System

Abstract

A system is provided.SOLUTION: A system, comprising: means for inputting personality information, values, interests, and skill sets; means for transmitting the inputted information to a server; means for analyzing the received information and storing the information in a database; means for extracting profile information from the database; means for generating a model based on the extracted profile information; means for making a suggestion to a user using the generated model; and means for presenting the suggested information to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Describe the "problem that the invention aims to solve" and the "means for solving the problem."

[0005] Important life decisions, such as furthering education, finding employment, and getting married, depend on gathering vast amounts of information and subjective judgment, resulting in a high degree of uncertainty. As a result, many people are unable to choose the best educational institution, workplace, or partner, and end up gambling on their life path. There is a need to solve this problem and maximize the value of individuals. [Means for solving the problem]

[0006] The present invention aims to help users make important life decisions more reliably and efficiently, and maximize their individual value, by using a system that includes a means for inputting personality information, values, interests, and skill sets, a means for sending the input information to a server, a means for analyzing the received information and storing it in a database, a means for extracting profile information from the database, a means for generating a model based on the extracted profile information, a means for making suggestions to the user using the generated model, and a means for presenting the suggested information to the user.

[0007] "Personality information" refers to information that represents personal characteristics such as a user's character, behavioral tendencies, values, and interests.

[0008] "Values" refers to information such as principles, beliefs, and moral standards that a user considers important.

[0009] "Interests" refers to information such as fields, activities, and themes that interest a user.

[0010] A "skill set" refers to information such as skills, abilities, and knowledge possessed by a user.

[0011] "Input means" refers to the interface or device through which a user provides information such as personality information, values, interests, skill sets, etc.

[0012] "Transmitting means" refers to the function or device for sending information provided by the user to the server.

[0013] "Means for analyzing and storing in a database" refers to the functions and processes by which the server analyzes the information received and stores it in a database in an appropriate format.

[0014] "Means for extracting profile information" refers to functions or processes for extracting specific information from a user database.

[0015] "Means for generating a model" refers to functions and processes for creating a prediction model or decision support model that reflects the characteristics and tendencies of the user based on the extracted profile information.

[0016] The "means for making suggestions" refers to the functions and processes for using the generated model to present optimal options and actions to the user.

[0017] "Means for presenting information" refers to an interface or device for visually or audibly displaying to the user the suggestions sent from the server.

[0018] "Area decision support means" refers to functions and processes that provide information on a particular area in which a user is interested and support optimal decision-making in that area.

[0019] "Means for updating proposals taking into account labor market trends" refers to functions and processes for adjusting and updating proposals to users in real time based on current labor market and future forecast data. [Brief explanation of the drawings]

[0020] [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

[0021] 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.

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

[0023] 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).

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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."

[0028] [First embodiment]

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

[0030] 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.

[0031] 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).

[0032] 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.

[0033] 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.

[0034] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form 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.

[0035] 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.

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

[0037] 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.

[0038] 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.

[0039] 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.

[0040] 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."

[0041] This invention is a system that supports important life decisions based on input information such as a user's personality, values, interests, and skill set. This system is a platform that stores the information provided by the user in a database and makes optimal suggestions using a generative AI model.

[0042] Program processing

[0043] Entering information

[0044] 1. User: Enter information about your personality, values, interests, skill set, etc.

[0045] Specifically, users use a web form or mobile app to answer questions or upload existing data (e.g., resume, personality test results).

[0046] Sending information

[0047] 1. Terminal: Sends the entered information to the server.

[0048] Specifically, the terminal converts the input data into JSON format and sends it to the server via a secure API.

[0049] Information analysis and storage

[0050] 1. Server: Analyzes the received information and stores it in a database.

[0051] The server validates the input data, formats it appropriately, and then stores it in the personality value database.

[0052] Extracting Profile Information

[0053] 1. Server: Extracts user profile information from the personality value database.

[0054] Specifically, the server uses an SQL query to retrieve profile information for the specified user from a database.

[0055] Generating the Model

[0056] 1. Server: Generates a LifePath model based on the extracted profile information.

[0057] Generative AI models are used to create predictive models based on user characteristics and past data, leveraging machine learning algorithms such as deep learning and reinforcement learning.

[0058] Selecting a field

[0059] 1. User: Select the area in which they would like support in making decisions such as further education, employment, and marriage.

[0060] Users are presented with options via a dashboard or chatbot.

[0061] Proposal Generation

[0062] 1. Server: Integrates user profile information with current information (e.g., labor market trends) to generate optimal recommendations.

[0063] Here, based on the user's profile information, the system considers multiple scenarios to determine optimal options for further education, career, partner, etc.

[0064] Presenting the proposal

[0065] 1. Terminal: Presents the generated proposals to the user.

[0066] The proposals are displayed in an easy-to-understand format, including text, graphs, and lists.

[0067] Specific examples

[0068] In the case of continuing education

[0069] 1. User: Enter your current grades and areas of interest.

[0070] 2. Terminal: Sends this information to the server.

[0071] 3. Server: Analyzes the user's information and stores it in a personality value database.

[0072] 4. Server: Extracts profile information from the database and generates a LifePath model using a generative AI model.

[0073] 5. User: Select further education.

[0074] 6. Server: Integrates user profiles with education market data to recommend the most suitable universities and courses.

[0075] 7. Terminal: Display a suggestion saying, "The best university for the user is Faculty B at University A. This faculty is a field that is expected to grow in the future."

[0076] In the case of employment

[0077] 1. User: Enter your skill set and desired location.

[0078] 2. Terminal: Sends this information to the server.

[0079] 3. Server: Analyzes the user's information and stores it in a personality value database.

[0080] 4. Server: Extracts profile information from the database and generates a LifePath model using a generative AI model.

[0081] 5. User: Selects employment.

[0082] 6. Server: Integrates user profiles with labor market data to suggest the most suitable jobs and companies.

[0083] 7. Terminal: Display a suggestion saying, "The best job for you is Position D at Company C. This company is in an industry that is expected to continue to grow."

[0084] In the case of marriage

[0085] 1. User: Enter your values ​​and interests.

[0086] 2. Terminal: Sends this information to the server.

[0087] 3. Server: Analyzes the user's information and stores it in a personality value database.

[0088] 4. Server: Extracts profile information from the database and generates a LifePath model using a generative AI model.

[0089] 5. User: Selects marriage.

[0090] 6. Server: Integrates user profiles and matching algorithms to suggest optimal partner candidates.

[0091] 7. Device: Display a suggestion saying, "The ideal partner candidate for you is E. He / she has very similar values ​​and interests to you."

[0092] By implementing the system of the present invention, users can make important life decisions more reliably and efficiently, maximizing their value.

[0093] The processing flow will be explained below.

[0094] Program processing

[0095] Flow from inputting information to presenting proposals

[0096] Step 1:

[0097] Users: Enter information about their personality, values, interests, skill sets, etc.

[0098] What it does: Answer questions or upload your resume or personality assessment using a web form or mobile app.

[0099] Step 2:

[0100] Terminal: Sends the entered information to the server.

[0101] Specific operation: Converts input data into JSON format and sends it to the server via a secure API.

[0102] Step 3:

[0103] Server: Analyzes the received information and stores it in a database.

[0104] Specific operation: Validates input data and stores it in the personality value database in the appropriate format.

[0105] Step 4:

[0106] Server: Extracts user profile information from the personality value database.

[0107] What it does: Retrieves the profile information for the specified user from the database using an SQL query.

[0108] Step 5:

[0109] Server: Generates a LifePath model based on the extracted profile information.

[0110] What it does: Use generative AI models to create predictive models using deep learning and reinforcement learning algorithms.

[0111] Step 6:

[0112] User: Select the area in which they would like support in making decisions such as further education, employment, and marriage.

[0113] Specific Action: Select an option on a dashboard or conversational chatbot.

[0114] Step 7:

[0115] Terminal: Sends the user's selections to the server.

[0116] Specific operation: The selected fields are sent to the server via API.

[0117] Step 8:

[0118] Server: Integrates user profile information with current information (such as labor market trends) to generate optimal proposals.

[0119] How it works: Uses generative AI models and up-to-date market data to generate multiple scenarios and options.

[0120] Step 9:

[0121] Terminal: Presents the generated suggestions to the user.

[0122] Specific behavior: Display suggestions to the user in text, graph, and list format.

[0123] Specific examples of procedures for continuing education

[0124] Step 1:

[0125] User: Enter current grades and areas of interest.

[0126] What it does: Answer questions about grades and interests in a web form.

[0127] Step 2:

[0128] Terminal: Sends the entered information to the server.

[0129] What it does: Converts input data from a web form into JSON format and sends it through a secure API.

[0130] Step 3:

[0131] Server: Analyzes user information and stores it in a personality value database.

[0132] Specific operation: Validate and clean input data and save it in the database in the appropriate format.

[0133] Step 4:

[0134] Server: Extracts profile information from the database and generates a LifePath model using a generative AI model.

[0135] Specific operations: Retrieves information from a database using SQL queries and generates a predictive model using machine learning algorithms.

[0136] Step 5:

[0137] User: Select support for further education.

[0138] Specific action: Select "Further education" on the dashboard or chatbot.

[0139] Step 6:

[0140] Server: Integrates user profile information with education market data to recommend the most suitable universities and courses.

[0141] Specific operation: Based on the generative AI model and education market data, proposals are generated taking into account multiple scenarios.

[0142] Step 7:

[0143] Terminal: Display a suggestion saying, "The best university for the user is Faculty B at University A. This faculty is a field that is expected to grow in the future."

[0144] What it does: Display the suggestions to the user in text format.

[0145] Through these specific processing steps, the LifePath-AI system makes suggestions to the user to support optimal decision-making.

[0146] Example 1

[0147] 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."

[0148] Previous systems supporting important life decisions were unable to properly utilize users' personality information, values, interests, skill sets, etc., and had difficulty making proposals that took real-time market trends into account. As a result, they were unable to provide optimal proposals to users, and their decision-making support functions were inadequate.

[0149] 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.

[0150] In this invention, the server includes means for inputting personality information, values, interests, and skill sets, means for converting the input information into a data format and transmitting it, means for validating the received information and storing it in a database, means for extracting profile information from the database, means for generating a predictive model using a generative AI model based on the extracted profile information, means for generating proposals suitable for the user for each field, and means for visualizing the generated proposals and presenting them to the user, thereby making it possible to provide appropriate and dynamic proposals to the user.

[0151] "Personality information" is information about the user's character and behavioral characteristics.

[0152] "Values" is information about the beliefs and ethics that users consider important.

[0153] "Interests" is information about areas or activities that interest a user.

[0154] A "skill set" is a collection of skills, knowledge, and abilities possessed by a user.

[0155] "Transmitting means" refers to the method or process for sending the input information to the server.

[0156] "Validation" is the process of verifying received data to ensure it is accurate and complete.

[0157] A "database" is a system for storing and managing structured data.

[0158] "Profile information" is comprehensive information including a user's personal information, past data, and history.

[0159] A "generative AI model" is a model that uses machine learning algorithms to make predictions and classifications.

[0160] A "predictive model" is a model that predicts future trends and options based on specific data.

[0161] A "means for generating suggestions" is a method or process for creating optimal options or suggested actions based on user information.

[0162] "Visualization" refers to the presentation of data or information in an easy-to-understand format, such as a graph, text, or list.

[0163] "Real-time market trend data" means the latest data on current market conditions and trends.

[0164] This invention is a system that supports important life decisions based on input of a user's personality information, values, interests, skill set, etc. This system uses a generative AI model to make optimal recommendations taking into account real-time market trends.

[0165] Hardware and Software Configuration

[0166] Hardware

[0167] Device: The device through which a user enters information (e.g., PC, smartphone, tablet).

[0168] Server: A computer system for analyzing information, storing it in a database, and running generative AI models.

[0169] software

[0170] Web form or mobile application: An interface where users enter their personality information, values, interests, and skill sets.

[0171] Database: A relational database (e.g., MySQL, PostgreSQL) to store user information.

[0172] Generative AI models: Machine learning algorithms (e.g., TensorFlow, PyTorch) that generate predictive models based on user profile information.

[0173] Secure API: A communication protocol (e.g. HTTPS) for sending data from a device to a server.

[0174] Program processing overview

[0175] Entering information

[0176] Users use web forms or mobile apps to enter information such as personality, values, interests, and skill sets. For example, users may answer questions about their personality, work history, and areas of interest.

[0177] Sending information

[0178] The device converts the input information into JSON format and sends it to the server via a secure API endpoint, for example, by sending a POST request to " / api / userdata."

[0179] Information analysis and storage

[0180] The server validates the information it receives, formats it appropriately, and stores it in a relational database, for example by checking the format of the input data and adding default values ​​if any fields are missing.

[0181] Extracting Profile Information

[0182] The server extracts the user's profile information from the personality value database, specifically, by using an SQL query to retrieve data based on the specified user ID.

[0183] Generating the Model

[0184] The server runs a generative AI model based on the extracted profile information to generate an individual predictive model, for example, using TensorFlow to predict educational and career options suited to the user's attributes.

[0185] Proposal Generation

[0186] The server integrates user profile information with real-time market trend data to generate optimal suggestions for each field, for example, suggesting the most suitable career options for users while taking into account the latest trends in the labor market.

[0187] Presenting the proposal

[0188] The terminal visualizes the suggestions received from the server and presents them to the user in the form of text, graphs, or lists.

[0189] Specific examples

[0190] Prompt Sentence Examples

[0191] "I'm good at math and interested in biology. I'd like to pursue a career in biotechnology. Can you suggest some educational options for me?"

[0192] Processing flow

[0193] 1. User: Enter your field of interest and grade information

[0194] "I'm good at math and interested in biology."

[0195] 2. Terminal: Sends input information to the server in JSON format

[0196] Example: Send a POST request to " / api / userdata"

[0197] 3. Server: Validate the information and store it in the database

[0198] Example: Saving data using an SQL query

[0199] 4. Server: Extracts user profile information and runs generative AI models

[0200] Example: Generating a predictive model with TensorFlow

[0201] 5. Server: Proposing the best school to go to

[0202] For example: "Faculty B at University A is suitable."

[0203] 6. Terminal: Visualize the proposal and present it to the user

[0204] Example: "The best university for the user is University A, Department B."

[0205] In this way, by utilizing generative AI models based on detailed user information and making specific and optimal suggestions that take real-time market trends into account, the system can effectively support users in making important life decisions.

