system

Abstract

Provide a system. [Solution] means for receiving data input from a user terminal and storing the data in a database; means for receiving psychological test results and storing the results in a database; means for generating a psychological profile of the user based on the stored data and psychological test results; A means for generating an AI model based on the generated psychological profile; a means for approving access requests for access to the AI ​​model by qualified users and providing an AI interface; A system including:

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] In modern society, when a parent or family member suddenly passes away, it is difficult to maintain that individual's presence and memories for an extended period of time. Furthermore, parents are no longer able to provide advice or messages tailored to the circumstances of the situation, especially after their children have grown up. In such situations, surviving family members lack the means to find psychological comfort. Furthermore, technology for incorporating an individual's thoughts and feelings into AI has not yet advanced sufficiently, making it difficult to model a specific individual's personality or ideology. This invention aims to solve these problems and provide a system for maintaining family ties. [Means for solving the problem]

[0005] The present invention provides a system including a means for receiving data input from a user terminal and storing the data in a database, a means for receiving psychological test results and storing the results in a database, a means for generating a user's psychological profile based on the stored data and psychological test results, a means for generating an AI model based on the generated psychological profile, and a means for approving access requests from limited users to access the AI ​​model and providing an AI interface. This allows users to accumulate their own thoughts and feelings in blog format and combine them with psychological test results to generate an AI model that reflects their personal thoughts and emotions. This AI model can be accessed only by limited users, allowing, for example, parents to leave messages for their future children, providing peace of mind for families.

[0006] A "user device" is an electronic device used by a user to enter data, take psychological tests, and interact with AI.

[0007] A "server" is a central control unit that receives, processes, and stores data sent from user terminals.

[0008] "Database" refers to data storage for storing and managing received blog entries and psychological test results.

[0009] A "blog entry" is information in which a user inputs their thoughts and feelings in text format and sends them to the system.

[0010] A "psychological test" is response data to questions for evaluating the user's psychological characteristics and state.

[0011] A "psychological profile" is a profile that indicates a user's psychological characteristics and tendencies, generated based on saved blog entries and psychological test results.

[0012] An "AI model" is an artificial intelligence system that is generated based on a psychological profile and simulates a user's thoughts and emotions.

[0013] "Access Request" means a request submitted by a qualified user to access an AI model.

[0014] An "AI interface" is a screen or platform through which users can interact with AI models.

[0015] A "restricted user" is a specific user who is authorized to use the system, such as a family member or close friend. [Brief explanation of the drawings]

[0016] [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

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] 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).

[0020] 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.

[0021] 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.

[0022] 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.

[0023] 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."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 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.

[0027] 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).

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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."

[0037] This invention is a system that generates a psychological profile for each user based on the user's blog entries and psychological test results, and then creates an AI model based on that profile. This system provides a platform for users to continuously input their thoughts and feelings and store them in a database. It also has a function for conducting psychological tests, and the test results are similarly stored in the database. The system then generates a psychological profile for the user based on this data and builds an AI model. This AI model is accessible only to a limited number of users, and is particularly useful when parents want to leave messages for their children.

[0038] Program processing

[0039] Data accumulation phase

[0040] 1. Write and submit your blog entry

[0041] The user uses a device to enter their thoughts and feelings into the blog entry form, and when they're done, they click the send button.

[0042] The device receives the user's input and generates an HTTP POST request to send it to the server. The device then sends the generated request to the server.

[0043] The server analyzes the received data, formats it, and saves it in the database. After saving is complete, it returns a response to the device indicating that the save was successful.

[0044] 2. Conduct and send the psychological test

[0045] The user accesses the psychological test administration page, enters answers to questions, and submits the results after answering all questions.

[0046] The terminal compiles the user's answers and generates result data, which is then sent to the server.

[0047] The server analyzes the received psychological test results, stores them in a database, and returns a response indicating that the test was successfully saved to the device.

[0048] AI model generation phase

[0049] 1. Creating a Psychological Profile

[0050] The server uses text analysis techniques and statistical analysis to generate a psychological profile of the user based on the blog entries and psychological test results stored in the database.

[0051] 2. Generating an AI model

[0052] The server uses the generated psychological profile to run an AI model generation algorithm, which learns the user's writing style and emotional expressions to build an AI model that reflects the user's personality and tendencies.

[0053] AI utilization phase

[0054] 1. Submitting an AI usage request

[0055] The user's family members log in to a dedicated access page and send requests to use the AI. For example, if they want to hear a specific message, they can enter the content and send it.

[0056] The terminal composes the input request content and sends it to the server.

[0057] 2. Request Approval and Response

[0058] The server analyzes the received request and verifies that the request is from a valid user. After verification, it provides the appropriate AI interface.

[0059] The terminal displays the AI ​​interface provided by the server, providing an environment in which the user can interact with the AI.

[0060] The server inputs user questions and requests into the AI ​​model, generates appropriate responses, and sends the responses back to the device, where they are displayed.

[0061] Specific examples

[0062] Example blog entry

[0063] User: "I'm so happy that my first big presentation was a success today."

[0064] Terminal: Send this to the server.

[0065] Server: Receives and stores in a database.

[0066] Psychological test examples

[0067] User: Completes the psychological test and submits.

[0068] Terminal: Sends the results to the server.

[0069] Server: Stores the results in a database.

[0070] Examples of AI use

[0071] User's child: "Today is the day I'm making a big decision influenced by my mom. I'd like to hear her advice."

[0072] Device: Sends a request to the server.

[0073] Server: Uses AI models to generate appropriate responses and send them back to the device.

[0074] Terminal: Displays the message "We believe you will move forward with your own will."

[0075] The processing flow will be explained below.

[0076] Data accumulation phase

[0077] Step 1:

[0078] The user enters his or her thoughts and feelings into the blog entry form on the device.

[0079] Step 2:

[0080] After completing the input, the user clicks the send button.

[0081] Step 3:

[0082] The terminal receives the blog entry entered by the user.

[0083] Step 4:

[0084] The data received by the terminal is sent to the server via an HTTP POST request.

[0085] Step 5:

[0086] The server analyzes the received blog entry data and stores it in a database.

[0087] Step 6:

[0088] The server sends a successful save response to the terminal.

[0089] Step 7:

[0090] The user accesses the psychological test administration page.

[0091] Step 8:

[0092] The user answers each question to complete the test.

[0093] Step 9:

[0094] The user submits the results.

[0095] Step 10:

[0096] The terminal compiles the user's responses and generates result data.

[0097] Step 11:

[0098] The device sends the result data to the server via an HTTP POST request.

[0099] Step 12:

[0100] The server analyzes the received psychological test result data and stores it in a database.

[0101] Step 13:

[0102] The server sends a successful save response to the terminal.

[0103] AI model generation phase

[0104] Step 14:

[0105] The server generates a psychological profile of the user based on the blog entries and psychological test results stored in the database using text analysis techniques and statistical analysis.

[0106] Step 15:

[0107] The server uses the generated psychological profile to run an artificial intelligence model generation algorithm.

[0108] Step 16:

[0109] Based on the data learned by the algorithm, the server builds an AI model that reflects the user's writing style and emotional expression.

[0110] AI utilization phase

[0111] Step 17:

[0112] The user's family members log in to a dedicated access page and submit a request to use AI.

[0113] Step 18:

[0114] The terminal composes the input request content and sends it to the server.

[0115] Step 19:

[0116] The server analyzes the received request and checks the access rights.

[0117] Step 20:

[0118] After the server verifies that the access rights are appropriate, it provides the corresponding AI interface.

[0119] Step 20:

[0120] The device displays the AI ​​interface provided by the server.

[0121] Step 21:

[0122] The user's family interacts with the AI ​​through an AI interface.

[0123] Step 22:

[0124] The server inputs user questions and requests into the AI ​​model, which generates appropriate responses.

[0125] Step 23:

[0126] The server sends the generated AI response back to the terminal.

[0127] Step 24:

[0128] The terminal displays the received response to the user.

[0129] Example 1

[0130] 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."

[0131] Currently, many users have more opportunities to record their thoughts and feelings in blog format, but there are limited ways to effectively utilize this data. In particular, technology that generates a user's psychological profile and builds an AI model based on that to provide personalized responses to the user has not yet been fully developed. Another challenge is providing the generated AI model only to specific users, thereby creating an environment where users can use it with peace of mind.

[0132] 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.

[0133] In this invention, the server includes means for receiving blog entries entered from a user terminal and storing them in a database, means for receiving psychological test results and storing them in a database, means for generating a psychological profile of the user based on the stored blog entries and psychological test results, means for generating an AI model based on the generated psychological profile, and means for approving requests for limited users to access the AI ​​model and providing an AI interface. This makes it possible to effectively utilize the user's psychological data and provide personalized responses, giving the user a sense of security.

[0134] A "user terminal" is an electronic device used by a user to create and transmit input data.

[0135] A "blog entry" is a piece of writing that expresses thoughts and feelings and that a user writes on a separate page on the Web.

[0136] "Psychological test results" are data obtained as a result of a user answering a psychological test.

[0137] A "database" is a system for storing and managing various data.

[0138] A "psychological profile" is information that indicates a user's psychological characteristics and tendencies, generated based on blog entries and psychological test results.

[0139] An "artificial intelligence model" is a machine learning model that is generated based on a user's psychological profile and trained to perform specific tasks.

[0140] A "request" refers to an operation or information request made by a user to a system.

[0141] An "interface" is the means or screen through which a user interacts with a system.

[0142] "Text analysis" is the process of analyzing text data using natural language processing techniques to extract useful information.

[0143] "Real-time" refers to immediate reactions and processing without delay.

[0144] The present invention is a system that allows users to input blog entries and psychological test results, generates a psychological profile for the user based on that data, and then builds an artificial intelligence model based on that profile. This system provides a platform for users to continuously input their own thoughts and feelings and store them in a database. It also has a function for conducting psychological tests, and the test results are similarly stored in the database.

[0145] To implement this invention, the following hardware and software are required: The terminal used by the user has a function for connecting to the Internet, and can access the blog entry input form and psychological test page using a web browser (e.g., GOOGLE CHROME (registered trademark), Mozilla Firefox). The terminal also has a built-in program for generating HTTP POST requests. The server has a database management system (e.g., MySQL (registered trademark), PostgreSQL) installed, and uses text analysis technology (e.g., NLTK, spaCy) and machine learning libraries (e.g., TENSORFLOW (registered trademark), PyTorch).

[0146] The specific operation of the system is as follows: when a user enters a blog entry through a device and clicks the send button, the device receives the input, generates an HTTP POST request, and sends it to the server. The server analyzes the received request, formats the text content, and saves it in a database. If the save is successful, the server returns a "save successful" response to the device. Similarly, when a user answers a psychological test and clicks the send button, the device tallys the answers and sends them to the server, which then saves the results in a database.

[0147] The server periodically retrieves blog entries and psychological test results stored in the database and analyzes the text using natural language processing technology. Based on the analysis results, it generates a psychological profile for the user and stores this profile in the database. Furthermore, it uses a machine learning library to build an artificial intelligence model based on the generated psychological profile. This AI model is also stored in the database.

[0148] To access the AI ​​model, limited users log in to a dedicated access page and submit a request. For example, a user's child might type, "Today is the day I'm making a big decision influenced by my mother. I'd like to hear some advice from my mother," and click the submit button. The device then sends the request to the server, which analyzes the request and verifies that it is from a valid user. After verification, the device uses the corresponding AI model to generate an appropriate response and sends it back to the device. The device then displays the received response to the user.

[0149] As specific examples, the input prompt for a blog entry is in the format "Please enter the content of your blog. For example, 'I had a great time with my friends today.'" The input prompt for a psychological test is in the format "Please answer the following questions. For example, questions include 'How are you feeling lately?'" The prompt for using AI is in the format "Please enter the message you want to ask the AI. For example, 'How should I make an important decision?'"

[0150] As described above, the present invention is a system that effectively utilizes the user's psychological data and provides personalized responses, thereby giving the user a sense of security.

[0151] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0152] Step 1:

[0153] Entering and submitting a blog entry

[0154] A user accesses a blog entry form and enters their thoughts and feelings. For example, they might enter, "I was so happy that my first big presentation was a success today."

[0155] Input: A user types a blog entry and clicks the submit button.

[0156] The terminal receives the input content and generates an HTTP POST request to send to the server. Specifically, it converts the input text into JSON format and sends it to the server as the payload of the HTTP request.

[0157] Output: The user's blog entry is sent to the server.

[0158] Step 2:

[0159] Receive and store blog entries

[0160] The server receives the HTTP POST request sent from the terminal.

[0161] Input: Blog entry data sent as an HTTP POST request.

[0162] The server analyzes the received data and formats the text content. Specifically, it performs JSON parsing and extracts the text data.

[0163] The server stores the formatted data in a database.

[0164] Output: The blog entry saved in the database.

[0165] Step 3:

[0166] Conduct and send psychological tests

[0167] A user visits a psychological test page and answers a series of questions, for example, "How are you feeling these days?" and answers "very good."

[0168] Input: The user answers the psychological test and clicks the submit button.

[0169] The device aggregates the user's answers to each question and generates the result data. Specifically, it compiles each answer as a JSON object and generates an HTTP POST request to send it to the server.

[0170] Output: The result data of the psychological test is sent to the server.

[0171] Step 4:

[0172] Receiving and storing psychological test results

[0173] The server receives an HTTP POST request including the psychological test result sent from the terminal.

[0174] Input: Psychological test result data sent as an HTTP POST request.

[0175] The server analyzes and formats the received result data, specifically by parsing it in JSON and extracting the response data.

[0176] The server stores the formatted data in a database.

[0177] Output: Psychological test results stored in a database.

[0178] Step 5:

[0179] Psychological profile generation

[0180] The server retrieves the blog entries and psychological test results stored in the database.

[0181] Input: Blog entries and psychological test results stored in a database.

[0182] The server analyzes the text using natural language processing technology (e.g., NLTK, spaCy). Specifically, it performs morphological analysis of the text data and performs emotion and sentiment analysis.

[0183] The server processes the analysis results using statistical analysis techniques to generate a psychological profile of the user.

[0184] Output: The generated psychological profile is stored in a database.

[0185] Step 6:

[0186] Generating artificial intelligence models

[0187] The server retrieves the generated psychological profile.

[0188] Input: Psychological profiles stored in a database.

[0189] The server uses machine learning libraries (e.g., TensorFlow, PyTorch) to execute artificial intelligence model generation algorithms.

[0190] The server learns the user's writing style and emotional expression, and builds an artificial intelligence model that reflects the user's personality and tendencies.

[0191] Output: The constructed artificial intelligence model is stored in a database.

[0192] Step 7:

[0193] Submitting an AI request

[0194] The user's family members log in to a dedicated access page.

[0195] Input: The user's family member enters a request to use AI and clicks the send button. For example, they might enter, "Today is the day I'm making a big decision influenced by my mother. I'd like to hear her advice."

[0196] The device sends the request content to the server as an HTTP POST request in JSON format.

[0197] Output: An AI-enabled request is sent to the server.

[0198] Step 8:

[0199] Request Approval and Response

[0200] The server analyzes the received request and verifies that it is from the user's family.

[0201] Input: AI-powered request data.

[0202] The server loads the appropriate AI model and generates an appropriate response to the request, such as the message, "I trust you to move forward with your own will."

[0203] The server generates a response and sends it back to the terminal.

[0204] Output: The generated response is sent to the terminal.

[0205] Step 9:

[0206] Viewing the response

[0207] The terminal receives the response provided by the server.

[0208] Input: The response data sent by the server.

[0209] The device displays the received response to the user, for example, a message saying "I believe you will move forward with your own will."

[0210] Output: The user receives the response.

[0211] (Application example 1)

[0212] 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."

[0213] Today, many users blog online and gain a deeper understanding of themselves through psychological tests. However, there is no established method for centrally analyzing this data and providing users with personalized content recommendations and appropriate responses in real time. In addition, there are demands for specific situations, such as when parents want to leave special messages for their children, or for content tailored to the user's psychological state. Therefore, there is a need for the development of a system that can generate a user's psychological profile, build an AI model based on that profile, and provide personalized services.

[0214] 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.

[0215] In this invention, the server includes means for receiving data input from a user terminal and storing the data in a database, means for receiving psychological test results and storing the results in a database, means for generating a psychological profile of the user based on the stored data and the psychological test results, means for generating an AI model based on the generated psychological profile, means for approving access requests for limited users to access the AI ​​model and providing an AI interface, and means for recommending personalized content based on the user's psychological profile, thereby making it possible to individually recommend content based on the user's psychological state and provide personalized messages for specific situations.

[0216] A "user terminal" is a device that can be operated by a user and is a device that connects to the Internet and inputs, transmits, and receives data. Examples include smartphones, tablets, and personal computers.

[0217] A "database" is a system for efficiently storing, managing, and searching data, and is a recording medium that stores information such as user blog entries and psychological test results.

[0218] The "psychological test results" are the user's response data and evaluation data obtained through the psychological test, and are information for understanding the user's psychological characteristics and tendencies based on this.

[0219] A "psychological profile" is profile data that indicates a user's psychological characteristics and tendencies, and is generated by analyzing the user's blog entries and psychological test results.

[0220] An "AI model" is a data model built using artificial intelligence technology, and is a computational algorithm for generating specific actions and responses based on a user's psychological profile.

[0221] An "Access Request" is a request sent by a qualified user to access an AI model, and the AI ​​interface is provided only if the access right is confirmed.

[0222] "Personalized content" refers to information and media such as videos, articles, and music that are optimized and recommended to a specific user based on the user's psychological profile.

[0223] An "AI interface" is a user interface that allows a user to interact with an AI model, and includes the screen and operating means displayed on the terminal.

[0224] "Text analysis" is the process of analyzing text data using natural language processing techniques to extract information such as meaning and sentiment.

[0225] "Content recommendation" is a system that uses a user's psychological profile to select and present the most suitable content for the user.

[0226] As an embodiment of the present invention, there is a system that receives data input from a user terminal, stores it in a database, generates a psychological profile based on the data, creates an AI model, and recommends personalized content. Specifically, this system operates as follows.

[0227] Hardware and Software Use

[0228] User devices can be smartphones, tablets, or personal computers. The applications that run on the devices are built using React Native. Server-side processing is done with Node.js and Express.js, and data is stored in MongoDB. Additionally, NLTK, a Python natural language processing library, and TensorFlow, a machine learning library, are used to generate the AI ​​model.

[0229] Entering and Saving Data

[0230] The user terminal accepts blog entries and psychological test results from users as input. The user enters this data through the application and clicks a button to submit it. The terminal then sends this input data to the server as an HTTP POST request.

[0231] The server parses and formats the data it receives and stores it in a MongoDB database. For example, if a user types "I'm very happy that my first big presentation was a success today," it will be stored in the database.

[0232] Psychological profile generation

[0233] The server uses text analysis technology to generate a psychological profile of the user based on the blog entries and psychological test results stored in the database. This profile is generated using Python's NLTK library, which analyzes the data and extracts the user's psychological characteristics and tendencies.

[0234] Generating and applying AI models

[0235] Based on the generated psychological profile, the server uses TensorFlow to generate an AI model that learns the user's language style and emotional expressions, and then recommends responses and content that reflect the user's characteristics.

[0236] Personalized content recommendations

[0237] The user device receives personalized content from the server based on the user's psychological profile. This content can include videos, articles, music, and more, and the most suitable content is recommended to the user.

[0238] AI usage example

[0239] Users can send requests for specific messages or advice through the application. For example, if a child of the user types, "Today is the day I'm making a big decision, influenced by my mother. I'd like to hear her advice," the server uses an AI model to generate an appropriate response and sends it back to the device. This results in the message, "I trust you to move forward with your own will."

[0240] Prompt Sentence Examples

[0241] "Generate personalized content suggestions for this user using their latest psychological profile. Their blog entry is 'I was so happy today that my first big presentation went well.'"

[0242] In this way, the present invention makes it possible to provide personalized services based on the user's psychological state.

[0243] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0244] Step 1:

[0245] A user types a blog entry and clicks the submit button.

[0246] Input: The blog entry entered by the user

[0247] Specific operation:

[0248] Users open the application on their smartphone or computer, enter their thoughts and feelings in the text area, and then click the send button to send the blog entry.

[0249] Step 2:

[0250] The terminal transmits the blog entry data to the server.

[0251] Input: User's blog entry

[0252] Output: Blog entry data sent to the server

[0253] Specific operation:

[0254] The device takes the user's input and sends it to the server as an HTTP POST request, which includes the text data of the blog entry.

[0255] Step 3:

[0256] The server receives the blog entry data and stores it in a database.

[0257] Input: Blog entry data sent from the device

[0258] Output: Blog entries stored in the database

[0259] Specific operation:

[0260] The server receives the HTTP POST request and analyzes its contents. After analyzing, it stores the formatted data in MongoDB. After storing the data, the server returns a success response to the device.

[0261] Step 4:

[0262] The user answers the psychological test and submits the results.

[0263] Input: Psychological test results answered by the user

[0264] Output: Psychological test data sent to the server

[0265] Specific operation:

[0266] The user answers the psychological test presented on the application, and after completing all the questions, clicks the send button to send the results.

[0267] Step 5:

[0268] The terminal transmits the psychological test results to the server.

[0269] Input: User's psychological test results

[0270] Output: Psychological test data sent to the server

[0271] Specific operation:

[0272] The device collects the user's psychological test results and sends them to the server as an HTTP POST request, which includes the psychological test result data.

[0273] Step 6:

[0274] The server receives the psychological test results and stores them in a database.

[0275] Input: Psychological test result data sent from the device

[0276] Output: Psychological test results stored in a database

[0277] Specific operation:

[0278] The server receives the test results and analyzes them. After analysis, the formatted data is saved in MongoDB. After saving is complete, the server returns a success response to the device.

[0279] Step 7:

[0280] The server generates a psychological profile based on the blog entries and psychological test results.

[0281] Input: Blog entries and psychological test results stored in a database

[0282] Output: Generated psychological profile

[0283] Specific operation:

[0284] The server retrieves blog entries and psychological test results from the database, performs text analysis using Python's NLTK library, and generates a psychological profile of the user based on the analyzed data.

[0285] Step 8:

[0286] The server generates an AI model based on the psychological profile.

[0287] Input: Generated psychological profile

[0288] Output: The generated AI model

[0289] Specific operation:

[0290] The server analyzes the psychological profile using the TensorFlow library and generates an AI model that learns the user's language style and emotional expressions.

[0291] Step 9:

[0292] Submit an access request for access to an AI model by a limited user.

