additionally, there is no system that allows users to resolve questions that arise on the spot while viewing business content, which reduces the user's learning efficiency.

[0593] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0594] In this invention, the server includes means for receiving a business-related question from a user, means for preprocessing the question, means for inputting the preprocessed question into a generative AI model, means for generating an answer to the question based on materials previously learned by the generative AI model, means for formatting the generated answer, means for sending the formatted answer to the user, means for inputting a question in real time while viewing business-related content on a smartphone and receiving an immediate answer, and means for receiving questions about videos or articles displayed on the smartphone, thereby enabling a user to immediately receive specific and appropriate business advice while viewing business content.

[0595] The "means for receiving business-related questions from users" refers to an interface that allows users to input specific business-related questions and have them received by the system.

[0596] The "means for preprocessing the question" is a process for analyzing the question received from the user and performing data cleaning, tokenization, etc.

[0597] "Means for inputting the preprocessed question into the generative AI model" refers to a method for inputting a question that has undergone preprocessing into the generative AI model.

[0598] "Means for generating answers to questions based on materials previously learned by the generative AI model" refers to a method for creating answers to questions using a generative AI model based on management-related literature and data previously learned.

[0599] The "means for formatting the generated answer" is a process for adjusting the grammar and format of the generated answer to make it easier to read.

[0600] The "means for transmitting the formatted answer to the user" is a communication method for providing the user with the answer that has been formatted.

[0601] "A means for viewing business-related content on a smartphone while inputting questions in real time and receiving instant answers" is a system that allows users to view videos or articles that are useful for business on their smartphones, input questions that arise while viewing them on the spot, and receive instant answers.

[0602] The "means for receiving questions about videos or articles displayed on the smartphone" refers to a function that allows a user to input questions about the content of business-related videos or articles while watching them on a smartphone, and the system receives those questions.

[0603] This invention provides a system that allows users to input business-related questions via smartphone and receive specific business advice in real time. Specific embodiments of this system will be described in detail below.

[0604] This system is implemented using a smartphone application. While users are watching business-related videos or articles within the application, they are provided with an interface where they can input questions. When users input a question, the smartphone sends it to the server.

[0605] The server then receives this question and performs text cleaning and tokenization. Text cleaning removes extra spaces and special characters. Tokenization breaks the question down into meaningful words and phrases, pre-processing the question and converting it into a format suitable for generative AI models.

[0606] After preprocessing, the question is input to a generative AI model by the server. The specific generative AI model used is OpenAI GPT. This model has been trained in advance on management-related literature and case studies, and is able to generate appropriate business advice in response to the user's question.

[0607] Once the generative AI model generates an answer based on the question, the server formats the answer grammatically and makes it easier for the user to understand, for example by dividing the answer into paragraphs or bullet points.

[0608] The formatted answers are then sent back to the smartphone, where the user can check them in real time on the application. This continuous interaction allows users to view business content while instantly resolving any questions that arise.

[0609] As a concrete example, suppose a user types, "Please tell me your strategy for entering a new market." The server passes this question to a generative AI model, which generates an answer such as, "When entering a new market, it is important to first conduct thorough market research and identify points of differentiation through competitive analysis." The server formats this answer and sends it to the smartphone. The user can view the answer on the app.

[0610] Examples of prompts that can be used include:

[0611] User Question: What is your strategy for entering new markets?

[0612] Business advice:

[0613] As can be seen from this example, this system provides users with a means to easily obtain specific business advice, greatly improving learning efficiency.

[0614] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0615] Step 1:

[0616] Question input from the user

[0617] The user enters a business question into a text box on the smartphone application and presses the submit button. The input data is the question text entered by the user, and this input is sent to the server.

[0618] Step 2:

[0619] Question Preprocessing

[0620] The server receives the question submitted by the user and performs text cleaning and tokenization. Specifically, it removes extra spaces and special characters and breaks the question into tokens. This process generates preprocessed question data. The input is the user's raw text question, and the output is the cleaned and tokenized text data.

[0621] Step 3:

[0622] Input to generative AI models

[0623] The server inputs the preprocessed question into a generative AI model (e.g., OpenAI's GPT). The input data is the preprocessed question text, and the generative AI model generates an answer to the question based on the pre-trained material. The output is the generated answer text.

[0624] Step 4:

[0625] Formatting answers

[0626] The server formats the answer text returned by the generative AI model. Specifically, it checks the grammar of the sentences, standardizes the format, and formats the answer by dividing it into paragraphs and bullet points. The input is the raw text answer from the generative AI model, and the output is the formatted text answer.

[0627] Step 5:

[0628] Submitting and viewing responses

[0629] The server sends the formatted answer back to the user's smartphone application, which displays the received answer on its screen. The input is the formatted text answer, and the output is a visual form on the user's smartphone display.

[0630] Step 6:

[0631] Entering questions while watching business-related content

[0632] Users can directly input questions while watching business-related videos or articles on a smartphone application. This question is also sent to the server as in step 1, and subsequent processing is executed. The input is a text question asked while watching the video or article, and the output is a display of the answer according to the flow up to step 5.

[0633] Through the above processing steps, the system realizing the present invention enables a user to obtain specific business advice in real time while viewing business content.

[0634] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0635] The present invention is a system that allows users to input business questions and obtain answers, and combines an emotion engine to provide advice that recognizes and takes into account the user's emotions. This system includes multiple processing steps, each of which is executed by a server, a terminal, and a user.

[0636] System Overview:

[0637] 1. User interface implementation:

[0638] The terminal provides an interface for the user to input a business question. The user interface includes a text box and a submit button.

[0639] 2. Details of the process listed on the server:

[0640] The server receives the question sent by the user, performs preprocessing, recognizes the user's emotions using an emotion engine, and generates an answer using a generative AI model. The generated answer is formatted and sent to the user.

[0641] A natural language description of what the program does:

[0642] 1. User inputs a question:

[0643] The user enters a business question into a text box on the terminal and presses the send button.

[0644] The terminal sends this input to the server.

[0645] Examples:

[0646] The user types in "What is your strategy for entering new markets?" and hits the submit button.

[0647] 2. Question preprocessing:

[0648] The server receives the question submitted by the user and performs text cleaning, removing unnecessary spaces and special characters to make the question clean.

[0649] The server tokenizes the question, breaking it down into meaningful words and phrases.

[0650] Examples:

[0651] Take the question, "What is your strategy for entering a new market?" and break it down into keywords such as "new market," "entry," and "strategy."

[0652] 3. Emotion recognition:

[0653] The server uses an emotion engine to recognize the user's emotion from the question sentence.

[0654] The emotion engine classifies emotions into categories such as positive, negative, and neutral.

[0655] Examples:

[0656] By analyzing the question, the emotion engine recognizes that the user is feeling anxious about the phrase "strategy for entering new markets."

[0657] 4. Input to the generative AI model:

[0658] The server inputs the preprocessed questions and emotional information from the emotion engine into the generative AI model.

[0659] The generative AI model generates appropriate answers to questions based on pre-trained literature and speech content from business executives, and also takes into consideration the user's emotions.

[0660] Examples:

[0661] For anxious users, the generative AI model generates a reassuring response such as, "Don't worry, when entering a new market, it's important to first conduct thorough market research and find points of differentiation through competitive analysis."

[0662] 5. Formatting your answer:

[0663] The server formats the generated answer, checking grammar and expression to make it easier for the user to understand.

[0664] Examples:

[0665] The server checks the generated answer, "Don't worry, when entering a new market, it's important to first conduct thorough market research and find your points of differentiation through competitive analysis," for grammar and formats it into a paragraph.

[0666] 6. Formatting your answers:

[0667] If necessary, the server will format the answer into paragraphs or bullet points, which makes the answer more readable.

[0668] 7. Submitting and Viewing Your Answers:

[0669] The server sends the formatted response to the user's terminal.

[0670] The terminal displays the received answer on the user's screen.

[0671] Examples:

[0672] The server then sends the formatted response to the user's device as, "Don't worry, when entering a new market, it's important to first conduct thorough market research and find points of differentiation through competitive analysis," and the device displays it.

[0673] This series of steps allows users to easily obtain specific business advice that takes into account the perspectives and feelings of well-known business leaders, which can be useful in formulating business strategies and developing organizations.

[0674] The processing flow will be explained below.

[0675] Step 1:

[0676] The user enters a question

[0677] The user enters a business question into a text box on the terminal and presses the send button. The terminal prepares the question as data to be sent to the server.

[0678] Step 2:

[0679] The device sends a question to the server

[0680] The terminal sends the user's input question to the server as a data packet, which contains the entire question.

[0681] Step 3:

[0682] The server receives the query

[0683] The server receives the query data sent from the terminal and verifies the integrity of the data. In this step, it checks whether any inappropriate data is included.

[0684] Step 4:

[0685] Server performs text cleaning

[0686] The server performs text cleaning on the received question, removing unnecessary spaces and special characters to make the question clean.

[0687] Step 5:

[0688] The server performs tokenization

[0689] The server tokenizes the question, breaking down the sentence into meaningful words and phrases, converting it into a format that is easy to feed into a generative AI model.

[0690] Step 6:

[0691] The server uses an emotion engine to recognize emotions.

[0692] The server inputs the question text into an emotion engine to recognize the user's emotion, which then classifies it into emotion categories such as positive, negative, and neutral.

[0693] Step 7:

[0694] The server inputs emotional information into the generative AI model

[0695] The server inputs the preprocessed question and the emotional information recognized by the emotion engine into the generative AI model, and the interaction of this data helps generate appropriate answers.

[0696] Step 8:

[0697] Generative AI models generate answers

[0698] The generative AI model generates optimal answers to questions based on pre-trained literature and lecture content, taking into account emotion recognition results.

[0699] Step 9:

[0700] The server formats the generated answer

[0701] The server formats the generated answers, correcting grammatical errors and making them more user-friendly.

[0702] Step 10:

[0703] The server formats the response

[0704] If necessary, the server will format the answer into paragraphs or bullet points, which will make the answer more readable and organized.

[0705] Step 11:

[0706] The server sends the formatted response to the device.

[0707] The server sends the formed and formatted response to the user's terminal.

[0708] Step 12:

[0709] The device receives and displays the answer

[0710] The terminal receives the response sent from the server and displays it on the user's screen, allowing the user to confirm specific business advice.

[0711] Examples:

[0712] The user types "What is your strategy for entering a new market?" into the device and presses the send button. The device sends this question to the server, which receives it. The server performs text cleaning and tokenization, and recognizes the user's emotions using an emotion engine. The server inputs the question and emotion information into a generative AI model, which then generates the answer, "Don't worry, when entering a new market, it's important to first conduct thorough market research and find points of differentiation through competitive analysis." The server then formats this answer and sends it to the device. The device receives the answer and displays it on the user's screen.

[0713] Example 2

[0714] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0715] Existing business advice systems do not provide answers that take the user's emotions into account, making it difficult to respond appropriately to the user's concerns and questions. Furthermore, simply generating answers based on literature or lecture content can sometimes lack consideration for the user's specific emotions and situation. There is a need for a system that can solve this problem and provide users with more personalized, emotion-sensitive, and appropriate business advice.

[0716] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0717] In this invention, the server includes means for receiving a business-related question from a user, means for preprocessing the question, means for inputting the preprocessed question into a sentiment analysis engine and recognizing the user's sentiment, means for inputting the sentiment information and the preprocessed question into a generative AI model, means for the generative AI model to generate an answer that takes into account the sentiment for the question based on materials it has previously learned, means for formatting the generated answer, and means for sending the formatted answer to the user, thereby enabling the provision of personalized business advice that takes into account the user's sentiment.

[0718] A "user" is an individual or entity that enters a business inquiry and uses the system.

[0719] "Business Questions" are specific inquiries or consultations related to business strategy, marketing, and management.

[0720] The "means for preprocessing questions" is a process for analyzing received questions and performing text cleaning and tokenization.

[0721] "Preprocessed questions" are question data that have been text cleaned and tokenized, and are formatted so that they can be analyzed.

[0722] An "emotion analysis engine" is a technology that analyzes and recognizes a user's emotions from input text and classifies them into emotional categories.

[0723] The "means for recognizing emotions" is a process of recognizing the user's emotions from preprocessed questions using an emotion analysis engine.

[0724] "Emotion information" refers to the emotion category (e.g., positive, negative, neutral) classified by the emotion analysis engine.

[0725] A "generative AI model" is an artificial intelligence model that generates appropriate answers to input data based on previously learned materials.

[0726] "Means for inputting to the generative AI model" refers to the process of providing preprocessed questions and emotion information to the generative AI model.

[0727] "Means of generation" refers to the process by which a generative AI model generates an appropriate answer based on input data.

[0728] The "formatting means" is a process of checking the grammar of the generated answer and making it into a format that is easy for the user to understand.

[0729] "Means for sending" refers to the process of sending the formatted answer to the user's terminal via communication.

[0730] The "system" is a set of devices and software that executes a series of processes to provide appropriate answers that take emotions into account to users' business-related questions.