[0206] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0207] Step 1:

[0208] A user enters information such as personality information, values, interests, and skill sets using a web form or mobile app.

[0209] Input: Information that a user enters into a form (e.g., personality test results, resume, areas of interest).

[0210] Output: The input information is saved on the form.

[0211] Specific behavior: The user answers each question and uploads existing data if necessary.

[0212] Step 2:

[0213] The device converts the input information into JSON format and sends it to the server via a secure API.

[0214] Input: User information saved in the form.

[0215] Output: The data is converted to JSON format and sent through a secure API.

[0216] Specific operation: The terminal serializes the input data in JSON format and sends a POST request to the server's API endpoint using the HTTPS protocol.

[0217] Step 3:

[0218] The server validates the received information and stores it in the database.

[0219] Input: JSON data sent from the terminal.

[0220] Output: Accurate and complete data stored in the database.

[0221] Specific operation: The server validates the received data, checks for incompleteness or inconsistencies, and then saves it to the database using an SQL query.

[0222] Step 4:

[0223] The server extracts the user's profile information from the database.

[0224] Input: User data in the database.

[0225] Output: The extracted profile information.

[0226] What happens: The server executes an SQL query to retrieve the profile based on the specified user ID.

[0227] Step 5:

[0228] The server runs a generative AI model based on the extracted profile information to generate a predictive model.

[0229] Input: Extracted profile information.

[0230] Output: The generated predictive model.

[0231] Specific operation: The server uses a machine learning framework such as TensorFlow or PyTorch to train and generate a predictive model using the profile information as input data.

[0232] Step 6:

[0233] Users select the areas in which they would like support, such as further education, employment, or marriage.

[0234] Input: User's choice.

[0235] Output: Selected field information.

[0236] Specific behavior: The user uses the dashboard or chatbot to select the desired field from the options displayed.

[0237] Step 7:

[0238] The server integrates the user's profile information with real-time market trend data to generate optimal offers.

[0239] Inputs: Profile information, real-time market trend data.

[0240] Output: The generated proposals.

[0241] How it works: The server uses a reinforcement learning algorithm to combine profiles and market data to run simulations and generate the most appropriate proposals.

[0242] Step 8:

[0243] The terminal visualizes the generated suggestions and presents them to the user.

[0244] Input: Proposal data from the server.

[0245] Output: A visualized proposal.

[0246] Specific operation: The device displays the suggestions received from the server in graph, list, and text format, making it easy for the user to understand.

[0247] (Application example 1)

[0248] 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."

[0249] Conventional personalization systems have been unable to fully utilize a wide range of information, such as a user's personality, values, interests, and skill set, to make optimal product recommendations. It has also been difficult for users to efficiently find products that best fit their lifestyles and purchasing behavior. Furthermore, it has been difficult to reflect market trends in real time and keep recommendations up to date.

[0250] 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.

[0251] In this invention, the server includes means for inputting personality information, values, interests, and skill sets, means for transmitting the input information to the server, means for analyzing the received information and storing it in a database, means for extracting profile information from the database, means for creating a generative AI model based on the extracted profile information, means for suggesting optimal products to the user using the generated generative AI model, and means for presenting the suggested product information to the user. This allows the user to efficiently receive suggestions of products optimal for their lifestyle, support their purchasing behavior, and receive the latest suggestions that reflect market trends.

[0252] "Personality information" is information that indicates the user's character and behavioral characteristics.

[0253] "Values" is information that indicates the beliefs and ethics that a user considers important.

[0254] "Interests" is information that indicates the fields and activities in which a user is interested.

[0255] A "skill set" is information that indicates the skills and abilities that a user possesses.

[0256] The "server" is a computer system that receives, analyzes, and stores data and makes recommendations using generative AI models.

[0257] A "generative AI model" is an artificial intelligence model that is generated based on a user's profile information and is used to make optimal suggestions to the user.

[0258] "Profile information" is a collective term for data about a user, such as personality information, values, interests, and skill sets.

[0259] A "product" is a good or service related to a user's lifestyle or purchasing behavior.

[0260] "Input means" is an interface through which a user provides personality information, values, interests, and skill sets to the system.

[0261] The "transmitting means" is a communication function for transferring the input information to the server.

[0262] The "analyzing means" refers to the algorithms or programs that process the received information and store it in a database.

[0263] "Storing means" is a function for saving analyzed information in a database.

[0264] The "means for extracting" is a function for extracting specific profile information from the database.

[0265] The "means of creation" is the process of forming a generative AI model based on the extracted information.

[0266] The "means of suggestion" is a function that uses a generative AI model to determine the best product for the user and generate that information.

[0267] The "presentation means" is an interface for displaying the generated proposal content in an easy-to-understand manner to the user.

[0268] "Purchasing behavior" refers to the actions and decisions a user makes when purchasing a product or service.

[0269] "Market trends" refer to current supply, demand, trends, and tendencies in the commercial market.

[0270] The present invention relates to a system that inputs information such as a user's personality, values, interests, and skill set, and then uses this information to suggest products that are optimal for the user's lifestyle and purchasing behavior. This system is composed of multiple components, which are described in detail below.

[0271] Entering information

[0272] Users use an interface to input their personality information, values, interests, and skill sets. This interface can be provided through a smartphone application, a web form, or a dedicated terminal. Users can enter information by answering questions.

[0273] Sending information

[0274] The terminal transmits the input information to the server. To do this, the terminal converts the input data into JSON format and transmits it to the server via a secure API. HTTPS is used as the communication protocol.

[0275] Information analysis and storage

[0276] The server analyzes the received information, processes the data as needed, and stores it in a database. Python and Flask are used for analyzing the information, and PostgreSQL is used as the database. The server also validates the input data and formats it appropriately.

[0277] Extracting profile information and creating a generative AI model

[0278] The server extracts user profile information from a database using SQL queries. It then creates a generative AI model based on the extracted profile information. This process is performed using a deep learning framework (e.g., TensorFlow or PyTorch).

[0279] Product proposals

[0280] The server uses the generated generative AI model to suggest optimal products to users. The suggestions are updated taking into account real-time market trends, ensuring that users are always provided with the best products.

[0281] Presenting the proposal

[0282] The device then presents the suggested product information to the user, which is displayed in an easy-to-understand format, including text, graphs, and lists, allowing the user to select and purchase the products.

[0283] Specific examples

[0284] For example, let's say an "extroverted, outdoorsy user" enters the following information:

[0285] Personality information: Extrovert

[0286] Values: Adventure

[0287] Interests: Outdoors

[0288] Skill Set: Rock Climbing

[0289] The server uses this information to create a generative AI model, which, taking into account the latest market trends, suggests products such as:

[0290] Latest hiking shoes

[0291] waterproof jacket

[0292] Users receive these suggestions through the app, with example prompts such as:

[0293] "Recommend the best products for users based on their personality, values, interests, and skill sets."

[0294] By implementing the system of the present invention, users can efficiently find products that best suit their lifestyles, supporting their purchasing behavior. In addition, they can receive the latest proposals that reflect market trends in real time.

[0295] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0296] Step 1:

[0297] Users enter personality information, values, interests, and skill sets.

[0298] Enter information through an input device, use a smartphone application, or use a web form. At this stage, the input data is in text format. For example, the user enters information such as "extroverted," "adventure," "outdoors," and "rock climbing."

[0299] Step 2:

[0300] The terminal transmits the input information to the server.

[0301] The input data is converted to JSON format and sent to the server via a secure API using the HTTPS protocol. Here, the input is JSON format data and the output is the data sent to the server.

[0302] Step 3:

[0303] The server analyzes the received information and stores it in a database.

[0304] The server validates the JSON data received and checks items such as personality information, values, interests, and skill sets. After checking, it converts it into the required format for database storage and saves it in the PostgreSQL database. At this point, the input is the JSON data sent to the server, and the output is the user information stored in the database.

[0305] Step 4:

[0306] The server extracts the profile information from the database.

[0307] User information stored in the database is extracted using an SQL query. Specifically, the target user's profile is obtained based on information such as "extroverted," "adventure," "outdoors," and "rock climbing." The input is the extraction query from the database, and the output is the extracted profile information.

[0308] Step 5:

[0309] A generative AI model is created based on the profile information extracted by the server.

[0310] The extracted information is processed using a deep learning framework (e.g., TensorFlow or PyTorch) to create a generative AI model that suggests optimal products to users. For example, a model is generated that suggests appropriate outdoor gear for "extrovert," "adventure," "outdoors," and "rock climbing." The input is the extracted profile information, and the output is the generated generative AI model.

[0311] Step 6:

[0312] The server uses the generated generative AI model to suggest the best products to the user.

[0313] Using a generative AI model and incorporating additional data such as market trends and purchasing history, the system selects the best products for a user. Specifically, it generates suggestions such as "latest hiking shoes" or "waterproof jacket." The input is the generative AI model and additional data, and the output is a list of product suggestions.

[0314] Step 7:

[0315] The terminal presents the suggested product information to the user.

[0316] The product proposal information sent from the server is displayed on the user's device. The display format is text, graph, or list format, making it easy for users to understand. The input is the product proposal information sent from the server, and the output is the product information presented to the user.

[0317] 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.

[0318] The present invention is a system that supports important life decisions based on a user's personality, values, interests, skill set, and emotional information. This system is a platform that stores information provided by the user in a database and uses a generative AI model and an emotional engine to make optimal suggestions.

[0319] Program processing

[0320] Entering information

[0321] 1. Users: Enter information about their personality, values, interests, skill sets, and emotions.

[0322] Specifically, users answer questions and upload existing data (e.g., resumes, personality test results, emotion recognition data) using a web form or mobile app.

[0323] Sending information

[0324] 1. Terminal: Sends the entered information to the server.

[0325] Specifically, the terminal converts the input data into JSON format and sends it to the server via a secure API.

[0326] Information analysis and storage

[0327] 1. Server: Analyzes the received information and stores it in the personality value database and emotion database.

[0328] The server validates the input data, formats it appropriately, and then stores it in the database.

[0329] Extracting profile and emotion information

[0330] 1. Server: Extracts user profile information and emotion information from the personality value database and emotion database.

[0331] Specifically, the profile information and emotion information of a specified user are retrieved from a database using an SQL query.

[0332] Generating the Model

[0333] 1. Server: Generates a LifePath model based on the extracted profile information and emotion information.

[0334] Generative AI models are used to create predictive models using deep learning and reinforcement learning algorithms.

[0335] Selecting a field

[0336] 1. User: Select the area in which they would like support in making decisions such as further education, employment, and marriage.

[0337] Users are presented with options via a dashboard or conversational chatbot.

[0338] Proposal Generation

[0339] 1. Server: Integrates user profile information, sentiment information, and current information (such as labor market trends) to generate optimal recommendations.

[0340] Specific operation: Generate multiple scenarios and options based on generative AI models and emotion engines.

[0341] Adjusting the proposal

[0342] 1. Server: Adjusts suggestions based on the user's emotions recognized by the emotion engine.

[0343] Specific behavior: The suggestions are adjusted to adapt to the user's current emotional state.

[0344] Presenting the proposal

[0345] 1. Terminal: Presents the generated proposals to the user.

[0346] The proposals are displayed in an easy-to-understand format, including text, graphs, and lists.

[0347] Specific examples

[0348] In the case of continuing education

[0349] 1. User: Enter current grades, areas of interest, and recent emotional state (e.g., stress level).

[0350] 2. Terminal: Sends this information to the server.

[0351] 3. Server: Analyzes the received information and stores it in the personality value database and emotion database.

[0352] 4. Server: Extracts profile information and emotional information from the database and generates a LifePath model using a generative AI model.

[0353] 5. User: Select support for further education.

[0354] 6. Server: Integrates user profile information, education market data, and sentiment information to suggest the most suitable universities and courses.

[0355] 7. Server: Adjust the suggestions based on the user's recent stress level.

[0356] 8. Device: Display the following suggestion: "The best university for you is Department B at University A. This department is a field that is expected to grow in the future. Also, taking into account your current stress level, University A has a comprehensive stress management program."

[0357] In the case of employment

[0358] 1. User: Enters skill set, desired location, and emotional state (e.g., motivation level).

[0359] 2. Terminal: Sends this information to the server.

[0360] 3. Server: Analyzes user information and stores it in the personality value database and emotion database.

[0361] 4. Server: Extracts profile information and emotional information from the database and generates a LifePath model using a generative AI model.

[0362] 5. User: Select job-finding support.

[0363] 6. Server: Integrates user profile information, labor market data, and sentiment information to suggest optimal occupations and companies.

[0364] 7. Server: Adjust the suggestions based on the user's motivation level.

[0365] 8. Device: Display a suggestion: "The perfect job for you is Position D at Company C. This company is in a growth industry and offers a flexible work schedule that matches your current motivation level."

[0366] In the case of marriage

[0367] 1. User: Enters values, interests, and emotional state (e.g., happiness).

[0368] 2. Terminal: Sends this information to the server.

[0369] 3. Server: Analyzes user information and stores it in the personality value database and emotion database.

[0370] 4. Server: Extracts profile information and emotional information from the database and generates a LifePath model using a generative AI model.

[0371] 5. User: Select Marriage Support.

[0372] 6. Server: Integrates user profile information, matching algorithms, and emotional information to propose optimal partner candidates.

[0373] 7. Server: Adjust the suggestions based on the user's happiness.

[0374] 8. Device: Display a suggestion saying, "The ideal partner candidate for you is E. He or she has very similar values ​​and interests to you and has the potential to increase your current happiness."

[0375] By implementing the system of the present invention, users can make important life decisions more reliably and efficiently by taking emotional information into consideration, thereby maximizing the value of their decisions.

[0376] The processing flow will be explained below.

[0377] Program processing

[0378] Flow from inputting information to presenting proposals

[0379] Step 1:

[0380] Users: Enter information about their personality, values, interests, skill sets, and emotions.

[0381] What happens: Answer questions or upload resumes, personality assessments, or emotion recognition data using a web form or mobile app.

[0382] Step 2:

[0383] Terminal: Sends the entered information to the server.

[0384] Specific operation: Converts input data into JSON format and sends it to the server via a secure API.

[0385] Step 3:

[0386] Server: Analyzes the received information and stores it in a database.

[0387] Specific operation: Validate the input data and store it in the personality value database and emotion database in the appropriate format.

[0388] Step 4:

[0389] Server: Extracts user profile information and emotion information from the personality value database and emotion database.