[0293] Input: Access request from a restricted user

[0294] Output: Access request sent to the server

[0295] Specific operation:

[0296] Users log in to a dedicated access page on the application and submit a request to access the AI ​​model.

[0297] Step 10:

[0298] The server approves the access request and provides the AI ​​interface.

[0299] Input: Access request sent by the user

[0300] Output: Provided AI interface

[0301] Specific operation:

[0302] The server analyzes the received request and checks the user's access rights, then provides the AI ​​interface to the device.

[0303] Step 11:

[0304] The server recommends personalized content based on the user's psychological profile.

[0305] Input: User's psychological profile

[0306] Output: personalized content

[0307] Specific operation:

[0308] The server then recommends the most suitable content for the user, such as videos, articles, and music, based on the generated psychological profile, using a generative AI model and prompts to select the appropriate content.

[0309] Example prompt sentence:

[0310] "Generate personalized content suggestions for this user using their latest psychological profile. Their blog entry is 'I was so happy today that my first big presentation went well.'"

[0311] 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.

[0312] This invention is a system that generates a psychological profile for each user based on the user's blog entries and psychological test results, and creates an AI model based on that profile. This system incorporates an emotion engine that recognizes the user's emotions, and can reflect the user's overall feelings and emotional changes in the AI ​​model.

[0313] Program processing

[0314] Data accumulation phase

[0315] 1. Write and submit your blog entry

[0316] The user enters their thoughts and feelings into the blog entry form on their device, and when they're done, clicks the send button.

[0317] The device receives the user's input and generates an HTTP POST request to send it to the server. The device then sends the generated request to the server.

[0318] The server analyzes the received data, formats it, and saves it in the database. After saving is complete, it returns a response to the device indicating that the save was successful.

[0319] 2. Emotion Recognition by Emotion Engine

[0320] The server sends the received blog entry data to an emotion engine to analyze the user's emotions.

[0321] The emotion engine generates emotion analysis results, which are also stored in the database.

[0322] 3. Conduct and send the psychological test

[0323] The user accesses the psychological test administration page, enters answers to questions, and submits the results after answering all questions.

[0324] The terminal compiles the user's answers and generates result data, which is then sent to the server.

[0325] The server analyzes the received psychological test results, stores them in a database, and returns a response indicating that the test was successfully saved to the device.

[0326] 4. Emotion recognition using emotion engine (psychological test)

[0327] The server sends the received psychological test results to the emotion engine to analyze the user's emotions.

[0328] The emotion engine generates emotion analysis results, which are also stored in the database.

[0329] AI model generation phase

[0330] 1. Creating a Psychological Profile

[0331] The server uses text analysis techniques and statistical analysis to generate a psychological profile of the user based on the blog entries, psychological test results, and sentiment analysis results stored in the database.

[0332] 2. Generating an AI model

[0333] The server uses the generated psychological profile to run an AI model generation algorithm, which learns the user's writing style and emotional expressions to build an AI model that reflects the user's personality and tendencies.

[0334] AI utilization phase

[0335] 1. Submitting an AI usage request

[0336] The user's family members log in to a dedicated access page and send requests to use the AI. For example, if they want to hear a specific message, they can enter the content and send it.

[0337] The terminal composes the input request content and sends it to the server.

[0338] 2. Request Approval and Response

[0339] The server analyzes the received request and checks the access rights.

[0340] After verifying that the access rights are appropriate, the corresponding AI interface will be provided.

[0341] 3. AI-User Interaction

[0342] The server inputs user questions and requests into the AI ​​model and generates appropriate responses.

[0343] The AI ​​model generates responses based on the analysis results of the emotion engine and provides messages that take the user's emotions into consideration.

[0344] The terminal displays the AI ​​interface provided by the server, providing an environment in which the user can interact with the AI.

[0345] The server sends the generated AI response back to the terminal, where it is displayed.

[0346] Specific examples

[0347] Example blog entry

[0348] User: "I was so happy today. I feel so much better after hearing the good news."

[0349] Terminal: Send this entry to the server.

[0350] Server: Receives and stores in the database. The emotion engine recognizes the emotion "happy" and stores the result.

[0351] Psychological test examples

[0352] User: Completes the psychological test and submits.

[0353] Terminal: Sends the results to the server.

[0354] Server: Save the results in the database. The emotion engine recognizes the emotion "nervous" and saves the result.

[0355] Examples of AI use

[0356] User's child: "I want to talk to my mom. I especially need encouragement today."

[0357] Device: Sends a request to the server.

[0358] Server: Accepts the request and uses the AI ​​model.

[0359] Device: Displays the message "We're always rooting for you. Believe in yourself and move forward."

[0360] The processing flow will be explained below.

[0361] Data accumulation phase

[0362] Step 1:

[0363] The user enters his or her thoughts and feelings into the blog entry form on the device.

[0364] Step 2:

[0365] After completing the input, the user clicks the send button.

[0366] Step 3:

[0367] The terminal receives the blog entry input by the user.

[0368] Step 4:

[0369] The data received by the terminal is sent to the server via an HTTP POST request.

[0370] Step 5:

[0371] The server analyzes the received blog entry data and stores it in a database.

[0372] Step 6:

[0373] The server sends a successful save response to the terminal.

[0374] Step 7:

[0375] The server sends the blog entry data to the emotion engine for emotion analysis.

[0376] Step 8:

[0377] The emotion engine generates the emotion analysis results of the blog entries and stores them in the database.

[0378] Step 9:

[0379] The user accesses the psychological test administration page.

[0380] Step 10:

[0381] The user answers each question, completes the test, and submits the results.

[0382] Step 11:

[0383] The terminal compiles the user's responses and generates result data.

[0384] Step 12:

[0385] The device sends the result data to the server via an HTTP POST request.

[0386] Step 13:

[0387] The server analyzes the received psychological test result data and stores it in a database.

[0388] Step 14:

[0389] The server sends a successful save response to the terminal.

[0390] Step 15:

[0391] The server sends the psychological test results to the emotion engine for emotion analysis.

[0392] Step 16:

[0393] The emotion engine performs emotion analysis of the psychological test results and stores the results in a database.

[0394] AI model generation phase

[0395] Step 17:

[0396] The server generates a psychological profile of the user using text analysis techniques and statistical analysis based on the blog entries, psychological test results, and sentiment analysis results stored in the database.

[0397] Step 18:

[0398] The server uses the generated psychological profile to run an artificial intelligence model generation algorithm.

[0399] Step 19:

[0400] Based on the data learned by the algorithm, the server builds an AI model that reflects the user's writing style and emotional expression.

[0401] AI utilization phase

[0402] Step 20:

[0403] The user's family members log in to a dedicated access page and submit a request to use AI.

[0404] Step 21:

[0405] The terminal composes the input request content and sends it to the server.

[0406] Step 22:

[0407] The server analyzes the received request and checks the access rights.

[0408] Step 23:

[0409] After the server verifies that the access rights are appropriate, it provides the corresponding AI interface.

[0410] Step 24:

[0411] The device displays the AI ​​interface provided by the server, providing an environment in which the user can interact with the AI.

[0412] Step 25:

[0413] The user's family interacts with the AI ​​through an AI interface.

[0414] Step 26:

[0415] The server inputs user questions and requests into the AI ​​model, which generates appropriate responses.

[0416] Step 27:

[0417] The server generates an AI response based on the analysis results of the emotion engine and provides a message that takes the user's emotions into consideration.

[0418] Step 28:

[0419] The server sends the generated AI response back to the terminal, where it is displayed.

[0420] Example 2

[0421] 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."

[0422] Conventional systems simply store users' blog entries and psychological test results in a database, making it difficult to deeply analyze users' emotional changes and psychological tendencies and reflect them in the AI ​​model. Furthermore, the responses generated by the AI ​​model did not take into account the user's emotions, resulting in a decline in the quality of the dialogue. This made it impossible to provide detailed psychological support to users.

[0423] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0424] In this invention, the server includes means for receiving data input from a user terminal and storing the data in a database, means for receiving psychological test results and storing the results in a database, means for generating a psychological profile of the user based on the stored data and the psychological test results, means for generating an AI model based on the generated psychological profile, means for emotionally analyzing the user's input data using an emotion engine, means for storing the analyzed emotion data in a database, means for emotionally analyzing the psychological test results using the emotion engine, and means for approving access requests for limited users to access the AI ​​model and providing an AI interface. This makes it possible to deeply analyze the user's emotional changes and psychological tendencies and generate an AI model that takes the user's emotions into consideration.

[0425] A "user terminal" is a device used by a user to input data, and includes devices such as personal computers and smartphones.

[0426] A "server" is a central processing unit that processes data received from user terminals and stores it in a database.

[0427] A "database" is a storage device for permanently storing received data, emotion analysis results, and psychological test results.

[0428] The "psychological test result" is data generated by the user answering the psychological test, and includes information indicating the user's psychological state.

[0429] A "psychological profile" is data that indicates a user's psychological tendencies and characteristics, generated based on the user's blog entries, psychological test results, and emotion analysis results.

[0430] An "AI model" is an artificial intelligence model generated based on a psychological profile, which learns the user's writing style and emotional expression.

[0431] The "emotion engine" is a system that uses natural language processing technology to analyze user input data and extract emotions as tags.

[0432] "Access Request" means a request submitted by a qualified user to access an AI model.

[0433] An "AI interface" is a user interface that provides an environment in which users can interact with an AI model.

[0434] This invention is a system that generates a user's psychological profile based on the user's blog entries and psychological test results, and creates an AI model based on that profile. This system incorporates an emotion engine that recognizes the user's emotions, and can reflect the user's overall feelings and emotional changes in the AI ​​model.

[0435] Hardware and Software Configuration

[0436] User terminal: A device such as a computer or smartphone that is used by a user to input data.

[0437] Server: A central processing unit that processes data and stores it in a database. A server usually consists of a computer with advanced computing power.

[0438] Database: A storage device for persistently storing received data, sentiment analysis results, and psychological test results. For example, an SQL database or NoSQL database is used.

[0439] Emotion engine: Software that uses natural language processing technology to analyze user input data and extract emotions as tags. For example, an NLP library implemented in Python or a dedicated sentiment analysis API is used.

[0440] System processing flow

[0441] 1. Write and submit your blog entry

[0442] The user enters their feelings and thoughts into the blog entry form on their device and clicks the send button.

[0443] The terminal receives the user's input data and sends it to the server as an HTTP POST request.

[0444] The server receives the data, analyzes and formats it, and saves it in the database. After saving is complete, it returns a successful save response to the device.

[0445] 2. Emotion Recognition by Emotion Engine

[0446] The server sends the blog entry data to an emotion engine to analyze the user's emotions.

[0447] The emotion engine uses NLP technology to extract emotions such as "happy" or "sad" as tags.

[0448] The server stores the emotion analysis results received from the emotion engine in a database.

[0449] 3. Conduct and send the psychological test

[0450] A user accesses a psychological test question page, enters answers to all questions, and submits the answer.

[0451] The terminal aggregates the user's response data, converts it into structured data (e.g., JSON format), and sends it to the server.

[0452] The server receives the psychological test results, analyzes and stores them, and returns a response indicating that the test results were successfully stored.

[0453] 4. Emotion recognition using emotion engine (psychological test)

[0454] The server sends the psychological test results to the emotion engine, which analyzes the user's emotions again.

[0455] The emotion engine generates analysis results and stores the results in a database.

[0456] 5. Generating a Psychological Profile

[0457] The server retrieves all blog entries, psychological test results, and sentiment analysis results from the database and generates a psychological profile using text analysis techniques (natural language processing and statistical analysis).

[0458] 6. Generating AI models

[0459] The server then runs an AI model generation algorithm (such as a neural network) based on the generated psychological profile. This model learns the user's writing style and emotional expressions, and reflects the user's personality and tendencies.

[0460] 7. Submitting an AI Request

[0461] The user's family members log in to a dedicated access page and send a request to use AI from their device.

[0462] 8. Request Approval and Response

[0463] The server analyzes the received request, checks the access rights, and then provides the corresponding AI interface.

[0464] 9. AI-User Interaction

[0465] The server inputs user questions and requests into the AI ​​model and generates appropriate responses.

[0466] The emotion engine is taken into account when generating responses, generating emotion-sensitive messages.

[0467] The server generates a response and sends it back to the terminal, which displays it.

[0468] Specific examples

[0469] Example blog entry

[0470] User: "I was very happy today. Hearing the good news brightened my mood." is entered and sent from the device to the server.

[0471] The server receives the data and stores it in a database. The emotion engine recognizes the emotion "happy" and stores the result.

[0472] Psychological test examples

[0473] The user answers a psychological test and sends the result of feeling "tense" from the terminal to the server.

[0474] The server stores the results in a database, and the emotion engine recognizes the emotion "nervous" and stores the result as well.

[0475] Examples of AI use

[0476] User's child: "I want to talk to my mother. I especially need encouragement today." Sends a request from the device to the server.

[0477] The server approves the request and uses an AI model to generate a message saying, "We're always rooting for you. Believe in yourself and move forward."

[0478] The server generates a message and returns it to the terminal, where it is displayed.

[0479] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0480] Step 1:

[0481] The user enters their feelings and thoughts into the blog entry form on their device and clicks the send button. This operation generates the entered text data.

[0482] Step 2:

[0483] The terminal receives the user's input and composes it into an HTTP POST request, which is then sent to the server as input data.

[0484] Step 3:

[0485] The server receives the HTTP POST request and analyzes the input data. Through analysis, text data is extracted. The text data is then formatted for storage in the database. The formatted data is saved in the database and a response indicating successful saving is returned to the terminal.

[0486] Step 4:

[0487] The server passes the formatted text data to the emotion engine, which analyzes the user's emotions. During this emotion analysis process, emotion tags are extracted from the text using NLP technology. The extracted emotion tags are generated as the emotion analysis results.

[0488] Step 5:

[0489] The server stores the emotion analysis results received from the emotion engine in a database.

[0490] Step 6:

[0491] The user accesses the psychological test question page through their device and enters their answers. After answering all the questions, the user submits the results. This operation generates the psychological test answer data.

[0492] Step 7:

[0493] The device collects the user's psychological test response data and converts it into structured data such as JSON format, then sends the generated structured data to the server as an HTTP POST request.

[0494] Step 8:

[0495] The server receives the HTTP POST request, analyzes the psychological test results, saves the analyzed results in a database, and returns a response indicating that the save was successful to the device.

[0496] Step 9:

[0497] The server sends the psychological test results to the emotion engine, which then analyzes the user's emotions again. The emotion engine extracts emotion tags from the psychological test results and generates emotion analysis results.

[0498] Step 10:

[0499] The server stores the emotion analysis results received from the emotion engine in a database.

[0500] Step 11:

[0501] The server retrieves blog entry data, psychological test results, and sentiment analysis results from the database, and then generates a psychological profile of the user using text analysis techniques and statistical analysis based on the retrieved data.

[0502] Step 12:

[0503] The server uses the generated psychological profile to run an AI model generation algorithm, which builds an AI model that learns the user's writing style and emotional expressions.

[0504] Step 13:

[0505] The user's family members log in to a dedicated access page and send requests for using AI from their devices, for example, by entering, "I want to hear an encouraging message from my mother."

[0506] Step 14:

[0507] The terminal composes the input request content as an HTTP POST request and sends it to the server.

[0508] Step 15:

[0509] The server analyzes the received request and checks the access rights. After the access rights are confirmed, the corresponding AI interface is provided.

[0510] Step 16:

[0511] The server inputs questions and requests from users into the AI ​​model and generates appropriate responses. The AI ​​model generates responses that take the user's emotions into consideration based on the analysis results of the emotion engine.

[0512] Step 17:

[0513] The server generates a response that is sent back to the terminal and displayed there.

[0514] Step 18:

[0515] The terminal displays the AI ​​interface provided by the server, creating an environment in which the user can interact with the AI.

[0516] (Application example 2)

[0517] 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."

[0518] Conventional advertising systems lack personalization that takes into account the user's emotions and psychological state, and deliver a large number of generic ads, making it difficult to quickly adapt to the user's interests and emotions. Furthermore, they lack technology that effectively utilizes users' daily blog entries and psychological test results to optimize ads. This results in a monotonous user experience and ineffective advertising.

[0519] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0520] In this invention, the server includes means for receiving data input from a user terminal and storing the data in a database, means for receiving psychological test results and storing the results in a database, means for generating a psychological profile of the user based on the stored data and the psychological test results, means for generating an AI model based on the generated psychological profile, means for approving access requests for limited users to access the AI ​​model and providing an AI interface, and means for generating and displaying personalized advertisements according to the user's psychological state, thereby making it possible to provide advertisements that reflect the user's emotions and psychological state and achieve a more effective advertising experience.

[0521] 1. "User terminal" means an input and display device used by a user, including mobile devices such as smartphones and tablets.

[0522] 2. "Database" means an information system for organizing and storing received data and analysis results, including relational databases and NoSQL databases.

[0523] 3. "Psychological test results" are data resulting from a user's responses to a psychological test, and are data that indicate the user's psychological state and personality traits.

[0524] 4. "Psychological profile" is a data set that shows the psychological characteristics of a user analyzed based on the user's blog entries and psychological test results.

[0525] 5. "AI Model" means a computer program generated by machine learning algorithms based on a user's psychological profile, capable of predicting and responding to a user's emotions and tendencies.

[0526] 6. "Access Request" means a request submitted by a User to access certain data or functionality.

[0527] 7. “AI Interface” means an interface through which a user interacts with an AI model, whether in text, voice, or other format.

[0528] 8. "Advertisement" means information displayed to promote a particular product or service to a user.

[0529] 9. "Personalized advertising" refers to individual advertising optimized based on a user's psychological state or characteristics.

[0530] 10. "Sentiment analysis" is the process of identifying emotions and emotional changes from user input data (such as blog entries or psychological test results).

[0531] 11. "Blog entry" is data in the form of text in which a user describes everyday events and feelings.

[0532] 12. "Text analysis" is the process of analyzing text data using natural language processing techniques to extract meaning and sentiment.

[0533] This invention is a system that generates a psychological profile for each user based on the user's blog entries and psychological test results, and then creates an AI model based on that profile. This system incorporates an emotion engine that recognizes the user's emotions, allowing the user's overall state of mind and emotional changes to be reflected in the AI ​​model. The specific program processing and required hardware and software are described in detail below.

[0534] Hardware and software used

[0535] User device: Mobile devices such as smartphones and tablets

[0536] Database: AWS (registered trademark) RDS (Relational Database Service)

[0537] Server: Node.js + Express.js

[0538] Emotion Engine API: IBM Watson(R) Natural Language Understanding

[0539] AI model generation: Scikit-learn, TensorFlow

[0540] Frontend: React Native

[0541] Program processing

[0542] 1. Data entry on the user's device

[0543] The application provides a form for users to write blog entries. Users use this form to input their daily events and feelings. Once the input is complete, they press the submit button to send the data to the server.

[0544] 2. Receiving and storing data on the server

[0545] The server receives data sent from the user terminal using an HTTP POST request and saves the received data in a database, which then stores the blog entry and its related information.

[0546] 3. Emotion analysis

[0547] The server sends the saved blog entry data to the emotion engine API to analyze the user's emotions. The emotion engine identifies the emotions in the text and sends the results back to the server. The acquired emotion analysis results are then stored in the database.

[0548] 4. Psychological profile generation

[0549] The server uses text analysis techniques and statistical analysis to generate a user's psychological profile based on the blog entries, psychological test results, and sentiment analysis results stored in the database. The generated profile details the user's psychological characteristics.

[0550] 5. AI model generation

[0551] The server then uses the generated psychological profile to run a machine learning algorithm, which learns the user's writing style and emotional expressions, and builds an AI model that reflects the user's personality and tendencies.

[0552] 6. Generating and displaying personalized advertisements

[0553] The server generates advertisements based on the user's psychological state. These advertisements reflect the user's emotions and interests and are delivered in real time from the server to the user's device. The user's device then displays the received advertisements on its screen, with content that the user finds interesting.

[0554] Specific examples

[0555] Example blog entry

[0556] User: "I was so happy today. I feel so much better after hearing the good news."

[0557] Server: Receives this entry and saves it in the database. The emotion engine recognizes the emotion "happy" and saves the result.

[0558] Examples of personalized ads

[0559] An advertisement for a "travel package with celebratory benefits" is displayed on the user's device based on the user's blog entry and sentiment analysis results.

[0560] Prompt Sentence Examples

[0561] "I'm very tired today. I don't feel well."

[0562] Sentiment analysis is performed from these blog entries to generate product advertisements with a relaxation theme.

[0563] As described above, the present invention makes it possible to provide advertisements that take into account the user's emotions and psychological state, thereby improving the user's advertising experience.

[0564] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0565] Step 1:

[0566] A user terminal inputs and transmits a blog entry.

[0567] The blog entry is in text format, and the user describes their daily events and feelings. The user clicks the submit button, which sends an HTTP POST request to the server.

[0568] Input: User's blog entry

[0569] Output: HTTP POST request sent to the server

[0570] Step 2:

[0571] The server receives blog entries from user terminals and stores them in a database.

[0572] The server parses the received blog entries and formats them for storage in a database, where the formatted data is stored in a relational table.

[0573] Input: User's blog entry data

[0574] Output: Data stored in a database table

[0575] Step 3:

[0576] The server sends the blog entry to the emotion engine API to perform emotion analysis.

[0577] The server sends the stored blog entries to an emotion engine API, such as IBM Watson, to identify the sentiment within the text, which then generates an emotion analysis result and sends it back to the server.

[0578] Input: Blog entry data

[0579] Output: Sentiment analysis result data

[0580] Step 4:

[0581] The server stores the emotion analysis results in a database.

[0582] The server formats the received sentiment analysis results for storage in a database, and stores them in association with the corresponding blog entry.

[0583] Input: Sentiment analysis result data

[0584] Output: Sentiment analysis results stored in a database table

[0585] Step 5:

[0586] The user takes the psychological test and submits the results.

[0587] The user accesses the psychological test page in the application, enters answers to questions, and after answering all questions, sends the results to the server.

[0588] Input: Psychological test response data

[0589] Output: Psychological test results sent to the server

[0590] Step 6:

[0591] The server receives the psychological test results and stores them in a database.

[0592] The server analyzes the received psychological test results and formats them for storage in a database, where the formatted data is stored in a related table.

[0593] Input: Psychological test result data

[0594] Output: Psychological test results stored in a database table

[0595] Step 7:

[0596] The server sends the psychological test results to the emotion engine API to perform emotion analysis.