[0731] The present invention is a system that allows users to input business-related questions and receive answers, and combines an emotion analysis engine to provide advice that recognizes and takes into account the user's emotions. This system is mainly processed using a server and terminals.

[0732] System configuration and specific details:

[0733] 1. User interface implementation:

[0734] The terminal provides an interface for users to input business-related questions. The interface includes a text box and a submit button. Through this interface, users can easily input their questions and proceed to the next processing step.

[0735] 2. Question preprocessing:

[0736] The server receives questions sent by users. The received questions are first text-cleaned to remove unnecessary spaces and special characters. Next, the server tokenizes the questions and breaks them down into meaningful words and phrases. Specifically, the question "What is your strategy for entering new markets?" is broken down into keywords such as "new market," "entry," and "strategy."

[0737] 3. Emotion recognition:

[0738] The server passes the preprocessed question to a sentiment analysis engine, which recognizes the user's emotions from the question and classifies them into sentiment categories such as positive, negative, and neutral. For example, the question "Please tell us your strategy for entering a new market" identifies the user's feelings of anxiety.

[0739] 4. Input to the generative AI model:

[0740] The server inputs the preprocessed question and emotion information into the generative AI model. Based on the pre-trained data, the generative AI model generates an appropriate answer to the question while also taking the user's emotions into consideration. For example, the generative AI model might generate the answer, "Don't worry, when entering a new market, it's important to first conduct thorough market research and identify points of differentiation through competitive analysis."

[0741] 5. Formatting your answer:

[0742] The generated answer is checked for grammar and expressions on the server, and is then formatted into a paragraph format that is easy for the user to understand.

[0743] 6. Formatting your answers:

[0744] If desired, the server can format the answer as paragraphs or bullet points, which makes the answer more readable.

[0745] 7. Submitting and Viewing Your Answers:

[0746] The formatted response is sent from the server to the user's device, which then displays the response on the user's screen. Specifically, the server sends the formatted response as "Don't worry, when entering a new market, it's important to first conduct thorough market research and find points of differentiation through competitive analysis," and the device displays it.

[0747] This system allows users to easily obtain specific business advice that takes into account the perspectives and sentiments of prominent business leaders, helping them to develop business strategies and develop their organizations. The system can be used on a variety of hardware, including servers, desktop computers, laptops, and mobile devices. The software used includes a generative AI model, a sentiment analysis engine, and a tokenization tool.

[0748] Examples:

[0749] When a user types "What is your strategy for entering a new market?" into a text box on their device and presses the send button, the server receives the question. The server then performs text cleaning and tokenization, and uses a sentiment analysis engine to identify the user's concerns. The generative AI model then generates an answer: "Don't worry, when entering a new market, it's important to first conduct thorough market research and identify points of differentiation through competitive analysis." The answer is then formatted by the server and sent to the user. The user can view this answer on their device.

[0750] Example prompt sentence:

[0751] "What is your strategy for entering new markets?"

[0752] This series of steps allows users to receive appropriate advice that takes their emotions into consideration.

[0753] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0754] Step 1:

[0755] A user enters a business question.

[0756] The user enters a question in the text box on the terminal and presses the send button.

[0757] Input: A business question from the user (e.g., "What is your strategy for entering new markets?")

[0758] Output: The entered question is sent from the terminal to the server. Specifically, the terminal constructs an HTTP request and sends the question data to the server.

[0759] Step 2:

[0760] The server receives the query and performs pre-processing.

[0761] The server performs text cleaning on the received question, removing unnecessary spaces and special characters.

[0762] Input: A question sent from the device (e.g., "What is your strategy for entering new markets?")

[0763] Output: Cleaned text (e.g., "What is your strategy for entering new markets?" with unnecessary spaces removed)

[0764] Specifically, the server uses regular expressions and other text processing techniques to remove unnecessary parts.

[0765] Step 3:

[0766] The server tokenizes the question.

[0767] Break down the cleaned question into words and phrases.

[0768] Input: Cleaned text (e.g., "What is your strategy for entering new markets?")

[0769] Output: Tokenized keywords (e.g., "new market," "entry," "strategy")

[0770] Specifically, the server applies a tokenization algorithm to break down the question into meaningful words and phrases.

[0771] Step 4:

[0772] The server uses an emotion analysis engine to recognize the user's emotions.

[0773] The server passes the preprocessed questions to a sentiment analysis engine for sentiment analysis.

[0774] Input: Tokenized keywords (e.g., "new market," "entry," "strategy")

[0775] Output: Emotional information (e.g., anxiety)

[0776] Specifically, the server inputs these keywords into an emotion analysis engine to obtain emotion categories such as positive, negative, and neutral.

[0777] Step 5:

[0778] The server inputs the preprocessed question and sentiment information into the generative AI model.

[0779] The generative AI model generates an answer.

[0780] Input: Tokenized keywords and sentiment information (e.g., "new market," "entry," "strategy," sentiment: anxiety)

[0781] Output: Generated answer (e.g., "Don't worry, when entering a new market, it's important to first conduct thorough market research and identify your points of differentiation through competitive analysis.")

[0782] Specifically, the server provides this data to the generative AI model and waits for it to generate an answer.

[0783] Step 6:

[0784] The server formats the generated answer.

[0785] The generated answers are checked for grammar and expression and formatted to make them easier to understand.

[0786] Input: Generated answer (e.g., "Don't worry, when entering a new market, it's important to first conduct thorough market research and identify your points of differentiation through competitive analysis.")

[0787] Output: Formatted answer (e.g. "Don't worry, when entering a new market, it's important to first conduct thorough market research and identify your points of differentiation through competitive analysis." formatted into a paragraph)

[0788] Specifically, the server performs a grammar check on the generated response and makes appropriate corrections.

[0789] Step 7:

[0790] The server sends the formatted response to the user's terminal.

[0791] The terminal displays.

[0792] Input: Formatted answer (e.g., "Don't worry, when entering a new market, it's important to first conduct thorough market research and identify your points of differentiation through competitive analysis.")

[0793] Output: The answer displayed on the user's screen

[0794] Specifically, the server sends the formatted answer as an HTTP response, and the terminal displays the received answer in the text area.

[0795] (Application example 2)

[0796] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0797] In modern manufacturing, it is difficult to obtain prompt and appropriate answers to real-time questions about factory operation and management. Furthermore, there is a lack of technology that provides advice that takes into account the emotions of workers and managers. As a result, efficiency on the shop floor and psychological stress among workers are negatively affected. The present invention aims to solve these problems by providing a system that enables factory workers and managers to ask questions in real time and receive answers that recognize their emotions.

[0798] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving an inquiry from a user, means for preprocessing the inquiry, means for inputting the preprocessed inquiry into a generative AI model, means for generating a response to the inquiry based on documents previously learned by the generative AI model, means for formatting the generated response, means for sending the formatted response to the user, and means including an emotion recognition engine for recognizing the user's emotions, the emotion recognition engine linking the user's emotion information to the generative AI model. This makes it possible to provide quick and appropriate answers to questions regarding factory operation and management and to provide advice that takes into consideration the emotions of workers.

[0799] "User" refers to the workers and managers who ask questions about factory operation and management to the system.

[0800] "Inquiry" means information that represents a question or request regarding factory operations and management.

[0801] "Preprocessing" refers to a series of steps that clean up queries and convert them into a format that is easy for generative AI models to interpret.

[0802] "Text cleaning" is the process of removing unnecessary spaces and special characters to make the text clean.

[0803] "Tokenization" is the process of breaking down text into meaningful words and phrases.

[0804] A "generative AI model" is an artificial intelligence model that generates appropriate responses to inquiries based on pre-trained data.

[0805] "Documents" refers to materials and records containing information relating to factory operations and management.

[0806] "Response" refers to the answer generated by a generative AI model in response to a query.

[0807] An "emotion recognition engine" is a technology that analyzes a user's emotions and classifies them into emotional categories such as positive, negative, and neutral.

[0808] "Integrating" refers to sharing the emotional information analyzed by the emotion recognition engine with the generative AI model and reflecting it in the response.

[0809] This invention provides a system that asks questions about factory operation and management and provides appropriate answers that recognize emotions. This system consists of a server, a user operation terminal, an emotion recognition engine, and a generative AI model.

[0810] First, the user (factory worker or manager) enters a question about factory operation and management into a text box on the terminal and presses the send button. This question is then sent from the terminal to the server. The terminal can be a regular PC, tablet, or smartphone.

[0811] The server receives the question submitted by the user and first performs text cleaning, removing unnecessary spaces and special characters to make the question clean. This step is part of preprocessing to enable accurate and efficient processing. Next, the server performs tokenization, breaking the question down into meaningful words and phrases.

[0812] After the question preprocessing is complete, the server uses an emotion recognition engine to analyze the preprocessed question and obtain the emotional information the user is feeling. This emotional information is classified into emotion categories such as positive, negative, and neutral. For example, if the question is "How can we operate a new assembly line efficiently?", the emotion recognition engine will analyze whether the user is feeling anxious or worried.

[0813] The server then inputs the preprocessed question and emotional information into a generative AI model. The generative AI model generates an appropriate answer based on document data related to factory operation and management that it has previously learned. In doing so, it also takes into account emotional information and generates an answer that takes the user's emotions into consideration. The generative AI model used here can be a large-scale language model such as GPT-3.

[0814] The generated answer is then formatted by the server, for example by checking grammar and adjusting paragraph structure. Finally, the formatted answer is sent from the server to the user's device and displayed on the device screen, allowing the user to receive specific advice that takes their feelings into consideration.

[0815] For example, if a worker types "Tell me about quality control" into a terminal and sends it, the server will clean and tokenize the question, and the emotion recognition engine will analyze the anxiety expressed in the question. The generative AI model will generate an answer such as "Quality control is important. Don't worry, please use the checklist to review your daily processes," which will then be formatted by the server and sent back to the user.

[0816] An example of a prompt might be, "How can we run our new assembly line efficiently? The workers are feeling impatient."

[0817] This system allows users to get appropriate answers to questions about factory operation and management in real time, improving work efficiency and reducing psychological stress.

[0818] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0819] Step 1:

[0820] The user enters a question about factory operation and management in the text box on the terminal and presses the send button.

[0821] Input: A user-typed question (e.g., "How can we run our new assembly line efficiently?")

[0822] Output: Question data sent from the terminal to the server

[0823] Step 2:

[0824] The server receives the questions sent by the user and performs text cleaning.

[0825] Input: Question data submitted by the user (e.g., "How can we run our new assembly line efficiently?")

[0826] Data processing: Remove unnecessary spaces and special characters to make the text clean.

[0827] Output: Cleaned question data (e.g., "How can we run our new assembly line efficiently?")

[0828] Step 3:

[0829] The server tokenizes the cleaned question, breaking it down into meaningful words and phrases.

[0830] Input: Cleaned question data

[0831] Data operations: Breaking down the question into words and phrases

[0832] Output: Tokenized question data (e.g., "New," "Assembly Line," "Efficient," "Operation," "What should I do?")

[0833] Step 4:

[0834] The emotion recognition engine analyzes the tokenized question data and reconnects with the user's emotions.

[0835] Input: Tokenized question data

[0836] Data Computing: Sentiment Analysis to Identify User Emotions

[0837] Output: Emotional information (e.g., impatience)

[0838] Step 5:

[0839] The server inputs the preprocessed question and emotion information into the generative AI model.

[0840] Input: Preprocessed question data and sentiment information

[0841] Data Computation: Generative AI models generate responses based on pre-trained data

[0842] Output: Generated response data (e.g., "Please proceed slowly. To operate a new assembly line efficiently, it is important to perform proper initial setup and regular maintenance.")

[0843] Step 6:

[0844] The server formats the generated answer.

[0845] Input: Generated response data

[0846] Data processing: checking grammar and adjusting paragraph structure

[0847] Output: Formatted response data (e.g., "Please proceed slowly. To operate a new assembly line efficiently, it is important to perform proper initial setup and regular maintenance.")

[0848] Step 7:

[0849] The server sends the formatted response to the user's terminal.

[0850] Input: Formatted answer data

[0851] Output: Answer data sent to the terminal (e.g., "Please proceed slowly. To operate a new assembly line efficiently, it is important to perform proper initial setup and regular maintenance.")

[0852] Step 8:

[0853] The terminal displays the received answer to the user.

[0854] Input: Formatted answer data sent from the server

[0855] Output: The answer displayed on the user's device (e.g., "Please proceed slowly. Proper initial setup and regular maintenance are essential to running your new assembly line efficiently.")

[0856] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0857] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0858] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0859] [Third embodiment]

[0860] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0861] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0862] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0863] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0864] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0865] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0866] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0867] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0868] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0869] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0870] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0871] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0872] The present invention is a system that allows users to input business questions and receive answers, and the server utilizes a generative AI model to enable users to easily receive specific business advice. This system includes multiple processing steps, each of which is executed by the server, a terminal, and the user.

[0873] System Overview:

[0874] 1. User interface implementation:

[0875] The terminal provides an interface for the user to input a business question. The user interface includes a text box and a submit button.