[0390] Specific operation: Use SQL queries to retrieve the required profile and sentiment information from the respective databases.

[0391] Step 5:

[0392] Server: Generates a LifePath model based on the extracted profile information and emotion information.

[0393] Specific operation: Using a generative AI model, a predictive model is created using deep learning and reinforcement learning algorithms based on the acquired data.

[0394] Step 6:

[0395] User: Select the area in which they would like support in making decisions such as further education, employment, and marriage.

[0396] What it does: Select your preferred area of ​​support using the dashboard and conversational chatbot.

[0397] Step 7:

[0398] Terminal: Sends the user's selections to the server.

[0399] Specific operation: The selection is sent to the server via API.

[0400] Step 8:

[0401] Server: Integrates user profile information, sentiment information, and current information (such as labor market trends) to generate optimal proposals.

[0402] How it works: Using a generative AI model and sentiment engine, it combines user information with the latest market data to generate multiple scenarios and options.

[0403] Step 9:

[0404] Server: Adjusts the suggestions based on the user's emotions recognized by the emotion engine.

[0405] Specific behavior: The suggestions are adjusted to adapt to the user's current emotional state.

[0406] Step 10:

[0407] Terminal: Presents the generated suggestions to the user.

[0408] What it does: Display suggestions to the user in text, graph, and list format.

[0409] Example: Going on to higher education

[0410] Step 1:

[0411] User: Enter current grades, areas of interest, and recent emotional state (e.g., stress level).

[0412] What it does: Answer questions on a web form about grades, interests, and emotional state.

[0413] Step 2:

[0414] Terminal: Sends the entered information to the server.

[0415] What it does: Converts input data from a web form into JSON format and sends it through a secure API.

[0416] Step 3:

[0417] Server: Analyzes user information and stores it in a personality value database and an emotion database.

[0418] Specific operation: Validate and clean input data and save it in the database in the appropriate format.

[0419] Step 4:

[0420] Server: Extracts profile information and emotional information from the database and generates a LifePath model using a generative AI model.

[0421] Specific operations: Retrieves information from a database using SQL queries and generates a predictive model using machine learning algorithms.

[0422] Step 5:

[0423] User: Select support for further education.

[0424] Specific action: Select "Further education" on the dashboard or chatbot.

[0425] Step 6:

[0426] Server: Integrates user profile information, educational market data, and sentiment information to recommend the most suitable universities and courses.

[0427] How it works: It combines generative AI models with the latest education market data to generate optimal proposals by considering multiple scenarios.

[0428] Step 7:

[0429] Server: Adjusts the suggestions based on the user's recent stress level.

[0430] Specific operation: Based on data from the emotion engine, the suggestions are adjusted to correspond to the user's stress level.

[0431] Step 8:

[0432] Device: Display the following suggestion: "The best university for you is Department B at University A. This department is a field that is expected to grow in the future. Also, taking into account your current stress level, University A has a comprehensive stress management program."

[0433] What it does: Displays suggestions in text format and provides additional information on stress management.

[0434] Through these specific processing steps, the LifePath-AI system makes suggestions to the user to support optimal decision-making that takes emotional information into account.

[0435] Example 2

[0436] 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."

[0437] Conventional systems considered a user's personality, values, interests, and skill set, but did not consider emotional information. This made it difficult for them to generate optimal recommendations that took emotional factors into account when users made decisions. Furthermore, they lacked the ability to reflect labor market trends and other related data in real time, making it impossible to provide advice based on the latest information. To solve these problems, a system that can analyze multifaceted data, including emotional information, and make optimal recommendations is needed.

[0438] 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.

[0439] In this invention, the server includes means for inputting personality information, values, interests, skill sets, and emotional information, means for transmitting the input information to the server, means for analyzing the received information and storing it in a database, means for extracting profile information and emotional information from the database, means for generating a model based on the extracted profile information and emotional information, means for making suggestions to the user using the generated model, and means for presenting the suggested information to the user. This enables the generation of optimal suggestions that take into account the user's emotional information, and real-time advice based on the latest labor market and related data.

[0440] "Personality information" refers to information such as the user's character, tendencies, behavioral patterns, and values.

[0441] "Values" is information about the beliefs, ethics, and standards of judgment that a user considers important.

[0442] "Interests" are information about areas or activities that interest a user.

[0443] A "skill set" is information about the skills, abilities, and expertise that a user possesses.

[0444] "Emotion information" is information that indicates the user's current emotional state and changes in emotions.

[0445] An "input method" is a method by which a user provides information to a system, including web forms, mobile applications, etc.

[0446] "Transmission means" refers to a method for transmitting information entered by a user to a server, and includes transmitting data through a secure API.

[0447] The "analysis means" is a method by which the server analyzes the information it receives, formats it appropriately, and stores it in the database.

[0448] "Storage means" refers to a method for storing the analyzed information in a database.

[0449] An "extraction means" is a method for extracting specific information from a database, such as using an SQL query.

[0450] "Generation means" refers to a method for creating a model based on extracted information, using deep learning or reinforcement learning algorithms.

[0451] The "proposal means" is a method for making optimal proposals to the user using the generated model.

[0452] A "presentation medium" is a method for visually displaying the proposed information to the user, including text, graphs, lists, and the like.

[0453] "Labor Market Trends" is the latest information on the labor market, including current employment conditions, job information by industry, and wage trends.

[0454] "Relevant data" is additional information that may influence a user's decision, such as education market data and trend information.

[0455] MODE FOR CARRYING OUT THE INVENTION

[0456] This invention is a system that supports important life decisions based on a user's personality, values, interests, skill set, and emotional information. This system is a platform that stores information provided by the user in a database and uses a generative AI model and an emotional engine to make optimal recommendations.

[0457] Entering information

[0458] 1. User: Enters information about personality, values, interests, skill sets, and emotions. Specifically, users answer questions or upload existing data (e.g., resumes, personality test results, emotion recognition data) using web forms or mobile apps. This allows for multifaceted information to be collected from users.

[0459] Sending information

[0460] 2. Terminal: The input information is sent to the server. Specifically, the terminal converts the input data into JSON format and sends it to the server via a secure API (HTTPS protocol). This transmission process uses encrypted communication to ensure the security of the information.

[0461] Information analysis and storage

[0462] 3. Server: Analyzes the received information and stores it in the personality value database and emotion database. Specifically, the server validates the input data, formats it appropriately, and then stores it in the database. This process is performed using Python scripts and a database management system (e.g., MySQL).

[0463] Extracting profile and emotion information

[0464] 4. Server: Extracts user profile information and emotion information from the personality value database and emotion database. Specifically, it uses SQL queries to retrieve the profile information and emotion information of a specified user from the database. During this process, it integrates related data using JOIN operations.

[0465] Generating the Model

[0466] 5. Server: Generates a LifePath model based on the extracted profile and emotional information. Using a generative AI model (e.g., TensorFlow, PyTorch), it creates a predictive model using deep learning and reinforcement learning algorithms. This model generates scenarios that help users make optimal decisions based on their profile and emotional information.

[0467] Proposal generation and refinement

[0468] 6. Server: Integrates user profile information, emotional information, and current information (such as labor market trends) to generate optimal proposals. Furthermore, the proposals are tailored based on the user's emotions as recognized by the emotion engine. By combining the generative AI model with the emotion engine, proposals optimized for individual needs are created.

[0469] Presenting the proposal

[0470] 7. Terminal: The generated suggestions are presented to the user in a visually appealing format, such as text, graphs, or lists. This is often done using front-end frameworks like React or Vue.js.

[0471] Specific examples

[0472] In the case of continuing education

[0473] Users input their current grades, areas of interest, and recent emotional state (e.g., stress level). This information is sent to the server, which analyzes and stores it in a database. Profile information and emotional information are then extracted from the database, and a LifePath model is generated using a generative AI model. When users select support for further education, the system integrates the user's profile information, educational market data, and emotional information to recommend the most suitable universities and courses. Finally, the system adjusts the recommendations based on the user's recent stress state, and finally presents the recommendations to the user.

[0474] Prompt Sentence Examples

[0475] The following text is presented as an example of how to select the best university.

[0476] "The best university for you is Department B at University A. This department is in a promising field, and given your current stress level, University A also has a strong stress management program."

[0477] In the case of employment

[0478] Users input their skill set, desired work location, and emotional state (e.g., motivation level). This information is sent to the server, which analyzes and stores the received information in a database. Profile information and emotional information are then extracted from the database, and a LifePath model is generated using a generative AI model. When a user selects job-hunting support, the system integrates the user's profile information, labor market data, and emotional information to suggest suitable occupations and companies. Finally, the system adjusts the suggestions based on the user's motivation level, and finally presents them to the user.

[0479] Prompt Sentence Examples

[0480] The following text is presented as an example of how to select the most suitable job.

[0481] "The perfect fit for you is Position D at Company C. This company is in a growth industry and offers a flexible work schedule that matches your current motivation level."

[0482] In the case of marriage

[0483] Users input their values, interests, and emotional state (e.g., happiness level). This information is sent to the server, which analyzes and stores it in a database. Profile information and emotional information are then extracted from the database, and a LifePath model is generated using a generative AI model. If marriage support is selected, the system integrates the user's profile information, matching algorithm, and emotional information to suggest suitable partner candidates. Finally, the system adjusts the suggestions based on the user's happiness level, and finally presents them to the user.

[0484] Prompt Sentence Examples

[0485] The following text is presented as an example of how to select the best partner:

[0486] "Your ideal partner candidate is Person E. Their values ​​and interests are very similar to yours, and they have the potential to increase your current happiness."

[0487] By implementing the system of the present invention, users can make important life decisions more reliably and efficiently by taking emotional information into consideration, thereby maximizing the value of their decisions.

[0488] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0489] Step 1:

[0490] Users input information about their personality, values, interests, skill sets, and emotions. Specifically, users use web forms or mobile apps to answer questions and upload existing data (e.g., resumes, personality test results, emotion recognition data). This input data is sent to the system.

[0491] Input: User-entered personality information, values, interests, skill sets, and emotional information.

[0492] Output: The input data is passed to the terminal.

[0493] Step 2:

[0494] Terminal: Sends the input information to the server. Specifically, the terminal converts the input data into JSON format and sends it to the server using a secure API (HTTPS protocol). During this process, encrypted communication is used to ensure the security of the data.

[0495] Input: Personality information, values, interests, skill sets, and emotional information provided by the user.

[0496] Output: JSON formatted data is sent to the server.

[0497] Step 3:

[0498] Server: Analyzes the received information and stores it in the personality value database and emotion database. Specifically, the server first validates the data to ensure that the format and content are appropriate. It then stores the prepared data in the database. This process uses Python scripts and a database management system (e.g., MySQL).

[0499] Input: Personality information, values, interests, skill sets, and emotional information sent from the device in JSON format.

[0500] Output: Parsed and validated data is stored in a database.

[0501] Step 4:

[0502] Server: Extracts user profile information and emotion information from the personality value database and emotion database. Specifically, it issues an SQL query to retrieve the profile information and emotion information of the specified user from the database. At this time, it integrates related data using a JOIN operation.

[0503] Input: User's personality information, values, interests, skill sets, and emotional information stored in a database.

[0504] Output: The extracted user profile information and emotion information are passed on to the next process.

[0505] Step 5:

[0506] Server: Generates a LifePath model based on the extracted profile and emotional information. Specifically, it uses a generative AI model (e.g., TensorFlow or PyTorch) to create a predictive model using deep learning and reinforcement learning algorithms. This model is designed to generate optimal scenarios based on the user's profile and emotional information.

[0507] Input: Extracted user profile information and sentiment information.

[0508] Output: The generated LifePath model is passed to the next step.

[0509] Step 6:

[0510] User: Selects the area in which they want support for decisions such as further education, employment, marriage, etc. Specifically, the user selects the area in which they need support through a dashboard or an interactive chatbot. The selected data is immediately sent to the server.

[0511] Input: User's choice of field such as further education, employment, marriage, etc.

[0512] Output: The selected field information is sent to the server.

[0513] Step 7:

[0514] Server: Integrates user profile information, sentiment information, and current information (such as labor market trends) to generate optimal recommendations. Specifically, the server retrieves real-time data (e.g., labor market trends and education market trends) from a database and generates multiple scenarios using a generative AI model and sentiment engine. These scenarios include multiple options for the field selected by the user.

[0515] Inputs: Generated LifePath model, user choices, real-time labor and education market data.

[0516] Output: The generated proposals are passed on to the next step.

[0517] Step 8:

[0518] Server: Adjusts suggestions based on the user's emotions recognized by the emotion engine. Specifically, the emotion engine analyzes the user's emotional state in real time and adjusts the suggestions to optimize them for the user's current emotional state. NLP (natural language processing) technology is heavily used in this process.

[0519] Input: Generated suggestions, user's real-time sentiment information.

[0520] Output: The adjusted proposal is sent to the device.

[0521] Step 9:

[0522] Terminal: Presents the generated suggestions to the user. Specifically, the terminal displays the suggestions received from the server in a visually easy-to-understand format, such as text, graphs, or lists. Front-end frameworks such as React or Vue.js are often used for this.

[0523] Input: adjusted proposal.

[0524] Output: The suggestions presented to the user.

[0525] (Application example 2)

[0526] 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."

[0527] In today's world, when users make a wide range of important decisions, such as furthering their education, finding employment, or getting married, they need support that takes into account their emotional state and personality, rather than simply providing information. However, conventional systems have difficulty responding to each user in detail and individually, and they do not adjust their suggestions in real time. This makes it difficult to provide optimal suggestions for users.

[0528] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting personality information, values, interests, and skill sets, means for transmitting the input information to the server, means for analyzing the received information and storing it in a database, means for extracting profile information from the database, means for generating a model using a generative AI model based on the extracted profile information, means for making suggestions to the user using the generated model, means for presenting the suggested information to the user, means for adjusting the content of the suggestions in real time in conjunction with a smart device, and means for overlaying the content of the suggestions in the user's field of view. This enables more accurate and efficient support by making personalized suggestions based on the user's emotional state and personality in real time.

[0529] "Personality information" is information that indicates the user's character traits and behavior patterns.

[0530] "Values" are information that indicates the beliefs, principles, and priorities that a user holds dear.

[0531] "Interests" is information about the subjects or fields in which a user is interested or concerned.

[0532] A "skill set" is information that indicates a collection of specific skills and abilities that a user possesses.

