System

Abstract

A system is provided.SOLUTION: A system, comprising: means for providing a user interface; means for building and training a generative AI model; means for analyzing a query from a user; means for generating a fortune-telling result based on an analysis result; and means for displaying the generated fortune-telling result on the user interface.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] Conventional fortune-telling systems have a strong occult element and are difficult to provide scientifically based predictions, leading many users to feel that they lack credibility. Furthermore, they do not adequately meet the needs of modern users who seek not only predictions but also problem-solving tips and objective advice. Furthermore, conventional systems have low accuracy in data collection and analysis, resulting in insufficient quality of results. [Means for solving the problem]

[0005] To solve the above problems, the present invention provides a system that includes a means for providing a user interface, a means for collecting data from multiple data sources, a means for cleaning and organizing the collected data, a means for building and training a generative AI model, a means for analyzing questions from users, a means for generating fortune-telling results based on the analysis results, and a means for displaying the generated fortune-telling results on a user interface. This system provides highly accurate fortune-telling results based on scientific evidence, enabling users to obtain the problem-solving hints and objective advice they desire. This will transform fortune-telling from the realm of the occult into the realm of science, and will support the mental stability of many users.

[0006] A "user interface" is an interface through which a user accesses a system and performs operations or inputs information.

[0007] "Data Sources" refers to multiple external or internal sources of information related to fortune-telling.

[0008] "Means for collecting data" refers to means for obtaining necessary information from multiple data sources.

[0009] "Data cleaning and preparation methods" are methods for converting collected data into an analyzable format and removing unnecessary information and noise.

[0010] A "generative AI model" is an artificial intelligence model built to generate fortune-telling results using machine learning technology.

[0011] "Training" is the training process by which a generative AI model uses collected data to improve its prediction accuracy.

[0012] A "means for analyzing questions" is a means for analyzing input from a user and understanding its meaning and relevance.

[0013] "Means for generating fortune-telling results" refers to means for generating appropriate answers or advice using a generative AI model based on the analyzed user's question.

[0014] The "means for displaying on a user interface" refers to a means for displaying the generated fortune-telling result in a format that allows the user to visually confirm it. [Brief explanation of the drawings]

[0015] [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

[0016] 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.

[0017] First, the terms used in the following description will be explained.

[0018] 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).

[0019] 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.

[0020] 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.

[0021] 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.

[0022] 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."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 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.

[0026] 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).

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 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.

[0033] 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.

[0034] 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.

[0035] 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."

[0036] The present invention relates to a system that provides highly accurate fortune-telling results using a generative AI model and a statistical model. The following describes in detail an embodiment of the present invention.

[0037] User Interface Design

[0038] Users access the fortune-telling system using a web browser or a dedicated application. This interface provides a form for users to enter their questions and concerns. The terminal also has the functionality to transmit the information entered by the user to the server. It also includes an interface for visually displaying the generated fortune-telling results.

[0039] Data collection and pre-processing

[0040] The server collects statistical and fortune-telling-related data from multiple data sources on the Internet, including astrology, tarot card readings, and numerology data. The collected data is stored in a database and converted into an analyzable format. The server then cleans and organizes the data, removing noise and filling in missing data.

[0041] Building and training generative AI models

[0042] The server uses the cleaned and organized data to build a generative AI model (e.g., a large-scale language model). This model repeatedly uses the data to train and improve its prediction accuracy. Once trained, the model is used to generate fortune-telling results.

[0043] Generating fortune-telling results

[0044] When a user inputs a question into the fortune-telling system, the device sends the information to the server. The server analyzes the user's input using natural language processing technology. This analysis extracts key keywords and their relationships. Based on the analysis results, the server uses a generative AI model to generate fortune-telling results. The generated results include an understanding of the current situation, future predictions, and specific advice.

[0045] Displaying the results

[0046] The server then transmits the generated fortune-telling results to the terminal, which then visually displays the results to the user, possibly using graphs or charts to make the results easier to understand.

[0047] Specific examples

[0048] scenario:

[0049] Imagine a woman in her 30s who is worried about her career.

[0050] 1. The user opens a web browser and accesses the fortune-telling system's homepage.

[0051] 2. The terminal displays a question input form and asks the user to enter the question, "Should I continue in my current job or consider changing jobs?"

[0052] 3. The user clicks the "Submit" button.

[0053] 4. The device sends the question to the server.

[0054] 5. The server analyzes the question and extracts related keywords.

[0055] 6. The server uses the generative AI model to generate fortune-telling results that include "the advantages and disadvantages of continuing in your current job" and "things to be aware of when changing jobs."

[0056] 7. The server sends the generated fortune-telling result to the terminal.

[0057] 8. The terminal displays the fortune-telling results to the user, visualizing things like "career prospects if you continue" and "risks and opportunities if you change jobs."

[0058] In this way, the system of the present invention can provide users with highly accurate fortune-telling results based on scientific evidence, and can give hints for solving problems and objective advice.

[0059] The processing flow will be explained below.

[0060] Step 1:

[0061] Users access the fortune-telling system using a dedicated web browser or application, and enter their questions and concerns into a form displayed on the user interface.

[0062] Step 2:

[0063] The terminal receives the questions and concerns entered by the user and generates transmission data, which includes the user's input.

[0064] Step 3:

[0065] The terminal transmits the transmission data to the server, where communication takes place over the network.

[0066] Step 4:

[0067] The server analyzes the received data and uses natural language processing technology to analyze the user's question and extract keywords.

[0068] Step 5:

[0069] The server retrieves relevant statistical and fortune-telling data based on the analysis results, using database queries.

[0070] Step 6:

[0071] The server generates fortune-telling results using a generative AI model, which generates predictions and advice in response to user questions based on the trained data.

[0072] Step 7:

[0073] The server then structures the generated fortune-telling results, dividing them into sections such as "Current situation analysis," "Future predictions," and "Specific advice."

[0074] Step 8:

[0075] The server then sends the structured fortune-telling results to the terminal, where network communication also takes place.

[0076] Step 9:

[0077] The terminal displays the received fortune-telling results to the user, and graphs and charts can be used to present the results in a visually easy-to-understand format.

[0078] Step 10:

[0079] Users can check the displayed fortune-telling results and use them as a reference for future actions and decisions. For example, they can receive specific advice about their careers.

[0080] This allows users to receive highly accurate fortune-telling results and obtain hints for solving problems and objective advice.

[0081] Example 1

[0082] 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."

[0083] Conventional fortune-telling systems require manual analysis of large amounts of data to obtain highly accurate fortune-telling results, which is labor-intensive and time-consuming. Furthermore, it is difficult to provide specific and useful advice in response to user questions. This results in users being unable to obtain fully satisfactory fortune-telling results, and the reliability of fortune-telling is also low.

[0084] 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.

[0085] In this invention, the server includes means for providing a user interface, means for collecting data from multiple data sources, means for cleaning and organizing the collected data, means for building and training a generative AI model, means for analyzing questions from users, means for generating fortune-telling results based on the analysis results, means for displaying the generated fortune-telling results on the user interface, means for displaying the user interface on a web browser or dedicated application and including a form for users to input questions, means for analyzing data including the questions input by the user using natural language processing technology and extracting key keywords, means for inputting the extracted keywords into the generative AI model and generating fortune-telling results including specific advice, and means for providing an interface for visually displaying the results and visually displaying the fortune-telling results using graphs and charts. This enables users to easily obtain fortune-telling results with high accuracy and specific advice.

[0086] A "user interface" is a mechanism by which a user interacts with a computer system, including forms into which questions or concerns can be entered and screens that visually display the results.

[0087] A "data source" is a source of information, such as an internet website or database, from which statistical data or fortune-telling-related information is collected.

[0088] "Cleaning and grooming" is the process of removing noise and missing values ​​from collected data and converting it into a form suitable for analysis and learning.

[0089] A "generative AI model" is an artificial intelligence model that learns from collected data to make predictions and generate new data, and specifically refers to one that uses natural language processing technology.

[0090] "Natural language processing technology" is a technology that allows computers to analyze, understand, and generate human language, and includes analyzing text data and extracting keywords.

[0091] The "fortune telling result" is an answer including predictions and advice generated by the generative AI model based on the questions and concerns entered by the user.

[0092] A "web browser" is application software for viewing web pages on the Internet.

[0093] "Purpose-built Application" means software designed for a specific purpose and used to access this System.

[0094] A "database" is a system for systematically storing collected data and efficiently managing and searching it.

[0095] "Graphs and charts" are a means of visually representing data and are used to display fortune-telling results in an easy-to-understand manner.

[0096] This invention relates to a system that provides highly accurate fortune-telling results using generative AI models and statistical models. This system is realized by the cooperation of users, terminals, and servers.

[0097] User Interface Design

[0098] Users access the fortune-telling system using a web browser or a dedicated application. The user interface provides a form for users to enter their questions and concerns about fortune-telling. This form includes text boxes and a submit button. The terminal displays the user interface and accepts user input.

[0099] Data collection and pre-processing

[0100] The server collects data related to astrology, tarot card readings, numerology, etc. from multiple data sources on the Internet. The collected data is stored in a database, which includes functions for efficiently managing and searching the data.

[0101] Data cleaning and preparation is performed using software such as Python and Pandas, which removes noise and imputes missing data, converting the data into a format suitable for analysis and model training.

[0102] Building and training generative AI models

[0103] The server uses the cleaned and prepared data to build a generative AI model. Frameworks such as TensorFlow and PyTorch are used to build the model. The training data is repeatedly used to train the model and improve its prediction accuracy. Once trained, the model is used to generate fortune-telling results.

[0104] Generating fortune-telling results

[0105] When a user enters a question into the fortune-telling system and clicks the submit button, the device sends the information to the server, which receives the request using a web framework such as Flask or Django and performs natural language processing using the Natural Language Toolkit (NLTK) or spaCy to extract key keywords from the user's question.

[0106] Based on the extracted keywords, the server uses a generative AI model to generate fortune-telling results, which include an understanding of the current situation, future predictions, and specific advice.

[0107] Displaying the results

[0108] The server then sends the generated fortune-telling results to the device, which then visually displays the results in a user interface using HTML and JavaScript, which can include graphs and charts to make the results easy to understand.

[0109] Specific scenarios and prompt examples

[0110] The following scenarios provide specific labels:

[0111] Imagine a woman in her 30s who is worried about her career.

[0112] 1. The user opens a web browser and accesses the fortune-telling system's homepage.

[0113] 2. The terminal displays a question input form and asks the user to enter the question, "Should I continue in my current job or consider changing jobs?"

[0114] 3. The user clicks the "Submit" button.

[0115] 4. The device sends the question to the server.

[0116] 5. The server analyzes the question and extracts related keywords.

[0117] 6. The server uses the generative AI model to generate fortune-telling results that include "the advantages and disadvantages of continuing in your current job" and "things to be aware of when changing jobs."

[0118] 7. The server sends the generated fortune-telling result to the terminal.

[0119] 8. The terminal displays the fortune-telling results to the user, visualizing things like "career prospects if you continue" and "risks and opportunities if you change jobs."

[0120] Example prompt sentence:

[0121] Should I stay in my current job or consider changing jobs?

[0122] How should I proceed with my relationship in the future?

[0123] "How should I invest now when the economic situation is so uncertain?"

[0124] As described above, the present invention is a system for providing users with highly accurate and specific fortune-telling results. By combining a generative AI model and natural language processing technology, this system is able to generate and visually display specific advice for users' concerns.

[0125] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0126] Step 1: Creating the User Interface

[0127] Users access the fortune-telling system using a web browser or a dedicated application. The terminal uses HTML, CSS, and JavaScript to display a form where users can enter their fortune-telling questions and concerns. The form includes text boxes and a submit button. The entered question is sent to the server when the submit button is clicked.

[0128] Input: Questions entered by the user in a browser or application

[0129] Output: Question input form displayed on the device

[0130] Step 2: Data collection

[0131] The server automatically collects data related to astrology, tarot card readings, numerology, etc. from multiple data sources on the Internet using web scraping tools such as Python and Scrapy, and stores this data in databases such as MySQL and PostgreSQL.

[0132] Input: Data from internet sources

[0133] Output: Raw data stored in a database

[0134] Step 3: Data Cleaning and Preparation

[0135] The server uses libraries such as Pandas and NumPy to clean and organize the collected data, removing noise and filling in missing data, resulting in structured data suitable for analysis and learning.

[0136] Input: Raw data stored in a database

[0137] Output: Cleaned and organized structured data

[0138] Step 4: Building and training a generative AI model

[0139] The server uses the cleaned and prepared data to build a generative AI model. The model is designed using machine learning frameworks such as TensorFlow and PyTorch, and trained using the training data repeatedly. This results in a highly accurate model that is used to generate fortune-telling results.

[0140] Input: Cleaned and structured data

[0141] Output: Trained AI model

[0142] Step 5: Analyzing the user question

[0143] When a user enters a question into the fortune-telling system and clicks the submit button, the device sends the information to the server, which receives the request using a web framework such as Flask or Django and performs natural language processing using the Natural Language Toolkit (NLTK) or spaCy, which extracts key keywords from the user's question.

[0144] Input: The question entered by the user

[0145] Output: Extracted main keywords

[0146] Step 6: Generate fortune-telling results

[0147] The server uses a generative AI model based on the extracted keywords to generate fortune-telling results, which include an understanding of the current situation, future predictions, and specific advice.

[0148] Input: Extracted keywords

[0149] Output: Generated fortune-telling result

[0150] Step 7: View the results

[0151] The server sends the generated fortune-telling results to the device, which then uses JavaScript to display the results in HTML so that the user can visually confirm them. Graphs and charts can be added as needed to make the results easier to understand.

[0152] Input: Generated fortune-telling result

[0153] Output: Fortune telling results displayed on the user interface

[0154] (Application example 1)

[0155] 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."

[0156] Conventional fortune-telling systems lack a mechanism for providing fortune-telling advice tailored to a user's individual payment behavior. As a result, users are inconvenienced by not receiving appropriate fortune-telling guidance for their daily payment behavior. Furthermore, fortune-telling advice based on payment behavior is not personalized, so it cannot be useful information for users.

[0157] 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.

[0158] In this invention, the server includes means for providing a user interface, means for collecting data from multiple data sources, means for cleaning and organizing the collected data, means for building and training a generative AI model, means for analyzing questions from users, means for generating fortune-telling results based on the analysis results, means for displaying the generated fortune-telling results on the user interface, means for providing fortune-related advice based on the user's payment behavior, and means for displaying the provided advice on the electronic payment means, thereby enabling users to receive fortune-telling advice that is individually personalized for their daily payment behavior.

[0159] A "user interface" is an interactive means by which a user accesses a system and inputs and displays information.

[0160] "Multiple Data Sources" refers to multiple sources of data collected from the internet and other sources.

[0161] "Data cleaning and maintenance procedures" are methods that implement procedures to remove noise from collected data and to impute missing data.

[0162] "Means for building and training generative AI models" refers to methods for building generative artificial intelligence models (e.g., large-scale language models) using collected and cleaned data and training them to improve their accuracy.

[0163] "Means for analyzing questions from users" refers to a method of analyzing questions entered into the system by users using natural language processing technology and extracting key keywords and their relevance.

[0164] "Means for generating fortune-telling results" refers to a method of using a generative AI model to derive fortune-telling results based on user input and analysis results.

[0165] The "means for displaying the generated fortune-telling result on a user interface" is a method for visually displaying the generated fortune-telling result to the user.

[0166] The "means for providing fortune-related advice based on payment behavior" is a method for analyzing a user's payment behavior and generating specific fortune-related advice based thereon.

[0167] "Means for displaying the provided advice on an electronic payment means" refers to a method for displaying the generated fortune advice on an electronic payment application or terminal used by the user.

[0168] The present invention relates to a system that provides fortune-related advice based on a user's payment behavior. This system uses generative AI models and statistical models to provide highly accurate advice, and details of the system are described below.

[0169] User Interface Design

[0170] Users access the system using a smartphone with an electronic payment application installed. The user interface has a function that visually displays advice based on the day's fortune when the user makes a payment.

[0171] Data collection and pre-processing

[0172] The server collects statistical data and fortune-telling-related data from multiple data sources on the Internet, including astrology, tarot card readings, numerology, etc. The collected data is stored in a database, where it is cleaned and organized by removing noise data and filling in missing data.

[0173] Building and training generative AI models

[0174] The server uses the cleaned and organized data to build a generative AI model. This model uses a large-scale language model (GPT-2, for example) to repeatedly train on the data to improve its prediction accuracy. Once trained, the model is used to generate fortune-related advice.

[0175] Generating Advice

[0176] When a user makes a payment using their smartphone, the device sends the information to the server. The server then analyzes the information based on this payment behavior using natural language processing technology. This analysis extracts key keywords and their relationships, and generates fortune-related advice using a generative AI model.

[0177] For example, when a user pays for lunch, the server generates advice such as "You're lucky to pay cashlessly today!" This advice is generated based on the user's payment behavior and fortune-telling results. The following prompt sentences can also be used:

[0178] "What payment advice would you like me to give you today?"

[0179] Displaying the results

[0180] The server sends the generated advice to the terminal, which visually displays the advice to the user, allowing the user to receive personalized fortune-telling advice for their daily payment behavior.

[0181] Specific examples

[0182] Specifically, imagine a scenario in which a user pays for lunch using an electronic payment application on their smartphone. When the user presses the payment button, the device sends the information to the server, which then generates fortune-telling advice based on the payment behavior and sends it back to the device. The device then displays the advice on its screen: "Cashless payment is your lucky day today!"

[0183] Based on highly accurate fortune-telling results, this system is able to provide specific advice related to users' payment behavior and provide useful information to users in real time.

[0184] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0185] Step 1:

[0186] The server collects data from multiple data sources on the Internet. The input here is the URLs and APIs of the multiple data sources, and the output is the collected raw data. Specifically, it collects statistical data and fortune-telling-related data (astrology, tarot card readings, numerology, etc.) and stores it in a database.

[0187] Step 2:

[0188] The server cleans and organizes the collected data. The input of this step is the raw data collected in step 1, and the output is cleaned and organized data. Specifically, it removes noise data and fills in missing data. It uses libraries such as Pandas to clean and organize the data.

[0189] Step 3:

[0190] The server uses the cleaned and prepared data to build and train a generative AI model. The input of this step is the clean data, and the output is a trained generative AI model. Specifically, it uses a large-scale language model such as GPT-2 to repeatedly train the data and improve the model's predictive accuracy. It utilizes Hugging Face's Transformers library.

[0191] Step 4:

[0192] A user makes a payment using an electronic payment application on a smartphone. The input here is the data of the user's payment action, and the output is that data is sent to the server. The terminal detects that the payment button has been pressed and sends the information to the server.

[0193] Step 5:

[0194] The server analyzes the submitted payment behavior information using natural language processing technology. The input for this step is the user's payment behavior data, and the output is the extracted important keywords and their relevance. NLP technology is used for the analysis. Specifically, the text is tokenized and key keywords are extracted.

[0195] Step 6:

[0196] The server uses a generative AI model based on the analysis results to generate fortune-related advice. The input for this step is the extracted keywords, and the output is the fortune-telling advice text. Specifically, the prompt "Please tell me some advice for today's payments" is input into the model, and appropriate advice is generated.

[0197] Step 7:

[0198] The server sends the generated fortune advice to the terminal. The input of this step is the generated advice, and the output is that the advice is sent to the terminal. The use of RESTful API is common.

[0199] Step 8:

[0200] The terminal visually displays the received advice to the user. The input of this step is the advice sent from the server, and the output is that the advice is displayed on the user's smartphone screen. Specifically, the electronic payment application displays the advice "Cashless payment is your lucky day today!" as a pop-up message or similar.