[0876] 2. Details of the process listed on the server:

[0877] The server receives the question submitted by the user, performs preprocessing, and generates an answer using a generative AI model, which is then formatted and sent to the user.

[0878] A natural language description of what the program does:

[0879] 1. User inputs a question:

[0880] The user enters a business question into a text box on the terminal and presses the send button.

[0881] The terminal sends this input to the server.

[0882] Examples:

[0883] The user types in "What is your strategy for entering new markets?" and hits the submit button.

[0884] 2. Question preprocessing:

[0885] The server receives the question submitted by the user and performs text cleaning, for example removing extra spaces and special characters.

[0886] The server tokenizes the question, breaking it down into meaningful words and phrases.

[0887] Examples:

[0888] Take the question, "What is your strategy for entering a new market?" and break it down into keywords such as "new market," "entry," and "strategy."

[0889] 3. Input to the generative AI model:

[0890] The server inputs the preprocessed questions into the generative AI model.

[0891] The generative AI model generates appropriate answers to questions based on pre-trained literature and speeches by business leaders.

[0892] Examples:

[0893] The server passes the question "What is your strategy for entering a new market?" to the generative AI model, which generates an answer such as "When entering a new market, it is important to first conduct thorough market research and find points of differentiation through competitive analysis."

[0894] 4. Formatting the answer:

[0895] The server formats the generated answer, ensuring grammar and formatting.

[0896] If necessary, format your answers by dividing them into paragraphs or bullet points.

[0897] Examples:

[0898] The server checks the generated answer, "When entering a new market, it is important to first conduct thorough market research and find points of differentiation through competitive analysis," for grammar and formats it into a paragraph.

[0899] 5. Submitting and Viewing Your Answers:

[0900] The server sends the formatted response to the user's terminal.

[0901] The terminal displays the received answer on the user's screen.

[0902] Examples:

[0903] The server then sends the formatted response to the user's device as, "When entering a new market, it is important to first conduct thorough market research and find points of differentiation through competitive analysis," and the device displays it.

[0904] Through this series of steps, users can easily obtain specific business advice based on the wisdom of well-known business leaders, which can be used to help formulate business strategies and develop their organizations.

[0905] The processing flow will be explained below.

[0906] Step 1:

[0907] The user enters a question

[0908] The user enters a business question into the input form on the terminal and presses the send button. The terminal prepares to send the question to the server.

[0909] Step 2:

[0910] The device sends a question to the server

[0911] The terminal transmits the question entered by the user to the server as data, which includes the content of the user's question.

[0912] Step 3:

[0913] The server receives the query

[0914] The server receives the query data sent from the terminal and checks the integrity of the data.

[0915] Step 4:

[0916] The server preprocesses the query

[0917] The server performs text cleaning on the received question, removing unnecessary spaces and special characters to make the question clean.

[0918] The server also tokenizes the question, breaking it down into meaningful words and phrases.

[0919] Step 5:

[0920] The server inputs questions into the generative AI model

[0921] The server inputs the preprocessed questions into a generative AI model, which generates appropriate answers based on pre-trained literature and speeches by business leaders.

[0922] Step 6:

[0923] Generative AI models generate answers

[0924] The generative AI model generates specific advice from the manager's perspective based on the input question, and the generated answers are temporarily stored on the server.

[0925] Step 7:

[0926] The server formats the generated answer

[0927] The server formats the answers received from the generative AI model, checking grammar and expression to make them easier for the user to understand.

[0928] Step 8:

[0929] The server formats the response

[0930] If necessary, the server will format the answer into paragraphs or bullet points, which makes the answer more readable.

[0931] Step 9:

[0932] The server sends the formatted response to the device.

[0933] The server sends the formatted response to the user's terminal.

[0934] Step 10:

[0935] The device receives and displays the answer

[0936] The terminal receives the response sent from the server and displays it on the user's screen, allowing the user to check specific business advice.

[0937] Examples:

[0938] The user types "What is your strategy for entering a new market?" into the device and presses the send button. The device sends this question to the server, which receives it. The server preprocesses the question and inputs it into the generative AI model. The generative AI model generates the answer "When entering a new market, it is important to first conduct thorough market research and identify points of differentiation through competitive analysis." The server formats this answer and sends it to the device. The device receives the answer and displays it on the user's screen.

[0939] Example 1

[0940] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0941] Currently, there are limited systems that provide fast and specific answers to business questions, making it difficult for many users to obtain appropriate business advice. In addition, existing systems often do not automate the preprocessing of questions or the formatting of answers, requiring manual intervention. Furthermore, the quality of the generated answers is inconsistent, making it difficult for users to obtain reliable information. A system that addresses these issues is needed.

[0942] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0943] In this invention, the server includes a means for receiving business questions from users at a terminal, a means for preprocessing the questions at the server, and a means for inputting the preprocessed questions into a generative AI model at the server. This allows users to quickly obtain appropriate business advice. Furthermore, by automating preprocessing including text cleaning and tokenization, semantic analysis of questions can be performed accurately and efficiently. The generative AI model generates high-quality answers based on pre-trained data, and by formatting the answers, it is possible to provide consistent information. This allows users to quickly receive reliable business advice, which can be useful for planning business strategies and developing organizations.

[0944] A "user" is a person or organization that utilizes the system to enter business questions and receive answers.

[0945] A "terminal" is a hardware device used by a user to input questions and receive answers from a server. Examples include computers and smartphones.

[0946] A "server" is a computer system whose role is to process questions entered by users, generate answers using generative AI models, format them, and then send them to the user's device.

[0947] "Question preprocessing" is the process of cleaning up the text data submitted by the user and converting it into an analyzable format using methods such as tokenization.

[0948] "Text cleaning" is the process of removing extra spaces and special characters from questions entered by users.

[0949] "Tokenization" is the process of breaking down text data into words and phrases and converting it into a form that is easier to analyze.

[0950] A "generative AI model" is an artificial intelligence model that learns large amounts of data in advance and generates appropriate answers for input text.

[0951] "Answer formatting" is the process of checking the generated answers for grammar and arranging them into paragraphs, bullet points, or other formats.

[0952] "Data" is information processed within the system, including questions entered by users and answers generated by the server.

[0953] This invention is a system that allows users to input business questions and receive answers. The server utilizes a generative AI model, allowing users to easily receive specific business advice. This system uses the following hardware and software:

[0954] Hardware and software used

[0955] Server: A computer system that processes user questions and generates answers, using cloud services as needed.

[0956] Device: A device on which a user enters questions and receives answers. Examples include computers, tablets, and smartphones.

[0957] Generative AI models: Artificial intelligence models that have been trained on large amounts of data in advance (e.g., OpenAI GPT-3). They are accessible through APIs.

[0958] System Configuration

[0959] The system consists of the following main components:

[0960] 1. User Interface:

[0961] The terminal provides an interface that includes a text box for the user to enter a question and a submit button.

[0962] 2. Receiving and Preprocessing Questions:

[0963] The server receives the questions sent from the terminal and performs text cleaning and tokenization. Text cleaning includes removing extra spaces and special characters, and tokenization uses libraries such as NLTK and SpaCy.

[0964] 3. Use of generative AI models:

[0965] The server inputs the preprocessed questions into the generative AI model, which generates answers based on pre-trained management literature and data.

[0966] 4. Formatting the answer:

[0967] The answers output by the generative AI model are formatted on the server, checking for grammar and formatting as needed, which includes formatting into paragraphs and bullet points using regular expressions and HTML tags.

[0968] 5. Submitting and Viewing Answers:

[0969] The server sends the formatted answer to the terminal, which then displays the answer on the user's screen.

[0970] Specific operation example

[0971] User enters and submits question:

[0972] The user types in "What is your strategy for entering new markets?" and hits the submit button.

[0973] Question preprocessing:

[0974] The server receives the question, cleans it of extra spaces and special characters, and tokenizes it into words like "new market," "entry," and "strategy."

[0975] Use of generative AI models:

[0976] The server inputs the preprocessed data into a generative AI model and generates answers such as, "When entering a new market, it is important to first conduct thorough market research and find points of differentiation through competitive analysis."

[0977] Formatting the answer:

[0978] The server then formats the answer grammatically and summarizes it in a paragraph: "When entering a new market, it's important to first conduct thorough market research and identify points of differentiation through competitive analysis."

[0979] Submit and view your answers:

[0980] The server sends the formatted response to the terminal, which displays it on the user's screen.

[0981] Prompt Sentence Examples

[0982] "What are the main steps in developing a marketing strategy?"

[0983] "What makes a startup successful?"

[0984] "What points should I pay attention to when introducing a new product to the market?"

[0985] In this way, the system helps users get quick, specific and reliable business advice.

[0986] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0987] Step 1:

[0988] The user enters a business question into a text box on the device and presses the submit button, providing a prompt such as "What is your strategy for entering new markets?"

[0989] The device generates an HTTP POST request to send this input to the server, and sends the question to the server. Specifically, the input text is included in the request body and sent to the specified endpoint.

[0990] Step 2:

[0991] The server receives the question sent from the device, which includes as input an HTTP POST request from the device.

[0992] The server first performs text cleaning. Specifically, it uses regular expressions to remove extra spaces and special characters. As a result of the data processing, it outputs the question in a clean format: "What is your strategy for entering a new market?"

[0993] Step 3:

[0994] The server then tokenizes the question, including the cleaned text as input.

[0995] Specifically, it uses libraries such as the Natural Language Toolkit (NLTK) and SpaCy to break down the question into words and phrases, such as "new market," "entry," and "strategy." This results in tokenized data being output.

[0996] Step 4:

[0997] The server inputs the preprocessed question into the generative AI model, which includes tokenized text data as input.

[0998] Specifically, the API of a generative AI model (e.g., OpenAI GPT-3) is called, and the tokenized data is passed as an argument. The generative AI model generates an answer to the question based on the data it has learned in advance. The API response then outputs text such as, "When entering a new market, it is important to first conduct thorough market research and identify points of differentiation through competitive analysis."

[0999] Step 5:

[1000] The server formats the generated answer, which includes the text output from the generative AI model as input.

[1001] Specifically, it uses a grammar checking engine (e.g., Grammarly API) and regular expressions and HTML tags to format text into paragraphs and bullet points. For example, it outputs a paragraph-formatted text such as "When entering a new market, it is important to first conduct thorough market research and identify points of differentiation through competitive analysis."

[1002] Step 6:

[1003] The server sends the formatted answer to the user's terminal, including the formatted answer text as input.

[1004] Specifically, it generates an HTTP response and sends it to the terminal, including the response data. The terminal then displays the received response on the user's screen. For example, a formatted response such as "When entering a new market, it is important to first conduct thorough market research and identify points of differentiation through competitive analysis" is displayed on the screen.

[1005] Through this series of steps, users can quickly obtain specific and reliable business advice.

[1006] (Application example 1)

[1007] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1008] Currently, there are limited ways to receive appropriate advice immediately in response to specific business questions, making it difficult for users to easily obtain reliable information. Additionally, there is no system that allows users to resolve questions that arise on the spot while viewing business content, which reduces the user's learning efficiency.

[1009] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1010] In this invention, the server includes means for receiving a business-related question from a user, means for preprocessing the question, means for inputting the preprocessed question into a generative AI model, means for generating an answer to the question based on materials previously learned by the generative AI model, means for formatting the generated answer, means for sending the formatted answer to the user, means for inputting a question in real time while viewing business-related content on a smartphone and receiving an immediate answer, and means for receiving questions about videos or articles displayed on the smartphone, thereby enabling a user to immediately receive specific and appropriate business advice while viewing business content.

[1011] The "means for receiving business-related questions from users" refers to an interface that allows users to input specific business-related questions and have them received by the system.

[1012] The "means for preprocessing the question" is a process for analyzing the question received from the user and performing data cleaning, tokenization, etc.

[1013] "Means for inputting the preprocessed question into the generative AI model" refers to a method for inputting a question that has undergone preprocessing into the generative AI model.

[1014] "Means for generating answers to questions based on materials previously learned by the generative AI model" refers to a method for creating answers to questions using a generative AI model based on management-related literature and data previously learned.

[1015] The "means for formatting the generated answer" is a process for adjusting the grammar and format of the generated answer to make it easier to read.

[1016] The "means for transmitting the formatted answer to the user" is a communication method for providing the user with the answer that has been formatted.

[1017] "A means for viewing business-related content on a smartphone while inputting questions in real time and receiving instant answers" is a system that allows users to view videos or articles that are useful for business on their smartphones, input questions that arise while viewing them on the spot, and receive instant answers.

[1018] The "means for receiving questions about videos or articles displayed on the smartphone" refers to a function that allows a user to input questions about the content of business-related videos or articles while watching them on a smartphone, and the system receives those questions.

[1019] This invention provides a system that allows users to input business-related questions via smartphone and receive specific business advice in real time. Specific embodiments of this system will be described in detail below.

[1020] This system is implemented using a smartphone application. While users are watching business-related videos or articles within the application, they are provided with an interface where they can input questions. When users input a question, the smartphone sends it to the server.