[0533] A "server" is a computer system that processes information submitted by users, stores it in a database, and generates recommendations.

[0534] A "database" is a structured storage scheme on a computer system for storing personality information, values, interests, skill sets, and other related information.

[0535] A "generative AI model" is an artificial intelligence algorithm that generates optimal suggestions based on user input information.

[0536] The "means of generation" is the process of using an AI model to create specific proposals based on specified profile information.

[0537] "Smart devices" are devices including wearable and mobile devices that allow users to visually obtain information and check proposal content in real time.

[0538] "Means for real-time adjustment" refers to a function that monitors the user's emotional state and profile information in real time and dynamically adjusts the content of suggestions.

[0539] "Overlay display" is a technology that displays information directly overlaid on the user's field of vision on the display of a smart device.

[0540] These definitions provide a clear understanding of the specific functions and features of the system provided by the invention.

[0541] The present invention relates to a system that provides a personalized shopping experience in a physical store based on a user's personality information, values, interests, skill set, and emotional information. The system collects information provided by the user and makes optimal suggestions using a generative AI model and an emotional engine. Specific embodiments for implementing the present invention are described below.

[0542] First, the user puts on a smart device (e.g., smart glasses). The smart glasses have a built-in camera and display, and are capable of recognizing the user's face and emotions. The user first registers their personality information, values, interests, skill set, and emotional information in the smart glasses application. This information is sent to the server, analyzed, and stored in a database.

[0543] The server analyzes the received information and stores it in a database. The database contains personality information, values, interests, skill sets, and emotional information. Based on this information, a generative AI model is used to extract user profile information and generate optimal recommendations. The generated model is created using a deep learning framework (e.g., TensorFlow or PyTorch).

[0544] The smart glasses' camera then performs real-time facial and emotional recognition of the user and sends the data to a server. The server then analyzes the data and adjusts the recommendations in real time based on a generative AI model and emotion engine. The recommendations are overlaid on the user's field of view, allowing the user to receive optimal shopping recommendations based on their emotional state and personality.

[0545] For example, if a user is stressed in a store, the server can analyze that information and provide information about stores that offer a relaxing environment or suggest products that have a relaxing effect. Also, if a user is looking for products specialized in a particular field, the server can provide specialized suggestions for that field. Below are some example prompts for inputting data into the generative AI model.

[0546] Specific examples

[0547] "User 12345's profile information states that their personality traits are INTJ, their values ​​emphasize creativity and innovation, and their interests are technology and fashion. Their skill set excels in programming and design. Their current emotional state is neutral, and their past emotional states are happy and neutral."

[0548] In this way, the system of the present invention can provide real-time personalized shopping suggestions that take into account the user's emotional state and personality information, thereby enabling the user to enjoy a more comfortable and satisfying shopping experience.

[0549] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0550] Step 1:

[0551] A user puts on a smart device, specifically smart glasses, and inputs their personality information, values, interests, skill sets, and emotional information, which are registered in advance in the smart glasses application. The input information is converted into JSON format and sent to the server.

[0552] Input: personality information, values, interests, skill sets, emotional information

[0553] Output: User data in JSON format

[0554] Step 2:

[0555] The device sends the information entered by the user to the server. Specifically, the smart glasses application uses a secure API to send the data to the server. The data is sent over an encrypted connection using protocols such as SSL / TLS.

[0556] Input: User data in JSON format

[0557] Output: User data sent to the server

[0558] Step 3:

[0559] The server parses the received information and stores it in a database. First, it validates the received data and then stores it in an SQL database (e.g., PostgreSQL). Validation involves checking whether all required fields are present, whether the format is correct, etc. Before saving it to the database, the information is properly formatted according to a specified schema.

[0560] Input: User data sent to the server

[0561] Output: User data stored in the database

[0562] Step 4:

[0563] The server extracts user profile information from the database. It uses an SQL query to retrieve the profile information of a specified user. This profile information includes personality information, values, interests, skill sets, and emotional information. The extracted information is used as input for the generative AI model.

[0564] Input: User data stored in the database

[0565] Output: Input data for the generative AI model

[0566] Step 5:

[0567] The server uses a generative AI model based on the extracted profile information to generate a model. Using a deep learning framework (e.g., TensorFlow or PyTorch), the extracted data is input and a model is generated that predicts the best suggestions for the user. This generative model is then used to generate personalized suggestions for each individual user.

[0568] Input: Input data for the generative AI model

[0569] Output: Generated proposed content model

[0570] Step 6:

[0571] The server uses the generated model to generate appropriate suggestions based on the user's profile information, taking into account the user's current emotional data. The generated suggestions are then tailored to best suit the user's needs.

[0572] Input: The generated proposal content model and the user's current emotion data.

[0573] Output: Generated proposals

[0574] Step 7:

[0575] The device displays the recommendations to the user in real time, overlaid on the smart glasses screen and displayed directly in the user's field of vision, allowing the user to check the optimal product recommendations and promotions at any time in the physical store.

[0576] Input: Generated proposal

[0577] Output: Proposal content overlaid on smart glasses

[0578] Step 8:

[0579] The smart device monitors the user's emotional state in real time and transmits the data to the server. The server analyzes the received emotional data and adjusts the suggestions to suit the user's current emotional state. This process optimizes the suggestions in real time.

[0580] Input: Real-time user emotion data

[0581] Output: Adjusted proposal

[0582] 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.

[0583] 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.

[0584] 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.

[0585] [Second embodiment]

[0586] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0587] 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.

[0588] 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).

[0589] 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.

[0590] 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.

[0591] 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).

[0592] 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. 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.

[0593] 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.

[0594] 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.

[0595] 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.

[0596] In the smart glasses 214, 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.

[0597] 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."

[0598] This invention is a system that supports important life decisions based on input information such as a user's personality, values, interests, and skill set. This system is a platform that stores the information provided by the user in a database and makes optimal suggestions using a generative AI model.

[0599] Program processing

[0600] Entering information

[0601] 1. User: Enter information about your personality, values, interests, skill set, etc.

[0602] Specifically, users use a web form or mobile app to answer questions or upload existing data (e.g., resume, personality test results).

[0603] Sending information

[0604] 1. Terminal: Sends the entered information to the server.

[0605] Specifically, the terminal converts the input data into JSON format and sends it to the server via a secure API.

[0606] Information analysis and storage

[0607] 1. Server: Analyzes the received information and stores it in a database.

[0608] The server validates the input data, formats it appropriately, and then stores it in the personality value database.

[0609] Extracting Profile Information

[0610] 1. Server: Extracts user profile information from the personality value database.

[0611] Specifically, the server uses an SQL query to retrieve profile information for the specified user from a database.

[0612] Generating the Model

[0613] 1. Server: Generates a LifePath model based on the extracted profile information.

[0614] Generative AI models are used to create predictive models based on user characteristics and past data, leveraging machine learning algorithms such as deep learning and reinforcement learning.

[0615] Selecting a field

[0616] 1. User: Select the area in which they would like support in making decisions such as further education, employment, and marriage.

[0617] Users are presented with options via a dashboard or chatbot.

[0618] Proposal Generation

[0619] 1. Server: Integrates user profile information with current information (e.g., labor market trends) to generate optimal recommendations.

[0620] Here, based on the user's profile information, the system considers multiple scenarios to determine optimal options for further education, career, partner, etc.

[0621] Presenting the proposal

[0622] 1. Terminal: Presents the generated proposals to the user.

[0623] The proposals are displayed in an easy-to-understand format, including text, graphs, and lists.

[0624] Specific examples

[0625] In the case of continuing education

[0626] 1. User: Enter your current grades and areas of interest.

[0627] 2. Terminal: Sends this information to the server.

[0628] 3. Server: Analyzes the user's information and stores it in a personality value database.

[0629] 4. Server: Extracts profile information from the database and generates a LifePath model using a generative AI model.

[0630] 5. User: Select further education.

[0631] 6. Server: Integrates user profiles with education market data to recommend the most suitable universities and courses.

[0632] 7. Terminal: Display a suggestion saying, "The best university for the user is Faculty B at University A. This faculty is a field that is expected to grow in the future."

[0633] In the case of employment

[0634] 1. User: Enter your skill set and desired location.

[0635] 2. Terminal: Sends this information to the server.

[0636] 3. Server: Analyzes the user's information and stores it in a personality value database.

[0637] 4. Server: Extracts profile information from the database and generates a LifePath model using a generative AI model.

[0638] 5. User: Selects employment.

[0639] 6. Server: Integrates user profiles with labor market data to suggest the most suitable jobs and companies.

[0640] 7. Terminal: Display a suggestion saying, "The best job for you is Position D at Company C. This company is in an industry that is expected to continue to grow."

[0641] In the case of marriage

[0642] 1. User: Enter your values ​​and interests.

[0643] 2. Terminal: Sends this information to the server.

[0644] 3. Server: Analyzes the user's information and stores it in a personality value database.

[0645] 4. Server: Extracts profile information from the database and generates a LifePath model using a generative AI model.

[0646] 5. User: Selects marriage.

[0647] 6. Server: Integrates user profiles and matching algorithms to suggest optimal partner candidates.

[0648] 7. Device: Display a suggestion saying, "The ideal partner candidate for you is E. He / she has very similar values ​​and interests to you."

[0649] By implementing the system of the present invention, users can make important life decisions more reliably and efficiently, maximizing their value.

[0650] The processing flow will be explained below.

[0651] Program processing

[0652] Flow from inputting information to presenting proposals

[0653] Step 1:

[0654] Users: Enter information about their personality, values, interests, skill sets, etc.

[0655] What it does: Answer questions or upload your resume or personality assessment using a web form or mobile app.

[0656] Step 2:

[0657] Terminal: Sends the entered information to the server.

[0658] Specific operation: Converts input data into JSON format and sends it to the server via a secure API.

[0659] Step 3:

[0660] Server: Analyzes the received information and stores it in a database.

[0661] Specific operation: Validates input data and stores it in the personality value database in the appropriate format.

[0662] Step 4:

[0663] Server: Extracts user profile information from the personality value database.

[0664] What it does: Retrieves the profile information for the specified user from the database using an SQL query.

[0665] Step 5:

[0666] Server: Generates a LifePath model based on the extracted profile information.

[0667] What it does: Use generative AI models to create predictive models using deep learning and reinforcement learning algorithms.

[0668] Step 6:

[0669] User: Select the area in which they would like support in making decisions such as further education, employment, and marriage.

[0670] Specific Action: Select an option on a dashboard or conversational chatbot.

[0671] Step 7:

[0672] Terminal: Sends the user's selections to the server.

[0673] Specific operation: The selected fields are sent to the server via API.

[0674] Step 8:

[0675] Server: Integrates user profile information with current information (such as labor market trends) to generate optimal proposals.

[0676] How it works: Uses generative AI models and up-to-date market data to generate multiple scenarios and options.

[0677] Step 9:

[0678] Terminal: Presents the generated suggestions to the user.

[0679] Specific behavior: Display suggestions to the user in text, graph, and list format.

[0680] Specific examples of procedures for continuing education

[0681] Step 1:

[0682] User: Enter current grades and areas of interest.

[0683] What it does: Answer questions about grades and interests in a web form.

[0684] Step 2:

[0685] Terminal: Sends the entered information to the server.

[0686] What it does: Converts input data from a web form into JSON format and sends it through a secure API.

[0687] Step 3:

[0688] Server: Analyzes user information and stores it in a personality value database.

[0689] Specific operation: Validate and clean input data and save it in the database in the appropriate format.

[0690] Step 4:

[0691] Server: Extracts profile information from the database and generates a LifePath model using a generative AI model.

[0692] Specific operations: Retrieves information from a database using SQL queries and generates a predictive model using machine learning algorithms.

[0693] Step 5:

[0694] User: Select support for further education.

[0695] Specific action: Select "Further education" on the dashboard or chatbot.

[0696] Step 6:

[0697] Server: Integrates user profile information with education market data to recommend the most suitable universities and courses.

[0698] Specific operation: Based on the generative AI model and education market data, proposals are generated taking into account multiple scenarios.

[0699] Step 7:

[0700] Terminal: Display a suggestion saying, "The best university for the user is Faculty B at University A. This faculty is a field that is expected to grow in the future."

[0701] What it does: Display the suggestions to the user in text format.

[0702] Through these specific processing steps, the LifePath-AI system makes suggestions to the user to support optimal decision-making.

[0703] Example 1

[0704] 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."

[0705] Previous systems supporting important life decisions were unable to properly utilize users' personality information, values, interests, skill sets, etc., and had difficulty making proposals that took real-time market trends into account. As a result, they were unable to provide optimal proposals to users, and their decision-making support functions were inadequate.

[0706] 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.

[0707] In this invention, the server includes means for inputting personality information, values, interests, and skill sets, means for converting the input information into a data format and transmitting it, means for validating the received information and storing it in a database, means for extracting profile information from the database, means for generating a predictive model using a generative AI model based on the extracted profile information, means for generating proposals suitable for the user for each field, and means for visualizing the generated proposals and presenting them to the user, thereby making it possible to provide appropriate and dynamic proposals to the user.

[0708] "Personality information" is information about the user's character and behavioral characteristics.

[0709] "Values" is information about the beliefs and ethics that users consider important.

[0710] "Interests" is information about areas or activities that interest a user.

[0711] A "skill set" is a collection of skills, knowledge, and abilities possessed by a user.

[0712] "Transmitting means" refers to the method or process for sending the input information to the server.

[0713] "Validation" is the process of verifying received data to ensure it is accurate and complete.

[0714] A "database" is a system for storing and managing structured data.

[0715] "Profile information" is comprehensive information including a user's personal information, past data, and history.

[0716] A "generative AI model" is a model that uses machine learning algorithms to make predictions and classifications.

[0717] A "predictive model" is a model that predicts future trends and options based on specific data.

[0718] A "means for generating suggestions" is a method or process for creating optimal options or suggested actions based on user information.

[0719] "Visualization" refers to the presentation of data or information in an easy-to-understand format, such as a graph, text, or list.

[0720] "Real-time market trend data" means the latest data on current market conditions and trends.

[0721] This invention is a system that supports important life decisions based on input of a user's personality information, values, interests, skill set, etc. This system uses a generative AI model to make optimal recommendations taking into account real-time market trends.

[0722] Hardware and Software Configuration

[0723] Hardware

[0724] Device: The device through which a user enters information (e.g., PC, smartphone, tablet).