[0597] The server sends the stored psychological test results to the emotion engine API to identify emotions in the text, and the emotion engine generates emotion analysis results and sends them back to the server.

[0598] Input: Psychological test result data

[0599] Output: Sentiment analysis result data

[0600] Step 8:

[0601] The server stores the emotion analysis results in a database.

[0602] The server formats the received emotion analysis results for storage in a database, and stores them in association with the corresponding psychological test results.

[0603] Input: Sentiment analysis result data

[0604] Output: Sentiment analysis results stored in a database table

[0605] Step 9:

[0606] The server generates a psychological profile of the user based on the blog entry, the psychological test results, and the emotion analysis results.

[0607] The server uses text analysis techniques and statistical analysis to generate a psychological profile of the user based on the data stored in the database.

[0608] Input: Blog entries stored in the database, psychological test results, and sentiment analysis results

[0609] Output: Psychological profile of the user

[0610] Step 10:

[0611] The server generates an AI model based on the generated psychological profile.

[0612] The server uses the psychological profile to run machine learning algorithms and build an AI model that reflects the user's emotions and tendencies.

[0613] Input: User's psychological profile

[0614] Output: User's AI model

[0615] Step 11:

[0616] The server generates and displays personalized advertisements according to the user's psychological state.

[0617] The server selects appropriate advertisements based on the user's psychological profile and AI model, and delivers the generated advertisements to the user's device in real time. The user's device then displays the received advertisements on its screen.

[0618] Input: User's psychological profile, AI model

[0619] Output: Personalized ads displayed on the user's device

[0620] 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.

[0621] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (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.

[0622] 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.

[0623] [Second embodiment]

[0624] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0625] 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.

[0626] 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).

[0627] 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.

[0628] 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.

[0629] 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).

[0630] 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.

[0631] 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.

[0632] 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.

[0633] 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.

[0634] 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.

[0635] 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."

[0636] This invention is a system that generates a psychological profile for each user based on the user's blog entries and psychological test results, and then creates an AI model based on that profile. This system provides a platform for users to continuously input their thoughts and feelings and store them in a database. It also has a function for conducting psychological tests, and the test results are similarly stored in the database. The system then generates a psychological profile for the user based on this data and builds an AI model. This AI model is accessible only to a limited number of users, and is particularly useful when parents want to leave messages for their children.

[0637] Program processing

[0638] Data accumulation phase

[0639] 1. Write and submit your blog entry

[0640] The user uses a device to enter their thoughts and feelings into the blog entry form, and when they're done, they click the send button.

[0641] The device receives the user's input and generates an HTTP POST request to send it to the server. The device then sends the generated request to the server.

[0642] The server analyzes the received data, formats it, and saves it in the database. After saving is complete, it returns a response to the device indicating that the save was successful.

[0643] 2. Conduct and send the psychological test

[0644] The user accesses the psychological test administration page, enters answers to questions, and submits the results after answering all questions.

[0645] The terminal compiles the user's answers and generates result data, which is then sent to the server.

[0646] The server analyzes the received psychological test results, stores them in a database, and returns a response indicating that the test was successfully saved to the device.

[0647] AI model generation phase

[0648] 1. Creating a Psychological Profile

[0649] The server uses text analysis techniques and statistical analysis to generate a psychological profile of the user based on the blog entries and psychological test results stored in the database.

[0650] 2. Generating an AI model

[0651] The server uses the generated psychological profile to run an AI model generation algorithm, which learns the user's writing style and emotional expressions to build an AI model that reflects the user's personality and tendencies.

[0652] AI utilization phase

[0653] 1. Submitting an AI usage request

[0654] The user's family members log in to a dedicated access page and send requests to use the AI. For example, if they want to hear a specific message, they can enter the content and send it.

[0655] The terminal composes the input request content and sends it to the server.

[0656] 2. Request Approval and Response

[0657] The server analyzes the received request and verifies that the request is from a valid user. After verification, it provides the appropriate AI interface.

[0658] The terminal displays the AI ​​interface provided by the server, providing an environment in which the user can interact with the AI.

[0659] The server inputs user questions and requests into the AI ​​model, generates appropriate responses, and sends the responses back to the device, where they are displayed.

[0660] Specific examples

[0661] Example blog entry

[0662] User: "I'm so happy that my first big presentation was a success today."

[0663] Terminal: Send this to the server.

[0664] Server: Receives and stores in a database.

[0665] Psychological test examples

[0666] User: Completes the psychological test and submits.

[0667] Terminal: Sends the results to the server.

[0668] Server: Stores the results in a database.

[0669] Examples of AI use

[0670] User's child: "Today is the day I'm making a big decision influenced by my mom. I'd like to hear her advice."

[0671] Device: Sends a request to the server.

[0672] Server: Uses AI models to generate appropriate responses and send them back to the device.

[0673] Terminal: Displays the message "We believe you will move forward with your own will."

[0674] The processing flow will be explained below.

[0675] Data accumulation phase

[0676] Step 1:

[0677] The user enters his or her thoughts and feelings into the blog entry form on the device.

[0678] Step 2:

[0679] After completing the input, the user clicks the send button.

[0680] Step 3:

[0681] The terminal receives the blog entry entered by the user.

[0682] Step 4:

[0683] The data received by the terminal is sent to the server via an HTTP POST request.

[0684] Step 5:

[0685] The server analyzes the received blog entry data and stores it in a database.

[0686] Step 6:

[0687] The server sends a successful save response to the terminal.

[0688] Step 7:

[0689] The user accesses the psychological test administration page.

[0690] Step 8:

[0691] The user answers each question to complete the test.

[0692] Step 9:

[0693] The user submits the results.

[0694] Step 10:

[0695] The terminal compiles the user's responses and generates result data.

[0696] Step 11:

[0697] The device sends the result data to the server via an HTTP POST request.

[0698] Step 12:

[0699] The server analyzes the received psychological test result data and stores it in a database.

[0700] Step 13:

[0701] The server sends a successful save response to the terminal.

[0702] AI model generation phase

[0703] Step 14:

[0704] The server generates a psychological profile of the user based on the blog entries and psychological test results stored in the database using text analysis techniques and statistical analysis.

[0705] Step 15:

[0706] The server uses the generated psychological profile to run an artificial intelligence model generation algorithm.

[0707] Step 16:

[0708] Based on the data learned by the algorithm, the server builds an AI model that reflects the user's writing style and emotional expression.

[0709] AI utilization phase

[0710] Step 17:

[0711] The user's family members log in to a dedicated access page and submit a request to use AI.

[0712] Step 18:

[0713] The terminal composes the input request content and sends it to the server.

[0714] Step 19:

[0715] The server analyzes the received request and checks the access rights.

[0716] Step 20:

[0717] After the server verifies that the access rights are appropriate, it provides the corresponding AI interface.

[0718] Step 20:

[0719] The device displays the AI ​​interface provided by the server.

[0720] Step 21:

[0721] The user's family interacts with the AI ​​through an AI interface.

[0722] Step 22:

[0723] The server inputs user questions and requests into the AI ​​model, which generates appropriate responses.

[0724] Step 23:

[0725] The server sends the generated AI response back to the terminal.

[0726] Step 24:

[0727] The terminal displays the received response to the user.

[0728] Example 1

[0729] 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."

[0730] Currently, many users have more opportunities to record their thoughts and feelings in blog format, but there are limited ways to effectively utilize this data. In particular, technology that generates a user's psychological profile and builds an AI model based on that to provide personalized responses to the user has not yet been fully developed. Another challenge is providing the generated AI model only to specific users, thereby creating an environment where users can use it with peace of mind.

[0731] 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.

[0732] In this invention, the server includes means for receiving blog entries entered from a user terminal and storing them in a database, means for receiving psychological test results and storing them in a database, means for generating a psychological profile of the user based on the stored blog entries and psychological test results, means for generating an AI model based on the generated psychological profile, and means for approving requests for limited users to access the AI ​​model and providing an AI interface. This makes it possible to effectively utilize the user's psychological data and provide personalized responses, giving the user a sense of security.

[0733] A "user terminal" is an electronic device used by a user to create and transmit input data.

[0734] A "blog entry" is a piece of writing that expresses thoughts and feelings and that a user writes on a separate page on the Web.

[0735] "Psychological test results" are data obtained as a result of a user answering a psychological test.

[0736] A "database" is a system for storing and managing various data.

[0737] A "psychological profile" is information that indicates a user's psychological characteristics and tendencies, generated based on blog entries and psychological test results.

[0738] An "artificial intelligence model" is a machine learning model that is generated based on a user's psychological profile and trained to perform specific tasks.

[0739] A "request" refers to an operation or information request made by a user to a system.

[0740] An "interface" is the means or screen through which a user interacts with a system.

[0741] "Text analysis" is the process of analyzing text data using natural language processing techniques to extract useful information.

[0742] "Real-time" refers to immediate reactions and processing without delay.

[0743] The present invention is a system that allows users to input blog entries and psychological test results, generates a psychological profile for the user based on that data, and then builds an artificial intelligence model based on that profile. This system provides a platform for users to continuously input their own thoughts and feelings and store them in a database. It also has a function for conducting psychological tests, and the test results are similarly stored in the database.

[0744] To implement this invention, the following hardware and software are required: The terminal used by the user has a function for connecting to the Internet and can access the blog entry input form and psychological test page using a web browser (e.g., Google (registered trademark) Chrome, Mozilla Firefox). The terminal also has a built-in program for generating HTTP POST requests. The server has a database management system (e.g., MySQL, PostgreSQL) installed, and uses text analysis technology (e.g., NLTK, spaCy) and machine learning libraries (e.g., TensorFlow, PyTorch).

[0745] The specific operation of the system is as follows: when a user enters a blog entry through a device and clicks the send button, the device receives the input, generates an HTTP POST request, and sends it to the server. The server analyzes the received request, formats the text content, and saves it in a database. If the save is successful, the server returns a "save successful" response to the device. Similarly, when a user answers a psychological test and clicks the send button, the device tallys the answers and sends them to the server, which then saves the results in a database.

[0746] The server periodically retrieves blog entries and psychological test results stored in the database and analyzes the text using natural language processing technology. Based on the analysis results, it generates a psychological profile for the user and stores this profile in the database. Furthermore, it uses a machine learning library to build an artificial intelligence model based on the generated psychological profile. This AI model is also stored in the database.

[0747] To access the AI ​​model, limited users log in to a dedicated access page and submit a request. For example, a user's child might type, "Today is the day I'm making a big decision influenced by my mother. I'd like to hear some advice from my mother," and click the submit button. The device then sends the request to the server, which analyzes the request and verifies that it is from a valid user. After verification, the device uses the corresponding AI model to generate an appropriate response and sends it back to the device. The device then displays the received response to the user.

[0748] As specific examples, the input prompt for a blog entry is in the format "Please enter the content of your blog. For example, 'I had a great time with my friends today.'" The input prompt for a psychological test is in the format "Please answer the following questions. For example, questions include 'How are you feeling lately?'" The prompt for using AI is in the format "Please enter the message you want to ask the AI. For example, 'How should I make an important decision?'"

[0749] As described above, the present invention is a system that effectively utilizes the user's psychological data and provides personalized responses, thereby giving the user a sense of security.

[0750] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0751] Step 1:

[0752] Entering and submitting a blog entry

[0753] A user accesses a blog entry form and enters their thoughts and feelings. For example, they might enter, "I was so happy that my first big presentation was a success today."

[0754] Input: A user types a blog entry and clicks the submit button.

[0755] The terminal receives the input content and generates an HTTP POST request to send to the server. Specifically, it converts the input text into JSON format and sends it to the server as the payload of the HTTP request.

[0756] Output: The user's blog entry is sent to the server.

[0757] Step 2:

[0758] Receive and store blog entries

[0759] The server receives the HTTP POST request sent from the terminal.

[0760] Input: Blog entry data sent as an HTTP POST request.

[0761] The server analyzes the received data and formats the text content. Specifically, it performs JSON parsing and extracts the text data.

[0762] The server stores the formatted data in a database.

[0763] Output: The blog entry saved in the database.

[0764] Step 3:

[0765] Conduct and send psychological tests

[0766] A user visits a psychological test page and answers a series of questions, for example, "How are you feeling these days?" and answers "very good."

[0767] Input: The user answers the psychological test and clicks the submit button.

[0768] The device aggregates the user's answers to each question and generates the result data. Specifically, it compiles each answer as a JSON object and generates an HTTP POST request to send it to the server.

[0769] Output: The result data of the psychological test is sent to the server.

[0770] Step 4:

[0771] Receiving and storing psychological test results

[0772] The server receives an HTTP POST request including the psychological test result sent from the terminal.

[0773] Input: Psychological test result data sent as an HTTP POST request.

[0774] The server analyzes and formats the received result data, specifically by parsing it in JSON and extracting the response data.

[0775] The server stores the formatted data in a database.

[0776] Output: Psychological test results stored in a database.

[0777] Step 5:

[0778] Psychological profile generation

[0779] The server retrieves the blog entries and psychological test results stored in the database.

[0780] Input: Blog entries and psychological test results stored in a database.

[0781] The server analyzes the text using natural language processing technology (e.g., NLTK, spaCy). Specifically, it performs morphological analysis of the text data and performs emotion and sentiment analysis.

[0782] The server processes the analysis results using statistical analysis techniques to generate a psychological profile of the user.

[0783] Output: The generated psychological profile is stored in a database.

[0784] Step 6:

[0785] Generating artificial intelligence models

[0786] The server retrieves the generated psychological profile.

[0787] Input: Psychological profiles stored in a database.

[0788] The server uses machine learning libraries (e.g., TensorFlow, PyTorch) to execute artificial intelligence model generation algorithms.

[0789] The server learns the user's writing style and emotional expression, and builds an artificial intelligence model that reflects the user's personality and tendencies.

[0790] Output: The constructed artificial intelligence model is stored in a database.

[0791] Step 7:

[0792] Submitting an AI request

[0793] The user's family members log in to a dedicated access page.

[0794] Input: The user's family member enters a request to use AI and clicks the send button. For example, they might enter, "Today is the day I'm making a big decision influenced by my mother. I'd like to hear her advice."

[0795] The device sends the request content to the server as an HTTP POST request in JSON format.

[0796] Output: An AI-enabled request is sent to the server.

[0797] Step 8:

[0798] Request Approval and Response

[0799] The server analyzes the received request and verifies that it is from the user's family.

[0800] Input: AI-powered request data.

[0801] The server loads the appropriate AI model and generates an appropriate response to the request, such as the message, "I trust you to move forward with your own will."

[0802] The server generates a response and sends it back to the terminal.

[0803] Output: The generated response is sent to the terminal.

[0804] Step 9:

[0805] Viewing the response

[0806] The terminal receives the response provided by the server.

[0807] Input: The response data sent by the server.

[0808] The device displays the received response to the user, for example, a message saying "I believe you will move forward with your own will."

[0809] Output: The user receives the response.

[0810] (Application example 1)

[0811] 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."

[0812] Today, many users blog online and gain a deeper understanding of themselves through psychological tests. However, there is no established method for centrally analyzing this data and providing users with personalized content recommendations and appropriate responses in real time. In addition, there are demands for specific situations, such as when parents want to leave special messages for their children, or for content tailored to the user's psychological state. Therefore, there is a need for the development of a system that can generate a user's psychological profile, build an AI model based on that profile, and provide personalized services.

[0813] 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.

[0814] In this invention, the server includes means for receiving data input from a user terminal and storing the data in a database, means for receiving psychological test results and storing the results in a database, means for generating a psychological profile of the user based on the stored data and the psychological test results, means for generating an AI model based on the generated psychological profile, means for approving access requests for limited users to access the AI ​​model and providing an AI interface, and means for recommending personalized content based on the user's psychological profile, thereby making it possible to individually recommend content based on the user's psychological state and provide personalized messages for specific situations.

[0815] A "user terminal" is a device that can be operated by a user and is a device that connects to the Internet and inputs, transmits, and receives data. Examples include smartphones, tablets, and personal computers.

[0816] A "database" is a system for efficiently storing, managing, and searching data, and is a recording medium that stores information such as user blog entries and psychological test results.

[0817] The "psychological test results" are the user's response data and evaluation data obtained through the psychological test, and are information for understanding the user's psychological characteristics and tendencies based on this.

[0818] A "psychological profile" is profile data that indicates a user's psychological characteristics and tendencies, and is generated by analyzing the user's blog entries and psychological test results.

[0819] An "AI model" is a data model built using artificial intelligence technology, and is a computational algorithm for generating specific actions and responses based on a user's psychological profile.

[0820] An "Access Request" is a request sent by a qualified user to access an AI model, and the AI ​​interface is provided only if the access right is confirmed.

[0821] "Personalized content" refers to information and media such as videos, articles, and music that are optimized and recommended to a specific user based on the user's psychological profile.

[0822] An "AI interface" is a user interface that allows a user to interact with an AI model, and includes the screen and operating means displayed on the terminal.

[0823] "Text analysis" is the process of analyzing text data using natural language processing techniques to extract information such as meaning and sentiment.

[0824] "Content recommendation" is a system that uses a user's psychological profile to select and present the most suitable content for the user.

[0825] As an embodiment of the present invention, there is a system that receives data input from a user terminal, stores it in a database, generates a psychological profile based on the data, creates an AI model, and recommends personalized content. Specifically, this system operates as follows.

[0826] Hardware and Software Use

[0827] User devices can be smartphones, tablets, or personal computers. The applications that run on the devices are built using React Native. Server-side processing is done with Node.js and Express.js, and data is stored in MongoDB. Additionally, NLTK, a Python natural language processing library, and TensorFlow, a machine learning library, are used to generate the AI ​​model.

[0828] Entering and Saving Data

[0829] The user terminal accepts blog entries and psychological test results from users as input. The user enters this data through the application and clicks a button to submit it. The terminal then sends this input data to the server as an HTTP POST request.

[0830] The server parses and formats the data it receives and stores it in a MongoDB database. For example, if a user types "I'm very happy that my first big presentation was a success today," it will be stored in the database.

[0831] Psychological profile generation

[0832] The server uses text analysis technology to generate a psychological profile of the user based on the blog entries and psychological test results stored in the database. This profile is generated using Python's NLTK library, which analyzes the data and extracts the user's psychological characteristics and tendencies.

[0833] Generating and applying AI models

[0834] Based on the generated psychological profile, the server uses TensorFlow to generate an AI model that learns the user's language style and emotional expressions, and then recommends responses and content that reflect the user's characteristics.

[0835] Personalized content recommendations

[0836] The user device receives personalized content from the server based on the user's psychological profile. This content can include videos, articles, music, and more, and the most suitable content is recommended to the user.

[0837] AI usage example

[0838] Users can send requests for specific messages or advice through the application. For example, if a child of the user types, "Today is the day I'm making a big decision, influenced by my mother. I'd like to hear her advice," the server uses an AI model to generate an appropriate response and sends it back to the device. This results in the message, "I trust you to move forward with your own will."

[0839] Prompt Sentence Examples

[0840] "Generate personalized content suggestions for this user using their latest psychological profile. Their blog entry is 'I was so happy today that my first big presentation went well.'"

[0841] In this way, the present invention makes it possible to provide personalized services based on the user's psychological state.

[0842] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0843] Step 1:

[0844] A user types a blog entry and clicks the submit button.

[0845] Input: The blog entry entered by the user

[0846] Specific operation:

[0847] Users open the application on their smartphone or computer, enter their thoughts and feelings in the text area, and then click the send button to send the blog entry.

[0848] Step 2:

[0849] The terminal transmits the blog entry data to the server.

[0850] Input: User's blog entry

[0851] Output: Blog entry data sent to the server

[0852] Specific operation:

[0853] The device takes the user's input and sends it to the server as an HTTP POST request, which includes the text data of the blog entry.

[0854] Step 3:

[0855] The server receives the blog entry data and stores it in a database.

[0856] Input: Blog entry data sent from the device

[0857] Output: Blog entries stored in the database

[0858] Specific operation:

[0859] The server receives the HTTP POST request and analyzes its contents. After analyzing, it stores the formatted data in MongoDB. After storing the data, the server returns a success response to the device.

[0860] Step 4:

[0861] The user answers the psychological test and submits the results.

[0862] Input: Psychological test results answered by the user

[0863] Output: Psychological test data sent to the server

[0864] Specific operation:

[0865] The user answers the psychological test presented on the application, and after completing all the questions, clicks the send button to send the results.

[0866] Step 5:

[0867] The terminal transmits the psychological test results to the server.

[0868] Input: User's psychological test results

[0869] Output: Psychological test data sent to the server

[0870] Specific operation:

[0871] The device collects the user's psychological test results and sends them to the server as an HTTP POST request, which includes the psychological test result data.

[0872] Step 6:

[0873] The server receives the psychological test results and stores them in a database.

[0874] Input: Psychological test result data sent from the device

[0875] Output: Psychological test results stored in a database

[0876] Specific operation:

[0877] The server receives the test results and analyzes them. After analysis, the formatted data is saved in MongoDB. After saving is complete, the server returns a success response to the device.

[0878] Step 7:

[0879] The server generates a psychological profile based on the blog entries and psychological test results.

[0880] Input: Blog entries and psychological test results stored in a database

[0881] Output: Generated psychological profile

[0882] Specific operation:

[0883] The server retrieves blog entries and psychological test results from the database, performs text analysis using Python's NLTK library, and generates a psychological profile of the user based on the analyzed data.

[0884] Step 8:

[0885] The server generates an AI model based on the psychological profile.

[0886] Input: Generated psychological profile

[0887] Output: The generated AI model

[0888] Specific operation:

[0889] The server analyzes the psychological profile using the TensorFlow library and generates an AI model that learns the user's language style and emotional expressions.

[0890] Step 9:

[0891] Submit an access request for access to an AI model by a limited user.

[0892] Input: Access request from a restricted user

[0893] Output: Access request sent to the server

[0894] Specific operation:

[0895] Users log in to a dedicated access page on the application and submit a request to access the AI ​​model.

[0896] Step 10:

[0897] The server approves the access request and provides the AI ​​interface.

[0898] Input: Access request sent by the user

[0899] Output: Provided AI interface

[0900] Specific operation:

[0901] The server analyzes the received request and checks the user's access rights, then provides the AI ​​interface to the device.

[0902] Step 11:

[0903] The server recommends personalized content based on the user's psychological profile.

[0904] Input: User's psychological profile

[0905] Output: personalized content

[0906] Specific operation:

[0907] The server then recommends the most suitable content for the user, such as videos, articles, and music, based on the generated psychological profile, using a generative AI model and prompts to select the appropriate content.