[1021] The server then receives this question and performs text cleaning and tokenization. Text cleaning removes extra spaces and special characters. Tokenization breaks the question down into meaningful words and phrases, pre-processing the question and converting it into a format suitable for generative AI models.

[1022] After preprocessing, the question is input to a generative AI model by the server. The specific generative AI model used is OpenAI GPT. This model has been trained in advance on management-related literature and case studies, and is able to generate appropriate business advice in response to the user's question.

[1023] Once the generative AI model generates an answer based on the question, the server formats the answer grammatically and makes it easier for the user to understand, for example by dividing the answer into paragraphs or bullet points.

[1024] The formatted answers are then sent back to the smartphone, where the user can check them in real time on the application. This continuous interaction allows users to view business content while instantly resolving any questions that arise.

[1025] As a concrete example, suppose a user types, "Please tell me your strategy for entering a new market." The server passes this question to a generative AI model, which generates an answer such as, "When entering a new market, it is important to first conduct thorough market research and identify points of differentiation through competitive analysis." The server formats this answer and sends it to the smartphone. The user can view the answer on the app.

[1026] Examples of prompts that can be used include:

[1027] User Question: What is your strategy for entering new markets?

[1028] Business advice:

[1029] As can be seen from this example, this system provides users with a means to easily obtain specific business advice, greatly improving learning efficiency.

[1030] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1031] Step 1:

[1032] Question input from the user

[1033] The user enters a business question into a text box on the smartphone application and presses the submit button. The input data is the question text entered by the user, and this input is sent to the server.

[1034] Step 2:

[1035] Question Preprocessing

[1036] The server receives the question submitted by the user and performs text cleaning and tokenization. Specifically, it removes extra spaces and special characters and breaks the question into tokens. This process generates preprocessed question data. The input is the user's raw text question, and the output is the cleaned and tokenized text data.

[1037] Step 3:

[1038] Input to generative AI models

[1039] The server inputs the preprocessed question into a generative AI model (e.g., OpenAI's GPT). The input data is the preprocessed question text, and the generative AI model generates an answer to the question based on the pre-trained material. The output is the generated answer text.

[1040] Step 4:

[1041] Formatting answers

[1042] The server formats the answer text returned by the generative AI model. Specifically, it checks the grammar of the sentences, standardizes the format, and formats the answer by dividing it into paragraphs and bullet points. The input is the raw text answer from the generative AI model, and the output is the formatted text answer.

[1043] Step 5:

[1044] Submitting and viewing responses

[1045] The server sends the formatted answer back to the user's smartphone application, which displays the received answer on its screen. The input is the formatted text answer, and the output is a visual form on the user's smartphone display.

[1046] Step 6:

[1047] Entering questions while watching business-related content

[1048] Users can directly input questions while watching business-related videos or articles on a smartphone application. This question is also sent to the server as in step 1, and subsequent processing is executed. The input is a text question asked while watching the video or article, and the output is a display of the answer according to the flow up to step 5.

[1049] Through the above processing steps, the system realizing the present invention enables a user to obtain specific business advice in real time while viewing business content.

[1050] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1051] The present invention is a system that allows users to input business questions and obtain answers, and combines an emotion engine to provide advice that recognizes and takes into account the user's emotions. This system includes multiple processing steps, each of which is executed by a server, a terminal, and a user.

[1052] System Overview:

[1053] 1. User interface implementation:

[1054] The terminal provides an interface for the user to input a business question. The user interface includes a text box and a submit button.

[1055] 2. Details of the process listed on the server:

[1056] The server receives the question sent by the user, performs preprocessing, recognizes the user's emotions using an emotion engine, and generates an answer using a generative AI model. The generated answer is formatted and sent to the user.

[1057] A natural language description of what the program does:

[1058] 1. User inputs a question:

[1059] The user enters a business question into a text box on the terminal and presses the send button.

[1060] The terminal sends this input to the server.

[1061] Examples:

[1062] The user types in "What is your strategy for entering new markets?" and hits the submit button.

[1063] 2. Question preprocessing:

[1064] The server receives the question submitted by the user and performs text cleaning, removing unnecessary spaces and special characters to make the question clean.

[1065] The server tokenizes the question, breaking it down into meaningful words and phrases.

[1066] Examples:

[1067] Take the question, "What is your strategy for entering a new market?" and break it down into keywords such as "new market," "entry," and "strategy."

[1068] 3. Emotion recognition:

[1069] The server uses an emotion engine to recognize the user's emotion from the question sentence.

[1070] The emotion engine classifies emotions into categories such as positive, negative, and neutral.

[1071] Examples:

[1072] By analyzing the question, the emotion engine recognizes that the user is feeling anxious about the phrase "strategy for entering new markets."

[1073] 4. Input to the generative AI model:

[1074] The server inputs the preprocessed questions and emotional information from the emotion engine into the generative AI model.

[1075] The generative AI model generates appropriate answers to questions based on pre-trained literature and speech content from business executives, and also takes into consideration the user's emotions.

[1076] Examples:

[1077] For anxious users, the generative AI model generates a reassuring response such as, "Don't worry, when entering a new market, it's important to first conduct thorough market research and find points of differentiation through competitive analysis."

[1078] 5. Formatting your answer:

[1079] The server formats the generated answer, checking grammar and expression to make it easier for the user to understand.

[1080] Examples:

[1081] The server checks the generated answer, "Don't worry, when entering a new market, it's important to first conduct thorough market research and find your points of differentiation through competitive analysis," for grammar and formats it into a paragraph.

[1082] 6. Formatting your answers:

[1083] If necessary, the server will format the answer into paragraphs or bullet points, which makes the answer more readable.

[1084] 7. Submitting and Viewing Your Answers:

[1085] The server sends the formatted response to the user's terminal.

[1086] The terminal displays the received answer on the user's screen.

[1087] Examples:

[1088] The server then sends the formatted response to the user's device as, "Don't worry, when entering a new market, it's important to first conduct thorough market research and find points of differentiation through competitive analysis," and the device displays it.

[1089] This series of steps allows users to easily obtain specific business advice that takes into account the perspectives and feelings of well-known business leaders, which can be useful in formulating business strategies and developing organizations.

[1090] The processing flow will be explained below.

[1091] Step 1:

[1092] The user enters a question

[1093] The user enters a business question into a text box on the terminal and presses the send button. The terminal prepares the question as data to be sent to the server.

[1094] Step 2:

[1095] The device sends a question to the server

[1096] The terminal sends the user's input question to the server as a data packet, which contains the entire question.

[1097] Step 3:

[1098] The server receives the query

[1099] The server receives the query data sent from the terminal and verifies the integrity of the data. In this step, it checks whether any inappropriate data is included.

[1100] Step 4:

[1101] Server performs text cleaning

[1102] The server performs text cleaning on the received question, removing unnecessary spaces and special characters to make the question clean.

[1103] Step 5:

[1104] The server performs tokenization

[1105] The server tokenizes the question, breaking down the sentence into meaningful words and phrases, converting it into a format that is easy to feed into a generative AI model.

[1106] Step 6:

[1107] The server uses an emotion engine to recognize emotions.

[1108] The server inputs the question text into an emotion engine to recognize the user's emotion, which then classifies it into emotion categories such as positive, negative, and neutral.

[1109] Step 7:

[1110] The server inputs emotional information into the generative AI model

[1111] The server inputs the preprocessed question and the emotional information recognized by the emotion engine into the generative AI model, and the interaction of this data helps generate appropriate answers.

[1112] Step 8:

[1113] Generative AI models generate answers

[1114] The generative AI model generates optimal answers to questions based on pre-trained literature and lecture content, taking into account emotion recognition results.

[1115] Step 9:

[1116] The server formats the generated answer

[1117] The server formats the generated answers, correcting grammatical errors and making them more user-friendly.

[1118] Step 10:

[1119] The server formats the response

[1120] If necessary, the server will format the answer into paragraphs or bullet points, which will make the answer more readable and organized.

[1121] Step 11:

[1122] The server sends the formatted response to the device.

[1123] The server sends the formed and formatted response to the user's terminal.

[1124] Step 12:

[1125] The device receives and displays the answer

[1126] The terminal receives the response sent from the server and displays it on the user's screen, allowing the user to confirm specific business advice.

[1127] Examples:

[1128] The user types "What is your strategy for entering a new market?" into the device and presses the send button. The device sends this question to the server, which receives it. The server performs text cleaning and tokenization, and recognizes the user's emotions using an emotion engine. The server inputs the question and emotion information into a generative AI model, which then generates the answer, "Don't worry, when entering a new market, it's important to first conduct thorough market research and find points of differentiation through competitive analysis." The server then formats this answer and sends it to the device. The device receives the answer and displays it on the user's screen.

[1129] Example 2

[1130] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1131] Existing business advice systems do not provide answers that take the user's emotions into account, making it difficult to respond appropriately to the user's concerns and questions. Furthermore, simply generating answers based on literature or lecture content can sometimes lack consideration for the user's specific emotions and situation. There is a need for a system that can solve this problem and provide users with more personalized, emotion-sensitive, and appropriate business advice.

[1132] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1133] In this invention, the server includes means for receiving a business-related question from a user, means for preprocessing the question, means for inputting the preprocessed question into a sentiment analysis engine and recognizing the user's sentiment, means for inputting the sentiment information and the preprocessed question into a generative AI model, means for the generative AI model to generate an answer that takes into account the sentiment for the question based on materials it has previously learned, means for formatting the generated answer, and means for sending the formatted answer to the user, thereby enabling the provision of personalized business advice that takes into account the user's sentiment.

[1134] A "user" is an individual or entity that enters a business inquiry and uses the system.

[1135] "Business Questions" are specific inquiries or consultations related to business strategy, marketing, and management.

[1136] The "means for preprocessing questions" is a process for analyzing received questions and performing text cleaning and tokenization.

[1137] "Preprocessed questions" are question data that have been text cleaned and tokenized, and are formatted so that they can be analyzed.

[1138] An "emotion analysis engine" is a technology that analyzes and recognizes a user's emotions from input text and classifies them into emotional categories.

[1139] The "means for recognizing emotions" is a process of recognizing the user's emotions from preprocessed questions using an emotion analysis engine.

[1140] "Emotion information" refers to the emotion category (e.g., positive, negative, neutral) classified by the emotion analysis engine.

[1141] A "generative AI model" is an artificial intelligence model that generates appropriate answers to input data based on previously learned materials.

[1142] "Means for inputting to the generative AI model" refers to the process of providing preprocessed questions and emotion information to the generative AI model.

[1143] "Means of generation" refers to the process by which a generative AI model generates an appropriate answer based on input data.

[1144] The "formatting means" is a process of checking the grammar of the generated answer and making it into a format that is easy for the user to understand.

[1145] "Means for sending" refers to the process of sending the formatted answer to the user's terminal via communication.

[1146] The "system" is a set of devices and software that executes a series of processes to provide appropriate answers that take emotions into account to users' business-related questions.

[1147] The present invention is a system that allows users to input business-related questions and receive answers, and combines an emotion analysis engine to provide advice that recognizes and takes into account the user's emotions. This system is mainly processed using a server and terminals.

[1148] System configuration and specific details:

[1149] 1. User interface implementation:

[1150] The terminal provides an interface for users to input business-related questions. The interface includes a text box and a submit button. Through this interface, users can easily input their questions and proceed to the next processing step.

[1151] 2. Question preprocessing:

[1152] The server receives questions sent by users. The received questions are first text-cleaned to remove unnecessary spaces and special characters. Next, the server tokenizes the questions and breaks them down into meaningful words and phrases. Specifically, the question "What is your strategy for entering new markets?" is broken down into keywords such as "new market," "entry," and "strategy."

[1153] 3. Emotion recognition:

[1154] The server passes the preprocessed question to a sentiment analysis engine, which recognizes the user's emotions from the question and classifies them into sentiment categories such as positive, negative, and neutral. For example, the question "Please tell us your strategy for entering a new market" identifies the user's feelings of anxiety.

[1155] 4. Input to the generative AI model:

[1156] The server inputs the preprocessed question and emotion information into the generative AI model. Based on the pre-trained data, the generative AI model generates an appropriate answer to the question while also taking the user's emotions into consideration. For example, the generative AI model might generate the answer, "Don't worry, when entering a new market, it's important to first conduct thorough market research and identify points of differentiation through competitive analysis."

[1157] 5. Formatting your answer:

[1158] The generated answer is checked for grammar and expressions on the server, and is then formatted into a paragraph format that is easy for the user to understand.

[1159] 6. Formatting your answers:

[1160] If desired, the server can format the answer as paragraphs or bullet points, which makes the answer more readable.

[1161] 7. Submitting and Viewing Your Answers:

[1162] The formatted response is sent from the server to the user's device, which then displays the response on the user's screen. Specifically, the server sends the formatted response as "Don't worry, when entering a new market, it's important to first conduct thorough market research and find points of differentiation through competitive analysis," and the device displays it.