[0201] Through these steps, users can receive personalized fortune-telling advice in real time for their daily payment behavior.

[0202] 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.

[0203] The present invention relates to a system that provides highly accurate fortune-telling results using a generative AI model and an emotion engine. The following describes in detail an embodiment of the present invention.

[0204] User Interface Design

[0205] Users access the fortune-telling system using a web browser or a dedicated application. This interface provides a form for users to enter their questions or concerns. It also provides a means for users to input their current emotional state by providing emoticons and sliders to express emotions. The terminal has the functionality to transmit the information and emotional data entered by the user to the server.

[0206] Data collection and pre-processing

[0207] The server collects statistical and fortune-telling-related data from multiple data sources on the Internet, including astrology, tarot card readings, and numerology data. The collected data is stored in a database and converted into an analyzable format. The server then cleans and organizes the data, removing noise and filling in missing data.

[0208] Introducing the Emotion Engine

[0209] The server is equipped with an emotion engine for analyzing the emotion data entered by the user. The emotion engine recognizes the user's emotion from the text and selected emotion icons, and feeds the analysis results back to the generative AI model. This makes it possible to generate personalized fortune-telling results based on the user's emotional state.

[0210] Building and training generative AI models

[0211] The server uses the cleaned and organized data to build a generative AI model (e.g., a large-scale language model). This model repeatedly uses the data to train and improve its prediction accuracy. Once trained, the model is used to generate fortune-telling results.

[0212] Generating fortune-telling results

[0213] When a user inputs a question and emotional data into the fortune-telling system, the device sends that information to the server. The server analyzes the user's input using natural language processing technology to extract key keywords and their associations. The emotion engine then analyzes the emotional data and feeds the analysis results back to the generative AI model. The server receives this feedback and generates personalized fortune-telling results based on the user's emotional state. The generated results include an understanding of the current situation, future predictions, and specific advice.

[0214] Displaying the results

[0215] The server then transmits the generated fortune-telling results to the terminal, which then visually displays the results to the user, possibly using graphs or charts to make the results easier to understand.

[0216] Specific examples

[0217] scenario:

[0218] Imagine a woman in her 30s worried about her career. She's feeling stressed and chooses emoticons that reflect that.

[0219] 1. The user opens a web browser and accesses the fortune-telling system's homepage.

[0220] 2. The terminal displays a question input form and asks the user to enter the question, "Should I continue in my current job or consider changing jobs?"

[0221] 3. The user selects an emoticon that expresses an emotion and inputs their current emotional state.

[0222] 4. The user clicks the "Submit" button.

[0223] 5. The device sends the question and emotion data to the server.

[0224] 6. The server analyzes the question and extracts related keywords.

[0225] 7. The server uses the emotion engine to analyze the emotion data and feeds the analysis results back to the generative AI model.

[0226] 8. The server uses the generative AI model to generate fortune-telling results, including "the pros and cons of staying in your current job" and "things to consider when changing jobs." This also includes specific advice based on the user's emotional state.

[0227] 9. The server sends the generated fortune-telling result to the terminal.

[0228] 10. The device displays the fortune-telling results to the user, visualizing things like "career prospects if you continue" and "risks and opportunities if you change jobs."

[0229] In this way, the system of the present invention can provide highly accurate fortune-telling results that take into account the user's emotions, and can give hints for resolving problems and objective advice.

[0230] The processing flow will be explained below.

[0231] Step 1:

[0232] Users access the fortune-telling system using a dedicated web browser or application, and enter their questions and concerns into a form displayed on the user interface.

[0233] Step 2:

[0234] The user inputs emotion data using emoticons and sliders that represent their current emotional state, selecting emotions such as "feeling stressed" or "feeling relieved."

[0235] Step 3:

[0236] The terminal receives the question content and emotion data entered by the user and generates transmission data, which includes the question in text format and the selected emotional state.

[0237] Step 4:

[0238] The terminal transmits the transmission data to the server via the network.

[0239] Step 5:

[0240] The server analyzes the received data and uses natural language processing technology to analyze the user's question and extract key keywords.

[0241] Step 6:

[0242] The server uses an emotion engine to analyze the emotion data entered by the user, which converts the entered emotional state into numerical data and analyzes that state.

[0243] Step 7:

[0244] The server compares the analyzed emotion data with keywords in the question and searches for relevant statistical and fortune-telling data from information in a database.

[0245] Step 8:

[0246] The server generates fortune-telling results using a generative AI model based on the acquired data. The generative AI model generates predictions and advice in response to the user's questions based on the trained data.

[0247] Step 9:

[0248] The server generates personalized fortune-telling results based on the user's emotional state, adjusting the content and presentation of advice based on the emotional data.

[0249] Step 10:

[0250] The server then structures the generated fortune-telling results, dividing them into sections such as "Current situation analysis," "Future predictions," and "Specific advice."

[0251] Step 11:

[0252] The server then sends the structured fortune-telling results to the terminal, where network communication also takes place.

[0253] Step 12:

[0254] The terminal displays the received fortune-telling results to the user, and graphs and charts can be used to present the results in a visually easy-to-understand format.

[0255] Step 13:

[0256] Users can check the displayed fortune-telling results and use them as a reference for future actions and decisions. For example, they can receive specific advice about their careers.

[0257] Through these specific processing steps, the user can receive highly accurate fortune-telling results that include emotional data, and can obtain hints for resolving problems and objective advice.

[0258] Example 2

[0259] 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."

[0260] Conventional fortune-telling systems provide uniform fortune-telling results without considering the user's emotional state, making it difficult to provide individually customized advice to users. Furthermore, they often fail to properly process noise and missing data in the collected data, resulting in inaccurate results. Therefore, there is a need for systems that can provide more accurate and personalized fortune-telling results that reflect the user's emotional state.

[0261] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for providing a user interface, a means for collecting data from multiple data sources, a means for cleaning and organizing the collected data, a means for analyzing the user's emotional state using an emotion engine, a means for building and training a generative AI model, a means for analyzing questions from the user, a means for generating fortune-telling results based on the analysis results and emotion analysis results, and a means for displaying the generated fortune-telling results on the user interface. This makes it possible to provide highly accurate personalized fortune-telling results that reflect the user's emotional state.

[0262] "User Interface" refers to the screens, forms, and other interactive means by which a user accesses a system, enters information, and receives results.

[0263] "Data Source" means any external or internal source of data related to divination, such as astrology, tarot cards, numerology, etc.

[0264] "Cleaning" refers to the process of removing noise and missing values ​​from collected data and preparing it into an analyzable format.

[0265] "Emotion engine" refers to software or algorithms for analyzing an emotional state from user-entered text and emoticons.

[0266] A "generative AI model" refers to a machine learning model that learns from collected and organized data and generates fortune-telling results based on the user's questions and emotional state.

[0267] "Analysis" refers to the process of recognizing key keywords and topics using natural language processing techniques and other methods to analyze questions and data provided by users.

[0268] "Fortune telling results" refers to information that includes answers and predictions to the user's questions, as well as specific advice.

[0269] This invention relates to a system that provides highly accurate fortune-telling results using a generative AI model and an emotion engine. This system is mainly composed of three elements: a server, a terminal, and a user, and operates in the following procedure.

[0270] User Interface Design

[0271] Users access the fortune-telling system using a web browser or a dedicated application. This interface provides a form for users to enter their questions or concerns. It also provides a means for users to input their current emotional state by providing emoticons and sliders to express emotions. The terminal has the functionality to transmit the information and emotional data entered by the user to the server.

[0272] Data collection and pre-processing

[0273] The server collects data related to astrology, tarot card readings, numerology, etc. from multiple data sources on the Internet. The collected data is stored in a database and converted into a format that can be analyzed. The server then cleans and organizes the data, removing noise and filling in missing data. This process is carried out using high-performance data server and database management software.

[0274] Introducing the Emotion Engine

[0275] The server is equipped with an emotion engine for analyzing the emotion data entered by the user. The emotion engine recognizes the user's emotional state using text analysis algorithms and selected emotion icons, and feeds the analysis results back to the generative AI model. This analysis uses natural language processing (NLP) techniques.

[0276] Building and training generative AI models

[0277] The server uses the cleaned and organized data to build a generative AI model. This model uses machine learning algorithms to repeatedly train on a variety of data to improve its prediction accuracy. Once trained, the model is used to generate fortune-telling results. This process utilizes machine learning frameworks such as TensorFlow and PyTorch.

[0278] Generating fortune-telling results

[0279] When a user inputs a question and emotional data into the fortune-telling system, the device sends the information to the server. The server analyzes the user's input and extracts key keywords and their associations. The emotion engine then analyzes the emotional data and feeds the analysis results back to the generative AI model. The server receives this feedback and generates personalized fortune-telling results based on the user's emotional state. The generated results include an understanding of the current situation, future predictions, and specific advice.

[0280] Displaying the results

[0281] The server then sends the generated fortune-telling results to the terminal, which then visually displays the results to the user using graphs and charts to help the user intuitively understand the results.

[0282] Specific examples

[0283] scenario

[0284] Imagine a woman in her 30s worried about her career. She's feeling stressed and chooses emoticons that reflect that.

[0285] 1. The user opens a web browser and accesses the fortune-telling system's homepage.

[0286] 2. The device displays a question input form and asks the user to enter the question, "Should I continue in my current job or consider changing jobs?"

[0287] 3. The user selects an emoticon that expresses an emotion and inputs their current emotional state.

[0288] 4. The user clicks the "Submit" button.

[0289] 5. The device sends the question and emotion data to the server.

[0290] 6. The server analyzes the question and extracts related keywords.

[0291] 7. The server uses the emotion engine to analyze the emotion data and feeds the analysis results back to the generative AI model.

[0292] 8. The server uses the generative AI model to generate fortune-telling results that include "the advantages and disadvantages of continuing in your current job" and "things to be aware of when changing jobs."

[0293] 9. The server sends the generated fortune-telling results to the terminal.

[0294] 10. The device displays the fortune-telling results to the user, visualizing things like "career prospects if you continue" and "risks and opportunities if you change jobs."

[0295] Prompt Sentence Examples

[0296] Should I stay in my current job or consider changing jobs?

[0297] "I want to know my future love luck"

[0298] In this way, the system of the present invention can provide highly accurate personalized fortune-telling results that take into account the user's emotions, and can give hints for solving problems and objective advice.

[0299] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0300] Step 1: Displaying the User Interface

[0301] The device displays the fortune-telling system's homepage in a web browser. This interface includes a form for users to enter their questions and concerns, as well as emoticons and sliders to express emotions.

[0302] Input: User interface URL

[0303] Output: A web page with a question form, emoticons, and a slider for entering emotions.

[0304] Specific operation: The device launches a web browser and accesses the specified URL. The fortune-telling system's homepage is displayed, along with an input form, emoticons for inputting emotions, and a slider.

[0305] Step 2: Enter and submit user data

[0306] Users enter their questions or concerns into the input form, select their current emotional state using emoticons or a slider, and click the "Submit" button when they're done.

[0307] Input: User question, emotional state (emoticons, slider)

[0308] Output: Question content and emotion data

[0309] What it does: A user enters a question into the form, such as "Should I stay in my current job or consider changing jobs?". Next, they click an emoticon that reflects their emotional state, adjust a slider to input the intensity of the emotion, and finally click the "Submit" button.

[0310] Step 3: Collect and preprocess data

[0311] The device sends the user's input questions and emotional data to a server, which then collects related data such as astrology, tarot card readings, and numerology from multiple data sources on the Internet and stores it in a database.

[0312] Input: User-entered data (questions, sentiment data), external data sources

[0313] Output: Cleaned and groomed data

[0314] Specific operation: The device sends the user's input information in a data format such as JSON to the server. The server analyzes the received data and collects relevant information from the necessary data sources. The collected data is stored in a database and converted into an analyzable format.

[0315] Step 4: Emotion analysis using the emotion engine

[0316] The server uses an emotion engine to analyze the emotion data entered by the user, which recognizes the user's emotional state using text analysis algorithms and selected emotion icons, and feeds the analysis results back to the generative AI model.

[0317] Input: User emotion data (text, emoticons)

[0318] Output: Sentiment analysis results (numerical data)

[0319] Specific operation: The server calls the emotion engine and sends the emotional information of the text and emoticons entered by the user. The emotion engine analyzes this data and outputs the emotional state as numerical data. The analysis results are fed back to the generative AI model.

[0320] Step 5: Building and training a generative AI model

[0321] The server uses the cleaned and curated data to build a generative AI model, which uses machine learning algorithms to repeatedly learn from diverse data and improve its prediction accuracy.

[0322] Input: Cleaned data

[0323] Output: Trained generative AI model

[0324] How it works: The server retrieves collected data from the database and performs a cleaning process. The cleaned data is then used to start training the generative AI model. Once training is complete, the model is ready to be used to generate fortune-telling results.

[0325] Step 6: Generate fortune-telling results

[0326] The server uses a generative AI model based on the user's question and sentiment analysis results to generate a fortune-telling result, which includes an understanding of the current situation, future predictions, and specific advice.

[0327] Input: User question, sentiment analysis results

[0328] Output: Fortune-telling results (current situation, future predictions, advice)

[0329] Specific operation: The server calls the generative AI model and provides the user's question and sentiment analysis results as input. The generative AI model processes this data and generates a fortune-telling result, which includes an answer to the user's question, a prediction, and specific advice.

[0330] Step 7: Send and view results

[0331] The server sends the generated fortune-telling results to the terminal, which then visually displays the results to the user using graphs and charts to help the user intuitively understand the results.

[0332] Input: Fortune-telling result

[0333] Output: Visual results (graphs, charts) that are displayed to the user

[0334] Specific operation: The server sends the generated fortune-telling results to the device in JSON format or similar. The device analyzes the received data and displays the results to the user. The results are visually displayed as graphs and charts, making them easier to understand.

[0335] Through the above steps, the system of the present invention can provide highly accurate and personalized fortune-telling results that take into account the user's emotions.

[0336] (Application example 2)

[0337] 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."

[0338] Conventional fortune-telling systems provide general results without considering the user's emotional state, making it difficult to provide personalized feedback or specific advice. Furthermore, some electronic payment systems lacked technology to provide specific suggestions based on the user's emotional state. This meant that they were unable to adequately help users select products or services that matched their current emotions.

[0339] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for providing a user interface, means for collecting data from multiple data sources, means for cleaning and organizing the collected data, means for building and training a generative AI model, means for analyzing questions and emotional data from the user, means for generating fortune-telling results according to the emotional state based on the analysis results, and means for displaying the generated fortune-telling results on the user interface. This makes it possible to provide personalized fortune-telling results and product / service recommendations that take the user's emotional state into consideration.

[0340] A "user interface" is the means by which a user accesses a system and performs input and output.

[0341] "Data Sources" refers to the various information sources and databases that provide information and statistics related to fortune-telling.

[0342] "Cleaning and maintenance" is a data processing step that removes noise data from collected data and fills in missing data.

[0343] A "generative AI model" is an artificial intelligence model that learns from large amounts of data and generates fortune-telling results and recommendations.

[0344] "Emotion data" is data that indicates the emotional state that the user is currently feeling, and is input in the form of text or icons.

[0345] "Fortune telling results" are personalized feedback and advice generated based on the user's questions and emotional data.

[0346] The "emotion engine" is an analytical engine that analyzes the emotional data entered by the user and feeds the results back to the generative AI model.

[0347] "Keywords" are key words or phrases extracted from the user's input and used for analysis.

[0348] A "prompt" is text that instructs a generative AI model to perform a specific task.

[0349] The present invention relates to a system that provides highly accurate fortune-telling results using a generative AI model and an emotion engine. The following describes in detail an embodiment of the present invention.

[0350] Overall system configuration

[0351] The server includes a means for providing a user interface, a means for collecting data from multiple data sources, a means for cleaning and organizing the collected data, a means for constructing and training a generative AI model, a means for analyzing questions and emotional data from the user, a means for generating fortune-telling results according to the emotional state based on the analysis results, and a means for displaying the generated fortune-telling results on the user interface.

[0352] User Interface Design

[0353] Users access the fortune-telling system using a dedicated smartphone application. This interface provides a form for users to enter their questions and concerns. It also provides a means for users to input their current emotional state using emoticons and a slider to express emotions. The device has the functionality to transmit the information and emotional data entered by the user to the server.

[0354] Data collection and cleaning

[0355] The server collects data on astrology, tarot card readings, numerology, etc. from multiple data sources on the Internet. The collected data undergoes a data processing process to remove noise data and fill in missing data before being stored in a database.

[0356] Introducing the Emotion Engine

[0357] The server is equipped with an emotion engine for analyzing the emotion data entered by the user. The emotion engine recognizes the user's emotion from the text and selected emotion icons, and feeds the analysis results back to the generative AI model. This makes it possible to generate personalized fortune-telling results based on the user's emotional state.

[0358] Building and training generative AI models

[0359] The server uses the cleaned and curated data to build a generative AI model that iteratively learns from the data and improves its prediction accuracy.

[0360] Generating and displaying fortune-telling results

[0361] When a user inputs a question and emotional data into the fortune-telling system, the device sends the information to the server. The server analyzes the user's input and extracts key keywords and their associations. The emotion engine then analyzes the emotional data and feeds the analysis results back to the generative AI model. The server receives this feedback and generates a personalized fortune-telling result based on the user's emotional state. The generated result is displayed on the device and provided visually to the user.

[0362] Hardware and Software Configuration

[0363] Hardware: Smartphones, servers

[0364] Software: Python, Transformers library (Hugging Face), TextBlob

[0365] Specific examples

[0366] Suppose a man in his 30s is feeling stressed and wants a product that will help him relax. The user types in "stress" and asks the following questions:

[0367] User Input: "Stress"

[0368] Question input: "What item would you like to use to relax?"

[0369] Example prompt for a generative AI model:

[0370] The user is in a stressful state and is asking the question: What item would help me relax? Based on this emotional state, make the best product recommendations.

[0371] In this way, the system of the present invention can provide personalized fortune-telling results and product and service recommendations that take into account the user's emotional state.

[0372] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0373] Step 1:

[0374] Users launch the smartphone app and enter their concerns or questions into a question input form. They also input their current emotional state using emojis and a slider to express their emotions.

[0375] Input: User question text, emotion data

[0376] Output: Question text and emotion data are sent to the device.

[0377] Step 2:

[0378] The device sends the question text and emotion data entered by the user to the server, where the data is encoded into an appropriate format.

[0379] Input: User-entered question text, sentiment data

[0380] Output: The encoded data sent to the server.

[0381] Step 3:

[0382] The server analyzes the received question text and sentiment data. First, it extracts key keywords from the question text.

[0383] Input: Encoded question text, sentiment data

[0384] Output: Extracted main keywords

[0385] Step 4:

[0386] The server analyzes the emotion data entered by the user using an emotion engine, which performs text analysis to recognize the user's emotional state and obtains the result.

[0387] Input: Emotion data

[0388] Output: Sentiment analysis result (e.g., "Stress")

[0389] Step 5:

[0390] The server uses a generative AI model to create a prompt based on the user's question text and the results of sentiment analysis. This prompt is then input into the generative AI model to generate a fortune-telling result.

[0391] Input: Question text, sentiment analysis results

[0392] Output: Generated fortune-telling result

[0393] Step 6:

[0394] The server then sends the generated fortune-telling results to the device and visually displays them to the user, including specific advice and product / service recommendations tailored to the user's emotional state.

[0395] Input: Fortune telling result

[0396] Output: Fortune telling results and recommendations displayed on the device

[0397] Step 7:

[0398] Users can check the fortune-telling results and recommended information displayed on their device and take appropriate action.