[0725] Server: A computer system for analyzing information, storing it in a database, and running generative AI models.

[0726] software

[0727] Web form or mobile application: An interface where users enter their personality information, values, interests, and skill sets.

[0728] Database: A relational database (e.g., MySQL, PostgreSQL) to store user information.

[0729] Generative AI models: Machine learning algorithms (e.g., TensorFlow, PyTorch) that generate predictive models based on user profile information.

[0730] Secure API: A communication protocol (e.g. HTTPS) for sending data from a device to a server.

[0731] Program processing overview

[0732] Entering information

[0733] Users use web forms or mobile apps to enter information such as personality, values, interests, and skill sets. For example, users may answer questions about their personality, work history, and areas of interest.

[0734] Sending information

[0735] The device converts the input information into JSON format and sends it to the server via a secure API endpoint, for example, by sending a POST request to " / api / userdata."

[0736] Information analysis and storage

[0737] The server validates the information it receives, formats it appropriately, and stores it in a relational database, for example by checking the format of the input data and adding default values ​​if any fields are missing.

[0738] Extracting Profile Information

[0739] The server extracts the user's profile information from the personality value database, specifically, by using an SQL query to retrieve data based on the specified user ID.

[0740] Generating the Model

[0741] The server runs a generative AI model based on the extracted profile information to generate an individual predictive model, for example, using TensorFlow to predict educational and career options suited to the user's attributes.

[0742] Proposal Generation

[0743] The server integrates user profile information with real-time market trend data to generate optimal suggestions for each field, for example, suggesting the most suitable career options for users while taking into account the latest trends in the labor market.

[0744] Presenting the proposal

[0745] The terminal visualizes the suggestions received from the server and presents them to the user in the form of text, graphs, or lists.

[0746] Specific examples

[0747] Prompt Sentence Examples

[0748] "I'm good at math and interested in biology. I'd like to pursue a career in biotechnology. Can you suggest some educational options for me?"

[0749] Processing flow

[0750] 1. User: Enter your field of interest and grade information

[0751] "I'm good at math and interested in biology."

[0752] 2. Terminal: Sends input information to the server in JSON format

[0753] Example: Send a POST request to " / api / userdata"

[0754] 3. Server: Validate the information and store it in the database

[0755] Example: Saving data using an SQL query

[0756] 4. Server: Extracts user profile information and runs generative AI models

[0757] Example: Generating a predictive model with TensorFlow

[0758] 5. Server: Proposing the best school to go to

[0759] For example: "Faculty B at University A is suitable."

[0760] 6. Terminal: Visualize the proposal and present it to the user

[0761] Example: "The best university for the user is University A, Department B."

[0762] In this way, by utilizing generative AI models based on detailed user information and making specific and optimal suggestions that take real-time market trends into account, the system can effectively support users in making important life decisions.

[0763] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0764] Step 1:

[0765] A user enters information such as personality information, values, interests, and skill sets using a web form or mobile app.

[0766] Input: Information that a user enters into a form (e.g., personality test results, resume, areas of interest).

[0767] Output: The input information is saved on the form.

[0768] Specific behavior: The user answers each question and uploads existing data if necessary.

[0769] Step 2:

[0770] The device converts the input information into JSON format and sends it to the server via a secure API.

[0771] Input: User information saved in the form.

[0772] Output: The data is converted to JSON format and sent through a secure API.

[0773] Specific operation: The terminal serializes the input data in JSON format and sends a POST request to the server's API endpoint using the HTTPS protocol.

[0774] Step 3:

[0775] The server validates the received information and stores it in the database.

[0776] Input: JSON data sent from the terminal.

[0777] Output: Accurate and complete data stored in the database.

[0778] Specific operation: The server validates the received data, checks for incompleteness or inconsistencies, and then saves it to the database using an SQL query.

[0779] Step 4:

[0780] The server extracts the user's profile information from the database.

[0781] Input: User data in the database.

[0782] Output: The extracted profile information.

[0783] What happens: The server executes an SQL query to retrieve the profile based on the specified user ID.

[0784] Step 5:

[0785] The server runs a generative AI model based on the extracted profile information to generate a predictive model.

[0786] Input: Extracted profile information.

[0787] Output: The generated predictive model.

[0788] Specific operation: The server uses a machine learning framework such as TensorFlow or PyTorch to train and generate a predictive model using the profile information as input data.

[0789] Step 6:

[0790] Users select the areas in which they would like support, such as further education, employment, or marriage.

[0791] Input: User's choice.

[0792] Output: Selected field information.

[0793] Specific behavior: The user uses the dashboard or chatbot to select the desired field from the options displayed.

[0794] Step 7:

[0795] The server integrates the user's profile information with real-time market trend data to generate optimal offers.

[0796] Inputs: Profile information, real-time market trend data.

[0797] Output: The generated proposals.

[0798] How it works: The server uses a reinforcement learning algorithm to combine profiles and market data to run simulations and generate the most appropriate proposals.

[0799] Step 8:

[0800] The terminal visualizes the generated suggestions and presents them to the user.

[0801] Input: Proposal data from the server.

[0802] Output: A visualized proposal.

[0803] Specific operation: The device displays the suggestions received from the server in graph, list, and text format, making it easy for the user to understand.

[0804] (Application example 1)

[0805] 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."

[0806] Conventional personalization systems have been unable to fully utilize a wide range of information, such as a user's personality, values, interests, and skill set, to make optimal product recommendations. It has also been difficult for users to efficiently find products that best fit their lifestyles and purchasing behavior. Furthermore, it has been difficult to reflect market trends in real time and keep recommendations up to date.

[0807] 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.

[0808] In this invention, the server includes means for inputting personality information, values, interests, and skill sets, means for transmitting the input information to the server, means for analyzing the received information and storing it in a database, means for extracting profile information from the database, means for creating a generative AI model based on the extracted profile information, means for suggesting optimal products to the user using the generated generative AI model, and means for presenting the suggested product information to the user. This allows the user to efficiently receive suggestions of products optimal for their lifestyle, support their purchasing behavior, and receive the latest suggestions that reflect market trends.

[0809] "Personality information" is information that indicates the user's character and behavioral characteristics.

[0810] "Values" is information that indicates the beliefs and ethics that a user considers important.

[0811] "Interests" is information that indicates the fields and activities in which a user is interested.

[0812] A "skill set" is information that indicates the skills and abilities that a user possesses.

[0813] The "server" is a computer system that receives, analyzes, and stores data and makes recommendations using generative AI models.

[0814] A "generative AI model" is an artificial intelligence model that is generated based on a user's profile information and is used to make optimal suggestions to the user.

[0815] "Profile information" is a collective term for data about a user, such as personality information, values, interests, and skill sets.

[0816] A "product" is a good or service related to a user's lifestyle or purchasing behavior.

[0817] "Input means" is an interface through which a user provides personality information, values, interests, and skill sets to the system.

[0818] The "transmitting means" is a communication function for transferring the input information to the server.

[0819] The "analyzing means" refers to the algorithms or programs that process the received information and store it in a database.

[0820] "Storing means" is a function for saving analyzed information in a database.

[0821] The "means for extracting" is a function for extracting specific profile information from the database.

[0822] The "means of creation" is the process of forming a generative AI model based on the extracted information.

[0823] The "means of suggestion" is a function that uses a generative AI model to determine the best product for the user and generate that information.

[0824] The "presentation means" is an interface for displaying the generated proposal content in an easy-to-understand manner to the user.

[0825] "Purchasing behavior" refers to the actions and decisions a user makes when purchasing a product or service.

[0826] "Market trends" refer to current supply, demand, trends, and tendencies in the commercial market.

[0827] The present invention relates to a system that inputs information such as a user's personality, values, interests, and skill set, and then uses this information to suggest products that are optimal for the user's lifestyle and purchasing behavior. This system is composed of multiple components, which are described in detail below.

[0828] Entering information

[0829] Users use an interface to input their personality information, values, interests, and skill sets. This interface can be provided through a smartphone application, a web form, or a dedicated terminal. Users can enter information by answering questions.

[0830] Sending information

[0831] The terminal transmits the input information to the server. To do this, the terminal converts the input data into JSON format and transmits it to the server via a secure API. HTTPS is used as the communication protocol.

[0832] Information analysis and storage

[0833] The server analyzes the received information, processes the data as needed, and stores it in a database. Python and Flask are used for analyzing the information, and PostgreSQL is used as the database. The server also validates the input data and formats it appropriately.

[0834] Extracting profile information and creating a generative AI model

[0835] The server extracts user profile information from a database using SQL queries. It then creates a generative AI model based on the extracted profile information. This process is performed using a deep learning framework (e.g., TensorFlow or PyTorch).

[0836] Product proposals

[0837] The server uses the generated generative AI model to suggest optimal products to users. The suggestions are updated taking into account real-time market trends, ensuring that users are always provided with the best products.

[0838] Presenting the proposal

[0839] The device then presents the suggested product information to the user, which is displayed in an easy-to-understand format, including text, graphs, and lists, allowing the user to select and purchase the products.

[0840] Specific examples

[0841] For example, let's say an "extroverted, outdoorsy user" enters the following information:

[0842] Personality information: Extrovert

[0843] Values: Adventure

[0844] Interests: Outdoors

[0845] Skill Set: Rock Climbing

[0846] The server uses this information to create a generative AI model, which, taking into account the latest market trends, suggests products such as:

[0847] Latest hiking shoes

[0848] waterproof jacket

[0849] Users receive these suggestions through the app, with example prompts such as:

[0850] "Recommend the best products for users based on their personality, values, interests, and skill sets."

[0851] By implementing the system of the present invention, users can efficiently find products that best suit their lifestyles, supporting their purchasing behavior. In addition, they can receive the latest proposals that reflect market trends in real time.

[0852] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0853] Step 1:

[0854] Users enter personality information, values, interests, and skill sets.

[0855] Enter information through an input device, use a smartphone application, or use a web form. At this stage, the input data is in text format. For example, the user enters information such as "extroverted," "adventure," "outdoors," and "rock climbing."

[0856] Step 2:

[0857] The terminal transmits the input information to the server.

[0858] The input data is converted to JSON format and sent to the server via a secure API using the HTTPS protocol. Here, the input is JSON format data and the output is the data sent to the server.

[0859] Step 3:

[0860] The server analyzes the received information and stores it in a database.

[0861] The server validates the JSON data received and checks items such as personality information, values, interests, and skill sets. After checking, it converts it into the required format for database storage and saves it in the PostgreSQL database. At this point, the input is the JSON data sent to the server, and the output is the user information stored in the database.

[0862] Step 4:

[0863] The server extracts the profile information from the database.

[0864] User information stored in the database is extracted using an SQL query. Specifically, the target user's profile is obtained based on information such as "extroverted," "adventure," "outdoors," and "rock climbing." The input is the extraction query from the database, and the output is the extracted profile information.

[0865] Step 5:

[0866] A generative AI model is created based on the profile information extracted by the server.

[0867] The extracted information is processed using a deep learning framework (e.g., TensorFlow or PyTorch) to create a generative AI model that suggests optimal products to users. For example, a model is generated that suggests appropriate outdoor gear for "extrovert," "adventure," "outdoors," and "rock climbing." The input is the extracted profile information, and the output is the generated generative AI model.

[0868] Step 6:

[0869] The server uses the generated generative AI model to suggest the best products to the user.

[0870] Using a generative AI model and incorporating additional data such as market trends and purchasing history, the system selects the best products for a user. Specifically, it generates suggestions such as "latest hiking shoes" or "waterproof jacket." The input is the generative AI model and additional data, and the output is a list of product suggestions.

[0871] Step 7:

[0872] The terminal presents the suggested product information to the user.

[0873] The product proposal information sent from the server is displayed on the user's device. The display format is text, graph, or list format, making it easy for users to understand. The input is the product proposal information sent from the server, and the output is the product information presented to the user.

[0874] 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.

[0875] The present invention is a system that supports important life decisions based on a user's personality, values, interests, skill set, and emotional information. This system is a platform that stores information provided by the user in a database and uses a generative AI model and an emotional engine to make optimal suggestions.

[0876] Program processing

[0877] Entering information

[0878] 1. Users: Enter information about their personality, values, interests, skill sets, and emotions.

[0879] Specifically, users answer questions and upload existing data (e.g., resumes, personality test results, emotion recognition data) using a web form or mobile app.

[0880] Sending information

[0881] 1. Terminal: Sends the entered information to the server.

[0882] Specifically, the terminal converts the input data into JSON format and sends it to the server via a secure API.

[0883] Information analysis and storage

[0884] 1. Server: Analyzes the received information and stores it in the personality value database and emotion database.

[0885] The server validates the input data, formats it appropriately, and then stores it in the database.

[0886] Extracting profile and emotion information

[0887] 1. Server: Extracts user profile information and emotion information from the personality value database and emotion database.

[0888] Specifically, the profile information and emotion information of a specified user are retrieved from a database using an SQL query.

[0889] Generating the Model

[0890] 1. Server: Generates a LifePath model based on the extracted profile information and emotion information.

[0891] Generative AI models are used to create predictive models using deep learning and reinforcement learning algorithms.

[0892] Selecting a field

[0893] 1. User: Select the area in which they would like support in making decisions such as further education, employment, and marriage.

[0894] Users are presented with options via a dashboard or conversational chatbot.

[0895] Proposal Generation

[0896] 1. Server: Integrates user profile information, sentiment information, and current information (such as labor market trends) to generate optimal recommendations.

[0897] Specific operation: Generate multiple scenarios and options based on generative AI models and emotion engines.

[0898] Adjusting the proposal

[0899] 1. Server: Adjusts suggestions based on the user's emotions recognized by the emotion engine.

[0900] Specific behavior: The suggestions are adjusted to adapt to the user's current emotional state.

[0901] Presenting the proposal

[0902] 1. Terminal: Presents the generated proposals to the user.

[0903] The proposals are displayed in an easy-to-understand format, including text, graphs, and lists.

[0904] Specific examples

[0905] In the case of continuing education

[0906] 1. User: Enter current grades, areas of interest, and recent emotional state (e.g., stress level).

[0907] 2. Terminal: Sends this information to the server.

[0908] 3. Server: Analyzes the received information and stores it in the personality value database and emotion database.

[0909] 4. Server: Extracts profile information and emotional information from the database and generates a LifePath model using a generative AI model.

[0910] 5. User: Select support for further education.