[1163] This system allows users to easily obtain specific business advice that takes into account the perspectives and sentiments of prominent business leaders, helping them to develop business strategies and develop their organizations. The system can be used on a variety of hardware, including servers, desktop computers, laptops, and mobile devices. The software used includes a generative AI model, a sentiment analysis engine, and a tokenization tool.

[1164] Examples:

[1165] When a user types "What is your strategy for entering a new market?" into a text box on their device and presses the send button, the server receives the question. The server then performs text cleaning and tokenization, and uses a sentiment analysis engine to identify the user's concerns. The generative AI model then generates an answer: "Don't worry, when entering a new market, it's important to first conduct thorough market research and identify points of differentiation through competitive analysis." The answer is then formatted by the server and sent to the user. The user can view this answer on their device.

[1166] Example prompt sentence:

[1167] "What is your strategy for entering new markets?"

[1168] This series of steps allows users to receive appropriate advice that takes their emotions into consideration.

[1169] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1170] Step 1:

[1171] A user enters a business question.

[1172] The user enters a question in the text box on the terminal and presses the send button.

[1173] Input: A business question from the user (e.g., "What is your strategy for entering new markets?")

[1174] Output: The entered question is sent from the terminal to the server. Specifically, the terminal constructs an HTTP request and sends the question data to the server.

[1175] Step 2:

[1176] The server receives the query and performs pre-processing.

[1177] The server performs text cleaning on the received question, removing unnecessary spaces and special characters.

[1178] Input: A question sent from the device (e.g., "What is your strategy for entering new markets?")

[1179] Output: Cleaned text (e.g., "What is your strategy for entering new markets?" with unnecessary spaces removed)

[1180] Specifically, the server uses regular expressions and other text processing techniques to remove unnecessary parts.

[1181] Step 3:

[1182] The server tokenizes the question.

[1183] Break down the cleaned question into words and phrases.

[1184] Input: Cleaned text (e.g., "What is your strategy for entering new markets?")

[1185] Output: Tokenized keywords (e.g., "new market," "entry," "strategy")

[1186] Specifically, the server applies a tokenization algorithm to break down the question into meaningful words and phrases.

[1187] Step 4:

[1188] The server uses an emotion analysis engine to recognize the user's emotions.

[1189] The server passes the preprocessed questions to a sentiment analysis engine for sentiment analysis.

[1190] Input: Tokenized keywords (e.g., "new market," "entry," "strategy")

[1191] Output: Emotional information (e.g., anxiety)

[1192] Specifically, the server inputs these keywords into an emotion analysis engine to obtain emotion categories such as positive, negative, and neutral.

[1193] Step 5:

[1194] The server inputs the preprocessed question and sentiment information into the generative AI model.

[1195] The generative AI model generates an answer.

[1196] Input: Tokenized keywords and sentiment information (e.g., "new market," "entry," "strategy," sentiment: anxiety)

[1197] Output: Generated answer (e.g., "Don't worry, when entering a new market, it's important to first conduct thorough market research and identify your points of differentiation through competitive analysis.")

[1198] Specifically, the server provides this data to the generative AI model and waits for it to generate an answer.

[1199] Step 6:

[1200] The server formats the generated answer.

[1201] The generated answers are checked for grammar and expression and formatted to make them easier to understand.

[1202] Input: Generated answer (e.g., "Don't worry, when entering a new market, it's important to first conduct thorough market research and identify your points of differentiation through competitive analysis.")

[1203] Output: Formatted answer (e.g. "Don't worry, when entering a new market, it's important to first conduct thorough market research and identify your points of differentiation through competitive analysis." formatted into a paragraph)

[1204] Specifically, the server performs a grammar check on the generated response and makes appropriate corrections.

[1205] Step 7:

[1206] The server sends the formatted response to the user's terminal.

[1207] The terminal displays.

[1208] Input: Formatted answer (e.g., "Don't worry, when entering a new market, it's important to first conduct thorough market research and identify your points of differentiation through competitive analysis.")

[1209] Output: The answer displayed on the user's screen

[1210] Specifically, the server sends the formatted answer as an HTTP response, and the terminal displays the received answer in the text area.

[1211] (Application example 2)

[1212] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1213] In modern manufacturing, it is difficult to obtain prompt and appropriate answers to real-time questions about factory operation and management. Furthermore, there is a lack of technology that provides advice that takes into account the emotions of workers and managers. As a result, efficiency on the shop floor and psychological stress among workers are negatively affected. The present invention aims to solve these problems by providing a system that enables factory workers and managers to ask questions in real time and receive answers that recognize their emotions.

[1214] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving an inquiry from a user, means for preprocessing the inquiry, means for inputting the preprocessed inquiry into a generative AI model, means for generating a response to the inquiry based on documents previously learned by the generative AI model, means for formatting the generated response, means for sending the formatted response to the user, and means including an emotion recognition engine for recognizing the user's emotions, the emotion recognition engine linking the user's emotion information to the generative AI model. This makes it possible to provide quick and appropriate answers to questions regarding factory operation and management and to provide advice that takes into consideration the emotions of workers.

[1215] "User" refers to the workers and managers who ask questions about factory operation and management to the system.

[1216] "Inquiry" means information that represents a question or request regarding factory operations and management.

[1217] "Preprocessing" refers to a series of steps that clean up queries and convert them into a format that is easy for generative AI models to interpret.

[1218] "Text cleaning" is the process of removing unnecessary spaces and special characters to make the text clean.

[1219] "Tokenization" is the process of breaking down text into meaningful words and phrases.

[1220] A "generative AI model" is an artificial intelligence model that generates appropriate responses to inquiries based on pre-trained data.

[1221] "Documents" refers to materials and records containing information relating to factory operations and management.

[1222] "Response" refers to the answer generated by a generative AI model in response to a query.

[1223] An "emotion recognition engine" is a technology that analyzes a user's emotions and classifies them into emotional categories such as positive, negative, and neutral.

[1224] "Integrating" refers to sharing the emotional information analyzed by the emotion recognition engine with the generative AI model and reflecting it in the response.

[1225] This invention provides a system that asks questions about factory operation and management and provides appropriate answers that recognize emotions. This system consists of a server, a user operation terminal, an emotion recognition engine, and a generative AI model.

[1226] First, the user (factory worker or manager) enters a question about factory operation and management into a text box on the terminal and presses the send button. This question is then sent from the terminal to the server. The terminal can be a regular PC, tablet, or smartphone.

[1227] The server receives the question submitted by the user and first performs text cleaning, removing unnecessary spaces and special characters to make the question clean. This step is part of preprocessing to enable accurate and efficient processing. Next, the server performs tokenization, breaking the question down into meaningful words and phrases.

[1228] After the question preprocessing is complete, the server uses an emotion recognition engine to analyze the preprocessed question and obtain the emotional information the user is feeling. This emotional information is classified into emotion categories such as positive, negative, and neutral. For example, if the question is "How can we operate a new assembly line efficiently?", the emotion recognition engine will analyze whether the user is feeling anxious or worried.

[1229] The server then inputs the preprocessed question and emotional information into a generative AI model. The generative AI model generates an appropriate answer based on document data related to factory operation and management that it has previously learned. In doing so, it also takes into account emotional information and generates an answer that takes the user's emotions into consideration. The generative AI model used here can be a large-scale language model such as GPT-3.

[1230] The generated answer is then formatted by the server, for example by checking grammar and adjusting paragraph structure. Finally, the formatted answer is sent from the server to the user's device and displayed on the device screen, allowing the user to receive specific advice that takes their feelings into consideration.

[1231] For example, if a worker types "Tell me about quality control" into a terminal and sends it, the server will clean and tokenize the question, and the emotion recognition engine will analyze the anxiety expressed in the question. The generative AI model will generate an answer such as "Quality control is important. Don't worry, please use the checklist to review your daily processes," which will then be formatted by the server and sent back to the user.

[1232] An example of a prompt might be, "How can we run our new assembly line efficiently? The workers are feeling impatient."

[1233] This system allows users to get appropriate answers to questions about factory operation and management in real time, improving work efficiency and reducing psychological stress.

[1234] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1235] Step 1:

[1236] The user enters a question about factory operation and management in the text box on the terminal and presses the send button.

[1237] Input: A user-typed question (e.g., "How can we run our new assembly line efficiently?")

[1238] Output: Question data sent from the terminal to the server

[1239] Step 2:

[1240] The server receives the questions sent by the user and performs text cleaning.

[1241] Input: Question data submitted by the user (e.g., "How can we run our new assembly line efficiently?")

[1242] Data processing: Remove unnecessary spaces and special characters to make the text clean.

[1243] Output: Cleaned question data (e.g., "How can we run our new assembly line efficiently?")

[1244] Step 3:

[1245] The server tokenizes the cleaned question, breaking it down into meaningful words and phrases.

[1246] Input: Cleaned question data

[1247] Data operations: Breaking down the question into words and phrases

[1248] Output: Tokenized question data (e.g., "New," "Assembly Line," "Efficient," "Operation," "What should I do?")

[1249] Step 4:

[1250] The emotion recognition engine analyzes the tokenized question data and reconnects with the user's emotions.

[1251] Input: Tokenized question data

[1252] Data Computing: Sentiment Analysis to Identify User Emotions

[1253] Output: Emotional information (e.g., impatience)

[1254] Step 5:

[1255] The server inputs the preprocessed question and emotion information into the generative AI model.

[1256] Input: Preprocessed question data and sentiment information

[1257] Data Computation: Generative AI models generate responses based on pre-trained data

[1258] Output: Generated response data (e.g., "Please proceed slowly. To operate a new assembly line efficiently, it is important to perform proper initial setup and regular maintenance.")

[1259] Step 6:

[1260] The server formats the generated answer.

[1261] Input: Generated response data

[1262] Data processing: checking grammar and adjusting paragraph structure

[1263] Output: Formatted response data (e.g., "Please proceed slowly. To operate a new assembly line efficiently, it is important to perform proper initial setup and regular maintenance.")

[1264] Step 7:

[1265] The server sends the formatted response to the user's terminal.

[1266] Input: Formatted answer data

[1267] Output: Answer data sent to the terminal (e.g., "Please proceed slowly. To operate a new assembly line efficiently, it is important to perform proper initial setup and regular maintenance.")

[1268] Step 8:

[1269] The terminal displays the received answer to the user.

[1270] Input: Formatted answer data sent from the server

[1271] Output: The answer displayed on the user's device (e.g., "Please proceed slowly. Proper initial setup and regular maintenance are essential to running your new assembly line efficiently.")

[1272] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1273] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1274] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1275] [Fourth embodiment]

[1276] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1277] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1278] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1279] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1280] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1281] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1282] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1283] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1284] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1285] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1286] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1287] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1288] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1289] The present invention is a system that allows users to input business questions and receive answers, and the server utilizes a generative AI model to enable users to easily receive specific business advice. This system includes multiple processing steps, each of which is executed by the server, a terminal, and the user.

[1290] System Overview:

[1291] 1. User interface implementation:

[1292] The terminal provides an interface for the user to input a business question. The user interface includes a text box and a submit button.

[1293] 2. Details of the process listed on the server:

[1294] The server receives the question submitted by the user, performs preprocessing, and generates an answer using a generative AI model, which is then formatted and sent to the user.

[1295] A natural language description of what the program does:

[1296] 1. User inputs a question:

[1297] The user enters a business question into a text box on the terminal and presses the send button.

[1298] The terminal sends this input to the server.

[1299] Examples:

[1300] The user types in "What is your strategy for entering new markets?" and hits the submit button.

[1301] 2. Question preprocessing:

[1302] The server receives the question submitted by the user and performs text cleaning, for example removing extra spaces and special characters.

[1303] The server tokenizes the question, breaking it down into meaningful words and phrases.

[1304] Examples:

[1305] Take the question, "What is your strategy for entering a new market?" and break it down into keywords such as "new market," "entry," and "strategy."

[1306] 3. Input to the generative AI model:

[1307] The server inputs the preprocessed questions into the generative AI model.

[1308] The generative AI model generates appropriate answers to questions based on pre-trained literature and speeches by business leaders.

[1309] Examples:

[1310] The server passes the question "What is your strategy for entering a new market?" to the generative AI model, which generates an answer such as "When entering a new market, it is important to first conduct thorough market research and find points of differentiation through competitive analysis."

[1311] 4. Formatting the answer:

[1312] The server formats the generated answer, ensuring grammar and formatting.

[1313] If necessary, format your answers by dividing them into paragraphs or bullet points.

[1314] Examples:

[1315] The server checks the generated answer, "When entering a new market, it is important to first conduct thorough market research and find points of differentiation through competitive analysis," for grammar and formats it into a paragraph.

[1316] 5. Submitting and Viewing Your Answers:

[1317] The server sends the formatted response to the user's terminal.

[1318] The terminal displays the received answer on the user's screen.

[1319] Examples:

[1320] The server then sends the formatted response to the user's device as, "When entering a new market, it is important to first conduct thorough market research and find points of differentiation through competitive analysis," and the device displays it.