[0399] Input: Fortune-telling results and recommendations displayed on the device

[0400] Output: User choices and actions

[0401] 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.

[0402] 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.

[0403] 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.

[0404] [Second embodiment]

[0405] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0406] 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.

[0407] 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).

[0408] 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.

[0409] 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.

[0410] 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).

[0411] 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.

[0412] 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.

[0413] 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.

[0414] 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.

[0415] 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.

[0416] 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."

[0417] The present invention relates to a system that provides highly accurate fortune-telling results using a generative AI model and a statistical model. The following describes in detail an embodiment of the present invention.

[0418] User Interface Design

[0419] Users access the fortune-telling system using a web browser or a dedicated application. This interface provides a form for users to enter their questions and concerns. The terminal also has the functionality to transmit the information entered by the user to the server. It also includes an interface for visually displaying the generated fortune-telling results.

[0420] Data collection and pre-processing

[0421] The server collects statistical and fortune-telling-related data from multiple data sources on the Internet, including astrology, tarot card readings, and numerology data. The collected data is stored in a database and converted into an analyzable format. The server then cleans and organizes the data, removing noise and filling in missing data.

[0422] Building and training generative AI models

[0423] The server uses the cleaned and organized data to build a generative AI model (e.g., a large-scale language model). This model repeatedly uses the data to train and improve its prediction accuracy. Once trained, the model is used to generate fortune-telling results.

[0424] Generating fortune-telling results

[0425] When a user inputs a question into the fortune-telling system, the device sends the information to the server. The server analyzes the user's input using natural language processing technology. This analysis extracts key keywords and their relationships. Based on the analysis results, the server uses a generative AI model to generate fortune-telling results. The generated results include an understanding of the current situation, future predictions, and specific advice.

[0426] Displaying the results

[0427] The server then transmits the generated fortune-telling results to the terminal, which then visually displays the results to the user, possibly using graphs or charts to make the results easier to understand.

[0428] Specific examples

[0429] scenario:

[0430] Imagine a woman in her 30s who is worried about her career.

[0431] 1. The user opens a web browser and accesses the fortune-telling system's homepage.

[0432] 2. The terminal displays a question input form and asks the user to enter the question, "Should I continue in my current job or consider changing jobs?"

[0433] 3. The user clicks the "Submit" button.

[0434] 4. The device sends the question to the server.

[0435] 5. The server analyzes the question and extracts related keywords.

[0436] 6. The server uses the generative AI model to generate fortune-telling results that include "the advantages and disadvantages of continuing in your current job" and "things to be aware of when changing jobs."

[0437] 7. The server sends the generated fortune-telling result to the terminal.

[0438] 8. The terminal displays the fortune-telling results to the user, visualizing things like "career prospects if you continue" and "risks and opportunities if you change jobs."

[0439] In this way, the system of the present invention can provide users with highly accurate fortune-telling results based on scientific evidence, and can give hints for solving problems and objective advice.

[0440] The processing flow will be explained below.

[0441] Step 1:

[0442] Users access the fortune-telling system using a dedicated web browser or application, and enter their questions and concerns into a form displayed on the user interface.

[0443] Step 2:

[0444] The terminal receives the questions and concerns entered by the user and generates transmission data, which includes the user's input.

[0445] Step 3:

[0446] The terminal transmits the transmission data to the server, where communication takes place over the network.

[0447] Step 4:

[0448] The server analyzes the received data and uses natural language processing technology to analyze the user's question and extract keywords.

[0449] Step 5:

[0450] The server retrieves relevant statistical and fortune-telling data based on the analysis results, using database queries.

[0451] Step 6:

[0452] The server generates fortune-telling results using a generative AI model, which generates predictions and advice in response to user questions based on the trained data.

[0453] Step 7:

[0454] The server then structures the generated fortune-telling results, dividing them into sections such as "Current situation analysis," "Future predictions," and "Specific advice."

[0455] Step 8:

[0456] The server then sends the structured fortune-telling results to the terminal, where network communication also takes place.

[0457] Step 9:

[0458] The terminal displays the received fortune-telling results to the user, and graphs and charts can be used to present the results in a visually easy-to-understand format.

[0459] Step 10:

[0460] Users can check the displayed fortune-telling results and use them as a reference for future actions and decisions. For example, they can receive specific advice about their careers.

[0461] This allows users to receive highly accurate fortune-telling results and obtain hints for solving problems and objective advice.

[0462] Example 1

[0463] 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."

[0464] Conventional fortune-telling systems require manual analysis of large amounts of data to obtain highly accurate fortune-telling results, which is labor-intensive and time-consuming. Furthermore, it is difficult to provide specific and useful advice in response to user questions. This results in users being unable to obtain fully satisfactory fortune-telling results, and the reliability of fortune-telling is also low.

[0465] 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.

[0466] In this invention, the server includes means for providing a user interface, means for collecting data from multiple data sources, means for cleaning and organizing the collected data, means for building and training a generative AI model, means for analyzing questions from users, means for generating fortune-telling results based on the analysis results, means for displaying the generated fortune-telling results on the user interface, means for displaying the user interface on a web browser or dedicated application and including a form for users to input questions, means for analyzing data including the questions input by the user using natural language processing technology and extracting key keywords, means for inputting the extracted keywords into the generative AI model and generating fortune-telling results including specific advice, and means for providing an interface for visually displaying the results and visually displaying the fortune-telling results using graphs and charts. This enables users to easily obtain fortune-telling results with high accuracy and specific advice.

[0467] A "user interface" is a mechanism by which a user interacts with a computer system, including forms into which questions or concerns can be entered and screens that visually display the results.

[0468] A "data source" is a source of information, such as an internet website or database, from which statistical data or fortune-telling-related information is collected.

[0469] "Cleaning and grooming" is the process of removing noise and missing values ​​from collected data and converting it into a form suitable for analysis and learning.

[0470] A "generative AI model" is an artificial intelligence model that learns from collected data to make predictions and generate new data, and specifically refers to one that uses natural language processing technology.

[0471] "Natural language processing technology" is a technology that allows computers to analyze, understand, and generate human language, and includes analyzing text data and extracting keywords.

[0472] The "fortune telling result" is an answer including predictions and advice generated by the generative AI model based on the questions and concerns entered by the user.

[0473] A "web browser" is application software for viewing web pages on the Internet.

[0474] "Purpose-built Application" means software designed for a specific purpose and used to access this System.

[0475] A "database" is a system for systematically storing collected data and efficiently managing and searching it.

[0476] "Graphs and charts" are a means of visually representing data and are used to display fortune-telling results in an easy-to-understand manner.

[0477] This invention relates to a system that provides highly accurate fortune-telling results using generative AI models and statistical models. This system is realized by the cooperation of users, terminals, and servers.

[0478] User Interface Design

[0479] Users access the fortune-telling system using a web browser or a dedicated application. The user interface provides a form for users to enter their questions and concerns about fortune-telling. This form includes text boxes and a submit button. The terminal displays the user interface and accepts user input.

[0480] Data collection and pre-processing

[0481] The server collects data related to astrology, tarot card readings, numerology, etc. from multiple data sources on the Internet. The collected data is stored in a database, which includes functions for efficiently managing and searching the data.

[0482] Data cleaning and preparation is performed using software such as Python and Pandas, which removes noise and imputes missing data, converting the data into a format suitable for analysis and model training.

[0483] Building and training generative AI models

[0484] The server uses the cleaned and prepared data to build a generative AI model. Frameworks such as TensorFlow and PyTorch are used to build the model. The training data is repeatedly used to train the model and improve its prediction accuracy. Once trained, the model is used to generate fortune-telling results.

[0485] Generating fortune-telling results

[0486] When a user enters a question into the fortune-telling system and clicks the submit button, the device sends the information to the server, which receives the request using a web framework such as Flask or Django and performs natural language processing using the Natural Language Toolkit (NLTK) or spaCy to extract key keywords from the user's question.

[0487] Based on the extracted keywords, the server uses a generative AI model to generate fortune-telling results, which include an understanding of the current situation, future predictions, and specific advice.

[0488] Displaying the results

[0489] The server then sends the generated fortune-telling results to the device, which then visually displays the results in a user interface using HTML and JavaScript, which can include graphs and charts to make the results easy to understand.

[0490] Specific scenarios and prompt examples

[0491] The following scenarios provide specific labels:

[0492] Imagine a woman in her 30s who is worried about her career.

[0493] 1. The user opens a web browser and accesses the fortune-telling system's homepage.

[0494] 2. The terminal displays a question input form and asks the user to enter the question, "Should I continue in my current job or consider changing jobs?"

[0495] 3. The user clicks the "Submit" button.

[0496] 4. The device sends the question to the server.

[0497] 5. The server analyzes the question and extracts related keywords.

[0498] 6. The server uses the generative AI model to generate fortune-telling results that include "the advantages and disadvantages of continuing in your current job" and "things to be aware of when changing jobs."

[0499] 7. The server sends the generated fortune-telling result to the terminal.

[0500] 8. The terminal displays the fortune-telling results to the user, visualizing things like "career prospects if you continue" and "risks and opportunities if you change jobs."

[0501] Example prompt sentence:

[0502] Should I stay in my current job or consider changing jobs?

[0503] How should I proceed with my relationship in the future?

[0504] "How should I invest now when the economic situation is so uncertain?"

[0505] As described above, the present invention is a system for providing users with highly accurate and specific fortune-telling results. By combining a generative AI model and natural language processing technology, this system is able to generate and visually display specific advice for users' concerns.

[0506] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0507] Step 1: Creating the User Interface

[0508] Users access the fortune-telling system using a web browser or a dedicated application. The terminal uses HTML, CSS, and JavaScript to display a form where users can enter their fortune-telling questions and concerns. The form includes text boxes and a submit button. The entered question is sent to the server when the submit button is clicked.

[0509] Input: Questions entered by the user in a browser or application

[0510] Output: Question input form displayed on the device

[0511] Step 2: Data collection

[0512] The server automatically collects data related to astrology, tarot card readings, numerology, etc. from multiple data sources on the Internet using web scraping tools such as Python and Scrapy, and stores this data in databases such as MySQL and PostgreSQL.

[0513] Input: Data from internet sources

[0514] Output: Raw data stored in a database

[0515] Step 3: Data Cleaning and Preparation

[0516] The server uses libraries such as Pandas and NumPy to clean and organize the collected data, removing noise and filling in missing data, resulting in structured data suitable for analysis and learning.

[0517] Input: Raw data stored in a database

[0518] Output: Cleaned and organized structured data

[0519] Step 4: Building and training a generative AI model

[0520] The server uses the cleaned and prepared data to build a generative AI model. The model is designed using machine learning frameworks such as TensorFlow and PyTorch, and trained using the training data repeatedly. This results in a highly accurate model that is used to generate fortune-telling results.

[0521] Input: Cleaned and structured data

[0522] Output: Trained AI model

[0523] Step 5: Analyzing the user question

[0524] When a user enters a question into the fortune-telling system and clicks the submit button, the device sends the information to the server, which receives the request using a web framework such as Flask or Django and performs natural language processing using the Natural Language Toolkit (NLTK) or spaCy, which extracts key keywords from the user's question.

[0525] Input: The question entered by the user

[0526] Output: Extracted main keywords

[0527] Step 6: Generate fortune-telling results

[0528] The server uses a generative AI model based on the extracted keywords to generate fortune-telling results, which include an understanding of the current situation, future predictions, and specific advice.

[0529] Input: Extracted keywords

[0530] Output: Generated fortune-telling result

[0531] Step 7: View the results

[0532] The server sends the generated fortune-telling results to the device, which then uses JavaScript to display the results in HTML so that the user can visually confirm them. Graphs and charts can be added as needed to make the results easier to understand.

[0533] Input: Generated fortune-telling result

[0534] Output: Fortune telling results displayed on the user interface

[0535] (Application example 1)

[0536] 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."

[0537] Conventional fortune-telling systems lack a mechanism for providing fortune-telling advice tailored to a user's individual payment behavior. As a result, users are inconvenienced by not receiving appropriate fortune-telling guidance for their daily payment behavior. Furthermore, fortune-telling advice based on payment behavior is not personalized, so it cannot be useful information for users.

[0538] 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.

[0539] In this invention, the server includes means for providing a user interface, means for collecting data from multiple data sources, means for cleaning and organizing the collected data, means for building and training a generative AI model, means for analyzing questions from users, means for generating fortune-telling results based on the analysis results, means for displaying the generated fortune-telling results on the user interface, means for providing fortune-related advice based on the user's payment behavior, and means for displaying the provided advice on the electronic payment means, thereby enabling users to receive fortune-telling advice that is individually personalized for their daily payment behavior.

[0540] A "user interface" is an interactive means by which a user accesses a system and inputs and displays information.

[0541] "Multiple Data Sources" refers to multiple sources of data collected from the internet and other sources.

[0542] "Data cleaning and maintenance procedures" are methods that implement procedures to remove noise from collected data and to impute missing data.

[0543] "Means for building and training generative AI models" refers to methods for building generative artificial intelligence models (e.g., large-scale language models) using collected and cleaned data and training them to improve their accuracy.

[0544] "Means for analyzing questions from users" refers to a method of analyzing questions entered into the system by users using natural language processing technology and extracting key keywords and their relevance.

[0545] "Means for generating fortune-telling results" refers to a method of using a generative AI model to derive fortune-telling results based on user input and analysis results.

[0546] The "means for displaying the generated fortune-telling result on a user interface" is a method for visually displaying the generated fortune-telling result to the user.

[0547] The "means for providing fortune-related advice based on payment behavior" is a method for analyzing a user's payment behavior and generating specific fortune-related advice based thereon.

[0548] "Means for displaying the provided advice on an electronic payment means" refers to a method for displaying the generated fortune advice on an electronic payment application or terminal used by the user.

[0549] The present invention relates to a system that provides fortune-related advice based on a user's payment behavior. This system uses generative AI models and statistical models to provide highly accurate advice, and details of the system are described below.

[0550] User Interface Design

[0551] Users access the system using a smartphone with an electronic payment application installed. The user interface has a function that visually displays advice based on the day's fortune when the user makes a payment.

[0552] Data collection and pre-processing

[0553] The server collects statistical data and fortune-telling-related data from multiple data sources on the Internet, including astrology, tarot card readings, numerology, etc. The collected data is stored in a database, where it is cleaned and organized by removing noise data and filling in missing data.

[0554] Building and training generative AI models

[0555] The server uses the cleaned and organized data to build a generative AI model. This model uses a large-scale language model (GPT-2, for example) to repeatedly train on the data to improve its prediction accuracy. Once trained, the model is used to generate fortune-related advice.

[0556] Generating Advice

[0557] When a user makes a payment using their smartphone, the device sends the information to the server. The server then analyzes the information based on this payment behavior using natural language processing technology. This analysis extracts key keywords and their relationships, and generates fortune-related advice using a generative AI model.

[0558] For example, when a user pays for lunch, the server generates advice such as "You're lucky to pay cashlessly today!" This advice is generated based on the user's payment behavior and fortune-telling results. The following prompt sentences can also be used:

[0559] "What payment advice would you like me to give you today?"

[0560] Displaying the results

[0561] The server sends the generated advice to the terminal, which visually displays the advice to the user, allowing the user to receive personalized fortune-telling advice for their daily payment behavior.

[0562] Specific examples

[0563] Specifically, imagine a scenario in which a user pays for lunch using an electronic payment application on their smartphone. When the user presses the payment button, the device sends the information to the server, which then generates fortune-telling advice based on the payment behavior and sends it back to the device. The device then displays the advice on its screen: "Cashless payment is your lucky day today!"

[0564] Based on highly accurate fortune-telling results, this system is able to provide specific advice related to users' payment behavior and provide useful information to users in real time.

[0565] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0566] Step 1:

[0567] The server collects data from multiple data sources on the Internet. The input here is the URLs and APIs of the multiple data sources, and the output is the collected raw data. Specifically, it collects statistical data and fortune-telling-related data (astrology, tarot card readings, numerology, etc.) and stores it in a database.

[0568] Step 2:

[0569] The server cleans and organizes the collected data. The input of this step is the raw data collected in step 1, and the output is cleaned and organized data. Specifically, it removes noise data and fills in missing data. It uses libraries such as Pandas to clean and organize the data.

[0570] Step 3:

[0571] The server uses the cleaned and prepared data to build and train a generative AI model. The input of this step is the clean data, and the output is a trained generative AI model. Specifically, it uses a large-scale language model such as GPT-2 to repeatedly train the data and improve the model's predictive accuracy. It utilizes Hugging Face's Transformers library.

[0572] Step 4:

[0573] A user makes a payment using an electronic payment application on a smartphone. The input here is the data of the user's payment action, and the output is that data is sent to the server. The terminal detects that the payment button has been pressed and sends the information to the server.

[0574] Step 5:

[0575] The server analyzes the submitted payment behavior information using natural language processing technology. The input for this step is the user's payment behavior data, and the output is the extracted important keywords and their relevance. NLP technology is used for the analysis. Specifically, the text is tokenized and key keywords are extracted.

[0576] Step 6:

[0577] The server uses a generative AI model based on the analysis results to generate fortune-related advice. The input for this step is the extracted keywords, and the output is the fortune-telling advice text. Specifically, the prompt "Please tell me some advice for today's payments" is input into the model, and appropriate advice is generated.

[0578] Step 7:

[0579] The server sends the generated fortune advice to the terminal. The input of this step is the generated advice, and the output is that the advice is sent to the terminal. The use of RESTful API is common.

[0580] Step 8:

[0581] The terminal visually displays the received advice to the user. The input of this step is the advice sent from the server, and the output is that the advice is displayed on the user's smartphone screen. Specifically, the electronic payment application displays the advice "Cashless payment is your lucky day today!" as a pop-up message or similar.

[0582] Through these steps, users can receive personalized fortune-telling advice in real time for their daily payment behavior.

[0583] 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.

[0584] The present invention relates to a system that provides highly accurate fortune-telling results using a generative AI model and an emotion engine. The following describes in detail an embodiment of the present invention.

[0585] User Interface Design

[0586] Users access the fortune-telling system using a web browser or a dedicated application. This interface provides a form for users to enter their questions or concerns. It also provides a means for users to input their current emotional state by providing emoticons and sliders to express emotions. The terminal has the functionality to transmit the information and emotional data entered by the user to the server.

[0587] Data collection and pre-processing

[0588] The server collects statistical and fortune-telling-related data from multiple data sources on the Internet, including astrology, tarot card readings, and numerology data. The collected data is stored in a database and converted into an analyzable format. The server then cleans and organizes the data, removing noise and filling in missing data.

[0589] Introducing the Emotion Engine

[0590] The server is equipped with an emotion engine for analyzing the emotion data entered by the user. The emotion engine recognizes the user's emotion from the text and selected emotion icons, and feeds the analysis results back to the generative AI model. This makes it possible to generate personalized fortune-telling results based on the user's emotional state.

[0591] Building and training generative AI models

[0592] The server uses the cleaned and organized data to build a generative AI model (e.g., a large-scale language model). This model repeatedly uses the data to train and improve its prediction accuracy. Once trained, the model is used to generate fortune-telling results.

[0593] Generating fortune-telling results

[0594] When a user inputs a question and emotional data into the fortune-telling system, the device sends that information to the server. The server analyzes the user's input using natural language processing technology to extract key keywords and their associations. The emotion engine then analyzes the emotional data and feeds the analysis results back to the generative AI model. The server receives this feedback and generates personalized fortune-telling results based on the user's emotional state. The generated results include an understanding of the current situation, future predictions, and specific advice.