[0908] Example prompt sentence:

[0909] "Generate personalized content suggestions for this user using their latest psychological profile. Their blog entry is 'I was so happy today that my first big presentation went well.'"

[0910] 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.

[0911] This invention is a system that generates a psychological profile for each user based on the user's blog entries and psychological test results, and creates an AI model based on that profile. This system incorporates an emotion engine that recognizes the user's emotions, and can reflect the user's overall feelings and emotional changes in the AI ​​model.

[0912] Program processing

[0913] Data accumulation phase

[0914] 1. Write and submit your blog entry

[0915] The user enters their thoughts and feelings into the blog entry form on their device, and when they're done, clicks the send button.

[0916] The device receives the user's input and generates an HTTP POST request to send it to the server. The device then sends the generated request to the server.

[0917] The server analyzes the received data, formats it, and saves it in the database. After saving is complete, it returns a response to the device indicating that the save was successful.

[0918] 2. Emotion Recognition by Emotion Engine

[0919] The server sends the received blog entry data to an emotion engine to analyze the user's emotions.

[0920] The emotion engine generates emotion analysis results, which are also stored in the database.

[0921] 3. Conduct and send the psychological test

[0922] The user accesses the psychological test administration page, enters answers to questions, and submits the results after answering all questions.

[0923] The terminal compiles the user's answers and generates result data, which is then sent to the server.

[0924] The server analyzes the received psychological test results, stores them in a database, and returns a response indicating that the test was successfully saved to the device.

[0925] 4. Emotion recognition using emotion engine (psychological test)

[0926] The server sends the received psychological test results to the emotion engine to analyze the user's emotions.

[0927] The emotion engine generates emotion analysis results, which are also stored in the database.

[0928] AI model generation phase

[0929] 1. Creating a Psychological Profile

[0930] The server uses text analysis techniques and statistical analysis to generate a psychological profile of the user based on the blog entries, psychological test results, and sentiment analysis results stored in the database.

[0931] 2. Generating an AI model

[0932] The server uses the generated psychological profile to run an AI model generation algorithm, which learns the user's writing style and emotional expressions to build an AI model that reflects the user's personality and tendencies.

[0933] AI utilization phase

[0934] 1. Submitting an AI usage request

[0935] The user's family members log in to a dedicated access page and send requests to use the AI. For example, if they want to hear a specific message, they can enter the content and send it.

[0936] The terminal composes the input request content and sends it to the server.

[0937] 2. Request Approval and Response

[0938] The server analyzes the received request and checks the access rights.

[0939] After verifying that the access rights are appropriate, the corresponding AI interface will be provided.

[0940] 3. AI-User Interaction

[0941] The server inputs user questions and requests into the AI ​​model and generates appropriate responses.

[0942] The AI ​​model generates responses based on the analysis results of the emotion engine and provides messages that take the user's emotions into consideration.

[0943] The terminal displays the AI ​​interface provided by the server, providing an environment in which the user can interact with the AI.

[0944] The server sends the generated AI response back to the terminal, where it is displayed.

[0945] Specific examples

[0946] Example blog entry

[0947] User: "I was so happy today. I feel so much better after hearing the good news."

[0948] Terminal: Send this entry to the server.

[0949] Server: Receives and stores in the database. The emotion engine recognizes the emotion "happy" and stores the result.

[0950] Psychological test examples

[0951] User: Completes the psychological test and submits.

[0952] Terminal: Sends the results to the server.

[0953] Server: Save the results in the database. The emotion engine recognizes the emotion "nervous" and saves the result.

[0954] Examples of AI use

[0955] User's child: "I want to talk to my mom. I especially need encouragement today."

[0956] Device: Sends a request to the server.

[0957] Server: Accepts the request and uses the AI ​​model.

[0958] Device: Displays the message "We're always rooting for you. Believe in yourself and move forward."

[0959] The processing flow will be explained below.

[0960] Data accumulation phase

[0961] Step 1:

[0962] The user enters his or her thoughts and feelings into the blog entry form on the device.

[0963] Step 2:

[0964] After completing the input, the user clicks the send button.

[0965] Step 3:

[0966] The terminal receives the blog entry input by the user.

[0967] Step 4:

[0968] The data received by the terminal is sent to the server via an HTTP POST request.

[0969] Step 5:

[0970] The server analyzes the received blog entry data and stores it in a database.

[0971] Step 6:

[0972] The server sends a successful save response to the terminal.

[0973] Step 7:

[0974] The server sends the blog entry data to the emotion engine for emotion analysis.

[0975] Step 8:

[0976] The emotion engine generates the emotion analysis results of the blog entries and stores them in the database.

[0977] Step 9:

[0978] The user accesses the psychological test administration page.

[0979] Step 10:

[0980] The user answers each question, completes the test, and submits the results.

[0981] Step 11:

[0982] The terminal compiles the user's responses and generates result data.

[0983] Step 12:

[0984] The device sends the result data to the server via an HTTP POST request.

[0985] Step 13:

[0986] The server analyzes the received psychological test result data and stores it in a database.

[0987] Step 14:

[0988] The server sends a successful save response to the terminal.

[0989] Step 15:

[0990] The server sends the psychological test results to the emotion engine for emotion analysis.

[0991] Step 16:

[0992] The emotion engine performs emotion analysis of the psychological test results and stores the results in a database.

[0993] AI model generation phase

[0994] Step 17:

[0995] The server generates a psychological profile of the user using text analysis techniques and statistical analysis based on the blog entries, psychological test results, and sentiment analysis results stored in the database.

[0996] Step 18:

[0997] The server uses the generated psychological profile to run an artificial intelligence model generation algorithm.

[0998] Step 19:

[0999] Based on the data learned by the algorithm, the server builds an AI model that reflects the user's writing style and emotional expression.

[1000] AI utilization phase

[1001] Step 20:

[1002] The user's family members log in to a dedicated access page and submit a request to use AI.

[1003] Step 21:

[1004] The terminal composes the input request content and sends it to the server.

[1005] Step 22:

[1006] The server analyzes the received request and checks the access rights.

[1007] Step 23:

[1008] After the server verifies that the access rights are appropriate, it provides the corresponding AI interface.

[1009] Step 24:

[1010] The device displays the AI ​​interface provided by the server, providing an environment in which the user can interact with the AI.

[1011] Step 25:

[1012] The user's family interacts with the AI ​​through an AI interface.

[1013] Step 26:

[1014] The server inputs user questions and requests into the AI ​​model, which generates appropriate responses.

[1015] Step 27:

[1016] The server generates an AI response based on the analysis results of the emotion engine and provides a message that takes the user's emotions into consideration.

[1017] Step 28:

[1018] The server sends the generated AI response back to the terminal, where it is displayed.

[1019] Example 2

[1020] 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."

[1021] Conventional systems simply store users' blog entries and psychological test results in a database, making it difficult to deeply analyze users' emotional changes and psychological tendencies and reflect them in the AI ​​model. Furthermore, the responses generated by the AI ​​model did not take into account the user's emotions, resulting in a decline in the quality of the dialogue. This made it impossible to provide detailed psychological support to users.

[1022] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1023] In this invention, the server includes means for receiving data input from a user terminal and storing the data in a database, means for receiving psychological test results and storing the results in a database, means for generating a psychological profile of the user based on the stored data and the psychological test results, means for generating an AI model based on the generated psychological profile, means for emotionally analyzing the user's input data using an emotion engine, means for storing the analyzed emotion data in a database, means for emotionally analyzing the psychological test results using the emotion engine, and means for approving access requests for limited users to access the AI ​​model and providing an AI interface. This makes it possible to deeply analyze the user's emotional changes and psychological tendencies and generate an AI model that takes the user's emotions into consideration.

[1024] A "user terminal" is a device used by a user to input data, and includes devices such as personal computers and smartphones.

[1025] A "server" is a central processing unit that processes data received from user terminals and stores it in a database.

[1026] A "database" is a storage device for permanently storing received data, emotion analysis results, and psychological test results.

[1027] The "psychological test result" is data generated by the user answering the psychological test, and includes information indicating the user's psychological state.

[1028] A "psychological profile" is data that indicates a user's psychological tendencies and characteristics, generated based on the user's blog entries, psychological test results, and emotion analysis results.

[1029] An "AI model" is an artificial intelligence model generated based on a psychological profile, which learns the user's writing style and emotional expression.

[1030] The "emotion engine" is a system that uses natural language processing technology to analyze user input data and extract emotions as tags.

[1031] "Access Request" means a request submitted by a qualified user to access an AI model.

[1032] An "AI interface" is a user interface that provides an environment in which users can interact with an AI model.

[1033] This invention is a system that generates a user's psychological profile based on the user's blog entries and psychological test results, and creates an AI model based on that profile. This system incorporates an emotion engine that recognizes the user's emotions, and can reflect the user's overall feelings and emotional changes in the AI ​​model.

[1034] Hardware and Software Configuration

[1035] User terminal: A device such as a computer or smartphone that is used by a user to input data.

[1036] Server: A central processing unit that processes data and stores it in a database. A server usually consists of a computer with advanced computing power.

[1037] Database: A storage device for persistently storing received data, sentiment analysis results, and psychological test results. For example, an SQL database or NoSQL database is used.

[1038] Emotion engine: Software that uses natural language processing technology to analyze user input data and extract emotions as tags. For example, an NLP library implemented in Python or a dedicated sentiment analysis API is used.

[1039] System processing flow

[1040] 1. Write and submit your blog entry

[1041] The user enters their feelings and thoughts into the blog entry form on their device and clicks the send button.

[1042] The terminal receives the user's input data and sends it to the server as an HTTP POST request.

[1043] The server receives the data, analyzes and formats it, and saves it in the database. After saving is complete, it returns a successful save response to the device.

[1044] 2. Emotion Recognition by Emotion Engine

[1045] The server sends the blog entry data to an emotion engine to analyze the user's emotions.

[1046] The emotion engine uses NLP technology to extract emotions such as "happy" or "sad" as tags.

[1047] The server stores the emotion analysis results received from the emotion engine in a database.

[1048] 3. Conduct and send the psychological test

[1049] A user accesses a psychological test question page, enters answers to all questions, and submits the answer.

[1050] The terminal aggregates the user's response data, converts it into structured data (e.g., JSON format), and sends it to the server.

[1051] The server receives the psychological test results, analyzes and stores them, and returns a response indicating that the test results were successfully stored.

[1052] 4. Emotion recognition using emotion engine (psychological test)

[1053] The server sends the psychological test results to the emotion engine, which analyzes the user's emotions again.

[1054] The emotion engine generates analysis results and stores the results in a database.

[1055] 5. Generating a Psychological Profile

[1056] The server retrieves all blog entries, psychological test results, and sentiment analysis results from the database and generates a psychological profile using text analysis techniques (natural language processing and statistical analysis).

[1057] 6. Generating AI models

[1058] The server then runs an AI model generation algorithm (such as a neural network) based on the generated psychological profile. This model learns the user's writing style and emotional expressions, and reflects the user's personality and tendencies.

[1059] 7. Submitting an AI Request

[1060] The user's family members log in to a dedicated access page and send a request to use AI from their device.

[1061] 8. Request Approval and Response

[1062] The server analyzes the received request, checks the access rights, and then provides the corresponding AI interface.

[1063] 9. AI-User Interaction

[1064] The server inputs user questions and requests into the AI ​​model and generates appropriate responses.

[1065] The emotion engine is taken into account when generating responses, generating emotion-sensitive messages.

[1066] The server generates a response and sends it back to the terminal, which displays it.

[1067] Specific examples

[1068] Example blog entry

[1069] User: "I was very happy today. Hearing the good news brightened my mood." is entered and sent from the device to the server.

[1070] The server receives the data and stores it in a database. The emotion engine recognizes the emotion "happy" and stores the result.

[1071] Psychological test examples

[1072] The user answers a psychological test and sends the result of feeling "tense" from the terminal to the server.

[1073] The server stores the results in a database, and the emotion engine recognizes the emotion "nervous" and stores the result as well.

[1074] Examples of AI use

[1075] User's child: "I want to talk to my mother. I especially need encouragement today." Sends a request from the device to the server.

[1076] The server approves the request and uses an AI model to generate a message saying, "We're always rooting for you. Believe in yourself and move forward."

[1077] The server generates a message and returns it to the terminal, where it is displayed.

[1078] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1079] Step 1:

[1080] The user enters their feelings and thoughts into the blog entry form on their device and clicks the send button. This operation generates the entered text data.

[1081] Step 2:

[1082] The terminal receives the user's input and composes it into an HTTP POST request, which is then sent to the server as input data.

[1083] Step 3:

[1084] The server receives the HTTP POST request and analyzes the input data. Through analysis, text data is extracted. The text data is then formatted for storage in the database. The formatted data is saved in the database and a response indicating successful saving is returned to the terminal.

[1085] Step 4:

[1086] The server passes the formatted text data to the emotion engine, which analyzes the user's emotions. During this emotion analysis process, emotion tags are extracted from the text using NLP technology. The extracted emotion tags are generated as the emotion analysis results.

[1087] Step 5:

[1088] The server stores the emotion analysis results received from the emotion engine in a database.

[1089] Step 6:

[1090] The user accesses the psychological test question page through their device and enters their answers. After answering all the questions, the user submits the results. This operation generates the psychological test answer data.

[1091] Step 7:

[1092] The device collects the user's psychological test response data and converts it into structured data such as JSON format, then sends the generated structured data to the server as an HTTP POST request.

[1093] Step 8:

[1094] The server receives the HTTP POST request, analyzes the psychological test results, saves the analyzed results in a database, and returns a response indicating that the save was successful to the device.

[1095] Step 9:

[1096] The server sends the psychological test results to the emotion engine, which then analyzes the user's emotions again. The emotion engine extracts emotion tags from the psychological test results and generates emotion analysis results.

[1097] Step 10:

[1098] The server stores the emotion analysis results received from the emotion engine in a database.

[1099] Step 11:

[1100] The server retrieves blog entry data, psychological test results, and sentiment analysis results from the database, and then generates a psychological profile of the user using text analysis techniques and statistical analysis based on the retrieved data.

[1101] Step 12:

[1102] The server uses the generated psychological profile to run an AI model generation algorithm, which builds an AI model that learns the user's writing style and emotional expressions.

[1103] Step 13:

[1104] The user's family members log in to a dedicated access page and send requests for using AI from their devices, for example, by entering, "I want to hear an encouraging message from my mother."

[1105] Step 14:

[1106] The terminal composes the input request content as an HTTP POST request and sends it to the server.

[1107] Step 15:

[1108] The server analyzes the received request and checks the access rights. After the access rights are confirmed, the corresponding AI interface is provided.

[1109] Step 16:

[1110] The server inputs questions and requests from users into the AI ​​model and generates appropriate responses. The AI ​​model generates responses that take the user's emotions into consideration based on the analysis results of the emotion engine.

[1111] Step 17:

[1112] The server generates a response that is sent back to the terminal and displayed there.

[1113] Step 18:

[1114] The terminal displays the AI ​​interface provided by the server, creating an environment in which the user can interact with the AI.

[1115] (Application example 2)

[1116] 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."

[1117] Conventional advertising systems lack personalization that takes into account the user's emotions and psychological state, and deliver a large number of generic ads, making it difficult to quickly adapt to the user's interests and emotions. Furthermore, they lack technology that effectively utilizes users' daily blog entries and psychological test results to optimize ads. This results in a monotonous user experience and ineffective advertising.

[1118] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1119] In this invention, the server includes means for receiving data input from a user terminal and storing the data in a database, means for receiving psychological test results and storing the results in a database, means for generating a psychological profile of the user based on the stored data and the psychological test results, means for generating an AI model based on the generated psychological profile, means for approving access requests for limited users to access the AI ​​model and providing an AI interface, and means for generating and displaying personalized advertisements according to the user's psychological state, thereby making it possible to provide advertisements that reflect the user's emotions and psychological state and achieve a more effective advertising experience.

[1120] 1. "User terminal" means an input and display device used by a user, including mobile devices such as smartphones and tablets.

[1121] 2. "Database" means an information system for organizing and storing received data and analysis results, including relational databases and NoSQL databases.

[1122] 3. "Psychological test results" are data resulting from a user's responses to a psychological test, and are data that indicate the user's psychological state and personality traits.

[1123] 4. "Psychological profile" is a data set that shows the psychological characteristics of a user analyzed based on the user's blog entries and psychological test results.

[1124] 5. "AI Model" means a computer program generated by machine learning algorithms based on a user's psychological profile, capable of predicting and responding to a user's emotions and tendencies.

[1125] 6. "Access Request" means a request submitted by a User to access certain data or functionality.

[1126] 7. “AI Interface” means an interface through which a user interacts with an AI model, whether in text, voice, or other format.

[1127] 8. "Advertisement" means information displayed to promote a particular product or service to a user.

[1128] 9. "Personalized advertising" refers to individual advertising optimized based on a user's psychological state or characteristics.

[1129] 10. "Sentiment analysis" is the process of identifying emotions and emotional changes from user input data (such as blog entries or psychological test results).

[1130] 11. "Blog entry" is data in the form of text in which a user describes everyday events and feelings.

[1131] 12. "Text analysis" is the process of analyzing text data using natural language processing techniques to extract meaning and sentiment.

[1132] This invention is a system that generates a psychological profile for each user based on the user's blog entries and psychological test results, and then creates an AI model based on that profile. This system incorporates an emotion engine that recognizes the user's emotions, allowing the user's overall state of mind and emotional changes to be reflected in the AI ​​model. The specific program processing and required hardware and software are described in detail below.

[1133] Hardware and software used

[1134] User device: Mobile devices such as smartphones and tablets

[1135] Database: AWS RDS (Relational Database Service)

[1136] Server: Node.js + Express.js

[1137] Emotion Engine API: IBM Watson Natural Language Understanding

[1138] AI model generation: Scikit-learn, TensorFlow

[1139] Frontend: React Native

[1140] Program processing

[1141] 1. Data entry on the user's device

[1142] The application provides a form for users to write blog entries. Users use this form to input their daily events and feelings. Once the input is complete, they press the submit button to send the data to the server.

[1143] 2. Receiving and storing data on the server

[1144] The server receives data sent from the user terminal using an HTTP POST request and saves the received data in a database, which then stores the blog entry and its related information.

[1145] 3. Emotion analysis

[1146] The server sends the saved blog entry data to the emotion engine API to analyze the user's emotions. The emotion engine identifies the emotions in the text and sends the results back to the server. The acquired emotion analysis results are then stored in the database.

[1147] 4. Psychological profile generation

[1148] The server uses text analysis techniques and statistical analysis to generate a user's psychological profile based on the blog entries, psychological test results, and sentiment analysis results stored in the database. The generated profile details the user's psychological characteristics.

[1149] 5. AI model generation

[1150] The server then uses the generated psychological profile to run a machine learning algorithm, which learns the user's writing style and emotional expressions, and builds an AI model that reflects the user's personality and tendencies.

[1151] 6. Generating and displaying personalized advertisements

[1152] The server generates advertisements based on the user's psychological state. These advertisements reflect the user's emotions and interests and are delivered in real time from the server to the user's device. The user's device then displays the received advertisements on its screen, with content that the user finds interesting.

[1153] Specific examples

[1154] Example blog entry

[1155] User: "I was so happy today. I feel so much better after hearing the good news."

[1156] Server: Receives this entry and saves it in the database. The emotion engine recognizes the emotion "happy" and saves the result.

[1157] Examples of personalized ads

[1158] An advertisement for a "travel package with celebratory benefits" is displayed on the user's device based on the user's blog entry and sentiment analysis results.

[1159] Prompt Sentence Examples

[1160] "I'm very tired today. I don't feel well."

[1161] Sentiment analysis is performed from these blog entries to generate product advertisements with a relaxation theme.

[1162] As described above, the present invention makes it possible to provide advertisements that take into account the user's emotions and psychological state, thereby improving the user's advertising experience.

[1163] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1164] Step 1:

[1165] A user terminal inputs and transmits a blog entry.

[1166] The blog entry is in text format, and the user describes their daily events and feelings. The user clicks the submit button, which sends an HTTP POST request to the server.

[1167] Input: User's blog entry

[1168] Output: HTTP POST request sent to the server

[1169] Step 2:

[1170] The server receives blog entries from user terminals and stores them in a database.

[1171] The server parses the received blog entries and formats them for storage in a database, where the formatted data is stored in a relational table.

[1172] Input: User's blog entry data

[1173] Output: Data stored in a database table

[1174] Step 3:

[1175] The server sends the blog entry to the emotion engine API to perform emotion analysis.

[1176] The server sends the stored blog entries to an emotion engine API, such as IBM Watson, to identify the sentiment within the text, which then generates an emotion analysis result and sends it back to the server.

[1177] Input: Blog entry data

[1178] Output: Sentiment analysis result data

[1179] Step 4:

[1180] The server stores the emotion analysis results in a database.

[1181] The server formats the received sentiment analysis results for storage in a database, and stores them in association with the corresponding blog entry.

[1182] Input: Sentiment analysis result data

[1183] Output: Sentiment analysis results stored in a database table

[1184] Step 5:

[1185] The user takes the psychological test and submits the results.

[1186] The user accesses the psychological test page in the application, enters answers to questions, and after answering all questions, sends the results to the server.

[1187] Input: Psychological test response data

[1188] Output: Psychological test results sent to the server

[1189] Step 6:

[1190] The server receives the psychological test results and stores them in a database.

[1191] The server analyzes the received psychological test results and formats them for storage in a database, where the formatted data is stored in a related table.

[1192] Input: Psychological test result data

[1193] Output: Psychological test results stored in a database table

[1194] Step 7:

[1195] The server sends the psychological test results to the emotion engine API to perform emotion analysis.

[1196] The server sends the stored psychological test results to the emotion engine API to identify emotions in the text, and the emotion engine generates emotion analysis results and sends them back to the server.

[1197] Input: Psychological test result data

[1198] Output: Sentiment analysis result data

[1199] Step 8:

[1200] The server stores the emotion analysis results in a database.

[1201] The server formats the received emotion analysis results for storage in a database, and stores them in association with the corresponding psychological test results.

[1202] Input: Sentiment analysis result data

[1203] Output: Sentiment analysis results stored in a database table

[1204] Step 9:

[1205] The server generates a psychological profile of the user based on the blog entry, the psychological test results, and the emotion analysis results.

[1206] The server uses text analysis techniques and statistical analysis to generate a psychological profile of the user based on the data stored in the database.