[0911] 6. Server: Integrates user profile information, education market data, and sentiment information to suggest the most suitable universities and courses.

[0912] 7. Server: Adjust the suggestions based on the user's recent stress level.

[0913] 8. Device: Display the following suggestion: "The best university for you is Department B at University A. This department is a field that is expected to grow in the future. Also, taking into account your current stress level, University A has a comprehensive stress management program."

[0914] In the case of employment

[0915] 1. User: Enters skill set, desired location, and emotional state (e.g., motivation level).

[0916] 2. Terminal: Sends this information to the server.

[0917] 3. Server: Analyzes user information and stores it in the personality value database and emotion database.

[0918] 4. Server: Extracts profile information and emotional information from the database and generates a LifePath model using a generative AI model.

[0919] 5. User: Select job-finding support.

[0920] 6. Server: Integrates user profile information, labor market data, and sentiment information to suggest optimal occupations and companies.

[0921] 7. Server: Adjust the suggestions based on the user's motivation level.

[0922] 8. Device: Display a suggestion: "The perfect job for you is Position D at Company C. This company is in a growth industry and offers a flexible work schedule that matches your current motivation level."

[0923] In the case of marriage

[0924] 1. User: Enters values, interests, and emotional state (e.g., happiness).

[0925] 2. Terminal: Sends this information to the server.

[0926] 3. Server: Analyzes user information and stores it in the personality value database and emotion database.

[0927] 4. Server: Extracts profile information and emotional information from the database and generates a LifePath model using a generative AI model.

[0928] 5. User: Select Marriage Support.

[0929] 6. Server: Integrates user profile information, matching algorithms, and emotional information to propose optimal partner candidates.

[0930] 7. Server: Adjust the suggestions based on the user's happiness.

[0931] 8. Device: Display a suggestion saying, "The ideal partner candidate for you is E. He or she has very similar values ​​and interests to you and has the potential to increase your current happiness."

[0932] By implementing the system of the present invention, users can make important life decisions more reliably and efficiently by taking emotional information into consideration, thereby maximizing the value of their decisions.

[0933] The processing flow will be explained below.

[0934] Program processing

[0935] Flow from inputting information to presenting proposals

[0936] Step 1:

[0937] Users: Enter information about their personality, values, interests, skill sets, and emotions.

[0938] What happens: Answer questions or upload resumes, personality assessments, or emotion recognition data using a web form or mobile app.

[0939] Step 2:

[0940] Terminal: Sends the entered information to the server.

[0941] Specific operation: Converts input data into JSON format and sends it to the server via a secure API.

[0942] Step 3:

[0943] Server: Analyzes the received information and stores it in a database.

[0944] Specific operation: Validate the input data and store it in the personality value database and emotion database in the appropriate format.

[0945] Step 4:

[0946] Server: Extracts user profile information and emotion information from the personality value database and emotion database.

[0947] Specific operation: Use SQL queries to retrieve the required profile and sentiment information from the respective databases.

[0948] Step 5:

[0949] Server: Generates a LifePath model based on the extracted profile information and emotion information.

[0950] Specific operation: Using a generative AI model, a predictive model is created using deep learning and reinforcement learning algorithms based on the acquired data.

[0951] Step 6:

[0952] User: Select the area in which they would like support in making decisions such as further education, employment, and marriage.

[0953] What it does: Select your preferred area of ​​support using the dashboard and conversational chatbot.

[0954] Step 7:

[0955] Terminal: Sends the user's selections to the server.

[0956] Specific operation: The selection is sent to the server via API.

[0957] Step 8:

[0958] Server: Integrates user profile information, sentiment information, and current information (such as labor market trends) to generate optimal proposals.

[0959] How it works: Using a generative AI model and sentiment engine, it combines user information with the latest market data to generate multiple scenarios and options.

[0960] Step 9:

[0961] Server: Adjusts the suggestions based on the user's emotions recognized by the emotion engine.

[0962] Specific behavior: The suggestions are adjusted to adapt to the user's current emotional state.

[0963] Step 10:

[0964] Terminal: Presents the generated suggestions to the user.

[0965] What it does: Display suggestions to the user in text, graph, and list format.

[0966] Example: Going on to higher education

[0967] Step 1:

[0968] User: Enter current grades, areas of interest, and recent emotional state (e.g., stress level).

[0969] What it does: Answer questions on a web form about grades, interests, and emotional state.

[0970] Step 2:

[0971] Terminal: Sends the entered information to the server.

[0972] What it does: Converts input data from a web form into JSON format and sends it through a secure API.

[0973] Step 3:

[0974] Server: Analyzes user information and stores it in a personality value database and an emotion database.

[0975] Specific operation: Validate and clean input data and save it in the database in the appropriate format.

[0976] Step 4:

[0977] Server: Extracts profile information and emotional information from the database and generates a LifePath model using a generative AI model.

[0978] Specific operations: Retrieves information from a database using SQL queries and generates a predictive model using machine learning algorithms.

[0979] Step 5:

[0980] User: Select support for further education.

[0981] Specific action: Select "Further education" on the dashboard or chatbot.

[0982] Step 6:

[0983] Server: Integrates user profile information, educational market data, and sentiment information to recommend the most suitable universities and courses.

[0984] How it works: It combines generative AI models with the latest education market data to generate optimal proposals by considering multiple scenarios.

[0985] Step 7:

[0986] Server: Adjusts the suggestions based on the user's recent stress level.

[0987] Specific operation: Based on data from the emotion engine, the suggestions are adjusted to correspond to the user's stress level.

[0988] Step 8:

[0989] Device: Display the following suggestion: "The best university for you is Department B at University A. This department is a field that is expected to grow in the future. Also, taking into account your current stress level, University A has a comprehensive stress management program."

[0990] What it does: Displays suggestions in text format and provides additional information on stress management.

[0991] Through these specific processing steps, the LifePath-AI system makes suggestions to the user to support optimal decision-making that takes emotional information into account.

[0992] Example 2

[0993] 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."

[0994] Conventional systems considered a user's personality, values, interests, and skill set, but did not consider emotional information. This made it difficult for them to generate optimal recommendations that took emotional factors into account when users made decisions. Furthermore, they lacked the ability to reflect labor market trends and other related data in real time, making it impossible to provide advice based on the latest information. To solve these problems, a system that can analyze multifaceted data, including emotional information, and make optimal recommendations is needed.

[0995] 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.

[0996] In this invention, the server includes means for inputting personality information, values, interests, skill sets, and emotional information, means for transmitting the input information to the server, means for analyzing the received information and storing it in a database, means for extracting profile information and emotional information from the database, means for generating a model based on the extracted profile information and emotional information, means for making suggestions to the user using the generated model, and means for presenting the suggested information to the user. This enables the generation of optimal suggestions that take into account the user's emotional information, and real-time advice based on the latest labor market and related data.

[0997] "Personality information" refers to information such as the user's character, tendencies, behavioral patterns, and values.

[0998] "Values" is information about the beliefs, ethics, and standards of judgment that a user considers important.

[0999] "Interests" are information about areas or activities that interest a user.

[1000] A "skill set" is information about the skills, abilities, and expertise that a user possesses.

[1001] "Emotion information" is information that indicates the user's current emotional state and changes in emotions.

[1002] An "input method" is a method by which a user provides information to a system, including web forms, mobile applications, etc.

[1003] "Transmission means" refers to a method for transmitting information entered by a user to a server, and includes transmitting data through a secure API.

[1004] The "analysis means" is a method by which the server analyzes the information it receives, formats it appropriately, and stores it in the database.

[1005] "Storage means" refers to a method for storing the analyzed information in a database.

[1006] An "extraction means" is a method for extracting specific information from a database, such as using an SQL query.

[1007] "Generation means" refers to a method for creating a model based on extracted information, using deep learning or reinforcement learning algorithms.

[1008] The "proposal means" is a method for making optimal proposals to the user using the generated model.

[1009] A "presentation medium" is a method for visually displaying the proposed information to the user, including text, graphs, lists, and the like.

[1010] "Labor Market Trends" is the latest information on the labor market, including current employment conditions, job information by industry, and wage trends.

[1011] "Relevant data" is additional information that may influence a user's decision, such as education market data and trend information.

[1012] MODE FOR CARRYING OUT THE INVENTION

[1013] This invention is a system that supports important life decisions based on a user's personality, values, interests, skill set, and emotional information. This system is a platform that stores information provided by the user in a database and uses a generative AI model and an emotional engine to make optimal recommendations.

[1014] Entering information

[1015] 1. User: Enters information about personality, values, interests, skill sets, and emotions. Specifically, users answer questions or upload existing data (e.g., resumes, personality test results, emotion recognition data) using web forms or mobile apps. This allows for multifaceted information to be collected from users.

[1016] Sending information

[1017] 2. Terminal: The input information is sent to the server. Specifically, the terminal converts the input data into JSON format and sends it to the server via a secure API (HTTPS protocol). This transmission process uses encrypted communication to ensure the security of the information.

[1018] Information analysis and storage

[1019] 3. Server: Analyzes the received information and stores it in the personality value database and emotion database. Specifically, the server validates the input data, formats it appropriately, and then stores it in the database. This process is performed using Python scripts and a database management system (e.g., MySQL).

[1020] Extracting profile and emotion information

[1021] 4. Server: Extracts user profile information and emotion information from the personality value database and emotion database. Specifically, it uses SQL queries to retrieve the profile information and emotion information of a specified user from the database. During this process, it integrates related data using JOIN operations.

[1022] Generating the Model

[1023] 5. Server: Generates a LifePath model based on the extracted profile and emotional information. Using a generative AI model (e.g., TensorFlow, PyTorch), it creates a predictive model using deep learning and reinforcement learning algorithms. This model generates scenarios that help users make optimal decisions based on their profile and emotional information.

[1024] Proposal generation and refinement

[1025] 6. Server: Integrates user profile information, emotional information, and current information (such as labor market trends) to generate optimal proposals. Furthermore, the proposals are tailored based on the user's emotions as recognized by the emotion engine. By combining the generative AI model with the emotion engine, proposals optimized for individual needs are created.

[1026] Presenting the proposal

[1027] 7. Terminal: The generated suggestions are presented to the user in a visually appealing format, such as text, graphs, or lists. This is often done using front-end frameworks like React or Vue.js.

[1028] Specific examples

[1029] In the case of continuing education

[1030] Users input their current grades, areas of interest, and recent emotional state (e.g., stress level). This information is sent to the server, which analyzes and stores it in a database. Profile information and emotional information are then extracted from the database, and a LifePath model is generated using a generative AI model. When users select support for further education, the system integrates the user's profile information, educational market data, and emotional information to recommend the most suitable universities and courses. Finally, the system adjusts the recommendations based on the user's recent stress state, and finally presents the recommendations to the user.

[1031] Prompt Sentence Examples

[1032] The following text is presented as an example of how to select the best university.

[1033] "The best university for you is Department B at University A. This department is in a promising field, and given your current stress level, University A also has a strong stress management program."

[1034] In the case of employment

[1035] Users input their skill set, desired work location, and emotional state (e.g., motivation level). This information is sent to the server, which analyzes and stores the received information in a database. Profile information and emotional information are then extracted from the database, and a LifePath model is generated using a generative AI model. When a user selects job-hunting support, the system integrates the user's profile information, labor market data, and emotional information to suggest suitable occupations and companies. Finally, the system adjusts the suggestions based on the user's motivation level, and finally presents them to the user.

[1036] Prompt Sentence Examples

[1037] The following text is presented as an example of how to select the most suitable job.

[1038] "The perfect fit for you is Position D at Company C. This company is in a growth industry and offers a flexible work schedule that matches your current motivation level."

[1039] In the case of marriage

[1040] Users input their values, interests, and emotional state (e.g., happiness level). This information is sent to the server, which analyzes and stores it in a database. Profile information and emotional information are then extracted from the database, and a LifePath model is generated using a generative AI model. If marriage support is selected, the system integrates the user's profile information, matching algorithm, and emotional information to suggest suitable partner candidates. Finally, the system adjusts the suggestions based on the user's happiness level, and finally presents them to the user.

[1041] Prompt Sentence Examples

[1042] The following text is presented as an example of how to select the best partner:

[1043] "Your ideal partner candidate is E. Their values ​​and interests are very similar to yours, and they have the potential to increase your current happiness."

[1044] By implementing the system of the present invention, users can make important life decisions more reliably and efficiently by taking emotional information into consideration, thereby maximizing the value of their decisions.

[1045] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1046] Step 1:

[1047] Users input information about their personality, values, interests, skill sets, and emotions. Specifically, users use web forms or mobile apps to answer questions and upload existing data (e.g., resumes, personality test results, emotion recognition data). This input data is sent to the system.

[1048] Input: User-entered personality information, values, interests, skill sets, and emotional information.

[1049] Output: The input data is passed to the terminal.

[1050] Step 2:

[1051] Terminal: Sends the input information to the server. Specifically, the terminal converts the input data into JSON format and sends it to the server using a secure API (HTTPS protocol). During this process, encrypted communication is used to ensure the security of the data.

[1052] Input: Personality information, values, interests, skill sets, and emotional information provided by the user.

[1053] Output: JSON formatted data is sent to the server.

[1054] Step 3:

[1055] Server: Analyzes the received information and stores it in the personality value database and emotion database. Specifically, the server first validates the data to ensure that the format and content are appropriate. It then stores the prepared data in the database. This process uses Python scripts and a database management system (e.g., MySQL).

[1056] Input: Personality information, values, interests, skill sets, and emotional information sent from the device in JSON format.

[1057] Output: Parsed and validated data is stored in a database.

[1058] Step 4:

[1059] Server: Extracts user profile information and emotion information from the personality value database and emotion database. Specifically, it issues an SQL query to retrieve the profile information and emotion information of the specified user from the database. At this time, it integrates related data using a JOIN operation.

[1060] Input: User's personality information, values, interests, skill sets, and emotional information stored in a database.

[1061] Output: The extracted user profile information and emotion information are passed on to the next process.

[1062] Step 5:

[1063] Server: Generates a LifePath model based on the extracted profile and emotional information. Specifically, it uses a generative AI model (e.g., TensorFlow or PyTorch) to create a predictive model using deep learning and reinforcement learning algorithms. This model is designed to generate optimal scenarios based on the user's profile and emotional information.

[1064] Input: Extracted user profile information and sentiment information.

[1065] Output: The generated LifePath model is passed to the next step.