[0595] Displaying the results

[0596] The server then transmits the generated fortune-telling results to the terminal, which then visually displays the results to the user, possibly using graphs or charts to make the results easier to understand.

[0597] Specific examples

[0598] scenario:

[0599] Imagine a woman in her 30s worried about her career. She's feeling stressed and chooses emoticons that reflect that.

[0600] 1. The user opens a web browser and accesses the fortune-telling system's homepage.

[0601] 2. The terminal displays a question input form and asks the user to enter the question, "Should I continue in my current job or consider changing jobs?"

[0602] 3. The user selects an emoticon that expresses an emotion and inputs their current emotional state.

[0603] 4. The user clicks the "Submit" button.

[0604] 5. The device sends the question and emotion data to the server.

[0605] 6. The server analyzes the question and extracts related keywords.

[0606] 7. The server uses the emotion engine to analyze the emotion data and feeds the analysis results back to the generative AI model.

[0607] 8. The server uses the generative AI model to generate fortune-telling results, including "the pros and cons of staying in your current job" and "things to consider when changing jobs." This also includes specific advice based on the user's emotional state.

[0608] 9. The server sends the generated fortune-telling result to the terminal.

[0609] 10. The device displays the fortune-telling results to the user, visualizing things like "career prospects if you continue" and "risks and opportunities if you change jobs."

[0610] In this way, the system of the present invention can provide highly accurate fortune-telling results that take into account the user's emotions, and can give hints for resolving problems and objective advice.

[0611] The processing flow will be explained below.

[0612] Step 1:

[0613] Users access the fortune-telling system using a dedicated web browser or application, and enter their questions and concerns into a form displayed on the user interface.

[0614] Step 2:

[0615] The user inputs emotion data using emoticons and sliders that represent their current emotional state, selecting emotions such as "feeling stressed" or "feeling relieved."

[0616] Step 3:

[0617] The terminal receives the question content and emotion data entered by the user and generates transmission data, which includes the question in text format and the selected emotional state.

[0618] Step 4:

[0619] The terminal transmits the transmission data to the server via the network.

[0620] Step 5:

[0621] The server analyzes the received data and uses natural language processing technology to analyze the user's question and extract key keywords.

[0622] Step 6:

[0623] The server uses an emotion engine to analyze the emotion data entered by the user, which converts the entered emotional state into numerical data and analyzes that state.

[0624] Step 7:

[0625] The server compares the analyzed emotion data with keywords in the question and searches for relevant statistical and fortune-telling data from information in a database.

[0626] Step 8:

[0627] The server generates fortune-telling results using a generative AI model based on the acquired data. The generative AI model generates predictions and advice in response to the user's questions based on the trained data.

[0628] Step 9:

[0629] The server generates personalized fortune-telling results based on the user's emotional state, adjusting the content and presentation of advice based on the emotional data.

[0630] Step 10:

[0631] The server then structures the generated fortune-telling results, dividing them into sections such as "Current situation analysis," "Future predictions," and "Specific advice."

[0632] Step 11:

[0633] The server then sends the structured fortune-telling results to the terminal, where network communication also takes place.

[0634] Step 12:

[0635] The terminal displays the received fortune-telling results to the user, and graphs and charts can be used to present the results in a visually easy-to-understand format.

[0636] Step 13:

[0637] Users can check the displayed fortune-telling results and use them as a reference for future actions and decisions. For example, they can receive specific advice about their careers.

[0638] Through these specific processing steps, the user can receive highly accurate fortune-telling results that include emotional data, and can obtain hints for resolving problems and objective advice.

[0639] Example 2

[0640] 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."

[0641] Conventional fortune-telling systems provide uniform fortune-telling results without considering the user's emotional state, making it difficult to provide individually customized advice to users. Furthermore, they often fail to properly process noise and missing data in the collected data, resulting in inaccurate results. Therefore, there is a need for systems that can provide more accurate and personalized fortune-telling results that reflect the user's emotional state.

[0642] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for providing a user interface, a means for collecting data from multiple data sources, a means for cleaning and organizing the collected data, a means for analyzing the user's emotional state using an emotion engine, a means for building and training a generative AI model, a means for analyzing questions from the user, a means for generating fortune-telling results based on the analysis results and emotion analysis results, and a means for displaying the generated fortune-telling results on the user interface. This makes it possible to provide highly accurate personalized fortune-telling results that reflect the user's emotional state.

[0643] "User Interface" refers to the screens, forms, and other interactive means by which a user accesses a system, enters information, and receives results.

[0644] "Data Source" means any external or internal source of data related to divination, such as astrology, tarot cards, numerology, etc.

[0645] "Cleaning" refers to the process of removing noise and missing values ​​from collected data and preparing it into an analyzable format.

[0646] "Emotion engine" refers to software or algorithms for analyzing an emotional state from user-entered text and emoticons.

[0647] A "generative AI model" refers to a machine learning model that learns from collected and organized data and generates fortune-telling results based on the user's questions and emotional state.

[0648] "Analysis" refers to the process of recognizing key keywords and topics using natural language processing techniques and other methods to analyze questions and data provided by users.

[0649] "Fortune telling results" refers to information that includes answers and predictions to the user's questions, as well as specific advice.

[0650] This invention relates to a system that provides highly accurate fortune-telling results using a generative AI model and an emotion engine. This system is mainly composed of three elements: a server, a terminal, and a user, and operates in the following procedure.

[0651] User Interface Design

[0652] Users access the fortune-telling system using a web browser or a dedicated application. This interface provides a form for users to enter their questions or concerns. It also provides a means for users to input their current emotional state by providing emoticons and sliders to express emotions. The terminal has the functionality to transmit the information and emotional data entered by the user to the server.

[0653] Data collection and pre-processing

[0654] The server collects data related to astrology, tarot card readings, numerology, etc. from multiple data sources on the Internet. The collected data is stored in a database and converted into a format that can be analyzed. The server then cleans and organizes the data, removing noise and filling in missing data. This process is carried out using high-performance data server and database management software.

[0655] Introducing the Emotion Engine

[0656] The server is equipped with an emotion engine for analyzing the emotion data entered by the user. The emotion engine recognizes the user's emotional state using text analysis algorithms and selected emotion icons, and feeds the analysis results back to the generative AI model. This analysis uses natural language processing (NLP) techniques.

[0657] Building and training generative AI models

[0658] The server uses the cleaned and organized data to build a generative AI model. This model uses machine learning algorithms to repeatedly train on a variety of data to improve its prediction accuracy. Once trained, the model is used to generate fortune-telling results. This process utilizes machine learning frameworks such as TensorFlow and PyTorch.

[0659] Generating fortune-telling results

[0660] When a user inputs a question and emotional data into the fortune-telling system, the device sends the information to the server. The server analyzes the user's input and extracts key keywords and their associations. The emotion engine then analyzes the emotional data and feeds the analysis results back to the generative AI model. The server receives this feedback and generates personalized fortune-telling results based on the user's emotional state. The generated results include an understanding of the current situation, future predictions, and specific advice.

[0661] Displaying the results

[0662] The server then sends the generated fortune-telling results to the terminal, which then visually displays the results to the user using graphs and charts to help the user intuitively understand the results.

[0663] Specific examples

[0664] scenario

[0665] Imagine a woman in her 30s worried about her career. She's feeling stressed and chooses emoticons that reflect that.

[0666] 1. The user opens a web browser and accesses the fortune-telling system's homepage.

[0667] 2. The device displays a question input form and asks the user to enter the question, "Should I continue in my current job or consider changing jobs?"

[0668] 3. The user selects an emoticon that expresses an emotion and inputs their current emotional state.

[0669] 4. The user clicks the "Submit" button.

[0670] 5. The device sends the question and emotion data to the server.

[0671] 6. The server analyzes the question and extracts related keywords.

[0672] 7. The server uses the emotion engine to analyze the emotion data and feeds the analysis results back to the generative AI model.

[0673] 8. The server uses the generative AI model to generate fortune-telling results that include "the advantages and disadvantages of continuing in your current job" and "things to be aware of when changing jobs."

[0674] 9. The server sends the generated fortune-telling results to the terminal.

[0675] 10. The device displays the fortune-telling results to the user, visualizing things like "career prospects if you continue" and "risks and opportunities if you change jobs."

[0676] Prompt Sentence Examples

[0677] Should I stay in my current job or consider changing jobs?

[0678] "I want to know my future love luck"

[0679] In this way, the system of the present invention can provide highly accurate personalized fortune-telling results that take into account the user's emotions, and can give hints for solving problems and objective advice.

[0680] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0681] Step 1: Displaying the User Interface

[0682] The device displays the fortune-telling system's homepage in a web browser. This interface includes a form for users to enter their questions and concerns, as well as emoticons and sliders to express emotions.

[0683] Input: User interface URL

[0684] Output: A web page with a question form, emoticons, and a slider for entering emotions.

[0685] Specific operation: The device launches a web browser and accesses the specified URL. The fortune-telling system's homepage is displayed, along with an input form, emoticons for inputting emotions, and a slider.

[0686] Step 2: Enter and submit user data

[0687] Users enter their questions or concerns into the input form, select their current emotional state using emoticons or a slider, and click the "Submit" button when they're done.

[0688] Input: User question, emotional state (emoticons, slider)

[0689] Output: Question content and emotion data

[0690] What it does: A user enters a question into the form, such as "Should I stay in my current job or consider changing jobs?". Next, they click an emoticon that reflects their emotional state, adjust a slider to input the intensity of the emotion, and finally click the "Submit" button.

[0691] Step 3: Collect and preprocess data

[0692] The device sends the user's input questions and emotional data to a server, which then collects related data such as astrology, tarot card readings, and numerology from multiple data sources on the Internet and stores it in a database.

[0693] Input: User-entered data (questions, sentiment data), external data sources

[0694] Output: Cleaned and groomed data

[0695] Specific operation: The device sends the user's input information in a data format such as JSON to the server. The server analyzes the received data and collects relevant information from the necessary data sources. The collected data is stored in a database and converted into an analyzable format.

[0696] Step 4: Emotion analysis using the emotion engine

[0697] The server uses an emotion engine to analyze the emotion data entered by the user, which recognizes the user's emotional state using text analysis algorithms and selected emotion icons, and feeds the analysis results back to the generative AI model.

[0698] Input: User emotion data (text, emoticons)

[0699] Output: Sentiment analysis results (numerical data)

[0700] Specific operation: The server calls the emotion engine and sends the emotional information of the text and emoticons entered by the user. The emotion engine analyzes this data and outputs the emotional state as numerical data. The analysis results are fed back to the generative AI model.

[0701] Step 5: Building and training a generative AI model

[0702] The server uses the cleaned and curated data to build a generative AI model, which uses machine learning algorithms to repeatedly learn from diverse data and improve its prediction accuracy.

[0703] Input: Cleaned data

[0704] Output: Trained generative AI model

[0705] How it works: The server retrieves collected data from the database and performs a cleaning process. The cleaned data is then used to start training the generative AI model. Once training is complete, the model is ready to be used to generate fortune-telling results.

[0706] Step 6: Generate fortune-telling results

[0707] The server uses a generative AI model based on the user's question and sentiment analysis results to generate a fortune-telling result, which includes an understanding of the current situation, future predictions, and specific advice.

[0708] Input: User question, sentiment analysis results

[0709] Output: Fortune-telling results (current situation, future predictions, advice)

[0710] Specific operation: The server calls the generative AI model and provides the user's question and sentiment analysis results as input. The generative AI model processes this data and generates a fortune-telling result, which includes an answer to the user's question, a prediction, and specific advice.

[0711] Step 7: Send and view results

[0712] The server sends the generated fortune-telling results to the terminal, which then visually displays the results to the user using graphs and charts to help the user intuitively understand the results.

[0713] Input: Fortune-telling result

[0714] Output: Visual results (graphs, charts) that are displayed to the user

[0715] Specific operation: The server sends the generated fortune-telling results to the device in JSON format or similar. The device analyzes the received data and displays the results to the user. The results are visually displayed as graphs and charts, making them easier to understand.

[0716] Through the above steps, the system of the present invention can provide highly accurate and personalized fortune-telling results that take into account the user's emotions.

[0717] (Application example 2)

[0718] 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."

[0719] Conventional fortune-telling systems provide general results without considering the user's emotional state, making it difficult to provide personalized feedback or specific advice. Furthermore, some electronic payment systems lacked technology to provide specific suggestions based on the user's emotional state. This meant that they were unable to adequately help users select products or services that matched their current emotions.

[0720] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for providing a user interface, means for collecting data from multiple data sources, means for cleaning and organizing the collected data, means for building and training a generative AI model, means for analyzing questions and emotional data from the user, means for generating fortune-telling results according to the emotional state based on the analysis results, and means for displaying the generated fortune-telling results on the user interface. This makes it possible to provide personalized fortune-telling results and product / service recommendations that take the user's emotional state into consideration.

[0721] A "user interface" is the means by which a user accesses a system and performs input and output.

[0722] "Data Sources" refers to the various information sources and databases that provide information and statistics related to fortune-telling.

[0723] "Cleaning and maintenance" is a data processing step that removes noise data from collected data and fills in missing data.

[0724] A "generative AI model" is an artificial intelligence model that learns from large amounts of data and generates fortune-telling results and recommendations.

[0725] "Emotion data" is data that indicates the emotional state that the user is currently feeling, and is input in the form of text or icons.

[0726] "Fortune telling results" are personalized feedback and advice generated based on the user's questions and emotional data.

[0727] The "emotion engine" is an analytical engine that analyzes the emotional data entered by the user and feeds the results back to the generative AI model.

[0728] "Keywords" are key words or phrases extracted from the user's input and used for analysis.

[0729] A "prompt" is text that instructs a generative AI model to perform a specific task.

[0730] The present invention relates to a system that provides highly accurate fortune-telling results using a generative AI model and an emotion engine. The following describes in detail an embodiment of the present invention.

[0731] Overall system configuration

[0732] The server includes a means for providing a user interface, a means for collecting data from multiple data sources, a means for cleaning and organizing the collected data, a means for constructing and training a generative AI model, a means for analyzing questions and emotional data from the user, a means for generating fortune-telling results according to the emotional state based on the analysis results, and a means for displaying the generated fortune-telling results on the user interface.

[0733] User Interface Design

[0734] Users access the fortune-telling system using a dedicated smartphone application. This interface provides a form for users to enter their questions and concerns. It also provides a means for users to input their current emotional state using emoticons and a slider to express emotions. The device has the functionality to transmit the information and emotional data entered by the user to the server.

[0735] Data collection and cleaning

[0736] The server collects data on astrology, tarot card readings, numerology, etc. from multiple data sources on the Internet. The collected data undergoes a data processing process to remove noise data and fill in missing data before being stored in a database.

[0737] Introducing the Emotion Engine

[0738] The server is equipped with an emotion engine for analyzing the emotion data entered by the user. The emotion engine recognizes the user's emotion from the text and selected emotion icons, and feeds the analysis results back to the generative AI model. This makes it possible to generate personalized fortune-telling results based on the user's emotional state.

[0739] Building and training generative AI models

[0740] The server uses the cleaned and curated data to build a generative AI model that iteratively learns from the data and improves its prediction accuracy.

[0741] Generating and displaying fortune-telling results

[0742] When a user inputs a question and emotional data into the fortune-telling system, the device sends the information to the server. The server analyzes the user's input and extracts key keywords and their associations. The emotion engine then analyzes the emotional data and feeds the analysis results back to the generative AI model. The server receives this feedback and generates a personalized fortune-telling result based on the user's emotional state. The generated result is displayed on the device and provided visually to the user.

[0743] Hardware and Software Configuration

[0744] Hardware: Smartphones, servers

[0745] Software: Python, Transformers library (Hugging Face), TextBlob

[0746] Specific examples

[0747] Suppose a man in his 30s is feeling stressed and wants a product that will help him relax. The user types in "stress" and asks the following questions:

[0748] User Input: "Stress"

[0749] Question input: "What item would you like to use to relax?"

[0750] Example prompt for a generative AI model:

[0751] The user is in a stressful state and is asking the question: What item would help me relax? Based on this emotional state, make the best product recommendations.

[0752] In this way, the system of the present invention can provide personalized fortune-telling results and product and service recommendations that take into account the user's emotional state.

[0753] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0754] Step 1:

[0755] Users launch the smartphone app and enter their concerns or questions into a question input form. They also input their current emotional state using emojis and a slider to express their emotions.

[0756] Input: User question text, emotion data

[0757] Output: Question text and emotion data are sent to the device.

[0758] Step 2:

[0759] The device sends the question text and emotion data entered by the user to the server, where the data is encoded into an appropriate format.

[0760] Input: User-entered question text, sentiment data

[0761] Output: The encoded data sent to the server.

[0762] Step 3:

[0763] The server analyzes the received question text and sentiment data. First, it extracts key keywords from the question text.

[0764] Input: Encoded question text, sentiment data

[0765] Output: Extracted main keywords

[0766] Step 4:

[0767] The server analyzes the emotion data entered by the user using an emotion engine, which performs text analysis to recognize the user's emotional state and obtains the result.

[0768] Input: Emotion data

[0769] Output: Sentiment analysis result (e.g., "Stress")

[0770] Step 5:

[0771] The server uses a generative AI model to create a prompt based on the user's question text and the results of sentiment analysis. This prompt is then input into the generative AI model to generate a fortune-telling result.

[0772] Input: Question text, sentiment analysis results

[0773] Output: Generated fortune-telling result

[0774] Step 6:

[0775] The server then sends the generated fortune-telling results to the device and visually displays them to the user, including specific advice and product / service recommendations tailored to the user's emotional state.

[0776] Input: Fortune telling result

[0777] Output: Fortune telling results and recommendations displayed on the device

[0778] Step 7:

[0779] Users can check the fortune-telling results and recommended information displayed on their device and take appropriate action.

[0780] Input: Fortune-telling results and recommendations displayed on the device

[0781] Output: User choices and actions

[0782] 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.

[0783] 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.

[0784] 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.

[0785] [Third embodiment]

[0786] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0787] 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.

[0788] 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).

[0789] 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.

[0790] 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.

[0791] 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).

[0792] 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.

[0793] 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.

[0794] 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.

[0795] 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.

[0796] 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.

[0797] 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."

[0798] The present invention relates to a system that provides highly accurate fortune-telling results using a generative AI model and a statistical model. The following describes in detail an embodiment of the present invention.

[0799] User Interface Design

[0800] Users access the fortune-telling system using a web browser or a dedicated application. This interface provides a form for users to enter their questions and concerns. The terminal also has the functionality to transmit the information entered by the user to the server. It also includes an interface for visually displaying the generated fortune-telling results.

[0801] Data collection and pre-processing

[0802] The server collects statistical and fortune-telling-related data from multiple data sources on the Internet, including astrology, tarot card readings, and numerology data. The collected data is stored in a database and converted into an analyzable format. The server then cleans and organizes the data, removing noise and filling in missing data.

[0803] Building and training generative AI models

[0804] The server uses the cleaned and organized data to build a generative AI model (e.g., a large-scale language model). This model repeatedly uses the data to train and improve its prediction accuracy. Once trained, the model is used to generate fortune-telling results.

[0805] Generating fortune-telling results

[0806] When a user inputs a question into the fortune-telling system, the device sends the information to the server. The server analyzes the user's input using natural language processing technology. This analysis extracts key keywords and their relationships. Based on the analysis results, the server uses a generative AI model to generate fortune-telling results. The generated results include an understanding of the current situation, future predictions, and specific advice.

[0807] Displaying the results

[0808] The server then transmits the generated fortune-telling results to the terminal, which then visually displays the results to the user, possibly using graphs or charts to make the results easier to understand.