[1207] Input: Blog entries stored in the database, psychological test results, and sentiment analysis results

[1208] Output: Psychological profile of the user

[1209] Step 10:

[1210] The server generates an AI model based on the generated psychological profile.

[1211] The server uses the psychological profile to run machine learning algorithms and build an AI model that reflects the user's emotions and tendencies.

[1212] Input: User's psychological profile

[1213] Output: User's AI model

[1214] Step 11:

[1215] The server generates and displays personalized advertisements according to the user's psychological state.

[1216] The server selects appropriate advertisements based on the user's psychological profile and AI model, and delivers the generated advertisements to the user's device in real time. The user's device then displays the received advertisements on its screen.

[1217] Input: User's psychological profile, AI model

[1218] Output: Personalized ads displayed on the user's device

[1219] 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.

[1220] 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.

[1221] 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.

[1222] [Third embodiment]

[1223] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1224] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[1225] 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).

[1226] 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.

[1227] 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.

[1228] 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).

[1229] 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.

[1230] 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.

[1231] 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.

[1232] 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.

[1233] 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.

[1234] 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."

[1235] This invention is a system that generates a psychological profile for each user based on the user's blog entries and psychological test results, and then creates an AI model based on that profile. This system provides a platform for users to continuously input their thoughts and feelings and store them in a database. It also has a function for conducting psychological tests, and the test results are similarly stored in the database. The system then generates a psychological profile for the user based on this data and builds an AI model. This AI model is accessible only to a limited number of users, and is particularly useful when parents want to leave messages for their children.

[1236] Program processing

[1237] Data accumulation phase

[1238] 1. Write and submit your blog entry

[1239] The user uses a device to enter their thoughts and feelings into the blog entry form, and when they're done, they click the send button.

[1240] The device receives the user's input and generates an HTTP POST request to send it to the server. The device then sends the generated request to the server.

[1241] The server analyzes the received data, formats it, and saves it in the database. After saving is complete, it returns a response to the device indicating that the save was successful.

[1242] 2. Conduct and send the psychological test

[1243] The user accesses the psychological test administration page, enters answers to questions, and submits the results after answering all questions.

[1244] The terminal compiles the user's answers and generates result data, which is then sent to the server.

[1245] The server analyzes the received psychological test results, stores them in a database, and returns a response indicating that the test was successfully saved to the device.

[1246] AI model generation phase

[1247] 1. Creating a Psychological Profile

[1248] The server uses text analysis techniques and statistical analysis to generate a psychological profile of the user based on the blog entries and psychological test results stored in the database.

[1249] 2. Generating an AI model

[1250] The server uses the generated psychological profile to run an AI model generation algorithm, which learns the user's writing style and emotional expressions to build an AI model that reflects the user's personality and tendencies.

[1251] AI utilization phase

[1252] 1. Submitting an AI usage request

[1253] The user's family members log in to a dedicated access page and send requests to use the AI. For example, if they want to hear a specific message, they can enter the content and send it.

[1254] The terminal composes the input request content and sends it to the server.

[1255] 2. Request Approval and Response

[1256] The server analyzes the received request and verifies that the request is from a valid user. After verification, it provides the appropriate AI interface.

[1257] The terminal displays the AI ​​interface provided by the server, providing an environment in which the user can interact with the AI.

[1258] The server inputs user questions and requests into the AI ​​model, generates appropriate responses, and sends the responses back to the device, where they are displayed.

[1259] Specific examples

[1260] Example blog entry

[1261] User: "I'm so happy that my first big presentation was a success today."

[1262] Terminal: Send this to the server.

[1263] Server: Receives and stores in a database.

[1264] Psychological test examples

[1265] User: Completes the psychological test and submits.

[1266] Terminal: Sends the results to the server.

[1267] Server: Stores the results in a database.

[1268] Examples of AI use

[1269] User's child: "Today is the day I'm making a big decision influenced by my mom. I'd like to hear her advice."

[1270] Device: Sends a request to the server.

[1271] Server: Uses AI models to generate appropriate responses and send them back to the device.

[1272] Terminal: Displays the message "We believe you will move forward with your own will."

[1273] The processing flow will be explained below.

[1274] Data accumulation phase

[1275] Step 1:

[1276] The user enters his or her thoughts and feelings into the blog entry form on the device.

[1277] Step 2:

[1278] After completing the input, the user clicks the send button.

[1279] Step 3:

[1280] The terminal receives the blog entry entered by the user.

[1281] Step 4:

[1282] The data received by the terminal is sent to the server via an HTTP POST request.

[1283] Step 5:

[1284] The server analyzes the received blog entry data and stores it in a database.

[1285] Step 6:

[1286] The server sends a successful save response to the terminal.

[1287] Step 7:

[1288] The user accesses the psychological test administration page.

[1289] Step 8:

[1290] The user answers each question to complete the test.

[1291] Step 9:

[1292] The user submits the results.

[1293] Step 10:

[1294] The terminal compiles the user's responses and generates result data.

[1295] Step 11:

[1296] The device sends the result data to the server via an HTTP POST request.

[1297] Step 12:

[1298] The server analyzes the received psychological test result data and stores it in a database.

[1299] Step 13:

[1300] The server sends a successful save response to the terminal.

[1301] AI model generation phase

[1302] Step 14:

[1303] The server generates a psychological profile of the user based on the blog entries and psychological test results stored in the database using text analysis techniques and statistical analysis.

[1304] Step 15:

[1305] The server uses the generated psychological profile to run an artificial intelligence model generation algorithm.

[1306] Step 16:

[1307] Based on the data learned by the algorithm, the server builds an AI model that reflects the user's writing style and emotional expression.

[1308] AI utilization phase

[1309] Step 17:

[1310] The user's family members log in to a dedicated access page and submit a request to use AI.

[1311] Step 18:

[1312] The terminal composes the input request content and sends it to the server.

[1313] Step 19:

[1314] The server analyzes the received request and checks the access rights.

[1315] Step 20:

[1316] After the server verifies that the access rights are appropriate, it provides the corresponding AI interface.

[1317] Step 20:

[1318] The device displays the AI ​​interface provided by the server.

[1319] Step 21:

[1320] The user's family interacts with the AI ​​through an AI interface.

[1321] Step 22:

[1322] The server inputs user questions and requests into the AI ​​model, which generates appropriate responses.

[1323] Step 23:

[1324] The server sends the generated AI response back to the terminal.

[1325] Step 24:

[1326] The terminal displays the received response to the user.

[1327] Example 1

[1328] 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."

[1329] Currently, many users have more opportunities to record their thoughts and feelings in blog format, but there are limited ways to effectively utilize this data. In particular, technology that generates a user's psychological profile and builds an AI model based on that to provide personalized responses to the user has not yet been fully developed. Another challenge is providing the generated AI model only to specific users, thereby creating an environment where users can use it with peace of mind.

[1330] 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.

[1331] In this invention, the server includes means for receiving blog entries entered from a user terminal and storing them in a database, means for receiving psychological test results and storing them in a database, means for generating a psychological profile of the user based on the stored blog entries and psychological test results, means for generating an AI model based on the generated psychological profile, and means for approving requests for limited users to access the AI ​​model and providing an AI interface. This makes it possible to effectively utilize the user's psychological data and provide personalized responses, giving the user a sense of security.

[1332] A "user terminal" is an electronic device used by a user to create and transmit input data.

[1333] A "blog entry" is a piece of writing that expresses thoughts and feelings and that a user writes on a separate page on the Web.

[1334] "Psychological test results" are data obtained as a result of a user answering a psychological test.

[1335] A "database" is a system for storing and managing various data.

[1336] A "psychological profile" is information that indicates a user's psychological characteristics and tendencies, generated based on blog entries and psychological test results.

[1337] An "artificial intelligence model" is a machine learning model that is generated based on a user's psychological profile and trained to perform specific tasks.

[1338] A "request" refers to an operation or information request made by a user to a system.

[1339] An "interface" is the means or screen through which a user interacts with a system.

[1340] "Text analysis" is the process of analyzing text data using natural language processing techniques to extract useful information.

[1341] "Real-time" refers to immediate reactions and processing without delay.

[1342] The present invention is a system that allows users to input blog entries and psychological test results, generates a psychological profile for the user based on that data, and then builds an artificial intelligence model based on that profile. This system provides a platform for users to continuously input their own thoughts and feelings and store them in a database. It also has a function for conducting psychological tests, and the test results are similarly stored in the database.

[1343] To implement this invention, the following hardware and software are required: The terminal used by the user has a function for connecting to the Internet and can access the blog entry input form and psychological test page using a web browser (e.g., Google Chrome, Mozilla Firefox). The terminal also has a built-in program for generating HTTP POST requests. The server has a database management system (e.g., MySQL, PostgreSQL) installed, and uses text analysis technology (e.g., NLTK, spaCy) and machine learning libraries (e.g., TensorFlow, PyTorch).

[1344] The specific operation of the system is as follows: when a user enters a blog entry through a device and clicks the send button, the device receives the input, generates an HTTP POST request, and sends it to the server. The server analyzes the received request, formats the text content, and saves it in a database. If the save is successful, the server returns a "save successful" response to the device. Similarly, when a user answers a psychological test and clicks the send button, the device tallys the answers and sends them to the server, which then saves the results in a database.

[1345] The server periodically retrieves blog entries and psychological test results stored in the database and analyzes the text using natural language processing technology. Based on the analysis results, it generates a psychological profile for the user and stores this profile in the database. Furthermore, it uses a machine learning library to build an artificial intelligence model based on the generated psychological profile. This AI model is also stored in the database.

[1346] To access the AI ​​model, limited users log in to a dedicated access page and submit a request. For example, a user's child might type, "Today is the day I'm making a big decision influenced by my mother. I'd like to hear some advice from my mother," and click the submit button. The device then sends the request to the server, which analyzes the request and verifies that it is from a valid user. After verification, the device uses the corresponding AI model to generate an appropriate response and sends it back to the device. The device then displays the received response to the user.

[1347] As specific examples, the input prompt for a blog entry is in the format "Please enter the content of your blog. For example, 'I had a great time with my friends today.'" The input prompt for a psychological test is in the format "Please answer the following questions. For example, questions include 'How are you feeling lately?'" The prompt for using AI is in the format "Please enter the message you want to ask the AI. For example, 'How should I make an important decision?'"

[1348] As described above, the present invention is a system that effectively utilizes the user's psychological data and provides personalized responses, thereby giving the user a sense of security.

[1349] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1350] Step 1:

[1351] Entering and submitting a blog entry

[1352] A user accesses a blog entry form and enters their thoughts and feelings. For example, they might enter, "I was so happy that my first big presentation was a success today."

[1353] Input: A user types a blog entry and clicks the submit button.

[1354] The terminal receives the input content and generates an HTTP POST request to send to the server. Specifically, it converts the input text into JSON format and sends it to the server as the payload of the HTTP request.

[1355] Output: The user's blog entry is sent to the server.

[1356] Step 2:

[1357] Receive and store blog entries

[1358] The server receives the HTTP POST request sent from the terminal.

[1359] Input: Blog entry data sent as an HTTP POST request.

[1360] The server analyzes the received data and formats the text content. Specifically, it performs JSON parsing and extracts the text data.

[1361] The server stores the formatted data in a database.

[1362] Output: The blog entry saved in the database.

[1363] Step 3:

[1364] Conduct and send psychological tests

[1365] A user visits a psychological test page and answers a series of questions, for example, "How are you feeling these days?" and answers "very good."

[1366] Input: The user answers the psychological test and clicks the submit button.

[1367] The device aggregates the user's answers to each question and generates the result data. Specifically, it compiles each answer as a JSON object and generates an HTTP POST request to send it to the server.

[1368] Output: The result data of the psychological test is sent to the server.

[1369] Step 4:

[1370] Receiving and storing psychological test results

[1371] The server receives an HTTP POST request including the psychological test result sent from the terminal.

[1372] Input: Psychological test result data sent as an HTTP POST request.

[1373] The server analyzes and formats the received result data, specifically by parsing it in JSON and extracting the response data.

[1374] The server stores the formatted data in a database.

[1375] Output: Psychological test results stored in a database.

[1376] Step 5:

[1377] Psychological profile generation

[1378] The server retrieves the blog entries and psychological test results stored in the database.

[1379] Input: Blog entries and psychological test results stored in a database.

[1380] The server analyzes the text using natural language processing technology (e.g., NLTK, spaCy). Specifically, it performs morphological analysis of the text data and performs emotion and sentiment analysis.

[1381] The server processes the analysis results using statistical analysis techniques to generate a psychological profile of the user.

[1382] Output: The generated psychological profile is stored in a database.

[1383] Step 6:

[1384] Generating artificial intelligence models

[1385] The server retrieves the generated psychological profile.

[1386] Input: Psychological profiles stored in a database.

[1387] The server uses machine learning libraries (e.g., TensorFlow, PyTorch) to execute artificial intelligence model generation algorithms.

[1388] The server learns the user's writing style and emotional expression, and builds an artificial intelligence model that reflects the user's personality and tendencies.

[1389] Output: The constructed artificial intelligence model is stored in a database.

[1390] Step 7:

[1391] Submitting an AI request

[1392] The user's family members log in to a dedicated access page.

[1393] Input: The user's family member enters a request to use AI and clicks the send button. For example, they might enter, "Today is the day I'm making a big decision influenced by my mother. I'd like to hear her advice."

[1394] The device sends the request content to the server as an HTTP POST request in JSON format.

[1395] Output: An AI-enabled request is sent to the server.

[1396] Step 8:

[1397] Request Approval and Response

[1398] The server analyzes the received request and verifies that it is from the user's family.

[1399] Input: AI-powered request data.

[1400] The server loads the appropriate AI model and generates an appropriate response to the request, such as the message, "I trust you to move forward with your own will."

[1401] The server generates a response and sends it back to the terminal.

[1402] Output: The generated response is sent to the terminal.

[1403] Step 9:

[1404] Viewing the response

[1405] The terminal receives the response provided by the server.

[1406] Input: The response data sent by the server.

[1407] The device displays the received response to the user, for example, a message saying "I believe you will move forward with your own will."

[1408] Output: The user receives the response.

[1409] (Application example 1)

[1410] 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."

[1411] Today, many users blog online and gain a deeper understanding of themselves through psychological tests. However, there is no established method for centrally analyzing this data and providing users with personalized content recommendations and appropriate responses in real time. In addition, there are demands for specific situations, such as when parents want to leave special messages for their children, or for content tailored to the user's psychological state. Therefore, there is a need for the development of a system that can generate a user's psychological profile, build an AI model based on that profile, and provide personalized services.

[1412] 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.

[1413] In this invention, the server includes means for receiving data input from a user terminal and storing the data in a database, means for receiving psychological test results and storing the results in a database, means for generating a psychological profile of the user based on the stored data and the psychological test results, means for generating an AI model based on the generated psychological profile, means for approving access requests for limited users to access the AI ​​model and providing an AI interface, and means for recommending personalized content based on the user's psychological profile, thereby making it possible to individually recommend content based on the user's psychological state and provide personalized messages for specific situations.

[1414] A "user terminal" is a device that can be operated by a user and is a device that connects to the Internet and inputs, transmits, and receives data. Examples include smartphones, tablets, and personal computers.

[1415] A "database" is a system for efficiently storing, managing, and searching data, and is a recording medium that stores information such as user blog entries and psychological test results.

[1416] The "psychological test results" are the user's response data and evaluation data obtained through the psychological test, and are information for understanding the user's psychological characteristics and tendencies based on this.

[1417] A "psychological profile" is profile data that indicates a user's psychological characteristics and tendencies, and is generated by analyzing the user's blog entries and psychological test results.

[1418] An "AI model" is a data model built using artificial intelligence technology, and is a computational algorithm for generating specific actions and responses based on a user's psychological profile.

[1419] An "Access Request" is a request sent by a qualified user to access an AI model, and the AI ​​interface is provided only if the access right is confirmed.

[1420] "Personalized content" refers to information and media such as videos, articles, and music that are optimized and recommended to a specific user based on the user's psychological profile.

[1421] An "AI interface" is a user interface that allows a user to interact with an AI model, and includes the screen and operating means displayed on the terminal.

[1422] "Text analysis" is the process of analyzing text data using natural language processing techniques to extract information such as meaning and sentiment.

[1423] "Content recommendation" is a system that uses a user's psychological profile to select and present the most suitable content for the user.

[1424] As an embodiment of the present invention, there is a system that receives data input from a user terminal, stores it in a database, generates a psychological profile based on the data, creates an AI model, and recommends personalized content. Specifically, this system operates as follows.

[1425] Hardware and Software Use

[1426] User devices can be smartphones, tablets, or personal computers. The applications that run on the devices are built using React Native. Server-side processing is done with Node.js and Express.js, and data is stored in MongoDB. Additionally, NLTK, a Python natural language processing library, and TensorFlow, a machine learning library, are used to generate the AI ​​model.

[1427] Entering and Saving Data

[1428] The user terminal accepts blog entries and psychological test results from users as input. The user enters this data through the application and clicks a button to submit it. The terminal then sends this input data to the server as an HTTP POST request.

[1429] The server parses and formats the data it receives and stores it in a MongoDB database. For example, if a user types "I'm very happy that my first big presentation was a success today," it will be stored in the database.

[1430] Psychological profile generation

[1431] The server uses text analysis technology to generate a psychological profile of the user based on the blog entries and psychological test results stored in the database. This profile is generated using Python's NLTK library, which analyzes the data and extracts the user's psychological characteristics and tendencies.

[1432] Generating and applying AI models

[1433] Based on the generated psychological profile, the server uses TensorFlow to generate an AI model that learns the user's language style and emotional expressions, and then recommends responses and content that reflect the user's characteristics.

[1434] Personalized content recommendations

[1435] The user device receives personalized content from the server based on the user's psychological profile. This content can include videos, articles, music, and more, and the most suitable content is recommended to the user.

[1436] AI usage example

[1437] Users can send requests for specific messages or advice through the application. For example, if a child of the user types, "Today is the day I'm making a big decision, influenced by my mother. I'd like to hear her advice," the server uses an AI model to generate an appropriate response and sends it back to the device. This results in the message, "I trust you to move forward with your own will."

[1438] Prompt Sentence Examples

[1439] "Generate personalized content suggestions for this user using their latest psychological profile. Their blog entry is 'I was so happy today that my first big presentation went well.'"

[1440] In this way, the present invention makes it possible to provide personalized services based on the user's psychological state.

[1441] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1442] Step 1:

[1443] A user types a blog entry and clicks the submit button.

[1444] Input: The blog entry entered by the user

[1445] Specific operation:

[1446] Users open the application on their smartphone or computer, enter their thoughts and feelings in the text area, and then click the send button to send the blog entry.

[1447] Step 2:

[1448] The terminal transmits the blog entry data to the server.

[1449] Input: User's blog entry

[1450] Output: Blog entry data sent to the server

[1451] Specific operation:

[1452] The device takes the user's input and sends it to the server as an HTTP POST request, which includes the text data of the blog entry.

[1453] Step 3:

[1454] The server receives the blog entry data and stores it in a database.

[1455] Input: Blog entry data sent from the device

[1456] Output: Blog entries stored in the database

[1457] Specific operation:

[1458] The server receives the HTTP POST request and analyzes its contents. After analyzing, it stores the formatted data in MongoDB. After storing the data, the server returns a success response to the device.

[1459] Step 4:

[1460] The user answers the psychological test and submits the results.

[1461] Input: Psychological test results answered by the user

[1462] Output: Psychological test data sent to the server

[1463] Specific operation:

[1464] The user answers the psychological test presented on the application, and after completing all the questions, clicks the send button to send the results.

[1465] Step 5:

[1466] The terminal transmits the psychological test results to the server.

[1467] Input: User's psychological test results

[1468] Output: Psychological test data sent to the server

[1469] Specific operation:

[1470] The device collects the user's psychological test results and sends them to the server as an HTTP POST request, which includes the psychological test result data.

[1471] Step 6:

[1472] The server receives the psychological test results and stores them in a database.

[1473] Input: Psychological test result data sent from the device

[1474] Output: Psychological test results stored in a database

[1475] Specific operation:

[1476] The server receives the test results and analyzes them. After analysis, the formatted data is saved in MongoDB. After saving is complete, the server returns a success response to the device.

[1477] Step 7:

[1478] The server generates a psychological profile based on the blog entries and psychological test results.

[1479] Input: Blog entries and psychological test results stored in a database

[1480] Output: Generated psychological profile

[1481] Specific operation:

[1482] The server retrieves blog entries and psychological test results from the database, performs text analysis using Python's NLTK library, and generates a psychological profile of the user based on the analyzed data.

[1483] Step 8:

[1484] The server generates an AI model based on the psychological profile.

[1485] Input: Generated psychological profile

[1486] Output: The generated AI model

[1487] Specific operation:

[1488] The server analyzes the psychological profile using the TensorFlow library and generates an AI model that learns the user's language style and emotional expressions.

[1489] Step 9:

[1490] Submit an access request for access to an AI model by a limited user.

[1491] Input: Access request from a restricted user

[1492] Output: Access request sent to the server

[1493] Specific operation:

[1494] Users log in to a dedicated access page on the application and submit a request to access the AI ​​model.

[1495] Step 10:

[1496] The server approves the access request and provides the AI ​​interface.

[1497] Input: Access request sent by the user

[1498] Output: Provided AI interface

[1499] Specific operation:

[1500] The server analyzes the received request and checks the user's access rights, then provides the AI ​​interface to the device.

[1501] Step 11:

[1502] The server recommends personalized content based on the user's psychological profile.

[1503] Input: User's psychological profile

[1504] Output: personalized content

[1505] Specific operation:

[1506] The server then recommends the most suitable content for the user, such as videos, articles, and music, based on the generated psychological profile, using a generative AI model and prompts to select the appropriate content.

[1507] Example prompt sentence:

[1508] "Generate personalized content suggestions for this user using their latest psychological profile. Their blog entry is 'I was so happy today that my first big presentation went well.'"

[1509] 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.

[1510] This invention is a system that generates a psychological profile for each user based on the user's blog entries and psychological test results, and creates an AI model based on that profile. This system incorporates an emotion engine that recognizes the user's emotions, and can reflect the user's overall feelings and emotional changes in the AI ​​model.

[1511] Program processing

[1512] Data accumulation phase

[1513] 1. Write and submit your blog entry

[1514] The user enters their thoughts and feelings into the blog entry form on their device, and when they're done, clicks the send button.

[1515] The device receives the user's input and generates an HTTP POST request to send it to the server. The device then sends the generated request to the server.