[1066] Step 6:

[1067] User: Selects the area in which they want support for decisions such as further education, employment, marriage, etc. Specifically, the user selects the area in which they need support through a dashboard or an interactive chatbot. The selected data is immediately sent to the server.

[1068] Input: User's choice of field such as further education, employment, marriage, etc.

[1069] Output: The selected field information is sent to the server.

[1070] Step 7:

[1071] Server: Integrates user profile information, sentiment information, and current information (such as labor market trends) to generate optimal recommendations. Specifically, the server retrieves real-time data (e.g., labor market trends and education market trends) from a database and generates multiple scenarios using a generative AI model and sentiment engine. These scenarios include multiple options for the field selected by the user.

[1072] Inputs: Generated LifePath model, user choices, real-time labor and education market data.

[1073] Output: The generated proposals are passed on to the next step.

[1074] Step 8:

[1075] Server: Adjusts suggestions based on the user's emotions recognized by the emotion engine. Specifically, the emotion engine analyzes the user's emotional state in real time and adjusts the suggestions to optimize them for the user's current emotional state. NLP (natural language processing) technology is heavily used in this process.

[1076] Input: Generated suggestions, user's real-time sentiment information.

[1077] Output: The adjusted proposal is sent to the device.

[1078] Step 9:

[1079] Terminal: Presents the generated suggestions to the user. Specifically, the terminal displays the suggestions received from the server in a visually easy-to-understand format, such as text, graphs, or lists. Front-end frameworks such as React or Vue.js are often used for this.

[1080] Input: adjusted proposal.

[1081] Output: The suggestions presented to the user.

[1082] (Application example 2)

[1083] 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."

[1084] In today's world, when users make a wide range of important decisions, such as furthering their education, finding employment, or getting married, they need support that takes into account their emotional state and personality, rather than simply providing information. However, conventional systems have difficulty responding to each user in detail and individually, and they do not adjust their suggestions in real time. This makes it difficult to provide optimal suggestions for users.

[1085] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting personality information, values, interests, and skill sets, means for transmitting the input information to the server, means for analyzing the received information and storing it in a database, means for extracting profile information from the database, means for generating a model using a generative AI model based on the extracted profile information, means for making suggestions to the user using the generated model, means for presenting the suggested information to the user, means for adjusting the content of the suggestions in real time in conjunction with a smart device, and means for overlaying the content of the suggestions in the user's field of view. This enables more accurate and efficient support by making personalized suggestions based on the user's emotional state and personality in real time.

[1086] "Personality information" is information that indicates the user's character traits and behavior patterns.

[1087] "Values" are information that indicates the beliefs, principles, and priorities that a user holds dear.

[1088] "Interests" is information about the subjects or fields in which a user is interested or concerned.

[1089] A "skill set" is information that indicates a collection of specific skills and abilities that a user possesses.

[1090] A "server" is a computer system that processes information submitted by users, stores it in a database, and generates recommendations.

[1091] A "database" is a structured storage scheme on a computer system for storing personality information, values, interests, skill sets, and other related information.

[1092] A "generative AI model" is an artificial intelligence algorithm that generates optimal suggestions based on user input information.

[1093] The "means of generation" is the process of using an AI model to create specific proposals based on specified profile information.

[1094] "Smart devices" are devices including wearable and mobile devices that allow users to visually obtain information and check proposal content in real time.

[1095] "Means for real-time adjustment" refers to a function that monitors the user's emotional state and profile information in real time and dynamically adjusts the content of suggestions.

[1096] "Overlay display" is a technology that displays information directly overlaid on the user's field of vision on the display of a smart device.

[1097] These definitions provide a clear understanding of the specific functions and features of the system provided by the invention.

[1098] The present invention relates to a system that provides a personalized shopping experience in a physical store based on a user's personality information, values, interests, skill set, and emotional information. The system collects information provided by the user and makes optimal suggestions using a generative AI model and an emotional engine. Specific embodiments for implementing the present invention are described below.

[1099] First, the user puts on a smart device (e.g., smart glasses). The smart glasses have a built-in camera and display, and are capable of recognizing the user's face and emotions. The user first registers their personality information, values, interests, skill set, and emotional information in the smart glasses application. This information is sent to the server, analyzed, and stored in a database.

[1100] The server analyzes the received information and stores it in a database. The database contains personality information, values, interests, skill sets, and emotional information. Based on this information, a generative AI model is used to extract user profile information and generate optimal recommendations. The generated model is created using a deep learning framework (e.g., TensorFlow or PyTorch).

[1101] The smart glasses' camera then performs real-time facial and emotional recognition of the user and sends the data to a server. The server then analyzes the data and adjusts the recommendations in real time based on a generative AI model and emotion engine. The recommendations are overlaid on the user's field of view, allowing the user to receive optimal shopping recommendations based on their emotional state and personality.

[1102] For example, if a user is stressed in a store, the server can analyze that information and provide information about stores that offer a relaxing environment or suggest products that have a relaxing effect. Also, if a user is looking for products specialized in a particular field, the server can provide specialized suggestions for that field. Below are some example prompts for inputting data into the generative AI model.

[1103] Specific examples

[1104] "User 12345's profile information states that their personality traits are INTJ, their values ​​emphasize creativity and innovation, and their interests are technology and fashion. Their skill set excels in programming and design. Their current emotional state is neutral, and their past emotional states are happy and neutral."

[1105] In this way, the system of the present invention can provide real-time personalized shopping suggestions that take into account the user's emotional state and personality information, thereby enabling the user to enjoy a more comfortable and satisfying shopping experience.

[1106] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1107] Step 1:

[1108] A user puts on a smart device, specifically smart glasses, and inputs their personality information, values, interests, skill sets, and emotional information, which are registered in advance in the smart glasses application. The input information is converted into JSON format and sent to the server.

[1109] Input: personality information, values, interests, skill sets, emotional information

[1110] Output: User data in JSON format

[1111] Step 2:

[1112] The device sends the information entered by the user to the server. Specifically, the smart glasses application uses a secure API to send the data to the server. The data is sent over an encrypted connection using protocols such as SSL / TLS.

[1113] Input: User data in JSON format

[1114] Output: User data sent to the server

[1115] Step 3:

[1116] The server parses the received information and stores it in a database. First, it validates the received data and then stores it in an SQL database (e.g., PostgreSQL). Validation involves checking whether all required fields are present, whether the format is correct, etc. Before saving it to the database, the information is properly formatted according to a specified schema.

[1117] Input: User data sent to the server

[1118] Output: User data stored in the database

[1119] Step 4:

[1120] The server extracts user profile information from the database. It uses an SQL query to retrieve the profile information of a specified user. This profile information includes personality information, values, interests, skill sets, and emotional information. The extracted information is used as input for the generative AI model.

[1121] Input: User data stored in the database

[1122] Output: Input data for the generative AI model

[1123] Step 5:

[1124] The server uses a generative AI model based on the extracted profile information to generate a model. Using a deep learning framework (e.g., TensorFlow or PyTorch), the extracted data is input and a model is generated that predicts the best suggestions for the user. This generative model is then used to generate personalized suggestions for each individual user.

[1125] Input: Input data for the generative AI model

[1126] Output: Generated proposed content model

[1127] Step 6:

[1128] The server uses the generated model to generate appropriate suggestions based on the user's profile information, taking into account the user's current emotional data. The generated suggestions are then tailored to best suit the user's needs.

[1129] Input: The generated proposal content model and the user's current emotion data.

[1130] Output: Generated proposals

[1131] Step 7:

[1132] The device displays the recommendations to the user in real time, overlaid on the smart glasses screen and displayed directly in the user's field of vision, allowing the user to check the optimal product recommendations and promotions at any time in the physical store.

[1133] Input: Generated proposal

[1134] Output: Proposal content overlaid on smart glasses

[1135] Step 8:

[1136] The smart device monitors the user's emotional state in real time and sends it to the server. The server analyzes the received emotional data and adjusts the suggestions to suit the user's current emotional state. Through this process, the suggestions are optimized in real time.

[1137] Input: Real-time user emotion data

[1138] Output: Adjusted proposal

[1139] 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.

[1140] 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.

[1141] 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.

[1142] [Third embodiment]

[1143] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1144] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[1145] 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).

[1146] 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.

[1147] 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.

[1148] 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).

[1149] 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. 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.

[1150] 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.

[1151] 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.

[1152] 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.

[1153] 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.

[1154] 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."

[1155] This invention is a system that supports important life decisions based on input information such as a user's personality, values, interests, and skill set. This system is a platform that stores the information provided by the user in a database and makes optimal suggestions using a generative AI model.

[1156] Program processing

[1157] Entering information

[1158] 1. User: Enter information about your personality, values, interests, skill set, etc.

[1159] Specifically, users use a web form or mobile app to answer questions or upload existing data (e.g., resume, personality test results).

[1160] Sending information

[1161] 1. Terminal: Sends the entered information to the server.

[1162] Specifically, the terminal converts the input data into JSON format and sends it to the server via a secure API.

[1163] Information analysis and storage

[1164] 1. Server: Analyzes the received information and stores it in a database.

[1165] The server validates the input data, formats it appropriately, and then stores it in the personality value database.

[1166] Extracting Profile Information

[1167] 1. Server: Extracts user profile information from the personality value database.

[1168] Specifically, the server uses an SQL query to retrieve profile information for the specified user from a database.

[1169] Generating the Model

[1170] 1. Server: Generates a LifePath model based on the extracted profile information.

[1171] Generative AI models are used to create predictive models based on user characteristics and past data, leveraging machine learning algorithms such as deep learning and reinforcement learning.

[1172] Selecting a field

[1173] 1. User: Select the area in which they would like support in making decisions such as further education, employment, and marriage.

[1174] Users are presented with options via a dashboard or chatbot.

[1175] Proposal Generation

[1176] 1. Server: Integrates user profile information with current information (e.g., labor market trends) to generate optimal recommendations.

[1177] Here, based on the user's profile information, the system considers multiple scenarios to determine optimal options for further education, career, partner, etc.

[1178] Presenting the proposal

[1179] 1. Terminal: Presents the generated proposals to the user.

[1180] The proposals are displayed in an easy-to-understand format, including text, graphs, and lists.

[1181] Specific examples

[1182] In the case of continuing education

[1183] 1. User: Enter your current grades and areas of interest.

[1184] 2. Terminal: Sends this information to the server.

[1185] 3. Server: Analyzes the user's information and stores it in a personality value database.

[1186] 4. Server: Extracts profile information from the database and generates a LifePath model using a generative AI model.

[1187] 5. User: Select further education.

[1188] 6. Server: Integrates user profiles with education market data to recommend the most suitable universities and courses.

[1189] 7. Terminal: Display a suggestion saying, "The best university for the user is Faculty B at University A. This faculty is a field that is expected to grow in the future."

[1190] In the case of employment

[1191] 1. User: Enter your skill set and desired location.

[1192] 2. Terminal: Sends this information to the server.

[1193] 3. Server: Analyzes the user's information and stores it in a personality value database.

[1194] 4. Server: Extracts profile information from the database and generates a LifePath model using a generative AI model.

[1195] 5. User: Selects employment.

[1196] 6. Server: Integrates user profiles with labor market data to suggest the most suitable jobs and companies.

[1197] 7. Terminal: Display a suggestion saying, "The best job for you is Position D at Company C. This company is in an industry that is expected to continue to grow."

[1198] In the case of marriage

[1199] 1. User: Enter your values ​​and interests.

[1200] 2. Terminal: Sends this information to the server.

[1201] 3. Server: Analyzes the user's information and stores it in a personality value database.

[1202] 4. Server: Extracts profile information from the database and generates a LifePath model using a generative AI model.

[1203] 5. User: Selects marriage.

[1204] 6. Server: Integrates user profiles and matching algorithms to suggest optimal partner candidates.

[1205] 7. Device: Display a suggestion saying, "The ideal partner candidate for you is E. He or she shares very similar values ​​and interests as you."

[1206] By implementing the system of the present invention, users can make important life decisions more reliably and efficiently, maximizing their value.

[1207] The processing flow will be explained below.

[1208] Program processing

[1209] Flow from inputting information to presenting proposals

[1210] Step 1:

[1211] Users: Enter information about their personality, values, interests, skill sets, etc.

[1212] What it does: Answer questions or upload your resume or personality assessment using a web form or mobile app.

[1213] Step 2:

[1214] Terminal: Sends the entered information to the server.

[1215] Specific operation: Converts input data into JSON format and sends it to the server via a secure API.

[1216] Step 3:

[1217] Server: Analyzes the received information and stores it in a database.

[1218] Specific operation: Validates input data and stores it in the personality value database in the appropriate format.

[1219] Step 4:

[1220] Server: Extracts user profile information from the personality value database.

[1221] What it does: Retrieves the profile information for the specified user from the database using an SQL query.

[1222] Step 5:

[1223] Server: Generates a LifePath model based on the extracted profile information.

[1224] What it does: Use generative AI models to create predictive models using deep learning and reinforcement learning algorithms.

[1225] Step 6:

[1226] User: Select the area in which they would like support in making decisions such as further education, employment, and marriage.

[1227] Specific Action: Select an option on a dashboard or conversational chatbot.

[1228] Step 7:

[1229] Terminal: Sends the user's selections to the server.

[1230] Specific operation: The selected fields are sent to the server via API.

[1231] Step 8:

[1232] Server: Integrates user profile information with current information (such as labor market trends) to generate optimal proposals.

[1233] How it works: Uses generative AI models and up-to-date market data to generate multiple scenarios and options.

[1234] Step 9:

[1235] Terminal: Presents the generated suggestions to the user.

[1236] Specific behavior: Display suggestions to the user in text, graph, and list format.

[1237] Specific examples of procedures for continuing education

[1238] Step 1:

[1239] User: Enter current grades and areas of interest.

[1240] What it does: Answer questions about grades and interests in a web form.

[1241] Step 2:

[1242] Terminal: Sends the entered information to the server.

[1243] What it does: Converts input data from a web form into JSON format and sends it through a secure API.

[1244] Step 3:

[1245] Server: Analyzes user information and stores it in a personality value database.

[1246] Specific operation: Validate and clean input data and save it in the database in the appropriate format.

[1247] Step 4:

[1248] Server: Extracts profile information from the database and generates a LifePath model using a generative AI model.

[1249] Specific operations: Retrieves information from a database using SQL queries and generates a predictive model using machine learning algorithms.