[0809] Specific examples

[0810] scenario:

[0811] Imagine a woman in her 30s who is worried about her career.

[0812] 1. The user opens a web browser and accesses the fortune-telling system's homepage.

[0813] 2. The terminal displays a question input form and asks the user to enter the question, "Should I continue in my current job or consider changing jobs?"

[0814] 3. The user clicks the "Submit" button.

[0815] 4. The device sends the question to the server.

[0816] 5. The server analyzes the question and extracts related keywords.

[0817] 6. The server uses the generative AI model to generate fortune-telling results that include "the advantages and disadvantages of continuing in your current job" and "things to be aware of when changing jobs."

[0818] 7. The server sends the generated fortune-telling result to the terminal.

[0819] 8. The terminal displays the fortune-telling results to the user, visualizing things like "career prospects if you continue" and "risks and opportunities if you change jobs."

[0820] In this way, the system of the present invention can provide users with highly accurate fortune-telling results based on scientific evidence, and can give hints for solving problems and objective advice.

[0821] The processing flow will be explained below.

[0822] Step 1:

[0823] Users access the fortune-telling system using a dedicated web browser or application, and enter their questions and concerns into a form displayed on the user interface.

[0824] Step 2:

[0825] The terminal receives the questions and concerns entered by the user and generates transmission data, which includes the user's input.

[0826] Step 3:

[0827] The terminal transmits the transmission data to the server, where communication takes place over the network.

[0828] Step 4:

[0829] The server analyzes the received data and uses natural language processing technology to analyze the user's question and extract keywords.

[0830] Step 5:

[0831] The server retrieves relevant statistical and fortune-telling data based on the analysis results, using database queries.

[0832] Step 6:

[0833] The server generates fortune-telling results using a generative AI model, which generates predictions and advice in response to user questions based on the trained data.

[0834] Step 7:

[0835] The server then structures the generated fortune-telling results, dividing them into sections such as "Current situation analysis," "Future predictions," and "Specific advice."

[0836] Step 8:

[0837] The server then sends the structured fortune-telling results to the terminal, where network communication also takes place.

[0838] Step 9:

[0839] The terminal displays the received fortune-telling results to the user, and graphs and charts can be used to present the results in a visually easy-to-understand format.

[0840] Step 10:

[0841] Users can check the displayed fortune-telling results and use them as a reference for future actions and decisions. For example, they can receive specific advice about their careers.

[0842] This allows users to receive highly accurate fortune-telling results and obtain hints for solving problems and objective advice.

[0843] Example 1

[0844] 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."

[0845] Conventional fortune-telling systems require manual analysis of large amounts of data to obtain highly accurate fortune-telling results, which is labor-intensive and time-consuming. Furthermore, it is difficult to provide specific and useful advice in response to user questions. This results in users being unable to obtain fully satisfactory fortune-telling results, and the reliability of fortune-telling is also low.

[0846] 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.

[0847] In this invention, the server includes means for providing a user interface, means for collecting data from multiple data sources, means for cleaning and organizing the collected data, means for building and training a generative AI model, means for analyzing questions from users, means for generating fortune-telling results based on the analysis results, means for displaying the generated fortune-telling results on the user interface, means for displaying the user interface on a web browser or dedicated application and including a form for users to input questions, means for analyzing data including the questions input by the user using natural language processing technology and extracting key keywords, means for inputting the extracted keywords into the generative AI model and generating fortune-telling results including specific advice, and means for providing an interface for visually displaying the results and visually displaying the fortune-telling results using graphs and charts. This enables users to easily obtain fortune-telling results with high accuracy and specific advice.

[0848] A "user interface" is a mechanism by which a user interacts with a computer system, including forms into which questions or concerns can be entered and screens that visually display the results.

[0849] A "data source" is a source of information, such as an internet website or database, from which statistical data or fortune-telling-related information is collected.

[0850] "Cleaning and grooming" is the process of removing noise and missing values ​​from collected data and converting it into a form suitable for analysis and learning.

[0851] A "generative AI model" is an artificial intelligence model that learns from collected data to make predictions and generate new data, and specifically refers to one that uses natural language processing technology.

[0852] "Natural language processing technology" is a technology that allows computers to analyze, understand, and generate human language, and includes analyzing text data and extracting keywords.

[0853] The "fortune telling result" is an answer including predictions and advice generated by the generative AI model based on the questions and concerns entered by the user.

[0854] A "web browser" is application software for viewing web pages on the Internet.

[0855] "Purpose-built Application" means software designed for a specific purpose and used to access this System.

[0856] A "database" is a system for systematically storing collected data and efficiently managing and searching it.

[0857] "Graphs and charts" are a means of visually representing data and are used to display fortune-telling results in an easy-to-understand manner.

[0858] This invention relates to a system that provides highly accurate fortune-telling results using generative AI models and statistical models. This system is realized by the cooperation of users, terminals, and servers.

[0859] User Interface Design

[0860] Users access the fortune-telling system using a web browser or a dedicated application. The user interface provides a form for users to enter their questions and concerns about fortune-telling. This form includes text boxes and a submit button. The terminal displays the user interface and accepts user input.

[0861] Data collection and pre-processing

[0862] The server collects data related to astrology, tarot card readings, numerology, etc. from multiple data sources on the Internet. The collected data is stored in a database, which includes functions for efficiently managing and searching the data.

[0863] Data cleaning and preparation is performed using software such as Python and Pandas, which removes noise and imputes missing data, converting the data into a format suitable for analysis and model training.

[0864] Building and training generative AI models

[0865] The server uses the cleaned and prepared data to build a generative AI model. Frameworks such as TensorFlow and PyTorch are used to build the model. The training data is repeatedly used to train the model and improve its prediction accuracy. Once trained, the model is used to generate fortune-telling results.

[0866] Generating fortune-telling results

[0867] When a user enters a question into the fortune-telling system and clicks the submit button, the device sends the information to the server, which receives the request using a web framework such as Flask or Django and performs natural language processing using the Natural Language Toolkit (NLTK) or spaCy to extract key keywords from the user's question.

[0868] Based on the extracted keywords, the server uses a generative AI model to generate fortune-telling results, which include an understanding of the current situation, future predictions, and specific advice.

[0869] Displaying the results

[0870] The server then sends the generated fortune-telling results to the device, which then visually displays the results in a user interface using HTML and JavaScript, which can include graphs and charts to make the results easy to understand.

[0871] Specific scenarios and prompt examples

[0872] The following scenarios provide specific labels:

[0873] Imagine a woman in her 30s who is worried about her career.

[0874] 1. The user opens a web browser and accesses the fortune-telling system's homepage.

[0875] 2. The terminal displays a question input form and asks the user to enter the question, "Should I continue in my current job or consider changing jobs?"

[0876] 3. The user clicks the "Submit" button.

[0877] 4. The device sends the question to the server.

[0878] 5. The server analyzes the question and extracts related keywords.

[0879] 6. The server uses the generative AI model to generate fortune-telling results that include "the advantages and disadvantages of continuing in your current job" and "things to be aware of when changing jobs."

[0880] 7. The server sends the generated fortune-telling result to the terminal.

[0881] 8. The terminal displays the fortune-telling results to the user, visualizing things like "career prospects if you continue" and "risks and opportunities if you change jobs."

[0882] Example prompt sentence:

[0883] Should I stay in my current job or consider changing jobs?

[0884] How should I proceed with my relationship in the future?

[0885] "How should I invest now when the economic situation is so uncertain?"

[0886] As described above, the present invention is a system for providing users with highly accurate and specific fortune-telling results. By combining a generative AI model and natural language processing technology, this system is able to generate and visually display specific advice for users' concerns.

[0887] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0888] Step 1: Creating the User Interface

[0889] Users access the fortune-telling system using a web browser or a dedicated application. The terminal uses HTML, CSS, and JavaScript to display a form where users can enter their fortune-telling questions and concerns. The form includes text boxes and a submit button. The entered question is sent to the server when the submit button is clicked.

[0890] Input: Questions entered by the user in a browser or application

[0891] Output: Question input form displayed on the device

[0892] Step 2: Data collection

[0893] The server automatically collects data related to astrology, tarot card readings, numerology, etc. from multiple data sources on the Internet using web scraping tools such as Python and Scrapy, and stores this data in databases such as MySQL and PostgreSQL.

[0894] Input: Data from internet sources

[0895] Output: Raw data stored in a database

[0896] Step 3: Data Cleaning and Preparation

[0897] The server uses libraries such as Pandas and NumPy to clean and organize the collected data, removing noise and filling in missing data, resulting in structured data suitable for analysis and learning.

[0898] Input: Raw data stored in a database

[0899] Output: Cleaned and organized structured data

[0900] Step 4: Building and training a generative AI model

[0901] The server uses the cleaned and prepared data to build a generative AI model. The model is designed using machine learning frameworks such as TensorFlow and PyTorch, and trained using the training data repeatedly. This results in a highly accurate model that is used to generate fortune-telling results.

[0902] Input: Cleaned and structured data

[0903] Output: Trained AI model

[0904] Step 5: Analyzing the user question

[0905] When a user enters a question into the fortune-telling system and clicks the submit button, the device sends the information to the server, which receives the request using a web framework such as Flask or Django and performs natural language processing using the Natural Language Toolkit (NLTK) or spaCy, which extracts key keywords from the user's question.

[0906] Input: The question entered by the user

[0907] Output: Extracted main keywords

[0908] Step 6: Generate fortune-telling results

[0909] The server uses a generative AI model based on the extracted keywords to generate fortune-telling results, which include an understanding of the current situation, future predictions, and specific advice.

[0910] Input: Extracted keywords

[0911] Output: Generated fortune-telling result

[0912] Step 7: View the results

[0913] The server sends the generated fortune-telling results to the device, which then uses JavaScript to display the results in HTML so that the user can visually confirm them. Graphs and charts can be added as needed to make the results easier to understand.

[0914] Input: Generated fortune-telling result

[0915] Output: Fortune telling results displayed on the user interface

[0916] (Application example 1)

[0917] 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."

[0918] Conventional fortune-telling systems lack a mechanism for providing fortune-telling advice tailored to a user's individual payment behavior. As a result, users are inconvenienced by not receiving appropriate fortune-telling guidance for their daily payment behavior. Furthermore, fortune-telling advice based on payment behavior is not personalized, so it cannot be useful information for users.

[0919] 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.

[0920] In this invention, the server includes means for providing a user interface, means for collecting data from multiple data sources, means for cleaning and organizing the collected data, means for building and training a generative AI model, means for analyzing questions from users, means for generating fortune-telling results based on the analysis results, means for displaying the generated fortune-telling results on the user interface, means for providing fortune-related advice based on the user's payment behavior, and means for displaying the provided advice on the electronic payment means, thereby enabling users to receive fortune-telling advice that is individually personalized for their daily payment behavior.

[0921] A "user interface" is an interactive means by which a user accesses a system and inputs and displays information.

[0922] "Multiple Data Sources" refers to multiple sources of data collected from the internet and other sources.

[0923] "Data cleaning and maintenance procedures" are methods that implement procedures to remove noise from collected data and to impute missing data.

[0924] "Means for building and training generative AI models" refers to methods for building generative artificial intelligence models (e.g., large-scale language models) using collected and cleaned data and training them to improve their accuracy.

[0925] "Means for analyzing questions from users" refers to a method of analyzing questions entered into the system by users using natural language processing technology and extracting key keywords and their relevance.

[0926] "Means for generating fortune-telling results" refers to a method of using a generative AI model to derive fortune-telling results based on user input and analysis results.

[0927] The "means for displaying the generated fortune-telling result on a user interface" is a method for visually displaying the generated fortune-telling result to the user.

[0928] The "means for providing fortune-related advice based on payment behavior" is a method for analyzing a user's payment behavior and generating specific fortune-related advice based thereon.

[0929] "Means for displaying the provided advice on an electronic payment means" refers to a method for displaying the generated fortune advice on an electronic payment application or terminal used by the user.

[0930] The present invention relates to a system that provides fortune-related advice based on a user's payment behavior. This system uses generative AI models and statistical models to provide highly accurate advice, and details of the system are described below.

[0931] User Interface Design

[0932] Users access the system using a smartphone with an electronic payment application installed. The user interface has a function that visually displays advice based on the day's fortune when the user makes a payment.

[0933] Data collection and pre-processing

[0934] The server collects statistical data and fortune-telling-related data from multiple data sources on the Internet, including astrology, tarot card readings, numerology, etc. The collected data is stored in a database, where it is cleaned and organized by removing noise data and filling in missing data.

[0935] Building and training generative AI models

[0936] The server uses the cleaned and organized data to build a generative AI model. This model uses a large-scale language model (GPT-2, for example) to repeatedly train on the data to improve its prediction accuracy. Once trained, the model is used to generate fortune-related advice.

[0937] Generating Advice

[0938] When a user makes a payment using their smartphone, the device sends the information to the server. The server then analyzes the information based on this payment behavior using natural language processing technology. This analysis extracts key keywords and their relationships, and generates fortune-related advice using a generative AI model.

[0939] For example, when a user pays for lunch, the server generates advice such as "You're lucky to pay cashlessly today!" This advice is generated based on the user's payment behavior and fortune-telling results. The following prompt sentences can also be used:

[0940] "What payment advice would you like me to give you today?"

[0941] Displaying the results

[0942] The server sends the generated advice to the terminal, which visually displays the advice to the user, allowing the user to receive personalized fortune-telling advice for their daily payment behavior.

[0943] Specific examples

[0944] Specifically, imagine a scenario in which a user pays for lunch using an electronic payment application on their smartphone. When the user presses the payment button, the device sends the information to the server, which then generates fortune-telling advice based on the payment behavior and sends it back to the device. The device then displays the advice on its screen: "Cashless payment is your lucky day today!"

[0945] Based on highly accurate fortune-telling results, this system is able to provide specific advice related to users' payment behavior and provide useful information to users in real time.

[0946] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0947] Step 1:

[0948] The server collects data from multiple data sources on the Internet. The input here is the URLs and APIs of the multiple data sources, and the output is the collected raw data. Specifically, it collects statistical data and fortune-telling-related data (astrology, tarot card readings, numerology, etc.) and stores it in a database.

[0949] Step 2:

[0950] The server cleans and organizes the collected data. The input of this step is the raw data collected in step 1, and the output is cleaned and organized data. Specifically, it removes noise data and fills in missing data. It uses libraries such as Pandas to clean and organize the data.

[0951] Step 3:

[0952] The server uses the cleaned and prepared data to build and train a generative AI model. The input of this step is the clean data, and the output is a trained generative AI model. Specifically, it uses a large-scale language model such as GPT-2 to repeatedly train the data and improve the model's predictive accuracy. It utilizes Hugging Face's Transformers library.

[0953] Step 4:

[0954] A user makes a payment using an electronic payment application on a smartphone. The input here is the data of the user's payment action, and the output is that data is sent to the server. The terminal detects that the payment button has been pressed and sends the information to the server.

[0955] Step 5:

[0956] The server analyzes the submitted payment behavior information using natural language processing technology. The input for this step is the user's payment behavior data, and the output is the extracted important keywords and their relevance. NLP technology is used for the analysis. Specifically, the text is tokenized and key keywords are extracted.

[0957] Step 6:

[0958] The server uses a generative AI model based on the analysis results to generate fortune-related advice. The input for this step is the extracted keywords, and the output is the fortune-telling advice text. Specifically, the prompt "Please tell me some advice for today's payments" is input into the model, and appropriate advice is generated.

[0959] Step 7:

[0960] The server sends the generated fortune advice to the terminal. The input of this step is the generated advice, and the output is that the advice is sent to the terminal. The use of RESTful API is common.

[0961] Step 8:

[0962] The terminal visually displays the received advice to the user. The input of this step is the advice sent from the server, and the output is that the advice is displayed on the user's smartphone screen. Specifically, the electronic payment application displays the advice "Cashless payment is your lucky day today!" as a pop-up message or similar.

[0963] Through these steps, users can receive personalized fortune-telling advice in real time for their daily payment behavior.

[0964] 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.

[0965] The present invention relates to a system that provides highly accurate fortune-telling results using a generative AI model and an emotion engine. The following describes in detail an embodiment of the present invention.

[0966] User Interface Design

[0967] Users access the fortune-telling system using a web browser or a dedicated application. This interface provides a form for users to enter their questions or concerns. It also provides a means for users to input their current emotional state by providing emoticons and sliders to express emotions. The terminal has the functionality to transmit the information and emotional data entered by the user to the server.

[0968] Data collection and pre-processing

[0969] The server collects statistical and fortune-telling-related data from multiple data sources on the Internet, including astrology, tarot card readings, and numerology data. The collected data is stored in a database and converted into an analyzable format. The server then cleans and organizes the data, removing noise and filling in missing data.

[0970] Introducing the Emotion Engine

[0971] The server is equipped with an emotion engine for analyzing the emotion data entered by the user. The emotion engine recognizes the user's emotion from the text and selected emotion icons, and feeds the analysis results back to the generative AI model. This makes it possible to generate personalized fortune-telling results based on the user's emotional state.

[0972] Building and training generative AI models

[0973] The server uses the cleaned and organized data to build a generative AI model (e.g., a large-scale language model). This model repeatedly uses the data to train and improve its prediction accuracy. Once trained, the model is used to generate fortune-telling results.

[0974] Generating fortune-telling results

[0975] When a user inputs a question and emotional data into the fortune-telling system, the device sends that information to the server. The server analyzes the user's input using natural language processing technology to extract key keywords and their associations. The emotion engine then analyzes the emotional data and feeds the analysis results back to the generative AI model. The server receives this feedback and generates personalized fortune-telling results based on the user's emotional state. The generated results include an understanding of the current situation, future predictions, and specific advice.

[0976] Displaying the results

[0977] The server then transmits the generated fortune-telling results to the terminal, which then visually displays the results to the user, possibly using graphs or charts to make the results easier to understand.

[0978] Specific examples

[0979] scenario:

[0980] Imagine a woman in her 30s worried about her career. She's feeling stressed and chooses emoticons that reflect that.

[0981] 1. The user opens a web browser and accesses the fortune-telling system's homepage.

[0982] 2. The terminal displays a question input form and asks the user to enter the question, "Should I continue in my current job or consider changing jobs?"

[0983] 3. The user selects an emoticon that expresses an emotion and inputs their current emotional state.

[0984] 4. The user clicks the "Submit" button.

[0985] 5. The device sends the question and emotion data to the server.

[0986] 6. The server analyzes the question and extracts related keywords.

[0987] 7. The server uses the emotion engine to analyze the emotion data and feeds the analysis results back to the generative AI model.

[0988] 8. The server uses the generative AI model to generate fortune-telling results, including "the pros and cons of staying in your current job" and "things to consider when changing jobs." This also includes specific advice based on the user's emotional state.

[0989] 9. The server sends the generated fortune-telling result to the terminal.

[0990] 10. The device displays the fortune-telling results to the user, visualizing things like "career prospects if you continue" and "risks and opportunities if you change jobs."

[0991] In this way, the system of the present invention can provide highly accurate fortune-telling results that take into account the user's emotions, and can give hints for resolving problems and objective advice.

[0992] The processing flow will be explained below.

[0993] Step 1:

[0994] Users access the fortune-telling system using a dedicated web browser or application, and enter their questions and concerns into a form displayed on the user interface.

[0995] Step 2:

[0996] The user inputs emotion data using emoticons and sliders that represent their current emotional state, selecting emotions such as "feeling stressed" or "feeling relieved."

[0997] Step 3:

[0998] The terminal receives the question content and emotion data entered by the user and generates transmission data, which includes the question in text format and the selected emotional state.