[1516] The server analyzes the received data, formats it, and saves it in the database. After saving is complete, it returns a response to the device indicating that the save was successful.

[1517] 2. Emotion Recognition by Emotion Engine

[1518] The server sends the received blog entry data to an emotion engine to analyze the user's emotions.

[1519] The emotion engine generates emotion analysis results, which are also stored in the database.

[1520] 3. Conduct and send the psychological test

[1521] The user accesses the psychological test administration page, enters answers to questions, and submits the results after answering all questions.

[1522] The terminal compiles the user's answers and generates result data, which is then sent to the server.

[1523] The server analyzes the received psychological test results, stores them in a database, and returns a response indicating that the test was successfully saved to the device.

[1524] 4. Emotion recognition using emotion engine (psychological test)

[1525] The server sends the received psychological test results to the emotion engine to analyze the user's emotions.

[1526] The emotion engine generates emotion analysis results, which are also stored in the database.

[1527] AI model generation phase

[1528] 1. Creating a Psychological Profile

[1529] The server uses text analysis techniques and statistical analysis to generate a psychological profile of the user based on the blog entries, psychological test results, and sentiment analysis results stored in the database.

[1530] 2. Generating an AI model

[1531] The server uses the generated psychological profile to run an AI model generation algorithm, which learns the user's writing style and emotional expressions to build an AI model that reflects the user's personality and tendencies.

[1532] AI utilization phase

[1533] 1. Submitting an AI usage request

[1534] The user's family members log in to a dedicated access page and send requests to use the AI. For example, if they want to hear a specific message, they can enter the content and send it.

[1535] The terminal composes the input request content and sends it to the server.

[1536] 2. Request Approval and Response

[1537] The server analyzes the received request and checks the access rights.

[1538] After verifying that the access rights are appropriate, the corresponding AI interface will be provided.

[1539] 3. AI-User Interaction

[1540] The server inputs user questions and requests into the AI ​​model and generates appropriate responses.

[1541] The AI ​​model generates responses based on the analysis results of the emotion engine and provides messages that take the user's emotions into consideration.

[1542] The terminal displays the AI ​​interface provided by the server, providing an environment in which the user can interact with the AI.

[1543] The server sends the generated AI response back to the terminal, where it is displayed.

[1544] Specific examples

[1545] Example blog entry

[1546] User: "I was so happy today. I feel so much better after hearing the good news."

[1547] Terminal: Send this entry to the server.

[1548] Server: Receives and stores in the database. The emotion engine recognizes the emotion "happy" and stores the result.

[1549] Psychological test examples

[1550] User: Completes the psychological test and submits.

[1551] Terminal: Sends the results to the server.

[1552] Server: Save the results in the database. The emotion engine recognizes the emotion "nervous" and saves the result.

[1553] Examples of AI use

[1554] User's child: "I want to talk to my mom. I especially need encouragement today."

[1555] Device: Sends a request to the server.

[1556] Server: Accepts the request and uses the AI ​​model.

[1557] Device: Displays the message "We're always rooting for you. Believe in yourself and move forward."

[1558] The processing flow will be explained below.

[1559] Data accumulation phase

[1560] Step 1:

[1561] The user enters his or her thoughts and feelings into the blog entry form on the device.

[1562] Step 2:

[1563] After completing the input, the user clicks the send button.

[1564] Step 3:

[1565] The terminal receives the blog entry input by the user.

[1566] Step 4:

[1567] The data received by the terminal is sent to the server via an HTTP POST request.

[1568] Step 5:

[1569] The server analyzes the received blog entry data and stores it in a database.

[1570] Step 6:

[1571] The server sends a successful save response to the terminal.

[1572] Step 7:

[1573] The server sends the blog entry data to the emotion engine for emotion analysis.

[1574] Step 8:

[1575] The emotion engine generates the emotion analysis results of the blog entries and stores them in the database.

[1576] Step 9:

[1577] The user accesses the psychological test administration page.

[1578] Step 10:

[1579] The user answers each question, completes the test, and submits the results.

[1580] Step 11:

[1581] The terminal compiles the user's responses and generates result data.

[1582] Step 12:

[1583] The device sends the result data to the server via an HTTP POST request.

[1584] Step 13:

[1585] The server analyzes the received psychological test result data and stores it in a database.

[1586] Step 14:

[1587] The server sends a successful save response to the terminal.

[1588] Step 15:

[1589] The server sends the psychological test results to the emotion engine for emotion analysis.

[1590] Step 16:

[1591] The emotion engine performs emotion analysis of the psychological test results and stores the results in a database.

[1592] AI model generation phase

[1593] Step 17:

[1594] The server generates a psychological profile of the user using text analysis techniques and statistical analysis based on the blog entries, psychological test results, and sentiment analysis results stored in the database.

[1595] Step 18:

[1596] The server uses the generated psychological profile to run an artificial intelligence model generation algorithm.

[1597] Step 19:

[1598] Based on the data learned by the algorithm, the server builds an AI model that reflects the user's writing style and emotional expression.

[1599] AI utilization phase

[1600] Step 20:

[1601] The user's family members log in to a dedicated access page and submit a request to use AI.

[1602] Step 21:

[1603] The terminal composes the input request content and sends it to the server.

[1604] Step 22:

[1605] The server analyzes the received request and checks the access rights.

[1606] Step 23:

[1607] After the server verifies that the access rights are appropriate, it provides the corresponding AI interface.

[1608] Step 24:

[1609] The device displays the AI ​​interface provided by the server, providing an environment in which the user can interact with the AI.

[1610] Step 25:

[1611] The user's family interacts with the AI ​​through an AI interface.

[1612] Step 26:

[1613] The server inputs user questions and requests into the AI ​​model, which generates appropriate responses.

[1614] Step 27:

[1615] The server generates an AI response based on the analysis results of the emotion engine and provides a message that takes the user's emotions into consideration.

[1616] Step 28:

[1617] The server sends the generated AI response back to the terminal, where it is displayed.

[1618] Example 2

[1619] 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."

[1620] Conventional systems simply store users' blog entries and psychological test results in a database, making it difficult to deeply analyze users' emotional changes and psychological tendencies and reflect them in the AI ​​model. Furthermore, the responses generated by the AI ​​model did not take into account the user's emotions, resulting in a decline in the quality of the dialogue. This made it impossible to provide detailed psychological support to users.

[1621] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1622] In this invention, the server includes means for receiving data input from a user terminal and storing the data in a database, means for receiving psychological test results and storing the results in a database, means for generating a psychological profile of the user based on the stored data and the psychological test results, means for generating an AI model based on the generated psychological profile, means for emotionally analyzing the user's input data using an emotion engine, means for storing the analyzed emotion data in a database, means for emotionally analyzing the psychological test results using the emotion engine, and means for approving access requests for limited users to access the AI ​​model and providing an AI interface. This makes it possible to deeply analyze the user's emotional changes and psychological tendencies and generate an AI model that takes the user's emotions into consideration.

[1623] A "user terminal" is a device used by a user to input data, and includes devices such as personal computers and smartphones.

[1624] A "server" is a central processing unit that processes data received from user terminals and stores it in a database.

[1625] A "database" is a storage device for permanently storing received data, emotion analysis results, and psychological test results.

[1626] The "psychological test result" is data generated by the user answering the psychological test, and includes information indicating the user's psychological state.

[1627] A "psychological profile" is data that indicates a user's psychological tendencies and characteristics, generated based on the user's blog entries, psychological test results, and emotion analysis results.

[1628] An "AI model" is an artificial intelligence model generated based on a psychological profile, which learns the user's writing style and emotional expression.

[1629] The "emotion engine" is a system that uses natural language processing technology to analyze user input data and extract emotions as tags.

[1630] "Access Request" means a request submitted by a qualified user to access an AI model.

[1631] An "AI interface" is a user interface that provides an environment in which users can interact with an AI model.

[1632] This invention is a system that generates a user's psychological profile based on the user's blog entries and psychological test results, and creates an AI model based on that profile. This system incorporates an emotion engine that recognizes the user's emotions, and can reflect the user's overall feelings and emotional changes in the AI ​​model.

[1633] Hardware and Software Configuration

[1634] User terminal: A device such as a computer or smartphone that is used by a user to input data.

[1635] Server: A central processing unit that processes data and stores it in a database. A server usually consists of a computer with advanced computing power.

[1636] Database: A storage device for persistently storing received data, sentiment analysis results, and psychological test results. For example, an SQL database or NoSQL database is used.

[1637] Emotion engine: Software that uses natural language processing technology to analyze user input data and extract emotions as tags. For example, an NLP library implemented in Python or a dedicated sentiment analysis API is used.

[1638] System processing flow

[1639] 1. Write and submit your blog entry

[1640] The user enters their feelings and thoughts into the blog entry form on their device and clicks the send button.

[1641] The terminal receives the user's input data and sends it to the server as an HTTP POST request.

[1642] The server receives the data, analyzes and formats it, and saves it in the database. After saving is complete, it returns a successful save response to the device.

[1643] 2. Emotion Recognition by Emotion Engine

[1644] The server sends the blog entry data to an emotion engine to analyze the user's emotions.

[1645] The emotion engine uses NLP technology to extract emotions such as "happy" or "sad" as tags.

[1646] The server stores the emotion analysis results received from the emotion engine in a database.

[1647] 3. Conduct and send the psychological test

[1648] A user accesses a psychological test question page, enters answers to all questions, and submits the answer.

[1649] The terminal aggregates the user's response data, converts it into structured data (e.g., JSON format), and sends it to the server.

[1650] The server receives the psychological test results, analyzes and stores them, and returns a response indicating that the test results were successfully stored.

[1651] 4. Emotion recognition using emotion engine (psychological test)

[1652] The server sends the psychological test results to the emotion engine, which analyzes the user's emotions again.

[1653] The emotion engine generates analysis results and stores the results in a database.

[1654] 5. Generating a Psychological Profile

[1655] The server retrieves all blog entries, psychological test results, and sentiment analysis results from the database and generates a psychological profile using text analysis techniques (natural language processing and statistical analysis).

[1656] 6. Generating AI models

[1657] The server then runs an AI model generation algorithm (such as a neural network) based on the generated psychological profile. This model learns the user's writing style and emotional expressions, and reflects the user's personality and tendencies.

[1658] 7. Submitting an AI Request

[1659] The user's family members log in to a dedicated access page and send a request to use AI from their device.

[1660] 8. Request Approval and Response

[1661] The server analyzes the received request, checks the access rights, and then provides the corresponding AI interface.

[1662] 9. AI-User Interaction

[1663] The server inputs user questions and requests into the AI ​​model and generates appropriate responses.

[1664] The emotion engine is taken into account when generating responses, generating emotion-sensitive messages.

[1665] The server generates a response and sends it back to the terminal, which displays it.

[1666] Specific examples

[1667] Example blog entry

[1668] User: "I was very happy today. Hearing the good news brightened my mood." is entered and sent from the device to the server.

[1669] The server receives the data and stores it in a database. The emotion engine recognizes the emotion "happy" and stores the result.

[1670] Psychological test examples

[1671] The user answers a psychological test and sends the result of feeling "tense" from the terminal to the server.

[1672] The server stores the results in a database, and the emotion engine recognizes the emotion "nervous" and stores the result as well.

[1673] Examples of AI use

[1674] User's child: "I want to talk to my mother. I especially need encouragement today." Sends a request from the device to the server.

[1675] The server approves the request and uses an AI model to generate a message saying, "We're always rooting for you. Believe in yourself and move forward."

[1676] The server generates a message and returns it to the terminal, where it is displayed.

[1677] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1678] Step 1:

[1679] The user enters their feelings and thoughts into the blog entry form on their device and clicks the send button. This operation generates the entered text data.

[1680] Step 2:

[1681] The terminal receives the user's input and composes it into an HTTP POST request, which is then sent to the server as input data.

[1682] Step 3:

[1683] The server receives the HTTP POST request and analyzes the input data. Through analysis, text data is extracted. The text data is then formatted for storage in the database. The formatted data is saved in the database and a response indicating successful saving is returned to the terminal.

[1684] Step 4:

[1685] The server passes the formatted text data to the emotion engine, which analyzes the user's emotions. During this emotion analysis process, emotion tags are extracted from the text using NLP technology. The extracted emotion tags are generated as the emotion analysis results.

[1686] Step 5:

[1687] The server stores the emotion analysis results received from the emotion engine in a database.

[1688] Step 6:

[1689] The user accesses the psychological test question page through their device and enters their answers. After answering all the questions, the user submits the results. This operation generates the psychological test answer data.

[1690] Step 7:

[1691] The device collects the user's psychological test response data and converts it into structured data such as JSON format, then sends the generated structured data to the server as an HTTP POST request.

[1692] Step 8:

[1693] The server receives the HTTP POST request, analyzes the psychological test results, saves the analyzed results in a database, and returns a response indicating that the save was successful to the device.

[1694] Step 9:

[1695] The server sends the psychological test results to the emotion engine, which then analyzes the user's emotions again. The emotion engine extracts emotion tags from the psychological test results and generates emotion analysis results.

[1696] Step 10:

[1697] The server stores the emotion analysis results received from the emotion engine in a database.

[1698] Step 11:

[1699] The server retrieves blog entry data, psychological test results, and sentiment analysis results from the database, and then generates a psychological profile of the user using text analysis techniques and statistical analysis based on the retrieved data.

[1700] Step 12:

[1701] The server uses the generated psychological profile to run an AI model generation algorithm, which builds an AI model that learns the user's writing style and emotional expressions.

[1702] Step 13:

[1703] The user's family members log in to a dedicated access page and send requests for using AI from their devices, for example, by entering, "I want to hear an encouraging message from my mother."

[1704] Step 14:

[1705] The terminal composes the input request content as an HTTP POST request and sends it to the server.

[1706] Step 15:

[1707] The server analyzes the received request and checks the access rights. After the access rights are confirmed, the corresponding AI interface is provided.

[1708] Step 16:

[1709] The server inputs questions and requests from users into the AI ​​model and generates appropriate responses. The AI ​​model generates responses that take the user's emotions into consideration based on the analysis results of the emotion engine.

[1710] Step 17:

[1711] The server generates a response that is sent back to the terminal and displayed there.

[1712] Step 18:

[1713] The terminal displays the AI ​​interface provided by the server, creating an environment in which the user can interact with the AI.

[1714] (Application example 2)

[1715] 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."

[1716] Conventional advertising systems lack personalization that takes into account the user's emotions and psychological state, and deliver a large number of generic ads, making it difficult to quickly adapt to the user's interests and emotions. Furthermore, they lack technology that effectively utilizes users' daily blog entries and psychological test results to optimize ads. This results in a monotonous user experience and ineffective advertising.

[1717] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1718] In this invention, the server includes means for receiving data input from a user terminal and storing the data in a database, means for receiving psychological test results and storing the results in a database, means for generating a psychological profile of the user based on the stored data and the psychological test results, means for generating an AI model based on the generated psychological profile, means for approving access requests for limited users to access the AI ​​model and providing an AI interface, and means for generating and displaying personalized advertisements according to the user's psychological state, thereby making it possible to provide advertisements that reflect the user's emotions and psychological state and achieve a more effective advertising experience.

[1719] 1. "User terminal" means an input and display device used by a user, including mobile devices such as smartphones and tablets.

[1720] 2. "Database" means an information system for organizing and storing received data and analysis results, including relational databases and NoSQL databases.

[1721] 3. "Psychological test results" are data resulting from a user's responses to a psychological test, and are data that indicate the user's psychological state and personality traits.

[1722] 4. "Psychological profile" is a data set that shows the psychological characteristics of a user analyzed based on the user's blog entries and psychological test results.

[1723] 5. "AI Model" means a computer program generated by machine learning algorithms based on a user's psychological profile, capable of predicting and responding to a user's emotions and tendencies.

[1724] 6. "Access Request" means a request submitted by a User to access certain data or functionality.

[1725] 7. “AI Interface” means an interface through which a user interacts with an AI model, whether in text, voice, or other format.

[1726] 8. "Advertisement" means information displayed to promote a particular product or service to a user.

[1727] 9. "Personalized advertising" refers to individual advertising optimized based on a user's psychological state or characteristics.

[1728] 10. "Sentiment analysis" is the process of identifying emotions and emotional changes from user input data (such as blog entries or psychological test results).

[1729] 11. "Blog entry" is data in the form of text in which a user describes everyday events and feelings.

[1730] 12. "Text analysis" is the process of analyzing text data using natural language processing techniques to extract meaning and sentiment.

[1731] This invention is a system that generates a psychological profile for each user based on the user's blog entries and psychological test results, and then creates an AI model based on that profile. This system incorporates an emotion engine that recognizes the user's emotions, allowing the user's overall state of mind and emotional changes to be reflected in the AI ​​model. The specific program processing and required hardware and software are described in detail below.

[1732] Hardware and software used

[1733] User device: Mobile devices such as smartphones and tablets

[1734] Database: AWS RDS (Relational Database Service)

[1735] Server: Node.js + Express.js

[1736] Emotion Engine API: IBM Watson Natural Language Understanding

[1737] AI model generation: Scikit-learn, TensorFlow

[1738] Frontend: React Native

[1739] Program processing

[1740] 1. Data entry on the user's device

[1741] The application provides a form for users to write blog entries. Users use this form to input their daily events and feelings. Once the input is complete, they press the submit button to send the data to the server.

[1742] 2. Receiving and storing data on the server

[1743] The server receives data sent from the user terminal using an HTTP POST request and saves the received data in a database, which then stores the blog entry and its related information.

[1744] 3. Emotion analysis

[1745] The server sends the saved blog entry data to the emotion engine API to analyze the user's emotions. The emotion engine identifies the emotions in the text and sends the results back to the server. The acquired emotion analysis results are then stored in the database.

[1746] 4. Psychological profile generation

[1747] The server uses text analysis techniques and statistical analysis to generate a user's psychological profile based on the blog entries, psychological test results, and sentiment analysis results stored in the database. The generated profile details the user's psychological characteristics.

[1748] 5. AI model generation

[1749] The server then uses the generated psychological profile to run a machine learning algorithm, which learns the user's writing style and emotional expressions, and builds an AI model that reflects the user's personality and tendencies.

[1750] 6. Generating and displaying personalized advertisements

[1751] The server generates advertisements based on the user's psychological state. These advertisements reflect the user's emotions and interests and are delivered in real time from the server to the user's device. The user's device then displays the received advertisements on its screen, with content that the user finds interesting.

[1752] Specific examples

[1753] Example blog entry

[1754] User: "I was so happy today. I feel so much better after hearing the good news."

[1755] Server: Receives this entry and saves it in the database. The emotion engine recognizes the emotion "happy" and saves the result.

[1756] Examples of personalized ads

[1757] An advertisement for a "travel package with celebratory benefits" is displayed on the user's device based on the user's blog entry and sentiment analysis results.

[1758] Prompt Sentence Examples

[1759] "I'm very tired today. I don't feel well."

[1760] Sentiment analysis is performed from these blog entries to generate product advertisements with a relaxation theme.

[1761] As described above, the present invention makes it possible to provide advertisements that take into account the user's emotions and psychological state, thereby improving the user's advertising experience.

[1762] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1763] Step 1:

[1764] A user terminal inputs and transmits a blog entry.

[1765] The blog entry is in text format, and the user describes their daily events and feelings. The user clicks the submit button, which sends an HTTP POST request to the server.

[1766] Input: User's blog entry

[1767] Output: HTTP POST request sent to the server

[1768] Step 2:

[1769] The server receives blog entries from user terminals and stores them in a database.

[1770] The server parses the received blog entries and formats them for storage in a database, where the formatted data is stored in a relational table.

[1771] Input: User's blog entry data

[1772] Output: Data stored in a database table

[1773] Step 3:

[1774] The server sends the blog entry to the emotion engine API to perform emotion analysis.

[1775] The server sends the stored blog entries to an emotion engine API, such as IBM Watson, to identify the sentiment within the text, which then generates an emotion analysis result and sends it back to the server.

[1776] Input: Blog entry data

[1777] Output: Sentiment analysis result data

[1778] Step 4:

[1779] The server stores the emotion analysis results in a database.

[1780] The server formats the received sentiment analysis results for storage in a database, and stores them in association with the corresponding blog entry.

[1781] Input: Sentiment analysis result data

[1782] Output: Sentiment analysis results stored in a database table

[1783] Step 5:

[1784] The user takes the psychological test and submits the results.

[1785] The user accesses the psychological test page in the application, enters answers to questions, and after answering all questions, sends the results to the server.

[1786] Input: Psychological test response data

[1787] Output: Psychological test results sent to the server

[1788] Step 6:

[1789] The server receives the psychological test results and stores them in a database.

[1790] The server analyzes the received psychological test results and formats them for storage in a database, where the formatted data is stored in a related table.

[1791] Input: Psychological test result data

[1792] Output: Psychological test results stored in a database table

[1793] Step 7:

[1794] The server sends the psychological test results to the emotion engine API to perform emotion analysis.

[1795] The server sends the stored psychological test results to the emotion engine API to identify emotions in the text, and the emotion engine generates emotion analysis results and sends them back to the server.

[1796] Input: Psychological test result data

[1797] Output: Sentiment analysis result data

[1798] Step 8:

[1799] The server stores the emotion analysis results in a database.

[1800] The server formats the received emotion analysis results for storage in a database, and stores them in association with the corresponding psychological test results.

[1801] Input: Sentiment analysis result data

[1802] Output: Sentiment analysis results stored in a database table

[1803] Step 9:

[1804] The server generates a psychological profile of the user based on the blog entry, the psychological test results, and the emotion analysis results.

[1805] The server uses text analysis techniques and statistical analysis to generate a psychological profile of the user based on the data stored in the database.

[1806] Input: Blog entries stored in the database, psychological test results, and sentiment analysis results

[1807] Output: Psychological profile of the user

[1808] Step 10:

[1809] The server generates an AI model based on the generated psychological profile.

[1810] The server uses the psychological profile to run machine learning algorithms and build an AI model that reflects the user's emotions and tendencies.

[1811] Input: User's psychological profile

[1812] Output: User's AI model

[1813] Step 11:

[1814] The server generates and displays personalized advertisements according to the user's psychological state.

[1815] The server selects appropriate advertisements based on the user's psychological profile and AI model, and delivers the generated advertisements to the user's device in real time. The user's device then displays the received advertisements on its screen.

[1816] Input: User's psychological profile, AI model

[1817] Output: Personalized ads displayed on the user's device

[1818] 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.

[1819] 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.

[1820] 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.

[1821] [Fourth embodiment]

[1822] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1823] 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.