[1321] Through this series of steps, users can easily obtain specific business advice based on the wisdom of well-known business leaders, which can be used to help formulate business strategies and develop their organizations.

[1322] The processing flow will be explained below.

[1323] Step 1:

[1324] The user enters a question

[1325] The user enters a business question into the input form on the terminal and presses the send button. The terminal prepares to send the question to the server.

[1326] Step 2:

[1327] The device sends a question to the server

[1328] The terminal transmits the question entered by the user to the server as data, which includes the content of the user's question.

[1329] Step 3:

[1330] The server receives the query

[1331] The server receives the query data sent from the terminal and checks the integrity of the data.

[1332] Step 4:

[1333] The server preprocesses the query

[1334] The server performs text cleaning on the received question, removing unnecessary spaces and special characters to make the question clean.

[1335] The server also tokenizes the question, breaking it down into meaningful words and phrases.

[1336] Step 5:

[1337] The server inputs questions into the generative AI model

[1338] The server inputs the preprocessed questions into a generative AI model, which generates appropriate answers based on pre-trained literature and speeches by business leaders.

[1339] Step 6:

[1340] Generative AI models generate answers

[1341] The generative AI model generates specific advice from the manager's perspective based on the input question, and the generated answers are temporarily stored on the server.

[1342] Step 7:

[1343] The server formats the generated answer

[1344] The server formats the answers received from the generative AI model, checking grammar and expression to make them easier for the user to understand.

[1345] Step 8:

[1346] The server formats the response

[1347] If necessary, the server will format the answer into paragraphs or bullet points, which makes the answer more readable.

[1348] Step 9:

[1349] The server sends the formatted response to the device.

[1350] The server sends the formatted response to the user's terminal.

[1351] Step 10:

[1352] The device receives and displays the answer

[1353] The terminal receives the response sent from the server and displays it on the user's screen, allowing the user to check specific business advice.

[1354] Examples:

[1355] The user types "What is your strategy for entering a new market?" into the device and presses the send button. The device sends this question to the server, which receives it. The server preprocesses the question and inputs it into the generative AI model. The generative AI model generates the answer "When entering a new market, it is important to first conduct thorough market research and identify points of differentiation through competitive analysis." The server formats this answer and sends it to the device. The device receives the answer and displays it on the user's screen.

[1356] Example 1

[1357] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1358] Currently, there are limited systems that provide fast and specific answers to business questions, making it difficult for many users to obtain appropriate business advice. In addition, existing systems often do not automate the preprocessing of questions or the formatting of answers, requiring manual intervention. Furthermore, the quality of the generated answers is inconsistent, making it difficult for users to obtain reliable information. A system that addresses these issues is needed.

[1359] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1360] In this invention, the server includes a means for receiving business questions from users at a terminal, a means for preprocessing the questions at the server, and a means for inputting the preprocessed questions into a generative AI model at the server. This allows users to quickly obtain appropriate business advice. Furthermore, by automating preprocessing including text cleaning and tokenization, semantic analysis of questions can be performed accurately and efficiently. The generative AI model generates high-quality answers based on pre-trained data, and by formatting the answers, it is possible to provide consistent information. This allows users to quickly receive reliable business advice, which can be useful for planning business strategies and developing organizations.

[1361] A "user" is a person or organization that utilizes the system to enter business questions and receive answers.

[1362] A "terminal" is a hardware device used by a user to input questions and receive answers from a server. Examples include computers and smartphones.

[1363] A "server" is a computer system whose role is to process questions entered by users, generate answers using generative AI models, format them, and then send them to the user's device.

[1364] "Question preprocessing" is the process of cleaning up the text data submitted by the user and converting it into an analyzable format using methods such as tokenization.

[1365] "Text cleaning" is the process of removing extra spaces and special characters from questions entered by users.

[1366] "Tokenization" is the process of breaking down text data into words and phrases and converting it into a form that is easier to analyze.

[1367] A "generative AI model" is an artificial intelligence model that learns large amounts of data in advance and generates appropriate answers for input text.

[1368] "Answer formatting" is the process of checking the generated answers for grammar and arranging them into paragraphs, bullet points, or other formats.

[1369] "Data" is information processed within the system, including questions entered by users and answers generated by the server.

[1370] This invention is a system that allows users to input business questions and receive answers. The server utilizes a generative AI model, allowing users to easily receive specific business advice. This system uses the following hardware and software:

[1371] Hardware and software used

[1372] Server: A computer system that processes user questions and generates answers, using cloud services as needed.

[1373] Device: A device on which a user enters questions and receives answers. Examples include computers, tablets, and smartphones.

[1374] Generative AI models: Artificial intelligence models that have been trained on large amounts of data in advance (e.g., OpenAI GPT-3). They are accessible through APIs.

[1375] System Configuration

[1376] The system consists of the following main components:

[1377] 1. User Interface:

[1378] The terminal provides an interface that includes a text box for the user to enter a question and a submit button.

[1379] 2. Receiving and Preprocessing Questions:

[1380] The server receives the questions sent from the terminal and performs text cleaning and tokenization. Text cleaning includes removing extra spaces and special characters, and tokenization uses libraries such as NLTK and SpaCy.

[1381] 3. Use of generative AI models:

[1382] The server inputs the preprocessed questions into the generative AI model, which generates answers based on pre-trained management literature and data.

[1383] 4. Formatting the answer:

[1384] The answers output by the generative AI model are formatted on the server, checking for grammar and formatting as needed, which includes formatting into paragraphs and bullet points using regular expressions and HTML tags.

[1385] 5. Submitting and Viewing Answers:

[1386] The server sends the formatted answer to the terminal, which then displays the answer on the user's screen.

[1387] Specific operation example

[1388] User enters and submits question:

[1389] The user types in "What is your strategy for entering new markets?" and hits the submit button.

[1390] Question preprocessing:

[1391] The server receives the question, cleans it of extra spaces and special characters, and tokenizes it into words like "new market," "entry," and "strategy."

[1392] Use of generative AI models:

[1393] The server inputs the preprocessed data into a generative AI model and generates answers such as, "When entering a new market, it is important to first conduct thorough market research and find points of differentiation through competitive analysis."

[1394] Formatting the answer:

[1395] The server then formats the answer grammatically and summarizes it in a paragraph: "When entering a new market, it's important to first conduct thorough market research and identify points of differentiation through competitive analysis."

[1396] Submit and view your answers:

[1397] The server sends the formatted response to the terminal, which displays it on the user's screen.

[1398] Prompt Sentence Examples

[1399] "What are the main steps in developing a marketing strategy?"

[1400] "What makes a startup successful?"

[1401] "What points should I pay attention to when introducing a new product to the market?"

[1402] In this way, the system helps users get quick, specific and reliable business advice.

[1403] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1404] Step 1:

[1405] The user enters a business question into a text box on the device and presses the submit button, providing a prompt such as "What is your strategy for entering new markets?"

[1406] The device generates an HTTP POST request to send this input to the server, and sends the question to the server. Specifically, the input text is included in the request body and sent to the specified endpoint.

[1407] Step 2:

[1408] The server receives the question sent from the device, which includes as input an HTTP POST request from the device.

[1409] The server first performs text cleaning. Specifically, it uses regular expressions to remove extra spaces and special characters. As a result of the data processing, it outputs the question in a clean format: "What is your strategy for entering a new market?"

[1410] Step 3:

[1411] The server then tokenizes the question, including the cleaned text as input.

[1412] Specifically, it uses libraries such as the Natural Language Toolkit (NLTK) and SpaCy to break down the question into words and phrases, such as "new market," "entry," and "strategy." This results in tokenized data being output.

[1413] Step 4:

[1414] The server inputs the preprocessed question into the generative AI model, which includes tokenized text data as input.

[1415] Specifically, the API of a generative AI model (e.g., OpenAI GPT-3) is called, and the tokenized data is passed as an argument. The generative AI model generates an answer to the question based on the data it has learned in advance. The API response then outputs text such as, "When entering a new market, it is important to first conduct thorough market research and identify points of differentiation through competitive analysis."

[1416] Step 5:

[1417] The server formats the generated answer, which includes the text output from the generative AI model as input.

[1418] Specifically, it uses a grammar checking engine (e.g., Grammarly API) and regular expressions and HTML tags to format text into paragraphs and bullet points. For example, it outputs a paragraph-formatted text such as "When entering a new market, it is important to first conduct thorough market research and identify points of differentiation through competitive analysis."

[1419] Step 6:

[1420] The server sends the formatted answer to the user's terminal, including the formatted answer text as input.

[1421] Specifically, it generates an HTTP response and sends it to the terminal, including the response data. The terminal then displays the received response on the user's screen. For example, a formatted response such as "When entering a new market, it is important to first conduct thorough market research and identify points of differentiation through competitive analysis" is displayed on the screen.

[1422] Through this series of steps, users can quickly obtain specific and reliable business advice.

[1423] (Application example 1)

[1424] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1425] Currently, there are limited ways to receive appropriate advice immediately in response to specific business questions, making it difficult for users to easily obtain reliable information. Additionally, there is no system that allows users to resolve questions that arise on the spot while viewing business content, which reduces the user's learning efficiency.

[1426] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1427] In this invention, the server includes means for receiving a business-related question from a user, means for preprocessing the question, means for inputting the preprocessed question into a generative AI model, means for generating an answer to the question based on materials previously learned by the generative AI model, means for formatting the generated answer, means for sending the formatted answer to the user, means for inputting a question in real time while viewing business-related content on a smartphone and receiving an immediate answer, and means for receiving questions about videos or articles displayed on the smartphone, thereby enabling a user to immediately receive specific and appropriate business advice while viewing business content.

[1428] The "means for receiving business-related questions from users" refers to an interface that allows users to input specific business-related questions and have them received by the system.

[1429] The "means for preprocessing the question" is a process for analyzing the question received from the user and performing data cleaning, tokenization, etc.

[1430] "Means for inputting the preprocessed question into the generative AI model" refers to a method for inputting a question that has undergone preprocessing into the generative AI model.

[1431] "Means for generating answers to questions based on materials previously learned by the generative AI model" refers to a method for creating answers to questions using a generative AI model based on management-related literature and data previously learned.

[1432] The "means for formatting the generated answer" is a process for adjusting the grammar and format of the generated answer to make it easier to read.

[1433] The "means for transmitting the formatted answer to the user" is a communication method for providing the user with the answer that has been formatted.

[1434] "A means for viewing business-related content on a smartphone while inputting questions in real time and receiving instant answers" is a system that allows users to view videos or articles that are useful for business on their smartphones, input questions that arise while viewing them on the spot, and receive instant answers.

[1435] The "means for receiving questions about videos or articles displayed on the smartphone" refers to a function that allows a user to input questions about the content of business-related videos or articles while watching them on a smartphone, and the system receives those questions.

[1436] This invention provides a system that allows users to input business-related questions via smartphone and receive specific business advice in real time. Specific embodiments of this system will be described in detail below.

[1437] This system is implemented using a smartphone application. While users are watching business-related videos or articles within the application, they are provided with an interface where they can input questions. When users input a question, the smartphone sends it to the server.

[1438] The server then receives this question and performs text cleaning and tokenization. Text cleaning removes extra spaces and special characters. Tokenization breaks the question down into meaningful words and phrases, pre-processing the question and converting it into a format suitable for generative AI models.

[1439] After preprocessing, the question is input to a generative AI model by the server. The specific generative AI model used is OpenAI GPT. This model has been trained in advance on management-related literature and case studies, and is able to generate appropriate business advice in response to the user's question.

[1440] Once the generative AI model generates an answer based on the question, the server formats the answer grammatically and makes it easier for the user to understand, for example by dividing the answer into paragraphs or bullet points.

[1441] The formatted answers are then sent back to the smartphone, where the user can check them in real time on the application. This continuous interaction allows users to view business content while instantly resolving any questions that arise.

[1442] As a concrete example, suppose a user types, "Please tell me your strategy for entering a new market." The server passes this question to a generative AI model, which generates an answer such as, "When entering a new market, it is important to first conduct thorough market research and identify points of differentiation through competitive analysis." The server formats this answer and sends it to the smartphone. The user can view the answer on the app.

[1443] Examples of prompts that can be used include:

[1444] User Question: What is your strategy for entering new markets?

[1445] Business advice:

[1446] As can be seen from this example, this system provides users with a means to easily obtain specific business advice, greatly improving learning efficiency.

[1447] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1448] Step 1:

[1449] Question input from the user

[1450] The user enters a business question into a text box on the smartphone application and presses the submit button. The input data is the question text entered by the user, and this input is sent to the server.

[1451] Step 2:

[1452] Question Preprocessing

[1453] The server receives the question submitted by the user and performs text cleaning and tokenization. Specifically, it removes extra spaces and special characters and breaks the question into tokens. This process generates preprocessed question data. The input is the user's raw text question, and the output is the cleaned and tokenized text data.

[1454] Step 3:

[1455] Input to generative AI models

[1456] The server inputs the preprocessed question into a generative AI model (e.g., OpenAI's GPT). The input data is the preprocessed question text, and the generative AI model generates an answer to the question based on the pre-trained material. The output is the generated answer text.