[0999] Step 4:

[1000] The terminal transmits the transmission data to the server via the network.

[1001] Step 5:

[1002] The server analyzes the received data and uses natural language processing technology to analyze the user's question and extract key keywords.

[1003] Step 6:

[1004] The server uses an emotion engine to analyze the emotion data entered by the user, which converts the entered emotional state into numerical data and analyzes that state.

[1005] Step 7:

[1006] The server compares the analyzed emotion data with keywords in the question and searches for relevant statistical and fortune-telling data from information in a database.

[1007] Step 8:

[1008] The server generates fortune-telling results using a generative AI model based on the acquired data. The generative AI model generates predictions and advice in response to the user's questions based on the trained data.

[1009] Step 9:

[1010] The server generates personalized fortune-telling results based on the user's emotional state, adjusting the content and presentation of advice based on the emotional data.

[1011] Step 10:

[1012] The server then structures the generated fortune-telling results, dividing them into sections such as "Current situation analysis," "Future predictions," and "Specific advice."

[1013] Step 11:

[1014] The server then sends the structured fortune-telling results to the terminal, where network communication also takes place.

[1015] Step 12:

[1016] The terminal displays the received fortune-telling results to the user, and graphs and charts can be used to present the results in a visually easy-to-understand format.

[1017] Step 13:

[1018] Users can check the displayed fortune-telling results and use them as a reference for future actions and decisions. For example, they can receive specific advice about their careers.

[1019] Through these specific processing steps, the user can receive highly accurate fortune-telling results that include emotional data, and can obtain hints for resolving problems and objective advice.

[1020] Example 2

[1021] 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."

[1022] Conventional fortune-telling systems provide uniform fortune-telling results without considering the user's emotional state, making it difficult to provide individually customized advice to users. Furthermore, they often fail to properly process noise and missing data in the collected data, resulting in inaccurate results. Therefore, there is a need for systems that can provide more accurate and personalized fortune-telling results that reflect the user's emotional state.

[1023] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for providing a user interface, a means for collecting data from multiple data sources, a means for cleaning and organizing the collected data, a means for analyzing the user's emotional state using an emotion engine, a means for building and training a generative AI model, a means for analyzing questions from the user, a means for generating fortune-telling results based on the analysis results and emotion analysis results, and a means for displaying the generated fortune-telling results on the user interface. This makes it possible to provide highly accurate personalized fortune-telling results that reflect the user's emotional state.

[1024] "User Interface" refers to the screens, forms, and other interactive means by which a user accesses a system, enters information, and receives results.

[1025] "Data Source" means any external or internal source of data related to divination, such as astrology, tarot cards, numerology, etc.

[1026] "Cleaning" refers to the process of removing noise and missing values ​​from collected data and preparing it into an analyzable format.

[1027] "Emotion engine" refers to software or algorithms for analyzing an emotional state from user-entered text and emoticons.

[1028] A "generative AI model" refers to a machine learning model that learns from collected and organized data and generates fortune-telling results based on the user's questions and emotional state.

[1029] "Analysis" refers to the process of recognizing key keywords and topics using natural language processing techniques and other methods to analyze questions and data provided by users.

[1030] "Fortune telling results" refers to information that includes answers and predictions to the user's questions, as well as specific advice.

[1031] This invention relates to a system that provides highly accurate fortune-telling results using a generative AI model and an emotion engine. This system is mainly composed of three elements: a server, a terminal, and a user, and operates in the following procedure.

[1032] User Interface Design

[1033] Users access the fortune-telling system using a web browser or a dedicated application. This interface provides a form for users to enter their questions or concerns. It also provides a means for users to input their current emotional state by providing emoticons and sliders to express emotions. The terminal has the functionality to transmit the information and emotional data entered by the user to the server.

[1034] Data collection and pre-processing

[1035] The server collects data related to astrology, tarot card readings, numerology, etc. from multiple data sources on the Internet. The collected data is stored in a database and converted into a format that can be analyzed. The server then cleans and organizes the data, removing noise and filling in missing data. This process is carried out using high-performance data server and database management software.

[1036] Introducing the Emotion Engine

[1037] The server is equipped with an emotion engine for analyzing the emotion data entered by the user. The emotion engine recognizes the user's emotional state using text analysis algorithms and selected emotion icons, and feeds the analysis results back to the generative AI model. This analysis uses natural language processing (NLP) techniques.

[1038] Building and training generative AI models

[1039] The server uses the cleaned and organized data to build a generative AI model. This model uses machine learning algorithms to repeatedly train on a variety of data to improve its prediction accuracy. Once trained, the model is used to generate fortune-telling results. This process utilizes machine learning frameworks such as TensorFlow and PyTorch.

[1040] Generating fortune-telling results

[1041] When a user inputs a question and emotional data into the fortune-telling system, the device sends the information to the server. The server analyzes the user's input and extracts key keywords and their associations. The emotion engine then analyzes the emotional data and feeds the analysis results back to the generative AI model. The server receives this feedback and generates personalized fortune-telling results based on the user's emotional state. The generated results include an understanding of the current situation, future predictions, and specific advice.

[1042] Displaying the results

[1043] The server then sends the generated fortune-telling results to the terminal, which then visually displays the results to the user using graphs and charts to help the user intuitively understand the results.

[1044] Specific examples

[1045] scenario

[1046] Imagine a woman in her 30s worried about her career. She's feeling stressed and chooses emoticons that reflect that.

[1047] 1. The user opens a web browser and accesses the fortune-telling system's homepage.

[1048] 2. The device displays a question input form and asks the user to enter the question, "Should I continue in my current job or consider changing jobs?"

[1049] 3. The user selects an emoticon that expresses an emotion and inputs their current emotional state.

[1050] 4. The user clicks the "Submit" button.

[1051] 5. The device sends the question and emotion data to the server.

[1052] 6. The server analyzes the question and extracts related keywords.

[1053] 7. The server uses the emotion engine to analyze the emotion data and feeds the analysis results back to the generative AI model.

[1054] 8. The server uses the generative AI model to generate fortune-telling results that include "the advantages and disadvantages of continuing in your current job" and "things to be aware of when changing jobs."

[1055] 9. The server sends the generated fortune-telling results to the terminal.

[1056] 10. The device displays the fortune-telling results to the user, visualizing things like "career prospects if you continue" and "risks and opportunities if you change jobs."

[1057] Prompt Sentence Examples

[1058] Should I stay in my current job or consider changing jobs?

[1059] "I want to know my future love luck"

[1060] In this way, the system of the present invention can provide highly accurate personalized fortune-telling results that take into account the user's emotions, and can give hints for solving problems and objective advice.

[1061] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1062] Step 1: Displaying the User Interface

[1063] The device displays the fortune-telling system's homepage in a web browser. This interface includes a form for users to enter their questions and concerns, as well as emoticons and sliders to express emotions.

[1064] Input: User interface URL

[1065] Output: A web page with a question form, emoticons, and a slider for entering emotions.

[1066] Specific operation: The device launches a web browser and accesses the specified URL. The fortune-telling system's homepage is displayed, along with an input form, emoticons for inputting emotions, and a slider.

[1067] Step 2: Enter and submit user data

[1068] Users enter their questions or concerns into the input form, select their current emotional state using emoticons or a slider, and click the "Submit" button when they're done.

[1069] Input: User question, emotional state (emoticons, slider)

[1070] Output: Question content and emotion data

[1071] What it does: A user enters a question into the form, such as "Should I stay in my current job or consider changing jobs?". Next, they click an emoticon that reflects their emotional state, adjust a slider to input the intensity of the emotion, and finally click the "Submit" button.

[1072] Step 3: Collect and preprocess data

[1073] The device sends the user's input questions and emotional data to a server, which then collects related data such as astrology, tarot card readings, and numerology from multiple data sources on the Internet and stores it in a database.

[1074] Input: User-entered data (questions, sentiment data), external data sources

[1075] Output: Cleaned and groomed data

[1076] Specific operation: The device sends the user's input information in a data format such as JSON to the server. The server analyzes the received data and collects relevant information from the necessary data sources. The collected data is stored in a database and converted into an analyzable format.

[1077] Step 4: Emotion analysis using the emotion engine

[1078] The server uses an emotion engine to analyze the emotion data entered by the user, which recognizes the user's emotional state using text analysis algorithms and selected emotion icons, and feeds the analysis results back to the generative AI model.

[1079] Input: User emotion data (text, emoticons)

[1080] Output: Sentiment analysis results (numerical data)

[1081] Specific operation: The server calls the emotion engine and sends the emotional information of the text and emoticons entered by the user. The emotion engine analyzes this data and outputs the emotional state as numerical data. The analysis results are fed back to the generative AI model.

[1082] Step 5: Building and training a generative AI model

[1083] The server uses the cleaned and curated data to build a generative AI model, which uses machine learning algorithms to repeatedly learn from diverse data and improve its prediction accuracy.

[1084] Input: Cleaned data

[1085] Output: Trained generative AI model

[1086] How it works: The server retrieves collected data from the database and performs a cleaning process. The cleaned data is then used to start training the generative AI model. Once training is complete, the model is ready to be used to generate fortune-telling results.

[1087] Step 6: Generate fortune-telling results

[1088] The server uses a generative AI model based on the user's question and sentiment analysis results to generate a fortune-telling result, which includes an understanding of the current situation, future predictions, and specific advice.

[1089] Input: User question, sentiment analysis results

[1090] Output: Fortune-telling results (current situation, future predictions, advice)

[1091] Specific operation: The server calls the generative AI model and provides the user's question and sentiment analysis results as input. The generative AI model processes this data and generates a fortune-telling result, which includes an answer to the user's question, a prediction, and specific advice.

[1092] Step 7: Send and view results

[1093] The server sends the generated fortune-telling results to the terminal, which then visually displays the results to the user using graphs and charts to help the user intuitively understand the results.

[1094] Input: Fortune-telling result

[1095] Output: Visual results (graphs, charts) that are displayed to the user

[1096] Specific operation: The server sends the generated fortune-telling results to the device in JSON format or similar. The device analyzes the received data and displays the results to the user. The results are visually displayed as graphs and charts, making them easier to understand.

[1097] Through the above steps, the system of the present invention can provide highly accurate and personalized fortune-telling results that take into account the user's emotions.

[1098] (Application example 2)

[1099] 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."

[1100] Conventional fortune-telling systems provide general results without considering the user's emotional state, making it difficult to provide personalized feedback or specific advice. Furthermore, some electronic payment systems lacked technology to provide specific suggestions based on the user's emotional state. This meant that they were unable to adequately help users select products or services that matched their current emotions.

[1101] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for providing a user interface, means for collecting data from multiple data sources, means for cleaning and organizing the collected data, means for building and training a generative AI model, means for analyzing questions and emotional data from the user, means for generating fortune-telling results according to the emotional state based on the analysis results, and means for displaying the generated fortune-telling results on the user interface. This makes it possible to provide personalized fortune-telling results and product / service recommendations that take the user's emotional state into consideration.

[1102] A "user interface" is the means by which a user accesses a system and performs input and output.

[1103] "Data Sources" refers to the various information sources and databases that provide information and statistics related to fortune-telling.

[1104] "Cleaning and maintenance" is a data processing step that removes noise data from collected data and fills in missing data.

[1105] A "generative AI model" is an artificial intelligence model that learns from large amounts of data and generates fortune-telling results and recommendations.

[1106] "Emotion data" is data that indicates the emotional state that the user is currently feeling, and is input in the form of text or icons.

[1107] "Fortune telling results" are personalized feedback and advice generated based on the user's questions and emotional data.

[1108] The "emotion engine" is an analytical engine that analyzes the emotional data entered by the user and feeds the results back to the generative AI model.

[1109] "Keywords" are key words or phrases extracted from the user's input and used for analysis.

[1110] A "prompt" is text that instructs a generative AI model to perform a specific task.

[1111] The present invention relates to a system that provides highly accurate fortune-telling results using a generative AI model and an emotion engine. The following describes in detail an embodiment of the present invention.

[1112] Overall system configuration

[1113] The server includes a means for providing a user interface, a means for collecting data from multiple data sources, a means for cleaning and organizing the collected data, a means for constructing and training a generative AI model, a means for analyzing questions and emotional data from the user, a means for generating fortune-telling results according to the emotional state based on the analysis results, and a means for displaying the generated fortune-telling results on the user interface.

[1114] User Interface Design

[1115] Users access the fortune-telling system using a dedicated smartphone application. This interface provides a form for users to enter their questions and concerns. It also provides a means for users to input their current emotional state using emoticons and a slider to express emotions. The device has the functionality to transmit the information and emotional data entered by the user to the server.

[1116] Data collection and cleaning

[1117] The server collects data on astrology, tarot card readings, numerology, etc. from multiple data sources on the Internet. The collected data undergoes a data processing process to remove noise data and fill in missing data before being stored in a database.

[1118] Introducing the Emotion Engine

[1119] The server is equipped with an emotion engine for analyzing the emotion data entered by the user. The emotion engine recognizes the user's emotion from the text and selected emotion icons, and feeds the analysis results back to the generative AI model. This makes it possible to generate personalized fortune-telling results based on the user's emotional state.

[1120] Building and training generative AI models

[1121] The server uses the cleaned and curated data to build a generative AI model that iteratively learns from the data and improves its prediction accuracy.

[1122] Generating and displaying fortune-telling results

[1123] When a user inputs a question and emotional data into the fortune-telling system, the device sends the information to the server. The server analyzes the user's input and extracts key keywords and their associations. The emotion engine then analyzes the emotional data and feeds the analysis results back to the generative AI model. The server receives this feedback and generates a personalized fortune-telling result based on the user's emotional state. The generated result is displayed on the device and provided visually to the user.

[1124] Hardware and Software Configuration

[1125] Hardware: Smartphones, servers

[1126] Software: Python, Transformers library (Hugging Face), TextBlob

[1127] Specific examples

[1128] Suppose a man in his 30s is feeling stressed and wants a product that will help him relax. The user types in "stress" and asks the following questions:

[1129] User Input: "Stress"

[1130] Question input: "What item would you like to use to relax?"

[1131] Example prompt for a generative AI model:

[1132] The user is in a stressful state and is asking the question: What item would help me relax? Based on this emotional state, make the best product recommendations.

[1133] In this way, the system of the present invention can provide personalized fortune-telling results and product and service recommendations that take into account the user's emotional state.

[1134] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1135] Step 1:

[1136] Users launch the smartphone app and enter their concerns or questions into a question input form. They also input their current emotional state using emojis and a slider to express their emotions.

[1137] Input: User question text, emotion data

[1138] Output: Question text and emotion data are sent to the device.

[1139] Step 2:

[1140] The device sends the question text and emotion data entered by the user to the server, where the data is encoded into an appropriate format.

[1141] Input: User-entered question text, sentiment data

[1142] Output: The encoded data sent to the server.

[1143] Step 3:

[1144] The server analyzes the received question text and sentiment data. First, it extracts key keywords from the question text.

[1145] Input: Encoded question text, sentiment data

[1146] Output: Extracted main keywords

[1147] Step 4:

[1148] The server analyzes the emotion data entered by the user using an emotion engine, which performs text analysis to recognize the user's emotional state and obtains the result.

[1149] Input: Emotion data

[1150] Output: Sentiment analysis result (e.g., "Stress")

[1151] Step 5:

[1152] The server uses a generative AI model to create a prompt based on the user's question text and the results of sentiment analysis. This prompt is then input into the generative AI model to generate a fortune-telling result.

[1153] Input: Question text, sentiment analysis results

[1154] Output: Generated fortune-telling result

[1155] Step 6:

[1156] The server then sends the generated fortune-telling results to the device and visually displays them to the user, including specific advice and product / service recommendations tailored to the user's emotional state.

[1157] Input: Fortune telling result

[1158] Output: Fortune telling results and recommendations displayed on the device

[1159] Step 7:

[1160] Users can check the fortune-telling results and recommended information displayed on their device and take appropriate action.

[1161] Input: Fortune-telling results and recommendations displayed on the device

[1162] Output: User choices and actions

[1163] 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.

[1164] 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.

[1165] 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.

[1166] [Fourth embodiment]

[1167] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1168] 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.

[1169] 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).

[1170] 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.

[1171] 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.

[1172] 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).

[1173] 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.

[1174] 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.

[1175] 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.

[1176] 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.

[1177] 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.

[1178] 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.

[1179] 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."

[1180] The present invention relates to a system that provides highly accurate fortune-telling results using a generative AI model and a statistical model. The following describes in detail an embodiment of the present invention.

[1181] User Interface Design

[1182] Users access the fortune-telling system using a web browser or a dedicated application. This interface provides a form for users to enter their questions and concerns. The terminal also has the functionality to transmit the information entered by the user to the server. It also includes an interface for visually displaying the generated fortune-telling results.

[1183] Data collection and pre-processing

[1184] The server collects statistical and fortune-telling-related data from multiple data sources on the Internet, including astrology, tarot card readings, and numerology data. The collected data is stored in a database and converted into an analyzable format. The server then cleans and organizes the data, removing noise and filling in missing data.

[1185] Building and training generative AI models

[1186] The server uses the cleaned and organized data to build a generative AI model (e.g., a large-scale language model). This model repeatedly uses the data to train and improve its prediction accuracy. Once trained, the model is used to generate fortune-telling results.

[1187] Generating fortune-telling results

[1188] When a user inputs a question into the fortune-telling system, the device sends the information to the server. The server analyzes the user's input using natural language processing technology. This analysis extracts key keywords and their relationships. Based on the analysis results, the server uses a generative AI model to generate fortune-telling results. The generated results include an understanding of the current situation, future predictions, and specific advice.

[1189] Displaying the results

[1190] The server then transmits the generated fortune-telling results to the terminal, which then visually displays the results to the user, possibly using graphs or charts to make the results easier to understand.

[1191] Specific examples

[1192] scenario:

[1193] Imagine a woman in her 30s who is worried about her career.

[1194] 1. The user opens a web browser and accesses the fortune-telling system's homepage.

[1195] 2. The terminal displays a question input form and asks the user to enter the question, "Should I continue in my current job or consider changing jobs?"

[1196] 3. The user clicks the "Submit" button.

[1197] 4. The device sends the question to the server.

[1198] 5. The server analyzes the question and extracts related keywords.

[1199] 6. The server uses the generative AI model to generate fortune-telling results that include "the advantages and disadvantages of continuing in your current job" and "things to be aware of when changing jobs."

[1200] 7. The server sends the generated fortune-telling result to the terminal.

[1201] 8. The terminal displays the fortune-telling results to the user, visualizing things like "career prospects if you continue" and "risks and opportunities if you change jobs."

[1202] In this way, the system of the present invention can provide users with highly accurate fortune-telling results based on scientific evidence, and can give hints for solving problems and objective advice.

[1203] The processing flow will be explained below.

[1204] Step 1:

[1205] Users access the fortune-telling system using a dedicated web browser or application, and enter their questions and concerns into a form displayed on the user interface.

[1206] Step 2:

[1207] The terminal receives the questions and concerns entered by the user and generates transmission data, which includes the user's input.

[1208] Step 3:

[1209] The terminal transmits the transmission data to the server, where communication takes place over the network.

[1210] Step 4:

[1211] The server analyzes the received data and uses natural language processing technology to analyze the user's question and extract keywords.

[1212] Step 5:

[1213] The server retrieves relevant statistical and fortune-telling data based on the analysis results, using database queries.

[1214] Step 6:

[1215] The server generates fortune-telling results using a generative AI model, which generates predictions and advice in response to user questions based on the trained data.