[1824] 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).

[1825] 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.

[1826] 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.

[1827] 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).

[1828] 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.

[1829] 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.

[1830] 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.

[1831] 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.

[1832] 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.

[1833] 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.

[1834] 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."

[1835] This invention is a system that generates a psychological profile for each user based on the user's blog entries and psychological test results, and then creates an AI model based on that profile. This system provides a platform for users to continuously input their thoughts and feelings and store them in a database. It also has a function for conducting psychological tests, and the test results are similarly stored in the database. The system then generates a psychological profile for the user based on this data and builds an AI model. This AI model is accessible only to a limited number of users, and is particularly useful when parents want to leave messages for their children.

[1836] Program processing

[1837] Data accumulation phase

[1838] 1. Write and submit your blog entry

[1839] The user uses a device to enter their thoughts and feelings into the blog entry form, and when they're done, they click the send button.

[1840] The device receives the user's input and generates an HTTP POST request to send it to the server. The device then sends the generated request to the server.

[1841] The server analyzes the received data, formats it, and saves it in the database. After saving is complete, it returns a response to the device indicating that the save was successful.

[1842] 2. Conduct and send the psychological test

[1843] The user accesses the psychological test administration page, enters answers to questions, and submits the results after answering all questions.

[1844] The terminal compiles the user's answers and generates result data, which is then sent to the server.

[1845] The server analyzes the received psychological test results, stores them in a database, and returns a response indicating that the test was successfully saved to the device.

[1846] AI model generation phase

[1847] 1. Creating a Psychological Profile

[1848] The server uses text analysis techniques and statistical analysis to generate a psychological profile of the user based on the blog entries and psychological test results stored in the database.

[1849] 2. Generating an AI model

[1850] The server uses the generated psychological profile to run an AI model generation algorithm, which learns the user's writing style and emotional expressions to build an AI model that reflects the user's personality and tendencies.

[1851] AI utilization phase

[1852] 1. Submitting an AI usage request

[1853] The user's family members log in to a dedicated access page and send requests to use the AI. For example, if they want to hear a specific message, they can enter the content and send it.

[1854] The terminal composes the input request content and sends it to the server.

[1855] 2. Request Approval and Response

[1856] The server analyzes the received request and verifies that the request is from a valid user. After verification, it provides the appropriate AI interface.

[1857] The terminal displays the AI ​​interface provided by the server, providing an environment in which the user can interact with the AI.

[1858] The server inputs user questions and requests into the AI ​​model, generates appropriate responses, and sends the responses back to the device, where they are displayed.

[1859] Specific examples

[1860] Example blog entry

[1861] User: "I'm so happy that my first big presentation was a success today."

[1862] Terminal: Send this to the server.

[1863] Server: Receives and stores in a database.

[1864] Psychological test examples

[1865] User: Completes the psychological test and submits.

[1866] Terminal: Sends the results to the server.

[1867] Server: Stores the results in a database.

[1868] Examples of AI use

[1869] User's child: "Today is the day I'm making a big decision influenced by my mom. I'd like to hear her advice."

[1870] Device: Sends a request to the server.

[1871] Server: Uses AI models to generate appropriate responses and send them back to the device.

[1872] Terminal: Displays the message "We believe you will move forward with your own will."

[1873] The processing flow will be explained below.

[1874] Data accumulation phase

[1875] Step 1:

[1876] The user enters his or her thoughts and feelings into the blog entry form on the device.

[1877] Step 2:

[1878] After completing the input, the user clicks the send button.

[1879] Step 3:

[1880] The terminal receives the blog entry entered by the user.

[1881] Step 4:

[1882] The data received by the terminal is sent to the server via an HTTP POST request.

[1883] Step 5:

[1884] The server analyzes the received blog entry data and stores it in a database.

[1885] Step 6:

[1886] The server sends a successful save response to the terminal.

[1887] Step 7:

[1888] The user accesses the psychological test administration page.

[1889] Step 8:

[1890] The user answers each question to complete the test.

[1891] Step 9:

[1892] The user submits the results.

[1893] Step 10:

[1894] The terminal compiles the user's responses and generates result data.

[1895] Step 11:

[1896] The device sends the result data to the server via an HTTP POST request.

[1897] Step 12:

[1898] The server analyzes the received psychological test result data and stores it in a database.

[1899] Step 13:

[1900] The server sends a successful save response to the terminal.

[1901] AI model generation phase

[1902] Step 14:

[1903] The server generates a psychological profile of the user based on the blog entries and psychological test results stored in the database using text analysis techniques and statistical analysis.

[1904] Step 15:

[1905] The server uses the generated psychological profile to run an artificial intelligence model generation algorithm.

[1906] Step 16:

[1907] Based on the data learned by the algorithm, the server builds an AI model that reflects the user's writing style and emotional expression.

[1908] AI utilization phase

[1909] Step 17:

[1910] The user's family members log in to a dedicated access page and submit a request to use AI.

[1911] Step 18:

[1912] The terminal composes the input request content and sends it to the server.

[1913] Step 19:

[1914] The server analyzes the received request and checks the access rights.

[1915] Step 20:

[1916] After the server verifies that the access rights are appropriate, it provides the corresponding AI interface.

[1917] Step 20:

[1918] The device displays the AI ​​interface provided by the server.

[1919] Step 21:

[1920] The user's family interacts with the AI ​​through an AI interface.

[1921] Step 22:

[1922] The server inputs user questions and requests into the AI ​​model, which generates appropriate responses.

[1923] Step 23:

[1924] The server sends the generated AI response back to the terminal.

[1925] Step 24:

[1926] The terminal displays the received response to the user.

[1927] Example 1

[1928] 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."

[1929] Currently, many users have more opportunities to record their thoughts and feelings in blog format, but there are limited ways to effectively utilize this data. In particular, technology that generates a user's psychological profile and builds an AI model based on that to provide personalized responses to the user has not yet been fully developed. Another challenge is providing the generated AI model only to specific users, thereby creating an environment where users can use it with peace of mind.

[1930] 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.

[1931] In this invention, the server includes means for receiving blog entries entered from a user terminal and storing them in a database, means for receiving psychological test results and storing them in a database, means for generating a psychological profile of the user based on the stored blog entries and psychological test results, means for generating an AI model based on the generated psychological profile, and means for approving requests for limited users to access the AI ​​model and providing an AI interface. This makes it possible to effectively utilize the user's psychological data and provide personalized responses, giving the user a sense of security.

[1932] A "user terminal" is an electronic device used by a user to create and transmit input data.

[1933] A "blog entry" is a piece of writing that expresses thoughts and feelings and that a user writes on a separate page on the Web.

[1934] "Psychological test results" are data obtained as a result of a user answering a psychological test.

[1935] A "database" is a system for storing and managing various data.

[1936] A "psychological profile" is information that indicates a user's psychological characteristics and tendencies, generated based on blog entries and psychological test results.

[1937] An "artificial intelligence model" is a machine learning model that is generated based on a user's psychological profile and trained to perform specific tasks.

[1938] A "request" refers to an operation or information request made by a user to a system.

[1939] An "interface" is the means or screen through which a user interacts with a system.

[1940] "Text analysis" is the process of analyzing text data using natural language processing techniques to extract useful information.

[1941] "Real-time" refers to immediate reactions and processing without delay.

[1942] The present invention is a system that allows users to input blog entries and psychological test results, generates a psychological profile for the user based on that data, and then builds an artificial intelligence model based on that profile. This system provides a platform for users to continuously input their own thoughts and feelings and store them in a database. It also has a function for conducting psychological tests, and the test results are similarly stored in the database.

[1943] To implement this invention, the following hardware and software are required: The terminal used by the user has a function for connecting to the Internet and can access the blog entry input form and psychological test page using a web browser (e.g., Google Chrome, Mozilla Firefox). The terminal also has a built-in program for generating HTTP POST requests. The server has a database management system (e.g., MySQL, PostgreSQL) installed, and uses text analysis technology (e.g., NLTK, spaCy) and machine learning libraries (e.g., TensorFlow, PyTorch).

[1944] The specific operation of the system is as follows: when a user enters a blog entry through a device and clicks the send button, the device receives the input, generates an HTTP POST request, and sends it to the server. The server analyzes the received request, formats the text content, and saves it in a database. If the save is successful, the server returns a "save successful" response to the device. Similarly, when a user answers a psychological test and clicks the send button, the device tallys the answers and sends them to the server, which then saves the results in a database.

[1945] The server periodically retrieves blog entries and psychological test results stored in the database and analyzes the text using natural language processing technology. Based on the analysis results, it generates a psychological profile for the user and stores this profile in the database. Furthermore, it uses a machine learning library to build an artificial intelligence model based on the generated psychological profile. This AI model is also stored in the database.

[1946] To access the AI ​​model, limited users log in to a dedicated access page and submit a request. For example, a user's child might type, "Today is the day I'm making a big decision influenced by my mother. I'd like to hear some advice from my mother," and click the submit button. The device then sends the request to the server, which analyzes the request and verifies that it is from a valid user. After verification, the device uses the corresponding AI model to generate an appropriate response and sends it back to the device. The device then displays the received response to the user.

[1947] As specific examples, the input prompt for a blog entry is in the format "Please enter the content of your blog. For example, 'I had a great time with my friends today.'" The input prompt for a psychological test is in the format "Please answer the following questions. For example, questions include 'How are you feeling lately?'" The prompt for using AI is in the format "Please enter the message you want to ask the AI. For example, 'How should I make an important decision?'"

[1948] As described above, the present invention is a system that effectively utilizes the user's psychological data and provides personalized responses, thereby giving the user a sense of security.

[1949] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1950] Step 1:

[1951] Entering and submitting a blog entry

[1952] A user accesses a blog entry form and enters their thoughts and feelings. For example, they might enter, "I was so happy that my first big presentation was a success today."

[1953] Input: A user types a blog entry and clicks the submit button.

[1954] The terminal receives the input content and generates an HTTP POST request to send to the server. Specifically, it converts the input text into JSON format and sends it to the server as the payload of the HTTP request.

[1955] Output: The user's blog entry is sent to the server.

[1956] Step 2:

[1957] Receive and store blog entries

[1958] The server receives the HTTP POST request sent from the terminal.

[1959] Input: Blog entry data sent as an HTTP POST request.

[1960] The server analyzes the received data and formats the text content. Specifically, it performs JSON parsing and extracts the text data.

[1961] The server stores the formatted data in a database.

[1962] Output: The blog entry saved in the database.

[1963] Step 3:

[1964] Conduct and send psychological tests

[1965] A user visits a psychological test page and answers a series of questions, for example, "How are you feeling these days?" and answers "very good."

[1966] Input: The user answers the psychological test and clicks the submit button.

[1967] The device aggregates the user's answers to each question and generates the result data. Specifically, it compiles each answer as a JSON object and generates an HTTP POST request to send it to the server.

[1968] Output: The result data of the psychological test is sent to the server.

[1969] Step 4:

[1970] Receiving and storing psychological test results

[1971] The server receives an HTTP POST request including the psychological test result sent from the terminal.

[1972] Input: Psychological test result data sent as an HTTP POST request.

[1973] The server analyzes and formats the received result data, specifically by parsing it in JSON and extracting the response data.

[1974] The server stores the formatted data in a database.

[1975] Output: Psychological test results stored in a database.

[1976] Step 5:

[1977] Psychological profile generation

[1978] The server retrieves the blog entries and psychological test results stored in the database.

[1979] Input: Blog entries and psychological test results stored in a database.

[1980] The server analyzes the text using natural language processing technology (e.g., NLTK, spaCy). Specifically, it performs morphological analysis of the text data and performs emotion and sentiment analysis.

[1981] The server processes the analysis results using statistical analysis techniques to generate a psychological profile of the user.

[1982] Output: The generated psychological profile is stored in a database.

[1983] Step 6:

[1984] Generating artificial intelligence models

[1985] The server retrieves the generated psychological profile.

[1986] Input: Psychological profiles stored in a database.

[1987] The server uses machine learning libraries (e.g., TensorFlow, PyTorch) to execute artificial intelligence model generation algorithms.

[1988] The server learns the user's writing style and emotional expression, and builds an artificial intelligence model that reflects the user's personality and tendencies.

[1989] Output: The constructed artificial intelligence model is stored in a database.

[1990] Step 7:

[1991] Submitting an AI request

[1992] The user's family members log in to a dedicated access page.

[1993] Input: The user's family member enters a request to use AI and clicks the send button. For example, they might enter, "Today is the day I'm making a big decision influenced by my mother. I'd like to hear her advice."

[1994] The device sends the request content to the server as an HTTP POST request in JSON format.

[1995] Output: An AI-enabled request is sent to the server.

[1996] Step 8:

[1997] Request Approval and Response

[1998] The server analyzes the received request and verifies that it is from the user's family.

[1999] Input: AI-powered request data.

[2000] The server loads the appropriate AI model and generates an appropriate response to the request, such as the message, "I trust you to move forward with your own will."

[2001] The server generates a response and sends it back to the terminal.

[2002] Output: The generated response is sent to the terminal.

[2003] Step 9:

[2004] Viewing the response

[2005] The terminal receives the response provided by the server.

[2006] Input: The response data sent by the server.

[2007] The device displays the received response to the user, for example, a message saying "I believe you will move forward with your own will."

[2008] Output: The user receives the response.

[2009] (Application example 1)

[2010] 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."

[2011] Today, many users blog online and gain a deeper understanding of themselves through psychological tests. However, there is no established method for centrally analyzing this data and providing users with personalized content recommendations and appropriate responses in real time. In addition, there are demands for specific situations, such as when parents want to leave special messages for their children, or for content tailored to the user's psychological state. Therefore, there is a need for the development of a system that can generate a user's psychological profile, build an AI model based on that profile, and provide personalized services.

[2012] 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.

[2013] In this invention, the server includes means for receiving data input from a user terminal and storing the data in a database, means for receiving psychological test results and storing the results in a database, means for generating a psychological profile of the user based on the stored data and the psychological test results, means for generating an AI model based on the generated psychological profile, means for approving access requests for limited users to access the AI ​​model and providing an AI interface, and means for recommending personalized content based on the user's psychological profile, thereby making it possible to individually recommend content based on the user's psychological state and provide personalized messages for specific situations.

[2014] A "user terminal" is a device that can be operated by a user and is a device that connects to the Internet and inputs, transmits, and receives data. Examples include smartphones, tablets, and personal computers.

[2015] A "database" is a system for efficiently storing, managing, and searching data, and is a recording medium that stores information such as user blog entries and psychological test results.

[2016] The "psychological test results" are the user's response data and evaluation data obtained through the psychological test, and are information for understanding the user's psychological characteristics and tendencies based on this.

[2017] A "psychological profile" is profile data that indicates a user's psychological characteristics and tendencies, and is generated by analyzing the user's blog entries and psychological test results.

[2018] An "AI model" is a data model built using artificial intelligence technology, and is a computational algorithm for generating specific actions and responses based on a user's psychological profile.

[2019] An "Access Request" is a request sent by a qualified user to access an AI model, and the AI ​​interface is provided only if the access right is confirmed.

[2020] "Personalized content" refers to information and media such as videos, articles, and music that are optimized and recommended to a specific user based on the user's psychological profile.

[2021] An "AI interface" is a user interface that allows a user to interact with an AI model, and includes the screen and operating means displayed on the terminal.

[2022] "Text analysis" is the process of analyzing text data using natural language processing techniques to extract information such as meaning and sentiment.

[2023] "Content recommendation" is a system that uses a user's psychological profile to select and present the most suitable content for the user.

[2024] As an embodiment of the present invention, there is a system that receives data input from a user terminal, stores it in a database, generates a psychological profile based on the data, creates an AI model, and recommends personalized content. Specifically, this system operates as follows.

[2025] Hardware and Software Use

[2026] User devices can be smartphones, tablets, or personal computers. The applications that run on the devices are built using React Native. Server-side processing is done with Node.js and Express.js, and data is stored in MongoDB. Additionally, NLTK, a Python natural language processing library, and TensorFlow, a machine learning library, are used to generate the AI ​​model.

[2027] Entering and Saving Data

[2028] The user terminal accepts blog entries and psychological test results from users as input. The user enters this data through the application and clicks a button to submit it. The terminal then sends this input data to the server as an HTTP POST request.

[2029] The server parses and formats the data it receives and stores it in a MongoDB database. For example, if a user types "I'm very happy that my first big presentation was a success today," it will be stored in the database.

[2030] Psychological profile generation

[2031] The server uses text analysis technology to generate a psychological profile of the user based on the blog entries and psychological test results stored in the database. This profile is generated using Python's NLTK library, which analyzes the data and extracts the user's psychological characteristics and tendencies.

[2032] Generating and applying AI models

[2033] Based on the generated psychological profile, the server uses TensorFlow to generate an AI model that learns the user's language style and emotional expressions, and then recommends responses and content that reflect the user's characteristics.

[2034] Personalized content recommendations

[2035] The user device receives personalized content from the server based on the user's psychological profile. This content can include videos, articles, music, and more, and the most suitable content is recommended to the user.

[2036] AI usage example

[2037] Users can send requests for specific messages or advice through the application. For example, if a child of the user types, "Today is the day I'm making a big decision, influenced by my mother. I'd like to hear her advice," the server uses an AI model to generate an appropriate response and sends it back to the device. This results in the message, "I trust you to move forward with your own will."

[2038] Prompt Sentence Examples

[2039] "Generate personalized content suggestions for this user using their latest psychological profile. Their blog entry is 'I was so happy today that my first big presentation went well.'"

[2040] In this way, the present invention makes it possible to provide personalized services based on the user's psychological state.

[2041] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2042] Step 1:

[2043] A user types a blog entry and clicks the submit button.

[2044] Input: The blog entry entered by the user

[2045] Specific operation:

[2046] Users open the application on their smartphone or computer, enter their thoughts and feelings in the text area, and then click the send button to send the blog entry.

[2047] Step 2:

[2048] The terminal transmits the blog entry data to the server.

[2049] Input: User's blog entry

[2050] Output: Blog entry data sent to the server

[2051] Specific operation:

[2052] The device takes the user's input and sends it to the server as an HTTP POST request, which includes the text data of the blog entry.

[2053] Step 3:

[2054] The server receives the blog entry data and stores it in a database.

[2055] Input: Blog entry data sent from the device

[2056] Output: Blog entries stored in the database

[2057] Specific operation:

[2058] The server receives the HTTP POST request and analyzes its contents. After analyzing, it stores the formatted data in MongoDB. After storing the data, the server returns a success response to the device.

[2059] Step 4:

[2060] The user answers the psychological test and submits the results.

[2061] Input: Psychological test results answered by the user

[2062] Output: Psychological test data sent to the server

[2063] Specific operation:

[2064] The user answers the psychological test presented on the application, and after completing all the questions, clicks the send button to send the results.

[2065] Step 5:

[2066] The terminal transmits the psychological test results to the server.

[2067] Input: User's psychological test results

[2068] Output: Psychological test data sent to the server

[2069] Specific operation:

[2070] The device collects the user's psychological test results and sends them to the server as an HTTP POST request, which includes the psychological test result data.

[2071] Step 6:

[2072] The server receives the psychological test results and stores them in a database.

[2073] Input: Psychological test result data sent from the device

[2074] Output: Psychological test results stored in a database

[2075] Specific operation:

[2076] The server receives the test results and analyzes them. After analysis, the formatted data is saved in MongoDB. After saving is complete, the server returns a success response to the device.

[2077] Step 7:

[2078] The server generates a psychological profile based on the blog entries and psychological test results.

[2079] Input: Blog entries and psychological test results stored in a database

[2080] Output: Generated psychological profile

[2081] Specific operation:

[2082] The server retrieves blog entries and psychological test results from the database, performs text analysis using Python's NLTK library, and generates a psychological profile of the user based on the analyzed data.

[2083] Step 8:

[2084] The server generates an AI model based on the psychological profile.

[2085] Input: Generated psychological profile

[2086] Output: The generated AI model

[2087] Specific operation:

[2088] The server analyzes the psychological profile using the TensorFlow library and generates an AI model that learns the user's language style and emotional expressions.

[2089] Step 9:

[2090] Submit an access request for access to an AI model by a limited user.

[2091] Input: Access request from a restricted user

[2092] Output: Access request sent to the server

[2093] Specific operation:

[2094] Users log in to a dedicated access page on the application and submit a request to access the AI ​​model.

[2095] Step 10:

[2096] The server approves the access request and provides the AI ​​interface.

[2097] Input: Access request sent by the user

[2098] Output: Provided AI interface

[2099] Specific operation:

[2100] The server analyzes the received request and checks the user's access rights, then provides the AI ​​interface to the device.

[2101] Step 11:

[2102] The server recommends personalized content based on the user's psychological profile.

[2103] Input: User's psychological profile

[2104] Output: personalized content

[2105] Specific operation:

[2106] The server then recommends the most suitable content for the user, such as videos, articles, and music, based on the generated psychological profile, using a generative AI model and prompts to select the appropriate content.

[2107] Example prompt sentence:

[2108] "Generate personalized content suggestions for this user using their latest psychological profile. Their blog entry is 'I was so happy today that my first big presentation went well.'"

[2109] 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.

[2110] This invention is a system that generates a psychological profile for each user based on the user's blog entries and psychological test results, and creates an AI model based on that profile. This system incorporates an emotion engine that recognizes the user's emotions, and can reflect the user's overall feelings and emotional changes in the AI ​​model.

[2111] Program processing

[2112] Data accumulation phase

[2113] 1. Write and submit your blog entry

[2114] The user enters their thoughts and feelings into the blog entry form on their device, and when they're done, clicks the send button.

[2115] The device receives the user's input and generates an HTTP POST request to send it to the server. The device then sends the generated request to the server.

[2116] The server analyzes the received data, formats it, and saves it in the database. After saving is complete, it returns a response to the device indicating that the save was successful.

[2117] 2. Emotion Recognition by Emotion Engine

[2118] The server sends the received blog entry data to an emotion engine to analyze the user's emotions.

[2119] The emotion engine generates emotion analysis results, which are also stored in the database.

[2120] 3. Conduct and send the psychological test

[2121] The user accesses the psychological test administration page, enters answers to questions, and submits the results after answering all questions.

[2122] The terminal compiles the user's answers and generates result data, which is then sent to the server.

[2123] The server analyzes the received psychological test results, stores them in a database, and returns a response indicating that the test was successfully saved to the device.

[2124] 4. Emotion recognition using emotion engine (psychological test)

[2125] The server sends the received psychological test results to the emotion engine to analyze the user's emotions.