[1457] Step 4:

[1458] Formatting answers

[1459] The server formats the answer text returned by the generative AI model. Specifically, it checks the grammar of the sentences, standardizes the format, and formats the answer by dividing it into paragraphs and bullet points. The input is the raw text answer from the generative AI model, and the output is the formatted text answer.

[1460] Step 5:

[1461] Submitting and viewing responses

[1462] The server sends the formatted answer back to the user's smartphone application, which displays the received answer on its screen. The input is the formatted text answer, and the output is a visual form on the user's smartphone display.

[1463] Step 6:

[1464] Entering questions while watching business-related content

[1465] Users can directly input questions while watching business-related videos or articles on a smartphone application. This question is also sent to the server as in step 1, and subsequent processing is executed. The input is a text question asked while watching the video or article, and the output is a display of the answer according to the flow up to step 5.

[1466] Through the above processing steps, the system realizing the present invention enables a user to obtain specific business advice in real time while viewing business content.

[1467] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1468] The present invention is a system that allows users to input business questions and obtain answers, and combines an emotion engine to provide advice that recognizes and takes into account the user's emotions. This system includes multiple processing steps, each of which is executed by a server, a terminal, and a user.

[1469] System Overview:

[1470] 1. User interface implementation:

[1471] The terminal provides an interface for the user to input a business question. The user interface includes a text box and a submit button.

[1472] 2. Details of the process listed on the server:

[1473] The server receives the question sent by the user, performs preprocessing, recognizes the user's emotions using an emotion engine, and generates an answer using a generative AI model. The generated answer is formatted and sent to the user.

[1474] A natural language description of what the program does:

[1475] 1. User inputs a question:

[1476] The user enters a business question into a text box on the terminal and presses the send button.

[1477] The terminal sends this input to the server.

[1478] Examples:

[1479] The user types in "What is your strategy for entering new markets?" and hits the submit button.

[1480] 2. Question preprocessing:

[1481] The server receives the question submitted by the user and performs text cleaning, removing unnecessary spaces and special characters to make the question clean.

[1482] The server tokenizes the question, breaking it down into meaningful words and phrases.

[1483] Examples:

[1484] Take the question, "What is your strategy for entering a new market?" and break it down into keywords such as "new market," "entry," and "strategy."

[1485] 3. Emotion recognition:

[1486] The server uses an emotion engine to recognize the user's emotion from the question sentence.

[1487] The emotion engine classifies emotions into categories such as positive, negative, and neutral.

[1488] Examples:

[1489] By analyzing the question, the emotion engine recognizes that the user is feeling anxious about the phrase "strategy for entering new markets."

[1490] 4. Input to the generative AI model:

[1491] The server inputs the preprocessed questions and emotional information from the emotion engine into the generative AI model.

[1492] The generative AI model generates appropriate answers to questions based on pre-trained literature and speech content from business executives, and also takes into consideration the user's emotions.

[1493] Examples:

[1494] For anxious users, the generative AI model generates a reassuring response such as, "Don't worry, when entering a new market, it's important to first conduct thorough market research and find points of differentiation through competitive analysis."

[1495] 5. Formatting your answer:

[1496] The server formats the generated answer, checking grammar and expression to make it easier for the user to understand.

[1497] Examples:

[1498] The server checks the generated answer, "Don't worry, when entering a new market, it's important to first conduct thorough market research and find your points of differentiation through competitive analysis," for grammar and formats it into a paragraph.

[1499] 6. Formatting your answers:

[1500] If necessary, the server will format the answer into paragraphs or bullet points, which makes the answer more readable.

[1501] 7. Submitting and Viewing Your Answers:

[1502] The server sends the formatted response to the user's terminal.

[1503] The terminal displays the received answer on the user's screen.

[1504] Examples:

[1505] The server then sends the formatted response to the user's device as, "Don't worry, when entering a new market, it's important to first conduct thorough market research and find points of differentiation through competitive analysis," and the device displays it.

[1506] This series of steps allows users to easily obtain specific business advice that takes into account the perspectives and feelings of well-known business leaders, which can be useful in formulating business strategies and developing organizations.

[1507] The processing flow will be explained below.

[1508] Step 1:

[1509] The user enters a question

[1510] The user enters a business question into a text box on the terminal and presses the send button. The terminal prepares the question as data to be sent to the server.

[1511] Step 2:

[1512] The device sends a question to the server

[1513] The terminal sends the user's input question to the server as a data packet, which contains the entire question.

[1514] Step 3:

[1515] The server receives the query

[1516] The server receives the query data sent from the terminal and verifies the integrity of the data. In this step, it checks whether any inappropriate data is included.

[1517] Step 4:

[1518] Server performs text cleaning

[1519] The server performs text cleaning on the received question, removing unnecessary spaces and special characters to make the question clean.

[1520] Step 5:

[1521] The server performs tokenization

[1522] The server tokenizes the question, breaking down the sentence into meaningful words and phrases, converting it into a format that is easy to feed into a generative AI model.

[1523] Step 6:

[1524] The server uses an emotion engine to recognize emotions.

[1525] The server inputs the question text into an emotion engine to recognize the user's emotion, which then classifies it into emotion categories such as positive, negative, and neutral.

[1526] Step 7:

[1527] The server inputs emotional information into the generative AI model

[1528] The server inputs the preprocessed question and the emotional information recognized by the emotion engine into the generative AI model, and the interaction of this data helps generate appropriate answers.

[1529] Step 8:

[1530] Generative AI models generate answers

[1531] The generative AI model generates optimal answers to questions based on pre-trained literature and lecture content, taking into account emotion recognition results.

[1532] Step 9:

[1533] The server formats the generated answer

[1534] The server formats the generated answers, correcting grammatical errors and making them more user-friendly.

[1535] Step 10:

[1536] The server formats the response

[1537] If necessary, the server will format the answer into paragraphs or bullet points, which will make the answer more readable and organized.

[1538] Step 11:

[1539] The server sends the formatted response to the device.

[1540] The server sends the formed and formatted response to the user's terminal.

[1541] Step 12:

[1542] The device receives and displays the answer

[1543] The terminal receives the response sent from the server and displays it on the user's screen, allowing the user to confirm specific business advice.

[1544] Examples:

[1545] The user types "What is your strategy for entering a new market?" into the device and presses the send button. The device sends this question to the server, which receives it. The server performs text cleaning and tokenization, and recognizes the user's emotions using an emotion engine. The server inputs the question and emotion information into a generative AI model, which then generates the answer, "Don't worry, when entering a new market, it's important to first conduct thorough market research and find points of differentiation through competitive analysis." The server then formats this answer and sends it to the device. The device receives the answer and displays it on the user's screen.

[1546] Example 2

[1547] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1548] Existing business advice systems do not provide answers that take the user's emotions into account, making it difficult to respond appropriately to the user's concerns and questions. Furthermore, simply generating answers based on literature or lecture content can sometimes lack consideration for the user's specific emotions and situation. There is a need for a system that can solve this problem and provide users with more personalized, emotion-sensitive, and appropriate business advice.

[1549] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1550] In this invention, the server includes means for receiving a business-related question from a user, means for preprocessing the question, means for inputting the preprocessed question into a sentiment analysis engine and recognizing the user's sentiment, means for inputting the sentiment information and the preprocessed question into a generative AI model, means for the generative AI model to generate an answer that takes into account the sentiment for the question based on materials it has previously learned, means for formatting the generated answer, and means for sending the formatted answer to the user, thereby enabling the provision of personalized business advice that takes into account the user's sentiment.

[1551] A "user" is an individual or entity that enters a business inquiry and uses the system.

[1552] "Business Questions" are specific inquiries or consultations related to business strategy, marketing, and management.

[1553] The "means for preprocessing questions" is a process for analyzing received questions and performing text cleaning and tokenization.

[1554] "Preprocessed questions" are question data that have been text cleaned and tokenized, and are formatted so that they can be analyzed.

[1555] An "emotion analysis engine" is a technology that analyzes and recognizes a user's emotions from input text and classifies them into emotional categories.

[1556] The "means for recognizing emotions" is a process of recognizing the user's emotions from preprocessed questions using an emotion analysis engine.

[1557] "Emotion information" refers to the emotion category (e.g., positive, negative, neutral) classified by the emotion analysis engine.

[1558] A "generative AI model" is an artificial intelligence model that generates appropriate answers to input data based on previously learned materials.

[1559] "Means for inputting to the generative AI model" refers to the process of providing preprocessed questions and emotion information to the generative AI model.

[1560] "Means of generation" refers to the process by which a generative AI model generates an appropriate answer based on input data.

[1561] The "formatting means" is a process of checking the grammar of the generated answer and making it into a format that is easy for the user to understand.

[1562] "Means for sending" refers to the process of sending the formatted answer to the user's terminal via communication.

[1563] The "system" is a set of devices and software that executes a series of processes to provide appropriate answers that take emotions into account to users' business-related questions.

[1564] The present invention is a system that allows users to input business-related questions and receive answers, and combines an emotion analysis engine to provide advice that recognizes and takes into account the user's emotions. This system is mainly processed using a server and terminals.

[1565] System configuration and specific details:

[1566] 1. User interface implementation:

[1567] The terminal provides an interface for users to input business-related questions. The interface includes a text box and a submit button. Through this interface, users can easily input their questions and proceed to the next processing step.

[1568] 2. Question preprocessing:

[1569] The server receives questions sent by users. The received questions are first text-cleaned to remove unnecessary spaces and special characters. Next, the server tokenizes the questions and breaks them down into meaningful words and phrases. Specifically, the question "What is your strategy for entering new markets?" is broken down into keywords such as "new market," "entry," and "strategy."

[1570] 3. Emotion recognition:

[1571] The server passes the preprocessed question to a sentiment analysis engine, which recognizes the user's emotions from the question and classifies them into sentiment categories such as positive, negative, and neutral. For example, the question "Please tell us your strategy for entering a new market" identifies the user's feelings of anxiety.

[1572] 4. Input to the generative AI model:

[1573] The server inputs the preprocessed question and emotion information into the generative AI model. Based on the pre-trained data, the generative AI model generates an appropriate answer to the question while also taking the user's emotions into consideration. For example, the generative AI model might generate the answer, "Don't worry, when entering a new market, it's important to first conduct thorough market research and identify points of differentiation through competitive analysis."

[1574] 5. Formatting your answer:

[1575] The generated answer is checked for grammar and expressions on the server, and is then formatted into a paragraph format that is easy for the user to understand.

[1576] 6. Formatting your answers:

[1577] If desired, the server can format the answer as paragraphs or bullet points, which makes the answer more readable.

[1578] 7. Submitting and Viewing Your Answers:

[1579] The formatted response is sent from the server to the user's device, which then displays the response on the user's screen. Specifically, the server sends the formatted response as "Don't worry, when entering a new market, it's important to first conduct thorough market research and find points of differentiation through competitive analysis," and the device displays it.

[1580] This system allows users to easily obtain specific business advice that takes into account the perspectives and sentiments of prominent business leaders, helping them to develop business strategies and develop their organizations. The system can be used on a variety of hardware, including servers, desktop computers, laptops, and mobile devices. The software used includes a generative AI model, a sentiment analysis engine, and a tokenization tool.

[1581] Examples:

[1582] When a user types "What is your strategy for entering a new market?" into a text box on their device and presses the send button, the server receives the question. The server then performs text cleaning and tokenization, and uses a sentiment analysis engine to identify the user's concerns. The generative AI model then generates an answer: "Don't worry, when entering a new market, it's important to first conduct thorough market research and identify points of differentiation through competitive analysis." The answer is then formatted by the server and sent to the user. The user can view this answer on their device.

[1583] Example prompt sentence:

[1584] "What is your strategy for entering new markets?"

[1585] This series of steps allows users to receive appropriate advice that takes their emotions into consideration.

[1586] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1587] Step 1:

[1588] A user enters a business question.

[1589] The user enters a question in the text box on the terminal and presses the send button.

[1590] Input: A business question from the user (e.g., "What is your strategy for entering new markets?")

[1591] Output: The entered question is sent from the terminal to the server. Specifically, the terminal constructs an HTTP request and sends the question data to the server.

[1592] Step 2:

[1593] The server receives the query and performs pre-processing.

[1594] The server performs text cleaning on the received question, removing unnecessary spaces and special characters.

[1595] Input: A question sent from the device (e.g., "What is your strategy for entering new markets?")

[1596] Output: Cleaned text (e.g., "What is your strategy for entering new markets?" with unnecessary spaces removed)

[1597] Specifically, the server uses regular expressions and other text processing techniques to remove unnecessary parts.

[1598] Step 3:

[1599] The server tokenizes the question.

[1600] Break down the cleaned question into words and phrases.

[1601] Input: Cleaned text (e.g., "What is your strategy for entering new markets?")

[1602] Output: Tokenized keywords (e.g., "new market," "entry," "strategy")

[1603] Specifically, the server applies a tokenization algorithm to break down the question into meaningful words and phrases.