[1216] Step 7:

[1217] The server then structures the generated fortune-telling results, dividing them into sections such as "Current situation analysis," "Future predictions," and "Specific advice."

[1218] Step 8:

[1219] The server then sends the structured fortune-telling results to the terminal, where network communication also takes place.

[1220] Step 9:

[1221] The terminal displays the received fortune-telling results to the user, and graphs and charts can be used to present the results in a visually easy-to-understand format.

[1222] Step 10:

[1223] Users can check the displayed fortune-telling results and use them as a reference for future actions and decisions. For example, they can receive specific advice about their careers.

[1224] This allows users to receive highly accurate fortune-telling results and obtain hints for solving problems and objective advice.

[1225] Example 1

[1226] 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."

[1227] Conventional fortune-telling systems require manual analysis of large amounts of data to obtain highly accurate fortune-telling results, which is labor-intensive and time-consuming. Furthermore, it is difficult to provide specific and useful advice in response to user questions. This results in users being unable to obtain fully satisfactory fortune-telling results, and the reliability of fortune-telling is also low.

[1228] 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.

[1229] In this invention, the server includes means for providing a user interface, means for collecting data from multiple data sources, means for cleaning and organizing the collected data, means for building and training a generative AI model, means for analyzing questions from users, means for generating fortune-telling results based on the analysis results, means for displaying the generated fortune-telling results on the user interface, means for displaying the user interface on a web browser or dedicated application and including a form for users to input questions, means for analyzing data including the questions input by the user using natural language processing technology and extracting key keywords, means for inputting the extracted keywords into the generative AI model and generating fortune-telling results including specific advice, and means for providing an interface for visually displaying the results and visually displaying the fortune-telling results using graphs and charts. This enables users to easily obtain fortune-telling results with high accuracy and specific advice.

[1230] A "user interface" is a mechanism by which a user interacts with a computer system, including forms into which questions or concerns can be entered and screens that visually display the results.

[1231] A "data source" is a source of information, such as an internet website or database, from which statistical data or fortune-telling-related information is collected.

[1232] "Cleaning and grooming" is the process of removing noise and missing values ​​from collected data and converting it into a form suitable for analysis and learning.

[1233] A "generative AI model" is an artificial intelligence model that learns from collected data to make predictions and generate new data, and specifically refers to one that uses natural language processing technology.

[1234] "Natural language processing technology" is a technology that allows computers to analyze, understand, and generate human language, and includes analyzing text data and extracting keywords.

[1235] The "fortune telling result" is an answer including predictions and advice generated by the generative AI model based on the questions and concerns entered by the user.

[1236] A "web browser" is application software for viewing web pages on the Internet.

[1237] "Purpose-built Application" means software designed for a specific purpose and used to access this System.

[1238] A "database" is a system for systematically storing collected data and efficiently managing and searching it.

[1239] "Graphs and charts" are a means of visually representing data and are used to display fortune-telling results in an easy-to-understand manner.

[1240] This invention relates to a system that provides highly accurate fortune-telling results using generative AI models and statistical models. This system is realized by the cooperation of users, terminals, and servers.

[1241] User Interface Design

[1242] Users access the fortune-telling system using a web browser or a dedicated application. The user interface provides a form for users to enter their questions and concerns about fortune-telling. This form includes text boxes and a submit button. The terminal displays the user interface and accepts user input.

[1243] Data collection and pre-processing

[1244] The server collects data related to astrology, tarot card readings, numerology, etc. from multiple data sources on the Internet. The collected data is stored in a database, which includes functions for efficiently managing and searching the data.

[1245] Data cleaning and preparation is performed using software such as Python and Pandas, which removes noise and imputes missing data, converting the data into a format suitable for analysis and model training.

[1246] Building and training generative AI models

[1247] The server uses the cleaned and prepared data to build a generative AI model. Frameworks such as TensorFlow and PyTorch are used to build the model. The training data is repeatedly used to train the model and improve its prediction accuracy. Once trained, the model is used to generate fortune-telling results.

[1248] Generating fortune-telling results

[1249] When a user enters a question into the fortune-telling system and clicks the submit button, the device sends the information to the server, which receives the request using a web framework such as Flask or Django and performs natural language processing using the Natural Language Toolkit (NLTK) or spaCy to extract key keywords from the user's question.

[1250] Based on the extracted keywords, the server uses a generative AI model to generate fortune-telling results, which include an understanding of the current situation, future predictions, and specific advice.

[1251] Displaying the results

[1252] The server then sends the generated fortune-telling results to the device, which then visually displays the results in a user interface using HTML and JavaScript, which can include graphs and charts to make the results easy to understand.

[1253] Specific scenarios and prompt examples

[1254] The following scenarios provide specific labels:

[1255] Imagine a woman in her 30s who is worried about her career.

[1256] 1. The user opens a web browser and accesses the fortune-telling system's homepage.

[1257] 2. The terminal displays a question input form and asks the user to enter the question, "Should I continue in my current job or consider changing jobs?"

[1258] 3. The user clicks the "Submit" button.

[1259] 4. The device sends the question to the server.

[1260] 5. The server analyzes the question and extracts related keywords.

[1261] 6. The server uses the generative AI model to generate fortune-telling results that include "the advantages and disadvantages of continuing in your current job" and "things to be aware of when changing jobs."

[1262] 7. The server sends the generated fortune-telling result to the terminal.

[1263] 8. The terminal displays the fortune-telling results to the user, visualizing things like "career prospects if you continue" and "risks and opportunities if you change jobs."

[1264] Example prompt sentence:

[1265] Should I stay in my current job or consider changing jobs?

[1266] How should I proceed with my relationship in the future?

[1267] "How should I invest now when the economic situation is so uncertain?"

[1268] As described above, the present invention is a system for providing users with highly accurate and specific fortune-telling results. By combining a generative AI model and natural language processing technology, this system is able to generate and visually display specific advice for users' concerns.

[1269] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1270] Step 1: Creating the User Interface

[1271] Users access the fortune-telling system using a web browser or a dedicated application. The terminal uses HTML, CSS, and JavaScript to display a form where users can enter their fortune-telling questions and concerns. The form includes text boxes and a submit button. The entered question is sent to the server when the submit button is clicked.

[1272] Input: Questions entered by the user in a browser or application

[1273] Output: Question input form displayed on the device

[1274] Step 2: Data collection

[1275] The server automatically collects data related to astrology, tarot card readings, numerology, etc. from multiple data sources on the Internet using web scraping tools such as Python and Scrapy, and stores this data in databases such as MySQL and PostgreSQL.

[1276] Input: Data from internet sources

[1277] Output: Raw data stored in a database

[1278] Step 3: Data Cleaning and Preparation

[1279] The server uses libraries such as Pandas and NumPy to clean and organize the collected data, removing noise and filling in missing data, resulting in structured data suitable for analysis and learning.

[1280] Input: Raw data stored in a database

[1281] Output: Cleaned and organized structured data

[1282] Step 4: Building and training a generative AI model

[1283] The server uses the cleaned and prepared data to build a generative AI model. The model is designed using machine learning frameworks such as TensorFlow and PyTorch, and trained using the training data repeatedly. This results in a highly accurate model that is used to generate fortune-telling results.

[1284] Input: Cleaned and structured data

[1285] Output: Trained AI model

[1286] Step 5: Analyzing the user question

[1287] When a user enters a question into the fortune-telling system and clicks the submit button, the device sends the information to the server, which receives the request using a web framework such as Flask or Django and performs natural language processing using the Natural Language Toolkit (NLTK) or spaCy, which extracts key keywords from the user's question.

[1288] Input: The question entered by the user

[1289] Output: Extracted main keywords

[1290] Step 6: Generate fortune-telling results

[1291] The server uses a generative AI model based on the extracted keywords to generate fortune-telling results, which include an understanding of the current situation, future predictions, and specific advice.

[1292] Input: Extracted keywords

[1293] Output: Generated fortune-telling result

[1294] Step 7: View the results

[1295] The server sends the generated fortune-telling results to the device, which then uses JavaScript to display the results in HTML so that the user can visually confirm them. Graphs and charts can be added as needed to make the results easier to understand.

[1296] Input: Generated fortune-telling result

[1297] Output: Fortune telling results displayed on the user interface

[1298] (Application example 1)

[1299] 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."

[1300] Conventional fortune-telling systems lack a mechanism for providing fortune-telling advice tailored to a user's individual payment behavior. As a result, users are inconvenienced by not receiving appropriate fortune-telling guidance for their daily payment behavior. Furthermore, fortune-telling advice based on payment behavior is not personalized, so it cannot be useful information for users.

[1301] 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.

[1302] In this invention, the server includes means for providing a user interface, means for collecting data from multiple data sources, means for cleaning and organizing the collected data, means for building and training a generative AI model, means for analyzing questions from users, means for generating fortune-telling results based on the analysis results, means for displaying the generated fortune-telling results on the user interface, means for providing fortune-related advice based on the user's payment behavior, and means for displaying the provided advice on the electronic payment means, thereby enabling users to receive fortune-telling advice that is individually personalized for their daily payment behavior.

[1303] A "user interface" is an interactive means by which a user accesses a system and inputs and displays information.

[1304] "Multiple Data Sources" refers to multiple sources of data collected from the internet and other sources.

[1305] "Data cleaning and maintenance procedures" are methods that implement procedures to remove noise from collected data and to impute missing data.

[1306] "Means for building and training generative AI models" refers to methods for building generative artificial intelligence models (e.g., large-scale language models) using collected and cleaned data and training them to improve their accuracy.

[1307] "Means for analyzing questions from users" refers to a method of analyzing questions entered into the system by users using natural language processing technology and extracting key keywords and their relevance.

[1308] "Means for generating fortune-telling results" refers to a method of using a generative AI model to derive fortune-telling results based on user input and analysis results.

[1309] The "means for displaying the generated fortune-telling result on a user interface" is a method for visually displaying the generated fortune-telling result to the user.

[1310] The "means for providing fortune-related advice based on payment behavior" is a method for analyzing a user's payment behavior and generating specific fortune-related advice based thereon.

[1311] "Means for displaying the provided advice on an electronic payment means" refers to a method for displaying the generated fortune advice on an electronic payment application or terminal used by the user.

[1312] The present invention relates to a system that provides fortune-related advice based on a user's payment behavior. This system uses generative AI models and statistical models to provide highly accurate advice, and details of the system are described below.

[1313] User Interface Design

[1314] Users access the system using a smartphone with an electronic payment application installed. The user interface has a function that visually displays advice based on the day's fortune when the user makes a payment.

[1315] Data collection and pre-processing

[1316] The server collects statistical data and fortune-telling-related data from multiple data sources on the Internet, including astrology, tarot card readings, numerology, etc. The collected data is stored in a database, where it is cleaned and organized by removing noise data and filling in missing data.

[1317] Building and training generative AI models

[1318] The server uses the cleaned and organized data to build a generative AI model. This model uses a large-scale language model (GPT-2, for example) to repeatedly train on the data to improve its prediction accuracy. Once trained, the model is used to generate fortune-related advice.

[1319] Generating Advice

[1320] When a user makes a payment using their smartphone, the device sends the information to the server. The server then analyzes the information based on this payment behavior using natural language processing technology. This analysis extracts key keywords and their relationships, and generates fortune-related advice using a generative AI model.

[1321] For example, when a user pays for lunch, the server generates advice such as "You're lucky to pay cashlessly today!" This advice is generated based on the user's payment behavior and fortune-telling results. The following prompt sentences can also be used:

[1322] "What payment advice would you like me to give you today?"

[1323] Displaying the results

[1324] The server sends the generated advice to the terminal, which visually displays the advice to the user, allowing the user to receive personalized fortune-telling advice for their daily payment behavior.

[1325] Specific examples

[1326] Specifically, imagine a scenario in which a user pays for lunch using an electronic payment application on their smartphone. When the user presses the payment button, the device sends the information to the server, which then generates fortune-telling advice based on the payment behavior and sends it back to the device. The device then displays the advice on its screen: "Cashless payment is your lucky day today!"

[1327] Based on highly accurate fortune-telling results, this system is able to provide specific advice related to users' payment behavior and provide useful information to users in real time.

[1328] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1329] Step 1:

[1330] The server collects data from multiple data sources on the Internet. The input here is the URLs and APIs of the multiple data sources, and the output is the collected raw data. Specifically, it collects statistical data and fortune-telling-related data (astrology, tarot card readings, numerology, etc.) and stores it in a database.

[1331] Step 2:

[1332] The server cleans and organizes the collected data. The input of this step is the raw data collected in step 1, and the output is cleaned and organized data. Specifically, it removes noise data and fills in missing data. It uses libraries such as Pandas to clean and organize the data.

[1333] Step 3:

[1334] The server uses the cleaned and prepared data to build and train a generative AI model. The input of this step is the clean data, and the output is a trained generative AI model. Specifically, it uses a large-scale language model such as GPT-2 to repeatedly train the data and improve the model's predictive accuracy. It utilizes Hugging Face's Transformers library.

[1335] Step 4:

[1336] A user makes a payment using an electronic payment application on a smartphone. The input here is the data of the user's payment action, and the output is that data is sent to the server. The terminal detects that the payment button has been pressed and sends the information to the server.

[1337] Step 5:

[1338] The server analyzes the submitted payment behavior information using natural language processing technology. The input for this step is the user's payment behavior data, and the output is the extracted important keywords and their relevance. NLP technology is used for the analysis. Specifically, the text is tokenized and key keywords are extracted.

[1339] Step 6:

[1340] The server uses a generative AI model based on the analysis results to generate fortune-related advice. The input for this step is the extracted keywords, and the output is the fortune-telling advice text. Specifically, the prompt "Please tell me some advice for today's payments" is input into the model, and appropriate advice is generated.

[1341] Step 7:

[1342] The server sends the generated fortune advice to the terminal. The input of this step is the generated advice, and the output is that the advice is sent to the terminal. The use of RESTful API is common.

[1343] Step 8:

[1344] The terminal visually displays the received advice to the user. The input of this step is the advice sent from the server, and the output is that the advice is displayed on the user's smartphone screen. Specifically, the electronic payment application displays the advice "Cashless payment is your lucky day today!" as a pop-up message or similar.

[1345] Through these steps, users can receive personalized fortune-telling advice in real time for their daily payment behavior.

[1346] 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.

[1347] The present invention relates to a system that provides highly accurate fortune-telling results using a generative AI model and an emotion engine. The following describes in detail an embodiment of the present invention.

[1348] User Interface Design

[1349] Users access the fortune-telling system using a web browser or a dedicated application. This interface provides a form for users to enter their questions or concerns. It also provides a means for users to input their current emotional state by providing emoticons and sliders to express emotions. The terminal has the functionality to transmit the information and emotional data entered by the user to the server.

[1350] Data collection and pre-processing

[1351] The server collects statistical and fortune-telling-related data from multiple data sources on the Internet, including astrology, tarot card readings, and numerology data. The collected data is stored in a database and converted into an analyzable format. The server then cleans and organizes the data, removing noise and filling in missing data.

[1352] Introducing the Emotion Engine

[1353] The server is equipped with an emotion engine for analyzing the emotion data entered by the user. The emotion engine recognizes the user's emotion from the text and selected emotion icons, and feeds the analysis results back to the generative AI model. This makes it possible to generate personalized fortune-telling results based on the user's emotional state.

[1354] Building and training generative AI models

[1355] The server uses the cleaned and organized data to build a generative AI model (e.g., a large-scale language model). This model repeatedly uses the data to train and improve its prediction accuracy. Once trained, the model is used to generate fortune-telling results.

[1356] Generating fortune-telling results

[1357] When a user inputs a question and emotional data into the fortune-telling system, the device sends that information to the server. The server analyzes the user's input using natural language processing technology to extract key keywords and their associations. The emotion engine then analyzes the emotional data and feeds the analysis results back to the generative AI model. The server receives this feedback and generates personalized fortune-telling results based on the user's emotional state. The generated results include an understanding of the current situation, future predictions, and specific advice.

[1358] Displaying the results

[1359] The server then transmits the generated fortune-telling results to the terminal, which then visually displays the results to the user, possibly using graphs or charts to make the results easier to understand.

[1360] Specific examples

[1361] scenario:

[1362] Imagine a woman in her 30s worried about her career. She's feeling stressed and chooses emoticons that reflect that.

[1363] 1. The user opens a web browser and accesses the fortune-telling system's homepage.

[1364] 2. The terminal displays a question input form and asks the user to enter the question, "Should I continue in my current job or consider changing jobs?"

[1365] 3. The user selects an emoticon that expresses an emotion and inputs their current emotional state.

[1366] 4. The user clicks the "Submit" button.

[1367] 5. The device sends the question and emotion data to the server.

[1368] 6. The server analyzes the question and extracts related keywords.

[1369] 7. The server uses the emotion engine to analyze the emotion data and feeds the analysis results back to the generative AI model.

[1370] 8. The server uses the generative AI model to generate fortune-telling results, including "the pros and cons of staying in your current job" and "things to consider when changing jobs." This also includes specific advice based on the user's emotional state.

[1371] 9. The server sends the generated fortune-telling result to the terminal.

[1372] 10. The device displays the fortune-telling results to the user, visualizing things like "career prospects if you continue" and "risks and opportunities if you change jobs."

[1373] In this way, the system of the present invention can provide highly accurate fortune-telling results that take into account the user's emotions, and can give hints for resolving problems and objective advice.

[1374] The processing flow will be explained below.

[1375] Step 1:

[1376] Users access the fortune-telling system using a dedicated web browser or application, and enter their questions and concerns into a form displayed on the user interface.

[1377] Step 2:

[1378] The user inputs emotion data using emoticons and sliders that represent their current emotional state, selecting emotions such as "feeling stressed" or "feeling relieved."

[1379] Step 3:

[1380] The terminal receives the question content and emotion data entered by the user and generates transmission data, which includes the question in text format and the selected emotional state.

[1381] Step 4:

[1382] The terminal transmits the transmission data to the server via the network.

[1383] Step 5:

[1384] The server analyzes the received data and uses natural language processing technology to analyze the user's question and extract key keywords.

[1385] Step 6:

[1386] The server uses an emotion engine to analyze the emotion data entered by the user, which converts the entered emotional state into numerical data and analyzes that state.

[1387] Step 7:

[1388] The server compares the analyzed emotion data with keywords in the question and searches for relevant statistical and fortune-telling data from information in a database.

[1389] Step 8:

[1390] The server generates fortune-telling results using a generative AI model based on the acquired data. The generative AI model generates predictions and advice in response to the user's questions based on the trained data.

[1391] Step 9:

[1392] The server generates personalized fortune-telling results based on the user's emotional state, adjusting the content and presentation of advice based on the emotional data.

[1393] Step 10:

[1394] The server then structures the generated fortune-telling results, dividing them into sections such as "Current situation analysis," "Future predictions," and "Specific advice."

[1395] Step 11:

[1396] The server then sends the structured fortune-telling results to the terminal, where network communication also takes place.

[1397] Step 12:

[1398] The terminal displays the received fortune-telling results to the user, and graphs and charts can be used to present the results in a visually easy-to-understand format.

[1399] Step 13:

[1400] Users can check the displayed fortune-telling results and use them as a reference for future actions and decisions. For example, they can receive specific advice about their careers.

[1401] Through these specific processing steps, the user can receive highly accurate fortune-telling results that include emotional data, and can obtain hints for resolving problems and objective advice.

[1402] Example 2

[1403] 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."

[1404] Conventional fortune-telling systems provide uniform fortune-telling results without considering the user's emotional state, making it difficult to provide individually customized advice to users. Furthermore, they often fail to properly process noise and missing data in the collected data, resulting in inaccurate results. Therefore, there is a need for systems that can provide more accurate and personalized fortune-telling results that reflect the user's emotional state.