[2126] The emotion engine generates emotion analysis results, which are also stored in the database.

[2127] AI model generation phase

[2128] 1. Creating a Psychological Profile

[2129] The server uses text analysis techniques and statistical analysis to generate a psychological profile of the user based on the blog entries, psychological test results, and sentiment analysis results stored in the database.

[2130] 2. Generating an AI model

[2131] The server uses the generated psychological profile to run an AI model generation algorithm, which learns the user's writing style and emotional expressions to build an AI model that reflects the user's personality and tendencies.

[2132] AI utilization phase

[2133] 1. Submitting an AI usage request

[2134] The user's family members log in to a dedicated access page and send requests to use the AI. For example, if they want to hear a specific message, they can enter the content and send it.

[2135] The terminal composes the input request content and sends it to the server.

[2136] 2. Request Approval and Response

[2137] The server analyzes the received request and checks the access rights.

[2138] After verifying that the access rights are appropriate, the corresponding AI interface will be provided.

[2139] 3. AI-User Interaction

[2140] The server inputs user questions and requests into the AI ​​model and generates appropriate responses.

[2141] The AI ​​model generates responses based on the analysis results of the emotion engine and provides messages that take the user's emotions into consideration.

[2142] The terminal displays the AI ​​interface provided by the server, providing an environment in which the user can interact with the AI.

[2143] The server sends the generated AI response back to the terminal, where it is displayed.

[2144] Specific examples

[2145] Example blog entry

[2146] User: "I was so happy today. I feel so much better after hearing the good news."

[2147] Terminal: Send this entry to the server.

[2148] Server: Receives and stores in the database. The emotion engine recognizes the emotion "happy" and stores the result.

[2149] Psychological test examples

[2150] User: Completes the psychological test and submits.

[2151] Terminal: Sends the results to the server.

[2152] Server: Save the results in the database. The emotion engine recognizes the emotion "nervous" and saves the result.

[2153] Examples of AI use

[2154] User's child: "I want to talk to my mom. I especially need encouragement today."

[2155] Device: Sends a request to the server.

[2156] Server: Accepts the request and uses the AI ​​model.

[2157] Device: Displays the message "We're always rooting for you. Believe in yourself and move forward."

[2158] The processing flow will be explained below.

[2159] Data accumulation phase

[2160] Step 1:

[2161] The user enters his or her thoughts and feelings into the blog entry form on the device.

[2162] Step 2:

[2163] After completing the input, the user clicks the send button.

[2164] Step 3:

[2165] The terminal receives the blog entry input by the user.

[2166] Step 4:

[2167] The data received by the terminal is sent to the server via an HTTP POST request.

[2168] Step 5:

[2169] The server analyzes the received blog entry data and stores it in a database.

[2170] Step 6:

[2171] The server sends a successful save response to the terminal.

[2172] Step 7:

[2173] The server sends the blog entry data to the emotion engine for emotion analysis.

[2174] Step 8:

[2175] The emotion engine generates the emotion analysis results of the blog entries and stores them in the database.

[2176] Step 9:

[2177] The user accesses the psychological test administration page.

[2178] Step 10:

[2179] The user answers each question, completes the test, and submits the results.

[2180] Step 11:

[2181] The terminal compiles the user's responses and generates result data.

[2182] Step 12:

[2183] The device sends the result data to the server via an HTTP POST request.

[2184] Step 13:

[2185] The server analyzes the received psychological test result data and stores it in a database.

[2186] Step 14:

[2187] The server sends a successful save response to the terminal.

[2188] Step 15:

[2189] The server sends the psychological test results to the emotion engine for emotion analysis.

[2190] Step 16:

[2191] The emotion engine performs emotion analysis of the psychological test results and stores the results in a database.

[2192] AI model generation phase

[2193] Step 17:

[2194] The server generates a psychological profile of the user using text analysis techniques and statistical analysis based on the blog entries, psychological test results, and sentiment analysis results stored in the database.

[2195] Step 18:

[2196] The server uses the generated psychological profile to run an artificial intelligence model generation algorithm.

[2197] Step 19:

[2198] Based on the data learned by the algorithm, the server builds an AI model that reflects the user's writing style and emotional expression.

[2199] AI utilization phase

[2200] Step 20:

[2201] The user's family members log in to a dedicated access page and submit a request to use AI.

[2202] Step 21:

[2203] The terminal composes the input request content and sends it to the server.

[2204] Step 22:

[2205] The server analyzes the received request and checks the access rights.

[2206] Step 23:

[2207] After the server verifies that the access rights are appropriate, it provides the corresponding AI interface.

[2208] Step 24:

[2209] The device displays the AI ​​interface provided by the server, providing an environment in which the user can interact with the AI.

[2210] Step 25:

[2211] The user's family interacts with the AI ​​through an AI interface.

[2212] Step 26:

[2213] The server inputs user questions and requests into the AI ​​model, which generates appropriate responses.

[2214] Step 27:

[2215] The server generates an AI response based on the analysis results of the emotion engine and provides a message that takes the user's emotions into consideration.

[2216] Step 28:

[2217] The server sends the generated AI response back to the terminal, where it is displayed.

[2218] Example 2

[2219] 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."

[2220] Conventional systems simply store users' blog entries and psychological test results in a database, making it difficult to deeply analyze users' emotional changes and psychological tendencies and reflect them in the AI ​​model. Furthermore, the responses generated by the AI ​​model did not take into account the user's emotions, resulting in a decline in the quality of the dialogue. This made it impossible to provide detailed psychological support to users.

[2221] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[2222] In this invention, the server includes means for receiving data input from a user terminal and storing the data in a database, means for receiving psychological test results and storing the results in a database, means for generating a psychological profile of the user based on the stored data and the psychological test results, means for generating an AI model based on the generated psychological profile, means for emotionally analyzing the user's input data using an emotion engine, means for storing the analyzed emotion data in a database, means for emotionally analyzing the psychological test results using the emotion engine, and means for approving access requests for limited users to access the AI ​​model and providing an AI interface. This makes it possible to deeply analyze the user's emotional changes and psychological tendencies and generate an AI model that takes the user's emotions into consideration.

[2223] A "user terminal" is a device used by a user to input data, and includes devices such as personal computers and smartphones.

[2224] A "server" is a central processing unit that processes data received from user terminals and stores it in a database.

[2225] A "database" is a storage device for permanently storing received data, emotion analysis results, and psychological test results.

[2226] The "psychological test result" is data generated by the user answering the psychological test, and includes information indicating the user's psychological state.

[2227] A "psychological profile" is data that indicates a user's psychological tendencies and characteristics, generated based on the user's blog entries, psychological test results, and emotion analysis results.

[2228] An "AI model" is an artificial intelligence model generated based on a psychological profile, which learns the user's writing style and emotional expression.

[2229] The "emotion engine" is a system that uses natural language processing technology to analyze user input data and extract emotions as tags.

[2230] "Access Request" means a request submitted by a qualified user to access an AI model.

[2231] An "AI interface" is a user interface that provides an environment in which users can interact with an AI model.

[2232] This invention is a system that generates a user's psychological profile based on the user's blog entries and psychological test results, and creates an AI model based on that profile. This system incorporates an emotion engine that recognizes the user's emotions, and can reflect the user's overall feelings and emotional changes in the AI ​​model.

[2233] Hardware and Software Configuration

[2234] User terminal: A device such as a computer or smartphone that is used by a user to input data.

[2235] Server: A central processing unit that processes data and stores it in a database. A server usually consists of a computer with advanced computing power.

[2236] Database: A storage device for persistently storing received data, sentiment analysis results, and psychological test results. For example, an SQL database or NoSQL database is used.

[2237] Emotion engine: Software that uses natural language processing technology to analyze user input data and extract emotions as tags. For example, an NLP library implemented in Python or a dedicated sentiment analysis API is used.

[2238] System processing flow

[2239] 1. Write and submit your blog entry

[2240] The user enters their feelings and thoughts into the blog entry form on their device and clicks the send button.

[2241] The terminal receives the user's input data and sends it to the server as an HTTP POST request.

[2242] The server receives the data, analyzes and formats it, and saves it in the database. After saving is complete, it returns a successful save response to the device.

[2243] 2. Emotion Recognition by Emotion Engine

[2244] The server sends the blog entry data to an emotion engine to analyze the user's emotions.

[2245] The emotion engine uses NLP technology to extract emotions such as "happy" or "sad" as tags.

[2246] The server stores the emotion analysis results received from the emotion engine in a database.

[2247] 3. Conduct and send the psychological test

[2248] A user accesses a psychological test question page, enters answers to all questions, and submits the answer.

[2249] The terminal aggregates the user's response data, converts it into structured data (e.g., JSON format), and sends it to the server.

[2250] The server receives the psychological test results, analyzes and stores them, and returns a response indicating that the test results were successfully stored.

[2251] 4. Emotion recognition using emotion engine (psychological test)

[2252] The server sends the psychological test results to the emotion engine, which analyzes the user's emotions again.

[2253] The emotion engine generates analysis results and stores the results in a database.

[2254] 5. Generating a Psychological Profile

[2255] The server retrieves all blog entries, psychological test results, and sentiment analysis results from the database and generates a psychological profile using text analysis techniques (natural language processing and statistical analysis).

[2256] 6. Generating AI models

[2257] The server then runs an AI model generation algorithm (such as a neural network) based on the generated psychological profile. This model learns the user's writing style and emotional expressions, and reflects the user's personality and tendencies.

[2258] 7. Submitting an AI Request

[2259] The user's family members log in to a dedicated access page and send a request to use AI from their device.

[2260] 8. Request Approval and Response

[2261] The server analyzes the received request, checks the access rights, and then provides the corresponding AI interface.

[2262] 9. AI-User Interaction

[2263] The server inputs user questions and requests into the AI ​​model and generates appropriate responses.

[2264] The emotion engine is taken into account when generating responses, generating emotion-sensitive messages.

[2265] The server generates a response and sends it back to the terminal, which displays it.

[2266] Specific examples

[2267] Example blog entry

[2268] User: "I was very happy today. Hearing the good news brightened my mood." is entered and sent from the device to the server.

[2269] The server receives the data and stores it in a database. The emotion engine recognizes the emotion "happy" and stores the result.

[2270] Psychological test examples

[2271] The user answers a psychological test and sends the result of feeling "tense" from the terminal to the server.

[2272] The server stores the results in a database, and the emotion engine recognizes the emotion "nervous" and stores the result as well.

[2273] Examples of AI use

[2274] User's child: "I want to talk to my mother. I especially need encouragement today." Sends a request from the device to the server.

[2275] The server approves the request and uses an AI model to generate a message saying, "We're always rooting for you. Believe in yourself and move forward."

[2276] The server generates a message and returns it to the terminal, where it is displayed.

[2277] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2278] Step 1:

[2279] The user enters their feelings and thoughts into the blog entry form on their device and clicks the send button. This operation generates the entered text data.

[2280] Step 2:

[2281] The terminal receives the user's input and composes it into an HTTP POST request, which is then sent to the server as input data.

[2282] Step 3:

[2283] The server receives the HTTP POST request and analyzes the input data. Through analysis, text data is extracted. The text data is then formatted for storage in the database. The formatted data is saved in the database and a response indicating successful saving is returned to the terminal.

[2284] Step 4:

[2285] The server passes the formatted text data to the emotion engine, which analyzes the user's emotions. During this emotion analysis process, emotion tags are extracted from the text using NLP technology. The extracted emotion tags are generated as the emotion analysis results.

[2286] Step 5:

[2287] The server stores the emotion analysis results received from the emotion engine in a database.

[2288] Step 6:

[2289] The user accesses the psychological test question page through their device and enters their answers. After answering all the questions, the user submits the results. This operation generates the psychological test answer data.

[2290] Step 7:

[2291] The device collects the user's psychological test response data and converts it into structured data such as JSON format, then sends the generated structured data to the server as an HTTP POST request.

[2292] Step 8:

[2293] The server receives the HTTP POST request, analyzes the psychological test results, saves the analyzed results in a database, and returns a response indicating that the save was successful to the device.

[2294] Step 9:

[2295] The server sends the psychological test results to the emotion engine, which then analyzes the user's emotions again. The emotion engine extracts emotion tags from the psychological test results and generates emotion analysis results.

[2296] Step 10:

[2297] The server stores the emotion analysis results received from the emotion engine in a database.

[2298] Step 11:

[2299] The server retrieves blog entry data, psychological test results, and sentiment analysis results from the database, and then generates a psychological profile of the user using text analysis techniques and statistical analysis based on the retrieved data.

[2300] Step 12:

[2301] The server uses the generated psychological profile to run an AI model generation algorithm, which builds an AI model that learns the user's writing style and emotional expressions.

[2302] Step 13:

[2303] The user's family members log in to a dedicated access page and send requests for using AI from their devices, for example, by entering, "I want to hear an encouraging message from my mother."

[2304] Step 14:

[2305] The terminal composes the input request content as an HTTP POST request and sends it to the server.

[2306] Step 15:

[2307] The server analyzes the received request and checks the access rights. After the access rights are confirmed, the corresponding AI interface is provided.

[2308] Step 16:

[2309] The server inputs questions and requests from users into the AI ​​model and generates appropriate responses. The AI ​​model generates responses that take the user's emotions into consideration based on the analysis results of the emotion engine.

[2310] Step 17:

[2311] The server generates a response that is sent back to the terminal and displayed there.

[2312] Step 18:

[2313] The terminal displays the AI ​​interface provided by the server, creating an environment in which the user can interact with the AI.

[2314] (Application example 2)

[2315] 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."

[2316] Conventional advertising systems lack personalization that takes into account the user's emotions and psychological state, and deliver a large number of generic ads, making it difficult to quickly adapt to the user's interests and emotions. Furthermore, they lack technology that effectively utilizes users' daily blog entries and psychological test results to optimize ads. This results in a monotonous user experience and ineffective advertising.

[2317] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[2318] In this invention, the server includes means for receiving data input from a user terminal and storing the data in a database, means for receiving psychological test results and storing the results in a database, means for generating a psychological profile of the user based on the stored data and the psychological test results, means for generating an AI model based on the generated psychological profile, means for approving access requests for limited users to access the AI ​​model and providing an AI interface, and means for generating and displaying personalized advertisements according to the user's psychological state, thereby making it possible to provide advertisements that reflect the user's emotions and psychological state and achieve a more effective advertising experience.

[2319] 1. "User terminal" means an input and display device used by a user, including mobile devices such as smartphones and tablets.

[2320] 2. "Database" means an information system for organizing and storing received data and analysis results, including relational databases and NoSQL databases.

[2321] 3. "Psychological test results" are data resulting from a user's responses to a psychological test, and are data that indicate the user's psychological state and personality traits.

[2322] 4. "Psychological profile" is a data set that shows the psychological characteristics of a user analyzed based on the user's blog entries and psychological test results.

[2323] 5. "AI Model" means a computer program generated by machine learning algorithms based on a user's psychological profile, capable of predicting and responding to a user's emotions and tendencies.

[2324] 6. "Access Request" means a request submitted by a User to access certain data or functionality.

[2325] 7. “AI Interface” means an interface through which a user interacts with an AI model, whether in text, voice, or other format.

[2326] 8. "Advertisement" means information displayed to promote a particular product or service to a user.

[2327] 9. "Personalized advertising" refers to individual advertising optimized based on a user's psychological state or characteristics.

[2328] 10. "Sentiment analysis" is the process of identifying emotions and emotional changes from user input data (such as blog entries or psychological test results).

[2329] 11. "Blog entry" is data in the form of text in which a user describes everyday events and feelings.

[2330] 12. "Text analysis" is the process of analyzing text data using natural language processing techniques to extract meaning and sentiment.

[2331] This invention is a system that generates a psychological profile for each user based on the user's blog entries and psychological test results, and then creates an AI model based on that profile. This system incorporates an emotion engine that recognizes the user's emotions, allowing the user's overall state of mind and emotional changes to be reflected in the AI ​​model. The specific program processing and required hardware and software are described in detail below.

[2332] Hardware and software used

[2333] User device: Mobile devices such as smartphones and tablets

[2334] Database: AWS RDS (Relational Database Service)

[2335] Server: Node.js + Express.js

[2336] Emotion Engine API: IBM Watson Natural Language Understanding

[2337] AI model generation: Scikit-learn, TensorFlow

[2338] Frontend: React Native

[2339] Program processing

[2340] 1. Data entry on the user's device

[2341] The application provides a form for users to write blog entries. Users use this form to input their daily events and feelings. Once the input is complete, they press the submit button to send the data to the server.

[2342] 2. Receiving and storing data on the server

[2343] The server receives data sent from the user terminal using an HTTP POST request and saves the received data in a database, which then stores the blog entry and its related information.

[2344] 3. Emotion analysis

[2345] The server sends the saved blog entry data to the emotion engine API to analyze the user's emotions. The emotion engine identifies the emotions in the text and sends the results back to the server. The acquired emotion analysis results are then stored in the database.

[2346] 4. Psychological profile generation

[2347] The server uses text analysis techniques and statistical analysis to generate a user's psychological profile based on the blog entries, psychological test results, and sentiment analysis results stored in the database. The generated profile details the user's psychological characteristics.

[2348] 5. AI model generation

[2349] The server then uses the generated psychological profile to run a machine learning algorithm, which learns the user's writing style and emotional expressions, and builds an AI model that reflects the user's personality and tendencies.

[2350] 6. Generating and displaying personalized advertisements

[2351] The server generates advertisements based on the user's psychological state. These advertisements reflect the user's emotions and interests and are delivered in real time from the server to the user's device. The user's device then displays the received advertisements on its screen, with content that the user finds interesting.

[2352] Specific examples

[2353] Example blog entry

[2354] User: "I was so happy today. I feel so much better after hearing the good news."

[2355] Server: Receives this entry and saves it in the database. The emotion engine recognizes the emotion "happy" and saves the result.

[2356] Examples of personalized ads

[2357] An advertisement for a "travel package with celebratory benefits" is displayed on the user's device based on the user's blog entry and sentiment analysis results.

[2358] Prompt Sentence Examples

[2359] "I'm very tired today. I don't feel well."

[2360] Sentiment analysis is performed from these blog entries to generate product advertisements with a relaxation theme.

[2361] As described above, the present invention makes it possible to provide advertisements that take into account the user's emotions and psychological state, thereby improving the user's advertising experience.

[2362] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2363] Step 1:

[2364] A user terminal inputs and transmits a blog entry.

[2365] The blog entry is in text format, and the user describes their daily events and feelings. The user clicks the submit button, which sends an HTTP POST request to the server.

[2366] Input: User's blog entry

[2367] Output: HTTP POST request sent to the server

[2368] Step 2:

[2369] The server receives blog entries from user terminals and stores them in a database.

[2370] The server parses the received blog entries and formats them for storage in a database, where the formatted data is stored in a relational table.

[2371] Input: User's blog entry data

[2372] Output: Data stored in a database table

[2373] Step 3:

[2374] The server sends the blog entry to the emotion engine API to perform emotion analysis.

[2375] The server sends the stored blog entries to an emotion engine API, such as IBM Watson, to identify the sentiment within the text, which then generates an emotion analysis result and sends it back to the server.

[2376] Input: Blog entry data

[2377] Output: Sentiment analysis result data

[2378] Step 4:

[2379] The server stores the emotion analysis results in a database.

[2380] The server formats the received sentiment analysis results for storage in a database, and stores them in association with the corresponding blog entry.

[2381] Input: Sentiment analysis result data

[2382] Output: Sentiment analysis results stored in a database table

[2383] Step 5:

[2384] The user takes the psychological test and submits the results.

[2385] The user accesses the psychological test page in the application, enters answers to questions, and after answering all questions, sends the results to the server.

[2386] Input: Psychological test response data

[2387] Output: Psychological test results sent to the server

[2388] Step 6:

[2389] The server receives the psychological test results and stores them in a database.

[2390] The server analyzes the received psychological test results and formats them for storage in a database, where the formatted data is stored in a related table.

[2391] Input: Psychological test result data

[2392] Output: Psychological test results stored in a database table

[2393] Step 7:

[2394] The server sends the psychological test results to the emotion engine API to perform emotion analysis.

[2395] The server sends the stored psychological test results to the emotion engine API to identify emotions in the text, and the emotion engine generates emotion analysis results and sends them back to the server.

[2396] Input: Psychological test result data

[2397] Output: Sentiment analysis result data

[2398] Step 8:

[2399] The server stores the emotion analysis results in a database.

[2400] The server formats the received emotion analysis results for storage in a database, and stores them in association with the corresponding psychological test results.

[2401] Input: Sentiment analysis result data

[2402] Output: Sentiment analysis results stored in a database table

[2403] Step 9:

[2404] The server generates a psychological profile of the user based on the blog entry, the psychological test results, and the emotion analysis results.

[2405] The server uses text analysis techniques and statistical analysis to generate a psychological profile of the user based on the data stored in the database.

[2406] Input: Blog entries stored in the database, psychological test results, and sentiment analysis results

[2407] Output: Psychological profile of the user

[2408] Step 10:

[2409] The server generates an AI model based on the generated psychological profile.

[2410] The server uses the psychological profile to run machine learning algorithms and build an AI model that reflects the user's emotions and tendencies.

[2411] Input: User's psychological profile

[2412] Output: User's AI model

[2413] Step 11:

[2414] The server generates and displays personalized advertisements according to the user's psychological state.

[2415] The server selects appropriate advertisements based on the user's psychological profile and AI model, and delivers the generated advertisements to the user's device in real time. The user's device then displays the received advertisements on its screen.

[2416] Input: User's psychological profile, AI model

[2417] Output: Personalized ads displayed on the user's device

[2418] 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.

[2419] 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.

[2420] 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.

[2421] 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.

[2422] 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.

[2423] 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.

[2424] 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).

[2425] 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.

[2426] 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."

[2427] 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.

[2428] 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).

[2429] 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.

[2430] 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.

[2431] 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.

[2432] 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.

[2433] 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 (Applicat...

Claims

1. means for receiving data input from a user terminal and storing the data in a database; means for receiving psychological test results and storing the results in a database; means for generating a psychological profile of the user based on the stored data and psychological test results; A means for generating an AI model based on the generated psychological profile; a means for approving access requests for access to the AI ​​model by qualified users and providing an AI interface; A system including:

2. 2. The system according to claim 1, further comprising means for analyzing text data entered in a blog format and modeling psychological characteristics of users.

3. The system of claim 1, further comprising means for transmitting the response generated by the user's AI model to the user terminal in real time and displaying the response content on the terminal.

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