[1604] Step 4:

[1605] The server uses an emotion analysis engine to recognize the user's emotions.

[1606] The server passes the preprocessed questions to a sentiment analysis engine for sentiment analysis.

[1607] Input: Tokenized keywords (e.g., "new market," "entry," "strategy")

[1608] Output: Emotional information (e.g., anxiety)

[1609] Specifically, the server inputs these keywords into an emotion analysis engine to obtain emotion categories such as positive, negative, and neutral.

[1610] Step 5:

[1611] The server inputs the preprocessed question and sentiment information into the generative AI model.

[1612] The generative AI model generates an answer.

[1613] Input: Tokenized keywords and sentiment information (e.g., "new market," "entry," "strategy," sentiment: anxiety)

[1614] Output: Generated answer (e.g., "Don't worry, when entering a new market, it's important to first conduct thorough market research and identify your points of differentiation through competitive analysis.")

[1615] Specifically, the server provides this data to the generative AI model and waits for it to generate an answer.

[1616] Step 6:

[1617] The server formats the generated answer.

[1618] The generated answers are checked for grammar and expression and formatted to make them easier to understand.

[1619] Input: Generated answer (e.g., "Don't worry, when entering a new market, it's important to first conduct thorough market research and identify your points of differentiation through competitive analysis.")

[1620] Output: Formatted answer (e.g. "Don't worry, when entering a new market, it's important to first conduct thorough market research and identify your points of differentiation through competitive analysis." formatted into a paragraph)

[1621] Specifically, the server performs a grammar check on the generated response and makes appropriate corrections.

[1622] Step 7:

[1623] The server sends the formatted response to the user's terminal.

[1624] The terminal displays.

[1625] Input: Formatted answer (e.g., "Don't worry, when entering a new market, it's important to first conduct thorough market research and identify your points of differentiation through competitive analysis.")

[1626] Output: The answer displayed on the user's screen

[1627] Specifically, the server sends the formatted answer as an HTTP response, and the terminal displays the received answer in the text area.

[1628] (Application example 2)

[1629] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1630] In modern manufacturing, it is difficult to obtain prompt and appropriate answers to real-time questions about factory operation and management. Furthermore, there is a lack of technology that provides advice that takes into account the emotions of workers and managers. As a result, efficiency on the shop floor and psychological stress among workers are negatively affected. The present invention aims to solve these problems by providing a system that enables factory workers and managers to ask questions in real time and receive answers that recognize their emotions.

[1631] The identification processing by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for receiving an inquiry from a user, means for preprocessing the inquiry, means for inputting the preprocessed inquiry into a generative AI model, means for generating a response to the inquiry based on documents previously learned by the generative AI model, means for formatting the generated response, means for sending the formatted response to the user, and means including an emotion recognition engine for recognizing the user's emotions, the emotion recognition engine linking the user's emotion information to the generative AI model. This makes it possible to provide quick and appropriate answers to questions regarding factory operation and management and to provide advice that takes into consideration the emotions of workers.

[1632] "User" refers to the workers and managers who ask questions about factory operation and management to the system.

[1633] "Inquiry" means information that represents a question or request regarding factory operations and management.

[1634] "Preprocessing" refers to a series of steps that clean up queries and convert them into a format that is easy for generative AI models to interpret.

[1635] "Text cleaning" is the process of removing unnecessary spaces and special characters to make the text clean.

[1636] "Tokenization" is the process of breaking down text into meaningful words and phrases.

[1637] A "generative AI model" is an artificial intelligence model that generates appropriate responses to inquiries based on pre-trained data.

[1638] "Documents" refers to materials and records containing information relating to factory operations and management.

[1639] "Response" refers to the answer generated by a generative AI model in response to a query.

[1640] An "emotion recognition engine" is a technology that analyzes a user's emotions and classifies them into emotional categories such as positive, negative, and neutral.

[1641] "Integrating" refers to sharing the emotional information analyzed by the emotion recognition engine with the generative AI model and reflecting it in the response.

[1642] This invention provides a system that asks questions about factory operation and management and provides appropriate answers that recognize emotions. This system consists of a server, a user operation terminal, an emotion recognition engine, and a generative AI model.

[1643] First, the user (factory worker or manager) enters a question about factory operation and management into a text box on the terminal and presses the send button. This question is then sent from the terminal to the server. The terminal can be a regular PC, tablet, or smartphone.

[1644] The server receives the question submitted by the user and first performs text cleaning, removing unnecessary spaces and special characters to make the question clean. This step is part of preprocessing to enable accurate and efficient processing. Next, the server performs tokenization, breaking the question down into meaningful words and phrases.

[1645] After the question preprocessing is complete, the server uses an emotion recognition engine to analyze the preprocessed question and obtain the emotional information the user is feeling. This emotional information is classified into emotion categories such as positive, negative, and neutral. For example, if the question is "How can we operate a new assembly line efficiently?", the emotion recognition engine will analyze whether the user is feeling anxious or worried.

[1646] The server then inputs the preprocessed question and emotional information into a generative AI model. The generative AI model generates an appropriate answer based on document data related to factory operation and management that it has previously learned. In doing so, it also takes into account emotional information and generates an answer that takes the user's emotions into consideration. The generative AI model used here can be a large-scale language model such as GPT-3.

[1647] The generated answer is then formatted by the server, for example by checking grammar and adjusting paragraph structure. Finally, the formatted answer is sent from the server to the user's device and displayed on the device screen, allowing the user to receive specific advice that takes their feelings into consideration.

[1648] For example, if a worker types "Tell me about quality control" into a terminal and sends it, the server will clean and tokenize the question, and the emotion recognition engine will analyze the anxiety expressed in the question. The generative AI model will generate an answer such as "Quality control is important. Don't worry, please use the checklist to review your daily processes," which will then be formatted by the server and sent back to the user.

[1649] An example of a prompt might be, "How can we run our new assembly line efficiently? The workers are feeling impatient."

[1650] This system allows users to get appropriate answers to questions about factory operation and management in real time, improving work efficiency and reducing psychological stress.

[1651] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1652] Step 1:

[1653] The user enters a question about factory operation and management in the text box on the terminal and presses the send button.

[1654] Input: A user-typed question (e.g., "How can we run our new assembly line efficiently?")

[1655] Output: Question data sent from the terminal to the server

[1656] Step 2:

[1657] The server receives the questions sent by the user and performs text cleaning.

[1658] Input: Question data submitted by the user (e.g., "How can we run our new assembly line efficiently?")

[1659] Data processing: Remove unnecessary spaces and special characters to make the text clean.

[1660] Output: Cleaned question data (e.g., "How can we run our new assembly line efficiently?")

[1661] Step 3:

[1662] The server tokenizes the cleaned question, breaking it down into meaningful words and phrases.

[1663] Input: Cleaned question data

[1664] Data operations: Breaking down the question into words and phrases

[1665] Output: Tokenized question data (e.g., "New," "Assembly Line," "Efficient," "Operation," "What should I do?")

[1666] Step 4:

[1667] The emotion recognition engine analyzes the tokenized question data and reconnects with the user's emotions.

[1668] Input: Tokenized question data

[1669] Data Computing: Sentiment Analysis to Identify User Emotions

[1670] Output: Emotional information (e.g., impatience)

[1671] Step 5:

[1672] The server inputs the preprocessed question and emotion information into the generative AI model.

[1673] Input: Preprocessed question data and sentiment information

[1674] Data Computation: Generative AI models generate responses based on pre-trained data

[1675] Output: Generated response data (e.g., "Please proceed slowly. To operate a new assembly line efficiently, it is important to perform proper initial setup and regular maintenance.")

[1676] Step 6:

[1677] The server formats the generated answer.

[1678] Input: Generated response data

[1679] Data processing: checking grammar and adjusting paragraph structure

[1680] Output: Formatted response data (e.g., "Please proceed slowly. To operate a new assembly line efficiently, it is important to perform proper initial setup and regular maintenance.")

[1681] Step 7:

[1682] The server sends the formatted response to the user's terminal.

[1683] Input: Formatted answer data

[1684] Output: Answer data sent to the terminal (e.g., "Please proceed slowly. To operate a new assembly line efficiently, it is important to perform proper initial setup and regular maintenance.")

[1685] Step 8:

[1686] The terminal displays the received answer to the user.

[1687] Input: Formatted answer data sent from the server

[1688] Output: The answer displayed on the user's device (e.g., "Please proceed slowly. Proper initial setup and regular maintenance are essential to running your new assembly line efficiently.")

[1689] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1690] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1691] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1692] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1693] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1694] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1695] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1696] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1697] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1698] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1699] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1700] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1701] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1702] 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.

[1703] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1704] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1705] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1706] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1707] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1708] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1709] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1710] The following is further disclosed regarding the above embodiment.

[1711] (Claim 1)

[1712] means for receiving business inquiries from users;

[1713] means for preprocessing the query;

[1714] means for inputting the preprocessed question into a generative AI model;

[1715] A means for generating an answer to the question based on literature and lecture content that the generative AI model has previously learned;

[1716] means for formatting the generated answer;

[1717] means for transmitting the formatted answer to a user.

[1718] (Claim 2)

[1719] 10. The system of claim 1, wherein the preprocessing includes text cleaning.

[1720] (Claim 3)

[1721] 10. The system of claim 1, wherein the preprocessing includes tokenization.

[1722] (Claim 4)

[1723] 2. The system of claim 1, wherein the generative AI model uses natural language processing technology.

[1724] (Claim 5)

[1725] 10. The system of claim 1, further comprising means for formatting the response.

[1726]

[1727] "Example 1"

[1728] (Claim 1)

[1729] means for receiving business questions from users at the terminal;

[1730] means for preprocessing the query at a server;

[1731] means for inputting the preprocessed questions into a generative AI model at a server;

[1732] A means for generating an answer to the question on a server based on data previously learned by the generative AI model;

[1733] means for formatting the generated answer at the server;

[1734] means for transmitting said formatted response to a user at a terminal.

[1735] (Claim 2)

[1736] 10. The system of claim 1, wherein the preprocessing includes text cleaning.

[1737] (Claim 3)

[1738] 10. The system of claim 1, wherein the preprocessing includes tokenization.

[1739] "Application Example 1"

[1740] (Claim 1)

[1741] means for receiving business inquiries from users;

[1742] means for preprocessing the query;

[1743] means for inputting the preprocessed question into a generative AI model;

[1744] A means for generating an answer to the question based on materials previously learned by the generative AI model;

[1745] means for formatting the generated answer;

[1746] means for transmitting the formatted response to a user;

[1747] A way to input questions in real time while viewing business-related content on a smartphone and get instant answers,

[1748] means for receiving questions about the video or article displayed on the smartphone;

[1749] A system including:

[1750] (Claim 2)

[1751] 10. The system of claim 1, wherein the preprocessing includes text cleaning.

[1752] (Claim 3)

[1753] 10. The system of claim 1, wherein the preprocessing includes tokenization.

[1754] "Example 2: Combining Emotion Engines"

[1755] (Claim 1)

[1756] means for receiving business inquiries from users;

[1757] means for preprocessing the query;

[1758] means for inputting the preprocessed question into a sentiment analysis engine to recognize a user's sentiment;

[1759] a means for inputting the emotion information and the preprocessed question into a generative AI model;

[1760] A means for generating an answer to the question that takes into account emotions based on materials that the generative AI model has previously learned;

[1761] means for formatting the generated answer;

[1762] means for transmitting the formatted answer to a user.

[1763] (Claim 2)

[1764] 10. The system of claim 1, wherein the preprocessing includes text cleaning.

[1765] (Claim 3)

[1766] 10. The system of claim 1, wherein the preprocessing includes tokenization.

[1767] "Application example 2 when combining emotion engines"

[1768] (Claim 1)

[1769] means for receiving inquiries from users;

[1770] means for preprocessing said query;

[1771] means for inputting the preprocessed query into a generative AI model;

[1772] means for generating a response to the query based on documents previously learned by the generative AI model;

[1773] means for formatting the generated reply;

[1774] means for sending the formatted reply to a user;

[1775] an emotion recognition engine for recognizing an emotion of a user;

[1776] The system includes a means for the emotion recognition engine to link user emotion information to a generative AI model.

[1777] (Claim 2)

[1778] 10. The system of claim 1, wherein the preprocessing includes text cleaning.

[1779] (Claim 3)

[1780] 10. The system of claim 1, wherein the preprocessing includes tokenization. [Explanation of symbols]

[1781] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving business inquiries from users; means for preprocessing the query; means for inputting the preprocessed question into a generative AI model; A means for generating an answer to the question based on literature and lecture content that the generative AI model has previously learned; means for formatting the generated answer; means for transmitting the formatted answer to a user.

2. The system of claim 1 , wherein the preprocessing includes text cleaning.

3. The system of claim 1 , wherein the preprocessing includes tokenization.

4. The system of claim 1, wherein the generative AI model uses natural language processing technology.

5. 10. The system of claim 1, further comprising means for formatting the response.

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