[1405] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes a means for providing a user interface, a means for collecting data from multiple data sources, a means for cleaning and organizing the collected data, a means for analyzing the user's emotional state using an emotion engine, a means for building and training a generative AI model, a means for analyzing questions from the user, a means for generating fortune-telling results based on the analysis results and emotion analysis results, and a means for displaying the generated fortune-telling results on the user interface. This makes it possible to provide highly accurate personalized fortune-telling results that reflect the user's emotional state.

[1406] "User Interface" refers to the screens, forms, and other interactive means by which a user accesses a system, enters information, and receives results.

[1407] "Data Source" means any external or internal source of data related to divination, such as astrology, tarot cards, numerology, etc.

[1408] "Cleaning" refers to the process of removing noise and missing values ​​from collected data and preparing it into an analyzable format.

[1409] "Emotion engine" refers to software or algorithms for analyzing an emotional state from user-entered text and emoticons.

[1410] A "generative AI model" refers to a machine learning model that learns from collected and organized data and generates fortune-telling results based on the user's questions and emotional state.

[1411] "Analysis" refers to the process of recognizing key keywords and topics using natural language processing techniques and other methods to analyze questions and data provided by users.

[1412] "Fortune telling results" refers to information that includes answers and predictions to the user's questions, as well as specific advice.

[1413] This invention relates to a system that provides highly accurate fortune-telling results using a generative AI model and an emotion engine. This system is mainly composed of three elements: a server, a terminal, and a user, and operates in the following procedure.

[1414] User Interface Design

[1415] Users access the fortune-telling system using a web browser or a dedicated application. This interface provides a form for users to enter their questions or concerns. It also provides a means for users to input their current emotional state by providing emoticons and sliders to express emotions. The terminal has the functionality to transmit the information and emotional data entered by the user to the server.

[1416] Data collection and pre-processing

[1417] The server collects data related to astrology, tarot card readings, numerology, etc. from multiple data sources on the Internet. The collected data is stored in a database and converted into a format that can be analyzed. The server then cleans and organizes the data, removing noise and filling in missing data. This process is carried out using high-performance data server and database management software.

[1418] Introducing the Emotion Engine

[1419] The server is equipped with an emotion engine for analyzing the emotion data entered by the user. The emotion engine recognizes the user's emotional state using text analysis algorithms and selected emotion icons, and feeds the analysis results back to the generative AI model. This analysis uses natural language processing (NLP) techniques.

[1420] Building and training generative AI models

[1421] The server uses the cleaned and organized data to build a generative AI model. This model uses machine learning algorithms to repeatedly train on a variety of data to improve its prediction accuracy. Once trained, the model is used to generate fortune-telling results. This process utilizes machine learning frameworks such as TensorFlow and PyTorch.

[1422] Generating fortune-telling results

[1423] When a user inputs a question and emotional data into the fortune-telling system, the device sends the information to the server. The server analyzes the user's input and extracts key keywords and their associations. The emotion engine then analyzes the emotional data and feeds the analysis results back to the generative AI model. The server receives this feedback and generates personalized fortune-telling results based on the user's emotional state. The generated results include an understanding of the current situation, future predictions, and specific advice.

[1424] Displaying the results

[1425] The server then sends the generated fortune-telling results to the terminal, which then visually displays the results to the user using graphs and charts to help the user intuitively understand the results.

[1426] Specific examples

[1427] scenario

[1428] Imagine a woman in her 30s worried about her career. She's feeling stressed and chooses emoticons that reflect that.

[1429] 1. The user opens a web browser and accesses the fortune-telling system's homepage.

[1430] 2. The device displays a question input form and asks the user to enter the question, "Should I continue in my current job or consider changing jobs?"

[1431] 3. The user selects an emoticon that expresses an emotion and inputs their current emotional state.

[1432] 4. The user clicks the "Submit" button.

[1433] 5. The device sends the question and emotion data to the server.

[1434] 6. The server analyzes the question and extracts related keywords.

[1435] 7. The server uses the emotion engine to analyze the emotion data and feeds the analysis results back to the generative AI model.

[1436] 8. The server uses the generative AI model to generate fortune-telling results that include "the advantages and disadvantages of continuing in your current job" and "things to be aware of when changing jobs."

[1437] 9. The server sends the generated fortune-telling results to the terminal.

[1438] 10. The device displays the fortune-telling results to the user, visualizing things like "career prospects if you continue" and "risks and opportunities if you change jobs."

[1439] Prompt Sentence Examples

[1440] Should I stay in my current job or consider changing jobs?

[1441] "I want to know my future love luck"

[1442] In this way, the system of the present invention can provide highly accurate personalized fortune-telling results that take into account the user's emotions, and can give hints for solving problems and objective advice.

[1443] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1444] Step 1: Displaying the User Interface

[1445] The device displays the fortune-telling system's homepage in a web browser. This interface includes a form for users to enter their questions and concerns, as well as emoticons and sliders to express emotions.

[1446] Input: User interface URL

[1447] Output: A web page with a question form, emoticons, and a slider for entering emotions.

[1448] Specific operation: The device launches a web browser and accesses the specified URL. The fortune-telling system's homepage is displayed, along with an input form, emoticons for inputting emotions, and a slider.

[1449] Step 2: Enter and submit user data

[1450] Users enter their questions or concerns into the input form, select their current emotional state using emoticons or a slider, and click the "Submit" button when they're done.

[1451] Input: User question, emotional state (emoticons, slider)

[1452] Output: Question content and emotion data

[1453] What it does: A user enters a question into the form, such as "Should I stay in my current job or consider changing jobs?". Next, they click an emoticon that reflects their emotional state, adjust a slider to input the intensity of the emotion, and finally click the "Submit" button.

[1454] Step 3: Collect and preprocess data

[1455] The device sends the user's input questions and emotional data to a server, which then collects related data such as astrology, tarot card readings, and numerology from multiple data sources on the Internet and stores it in a database.

[1456] Input: User-entered data (questions, sentiment data), external data sources

[1457] Output: Cleaned and groomed data

[1458] Specific operation: The device sends the user's input information in a data format such as JSON to the server. The server analyzes the received data and collects relevant information from the necessary data sources. The collected data is stored in a database and converted into an analyzable format.

[1459] Step 4: Emotion analysis using the emotion engine

[1460] The server uses an emotion engine to analyze the emotion data entered by the user, which recognizes the user's emotional state using text analysis algorithms and selected emotion icons, and feeds the analysis results back to the generative AI model.

[1461] Input: User emotion data (text, emoticons)

[1462] Output: Sentiment analysis results (numerical data)

[1463] Specific operation: The server calls the emotion engine and sends the emotional information of the text and emoticons entered by the user. The emotion engine analyzes this data and outputs the emotional state as numerical data. The analysis results are fed back to the generative AI model.

[1464] Step 5: Building and training a generative AI model

[1465] The server uses the cleaned and curated data to build a generative AI model, which uses machine learning algorithms to repeatedly learn from diverse data and improve its prediction accuracy.

[1466] Input: Cleaned data

[1467] Output: Trained generative AI model

[1468] How it works: The server retrieves collected data from the database and performs a cleaning process. The cleaned data is then used to start training the generative AI model. Once training is complete, the model is ready to be used to generate fortune-telling results.

[1469] Step 6: Generate fortune-telling results

[1470] The server uses a generative AI model based on the user's question and sentiment analysis results to generate a fortune-telling result, which includes an understanding of the current situation, future predictions, and specific advice.

[1471] Input: User question, sentiment analysis results

[1472] Output: Fortune-telling results (current situation, future predictions, advice)

[1473] Specific operation: The server calls the generative AI model and provides the user's question and sentiment analysis results as input. The generative AI model processes this data and generates a fortune-telling result, which includes an answer to the user's question, a prediction, and specific advice.

[1474] Step 7: Send and view results

[1475] The server sends the generated fortune-telling results to the terminal, which then visually displays the results to the user using graphs and charts to help the user intuitively understand the results.

[1476] Input: Fortune-telling result

[1477] Output: Visual results (graphs, charts) that are displayed to the user

[1478] Specific operation: The server sends the generated fortune-telling results to the device in JSON format or similar. The device analyzes the received data and displays the results to the user. The results are visually displayed as graphs and charts, making them easier to understand.

[1479] Through the above steps, the system of the present invention can provide highly accurate and personalized fortune-telling results that take into account the user's emotions.

[1480] (Application example 2)

[1481] 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."

[1482] Conventional fortune-telling systems provide general results without considering the user's emotional state, making it difficult to provide personalized feedback or specific advice. Furthermore, some electronic payment systems lacked technology to provide specific suggestions based on the user's emotional state. This meant that they were unable to adequately help users select products or services that matched their current emotions.

[1483] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for providing a user interface, means for collecting data from multiple data sources, means for cleaning and organizing the collected data, means for building and training a generative AI model, means for analyzing questions and emotional data from the user, means for generating fortune-telling results according to the emotional state based on the analysis results, and means for displaying the generated fortune-telling results on the user interface. This makes it possible to provide personalized fortune-telling results and product / service recommendations that take the user's emotional state into consideration.

[1484] A "user interface" is the means by which a user accesses a system and performs input and output.

[1485] "Data Sources" refers to the various information sources and databases that provide information and statistics related to fortune-telling.

[1486] "Cleaning and maintenance" is a data processing step that removes noise data from collected data and fills in missing data.

[1487] A "generative AI model" is an artificial intelligence model that learns from large amounts of data and generates fortune-telling results and recommendations.

[1488] "Emotion data" is data that indicates the emotional state that the user is currently feeling, and is input in the form of text or icons.

[1489] "Fortune telling results" are personalized feedback and advice generated based on the user's questions and emotional data.

[1490] The "emotion engine" is an analytical engine that analyzes the emotional data entered by the user and feeds the results back to the generative AI model.

[1491] "Keywords" are key words or phrases extracted from the user's input and used for analysis.

[1492] A "prompt" is text that instructs a generative AI model to perform a specific task.

[1493] The present invention relates to a system that provides highly accurate fortune-telling results using a generative AI model and an emotion engine. The following describes in detail an embodiment of the present invention.

[1494] Overall system configuration

[1495] The server includes a means for providing a user interface, a means for collecting data from multiple data sources, a means for cleaning and organizing the collected data, a means for constructing and training a generative AI model, a means for analyzing questions and emotional data from the user, a means for generating fortune-telling results according to the emotional state based on the analysis results, and a means for displaying the generated fortune-telling results on the user interface.

[1496] User Interface Design

[1497] Users access the fortune-telling system using a dedicated smartphone application. This interface provides a form for users to enter their questions and concerns. It also provides a means for users to input their current emotional state using emoticons and a slider to express emotions. The device has the functionality to transmit the information and emotional data entered by the user to the server.

[1498] Data collection and cleaning

[1499] The server collects data on astrology, tarot card readings, numerology, etc. from multiple data sources on the Internet. The collected data undergoes a data processing process to remove noise data and fill in missing data before being stored in a database.

[1500] Introducing the Emotion Engine

[1501] The server is equipped with an emotion engine for analyzing the emotion data entered by the user. The emotion engine recognizes the user's emotion from the text and selected emotion icons, and feeds the analysis results back to the generative AI model. This makes it possible to generate personalized fortune-telling results based on the user's emotional state.

[1502] Building and training generative AI models

[1503] The server uses the cleaned and curated data to build a generative AI model that iteratively learns from the data and improves its prediction accuracy.

[1504] Generating and displaying fortune-telling results

[1505] When a user inputs a question and emotional data into the fortune-telling system, the device sends the information to the server. The server analyzes the user's input and extracts key keywords and their associations. The emotion engine then analyzes the emotional data and feeds the analysis results back to the generative AI model. The server receives this feedback and generates a personalized fortune-telling result based on the user's emotional state. The generated result is displayed on the device and provided visually to the user.

[1506] Hardware and Software Configuration

[1507] Hardware: Smartphones, servers

[1508] Software: Python, Transformers library (Hugging Face), TextBlob

[1509] Specific examples

[1510] Suppose a man in his 30s is feeling stressed and wants a product that will help him relax. The user types in "stress" and asks the following questions:

[1511] User Input: "Stress"

[1512] Question input: "What item would you like to use to relax?"

[1513] Example prompt for a generative AI model:

[1514] The user is in a stressful state and is asking the question: What item would help me relax? Based on this emotional state, make the best product recommendations.

[1515] In this way, the system of the present invention can provide personalized fortune-telling results and product and service recommendations that take into account the user's emotional state.

[1516] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1517] Step 1:

[1518] Users launch the smartphone app and enter their concerns or questions into a question input form. They also input their current emotional state using emojis and a slider to express their emotions.

[1519] Input: User question text, emotion data

[1520] Output: Question text and emotion data are sent to the device.

[1521] Step 2:

[1522] The device sends the question text and emotion data entered by the user to the server, where the data is encoded into an appropriate format.

[1523] Input: User-entered question text, sentiment data

[1524] Output: The encoded data sent to the server.

[1525] Step 3:

[1526] The server analyzes the received question text and sentiment data. First, it extracts key keywords from the question text.

[1527] Input: Encoded question text, sentiment data

[1528] Output: Extracted main keywords

[1529] Step 4:

[1530] The server analyzes the emotion data entered by the user using an emotion engine, which performs text analysis to recognize the user's emotional state and obtains the result.

[1531] Input: Emotion data

[1532] Output: Sentiment analysis result (e.g., "Stress")

[1533] Step 5:

[1534] The server uses a generative AI model to create a prompt based on the user's question text and the results of sentiment analysis. This prompt is then input into the generative AI model to generate a fortune-telling result.

[1535] Input: Question text, sentiment analysis results

[1536] Output: Generated fortune-telling result

[1537] Step 6:

[1538] The server then sends the generated fortune-telling results to the device and visually displays them to the user, including specific advice and product / service recommendations tailored to the user's emotional state.

[1539] Input: Fortune telling result

[1540] Output: Fortune telling results and recommendations displayed on the device

[1541] Step 7:

[1542] Users can check the fortune-telling results and recommended information displayed on their device and take appropriate action.

[1543] Input: Fortune-telling results and recommendations displayed on the device

[1544] Output: User choices and actions

[1545] 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.

[1546] 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.

[1547] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1548] 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.

[1549] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1550] 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.

[1551] 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).

[1552] 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.

[1553] 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."

[1554] 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.

[1555] 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).

[1556] 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.

[1557] 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.

[1558] 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.

[1559] 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.

[1560] 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.

[1561] 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.

[1562] 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.

[1563] 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.

[1564] 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.

[1565] 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.

[1566] The following is further disclosed regarding the above embodiment.

[1567] (Claim 1)

[1568] means for providing a user interface;

[1569] a means for collecting data from multiple data sources;

[1570] A means of cleaning and organizing the collected data;

[1571] A means of building and training a generative AI model; and

[1572] means for analyzing user questions;

[1573] A means for generating a fortune-telling result based on the analysis result;

[1574] The system includes a means for displaying the generated fortune-telling results on a user interface.

[1575] (Claim 2)

[1576] The system of claim 1, wherein the system uses statistical data and fortune-telling data to generate highly accurate fortune-telling results using a machine learning model.

[1577] (Claim 3)

[1578] 10. The system of claim 1, wherein the system generates a fortune-telling result by analyzing a user's input and extracting keywords related to the input.

[1579] "Example 1"

[1580] The following claims are based on the content of the new invention.

[1581] (Claim 1)

[1582] means for providing a user interface;

[1583] a means for collecting data from multiple data sources;

[1584] A means of cleaning and organizing the collected data;

[1585] A means of building and training a generative AI model; and

[1586] means for analyzing user questions;

[1587] A means for generating a fortune-telling result based on the analysis result;

[1588] a means for displaying the generated fortune-telling result on a user interface;

[1589] a means for displaying a user interface in a web browser or a dedicated application, the user interface including a form for inputting a question;

[1590] A means for analyzing data including questions entered by users using natural language processing technology and extracting key keywords;

[1591] A means for inputting the extracted keywords into a generative AI model to generate fortune-telling results including specific advice;

[1592] The system provides an interface for visually displaying the results and includes means for visually displaying the fortune-telling results using graphs and charts.

[1593] (Claim 2)

[1594] The system of claim 1, wherein the system uses statistical data and fortune-telling data to generate highly accurate fortune-telling results using a machine learning model.

[1595] (Claim 3)

[1596] 10. The system of claim 1, wherein the system generates a fortune-telling result by analyzing a user's input and extracting keywords related to the input.

[1597] "Application Example 1"

[1598] (Claim 1)

[1599] means for providing a user interface;

[1600] a means for collecting data from multiple data sources;

[1601] A means of cleaning and organizing the collected data;

[1602] A means of building and training a generative AI model; and

[1603] means for analyzing user questions;

[1604] A means for generating a fortune-telling result based on the analysis result;

[1605] a means for displaying the generated fortune-telling result on a user interface;

[1606] means for providing fortune-related advice based on the user's payment behavior;

[1607] The system includes a means for displaying the provided advice on an electronic payment instrument.

[1608] (Claim 2)

[1609] The system of claim 1, wherein the system uses statistical data and fortune-telling data to generate highly accurate fortune-telling results using a machine learning model.

[1610] (Claim 3)

[1611] 10. The system of claim 1, wherein the system generates a fortune-telling result by analyzing a user's input and extracting keywords related to the input.

[1612] "Example 2: Combining Emotion Engines"

[1613] (Claim 1)

[1614] means for providing a user interface;

[1615] a means for collecting data from multiple data sources;

[1616] A means of cleaning and organizing the collected data;

[1617] means for analyzing a user's emotional state using an emotion engine;

[1618] A means of building and training a generative AI model; and

[1619] means for analyzing user questions;

[1620] A means for generating a fortune-telling result based on the analysis result and the emotion analysis result;

[1621] The system includes a means for displaying the generated fortune-telling results on a user interface.

[1622] (Claim 2)

[1623] The system of claim 1, wherein the system uses statistical data and fortune-telling data to generate highly accurate fortune-telling results using a machine learning model.

[1624] (Claim 3)

[1625] 10. The system of claim 1, wherein the system generates a fortune-telling result by analyzing a user's input and extracting keywords related to the input.

[1626] "Application example 2 when combining emotion engines"

[1627] (Claim 1)

[1628] means for providing a user interface;

[1629] a means for collecting data from multiple data sources;

[1630] A means of cleaning and organizing the collected data;

[1631] A means of building and training a generative AI model; and

[1632] means for analyzing user questions and emotion data;

[1633] A means for generating a fortune-telling result according to the emotional state based on the analysis result;

[1634] The system includes a means for displaying the generated fortune-telling results on a user interface.

[1635] (Claim 2)

[1636] 2. The system of claim 1, wherein the system uses statistical data and fortune-telling data to generate highly accurate fortune-telling results using an emotion engine and a machine learning model.

[1637] (Claim 3)

[1638] 10. The system of claim 1, wherein the system generates a fortune-telling result by analyzing a user's input and extracting keywords and emotional states associated with the input. [Explanation of symbols]

[1639] 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 providing a user interface; a means for collecting data from multiple data sources; A means of cleaning and organizing the collected data; A means of building and training a generative AI model; and means for analyzing user questions; A means for generating a fortune-telling result based on the analysis result; The system includes a means for displaying the generated fortune-telling results on a user interface.

2. The system of claim 1 , wherein the system uses statistical data and fortune-telling data to generate highly accurate fortune-telling results using a machine learning model.

3. 2. The system according to claim 1, wherein the system generates a fortune-telling result by analyzing a user's input and extracting keywords related to the input.

Images