[1250] Step 5:

[1251] User: Select support for further education.

[1252] Specific action: Select "Further education" on the dashboard or chatbot.

[1253] Step 6:

[1254] Server: Integrates user profile information with education market data to recommend the most suitable universities and courses.

[1255] Specific operation: Based on the generative AI model and education market data, proposals are generated taking into account multiple scenarios.

[1256] Step 7:

[1257] Terminal: Display a suggestion saying, "The best university for the user is Faculty B at University A. This faculty is a field that is expected to grow in the future."

[1258] What it does: Display the suggestions to the user in text format.

[1259] Through these specific processing steps, the LifePath-AI system makes suggestions to the user to support optimal decision-making.

[1260] Example 1

[1261] 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."

[1262] Previous systems supporting important life decisions were unable to properly utilize users' personality information, values, interests, skill sets, etc., and had difficulty making proposals that took real-time market trends into account. As a result, they were unable to provide optimal proposals to users, and their decision-making support functions were inadequate.

[1263] 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.

[1264] In this invention, the server includes means for inputting personality information, values, interests, and skill sets, means for converting the input information into a data format and transmitting it, means for validating the received information and storing it in a database, means for extracting profile information from the database, means for generating a predictive model using a generative AI model based on the extracted profile information, means for generating proposals suitable for the user for each field, and means for visualizing the generated proposals and presenting them to the user, thereby making it possible to provide appropriate and dynamic proposals to the user.

[1265] "Personality information" is information about the user's character and behavioral characteristics.

[1266] "Values" is information about the beliefs and ethics that users consider important.

[1267] "Interests" is information about areas or activities that interest a user.

[1268] A "skill set" is a collection of skills, knowledge, and abilities possessed by a user.

[1269] "Transmitting means" refers to the method or process for sending the input information to the server.

[1270] "Validation" is the process of verifying received data to ensure it is accurate and complete.

[1271] A "database" is a system for storing and managing structured data.

[1272] "Profile information" is comprehensive information including a user's personal information, past data, and history.

[1273] A "generative AI model" is a model that uses machine learning algorithms to make predictions and classifications.

[1274] A "predictive model" is a model that predicts future trends and options based on specific data.

[1275] A "means for generating suggestions" is a method or process for creating optimal options or suggested actions based on user information.

[1276] "Visualization" refers to the presentation of data or information in an easy-to-understand format, such as a graph, text, or list.

[1277] "Real-time market trend data" means the latest data on current market conditions and trends.

[1278] This invention is a system that supports important life decisions based on input of a user's personality information, values, interests, skill set, etc. This system uses a generative AI model to make optimal recommendations taking into account real-time market trends.

[1279] Hardware and Software Configuration

[1280] Hardware

[1281] Device: The device through which a user enters information (e.g., PC, smartphone, tablet).

[1282] Server: A computer system for analyzing information, storing it in a database, and running generative AI models.

[1283] software

[1284] Web form or mobile application: An interface where users enter their personality information, values, interests, and skill sets.

[1285] Database: A relational database (e.g., MySQL, PostgreSQL) to store user information.

[1286] Generative AI models: Machine learning algorithms (e.g., TensorFlow, PyTorch) that generate predictive models based on user profile information.

[1287] Secure API: A communication protocol (e.g. HTTPS) for sending data from a device to a server.

[1288] Program processing overview

[1289] Entering information

[1290] Users use web forms or mobile apps to enter information such as personality, values, interests, and skill sets. For example, users may answer questions about their personality, work history, and areas of interest.

[1291] Sending information

[1292] The device converts the input information into JSON format and sends it to the server via a secure API endpoint, for example, by sending a POST request to " / api / userdata."

[1293] Information analysis and storage

[1294] The server validates the information it receives, formats it appropriately, and stores it in a relational database, for example by checking the format of the input data and adding default values ​​if any fields are missing.

[1295] Extracting Profile Information

[1296] The server extracts the user's profile information from the personality value database, specifically, by using an SQL query to retrieve data based on the specified user ID.

[1297] Generating the Model

[1298] The server runs a generative AI model based on the extracted profile information to generate an individual predictive model, for example, using TensorFlow to predict educational and career options suited to the user's attributes.

[1299] Proposal Generation

[1300] The server integrates user profile information with real-time market trend data to generate optimal suggestions for each field, for example, suggesting the most suitable career options for users while taking into account the latest trends in the labor market.

[1301] Presenting the proposal

[1302] The terminal visualizes the suggestions received from the server and presents them to the user in the form of text, graphs, or lists.

[1303] Specific examples

[1304] Prompt Sentence Examples

[1305] "I'm good at math and interested in biology. I'd like to pursue a career in biotechnology. Can you suggest some educational options for me?"

[1306] Processing flow

[1307] 1. User: Enter your field of interest and grade information

[1308] "I'm good at math and interested in biology."

[1309] 2. Terminal: Sends input information to the server in JSON format

[1310] Example: Send a POST request to " / api / userdata"

[1311] 3. Server: Validate the information and store it in the database

[1312] Example: Saving data using an SQL query

[1313] 4. Server: Extracts user profile information and runs generative AI models

[1314] Example: Generating a predictive model with TensorFlow

[1315] 5. Server: Proposing the best school to go to

[1316] For example: "Faculty B at University A is suitable."

[1317] 6. Terminal: Visualize the proposal and present it to the user

[1318] Example: "The best university for the user is University A, Department B."

[1319] In this way, by utilizing generative AI models based on detailed user information and making specific and optimal suggestions that take real-time market trends into account, the system can effectively support users in making important life decisions.

[1320] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1321] Step 1:

[1322] A user enters information such as personality information, values, interests, and skill sets using a web form or mobile app.

[1323] Input: Information that a user enters into a form (e.g., personality test results, resume, areas of interest).

[1324] Output: The input information is saved on the form.

[1325] Specific behavior: The user answers each question and uploads existing data if necessary.

[1326] Step 2:

[1327] The device converts the input information into JSON format and sends it to the server via a secure API.

[1328] Input: User information saved in the form.

[1329] Output: The data is converted to JSON format and sent through a secure API.

[1330] Specific operation: The terminal serializes the input data in JSON format and sends a POST request to the server's API endpoint using the HTTPS protocol.

[1331] Step 3:

[1332] The server validates the received information and stores it in the database.

[1333] Input: JSON data sent from the terminal.

[1334] Output: Accurate and complete data stored in the database.

[1335] Specific operation: The server validates the received data, checks for incompleteness or inconsistencies, and then saves it to the database using an SQL query.

[1336] Step 4:

[1337] The server extracts the user's profile information from the database.

[1338] Input: User data in the database.

[1339] Output: The extracted profile information.

[1340] What happens: The server executes an SQL query to retrieve the profile based on the specified user ID.

[1341] Step 5:

[1342] The server runs a generative AI model based on the extracted profile information to generate a predictive model.

[1343] Input: Extracted profile information.

[1344] Output: The generated predictive model.

[1345] Specific operation: The server uses a machine learning framework such as TensorFlow or PyTorch to train and generate a predictive model using the profile information as input data.

[1346] Step 6:

[1347] Users select the areas in which they would like support, such as further education, employment, or marriage.

[1348] Input: User's choice.

[1349] Output: Selected field information.

[1350] Specific behavior: The user uses the dashboard or chatbot to select the desired field from the options displayed.

[1351] Step 7:

[1352] The server integrates the user's profile information with real-time market trend data to generate optimal offers.

[1353] Inputs: Profile information, real-time market trend data.

[1354] Output: The generated proposals.

[1355] How it works: The server uses a reinforcement learning algorithm to combine profiles and market data to run simulations and generate the most appropriate proposals.

[1356] Step 8:

[1357] The terminal visualizes the generated suggestions and presents them to the user.

[1358] Input: Proposal data from the server.

[1359] Output: A visualized proposal.

[1360] Specific operation: The device displays the suggestions received from the server in graph, list, and text format, making it easy for the user to understand.

[1361] (Application example 1)

[1362] 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."

[1363] Conventional personalization systems have been unable to fully utilize a wide range of information, such as a user's personality, values, interests, and skill set, to make optimal product recommendations. It has also been difficult for users to efficiently find products that best fit their lifestyles and purchasing behavior. Furthermore, it has been difficult to reflect market trends in real time and keep recommendations up to date.

[1364] 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.

[1365] In this invention, the server includes means for inputting personality information, values, interests, and skill sets, means for transmitting the input information to the server, means for analyzing the received information and storing it in a database, means for extracting profile information from the database, means for creating a generative AI model based on the extracted profile information, means for suggesting optimal products to the user using the generated generative AI model, and means for presenting the suggested product information to the user. This allows the user to efficiently receive suggestions of products optimal for their lifestyle, support their purchasing behavior, and receive the latest suggestions that reflect market trends.

[1366] "Personality information" is information that indicates the user's character and behavioral characteristics.

[1367] "Values" is information that indicates the beliefs and ethics that a user considers important.

[1368] "Interests" is information that indicates the fields and activities in which a user is interested.

[1369] A "skill set" is information that indicates the skills and abilities that a user possesses.

[1370] The "server" is a computer system that receives, analyzes, and stores data and makes recommendations using generative AI models.

[1371] A "generative AI model" is an artificial intelligence model that is generated based on a user's profile information and is used to make optimal suggestions to the user.

[1372] "Profile information" is a collective term for data about a user, such as personality information, values, interests, and skill sets.

[1373] A "product" is a good or service related to a user's lifestyle or purchasing behavior.

[1374] "Input means" is an interface through which a user provides personality information, values, interests, and skill sets to the system.

[1375] The "transmitting means" is a communication function for transferring the input information to the server.

[1376] The "analyzing means" refers to the algorithms or programs that process the received information and store it in a database.

[1377] "Storing means" is a function for saving analyzed information in a database.

[1378] The "means for extracting" is a function for extracting specific profile information from the database.

[1379] The "means of creation" is the process of forming a generative AI model based on the extracted information.

[1380] The "means of suggestion" is a function that uses a generative AI model to determine the best product for the user and generate that information.

[1381] The "presentation means" is an interface for displaying the generated proposal content in an easy-to-understand manner to the user.

[1382] "Purchasing behavior" refers to the actions and decisions a user makes when purchasing a product or service.

[1383] "Market trends" refer to current supply, demand, trends, and tendencies in the commercial market.

[1384] The present invention relates to a system that inputs information such as a user's personality, values, interests, and skill set, and then uses this information to suggest products that are optimal for the user's lifestyle and purchasing behavior. This system is composed of multiple components, which are described in detail below.

[1385] Entering information

[1386] Users use an interface to input their personality information, values, interests, and skill sets. This interface can be provided through a smartphone application, a web form, or a dedicated terminal. Users can enter information by answering questions.

[1387] Sending information

[1388] The terminal transmits the input information to the server. To do this, the terminal converts the input data into JSON format and transmits it to the server via a secure API. HTTPS is used as the communication protocol.

[1389] Information analysis and storage

[1390] The server analyzes the received information, processes the data as needed, and stores it in a database. Python and Flask are used for analyzing the information, and PostgreSQL is used as the database. The server also validates the input data and formats it appropriately.

[1391] Extracting profile information and creating a generative AI model

[1392] The server extracts user profile information from a database using SQL queries. It then creates a generative AI model based on the extracted profile information. This process is performed using a deep learning framework (e.g., TensorFlow or PyTorch).

[1393] Product proposals

[1394] The server uses the generated generative AI model to suggest optimal products to users. The suggestions are updated taking into account real-time market trends, ensuring that users are always provided with the best products.

[1395] Presenting the proposal

[1396] The device then presents the suggested product information to the user, which is displayed in an easy-to-understand format, including text, graphs, and lists, allowing the user to select and purchase the products.

[1397] Specific examples

[1398] For example, let's say an "extroverted, outdoorsy user" enters the following information:

[1399] Personality information: Extrovert

[1400] Values: Adventure

[1401] Interests: Outdoors

[1402] Skill Set: Rock Climbing

[1403] The server uses this information to create a generative AI model, which, taking into account the latest market trends, suggests products such as:

[1404] Latest hiking shoes

[1405] waterproof jacket

[1406] Users receive these suggestions through the app, with example prompts such as:

[1407] "Recommend the best products for users based on their personality, values, interests, and skill sets."

[1408] By implementing the system of the present invention, users can efficiently find products that best suit their lifestyles, supporting their purchasing behavior. In addition, they can receive the latest proposals that reflect market trends in real time.

[1409] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1410] Step 1:

[1411] Users enter personality information, values, interests, and skill sets.

[1412] Enter information through an input device, use a smartphone application, or use a web form. At this stage, the input data is in text format. For example, the user enters information such as "extroverted," "adventure," "outdoors," and "rock climbing."

[1413] Step 2:

[1414] The terminal transmits the input information to the server.

[1415] The input data is converted to JSON format and sent to the server via a secure API using the HTTPS protocol. Here, the input is JSON format data and the output is the data sent to the server.

[1416] Step 3:

[1417] The server analyzes the received information and stores it in a database.

[1418] The server validates the JSON data received and checks items such as personality information, values, interests, and skill sets. After checking, it converts it into the required format for database storage and saves it in the PostgreSQL database. At this point, the input is the JSON data sent to the server, and the output is the user information stored in the database.

[1419] Step 4:

[1420] The server extracts the profile information from the database.

[1421] User information stored in the database is extracted using an SQL query. Specifically, the target user's profile is obtained based on information such as "extroverted," "adventure," "outdoors," and "rock climbing." The input is the extraction query from the database, and the output is the extracted profile information.

[1422] Step 5:

[1423] A generative AI model is created based on the profile information extracted by the server.

[1424] The extracted information is processed using a deep learning framework (e.g., TensorFlow or PyTorch) to create a generative AI model that suggests optimal products to users. For example, a model is generated that suggests appropriate outdoor gear for "extrovert," "adventure," "outdoors," and "rock climbing." The input is the extracted profile information, and the output is the generated generative AI model.

[1425] Step 6:

[1426] The server uses the generated generative AI model to suggest the best products to the user.

[1427] Using a generative AI model and incorporating additional data such as market trends and purchasing history, the system selects the best products for a user. Specifically, it generates suggestions such as "latest hiking shoes" or "waterproof jacket." The input is the generative AI model and additional data, and the output is a list of product suggestions.

[1428] Step 7:

[1429] The terminal presents the suggested product information to the user.

[1430] The product proposal information sent from the server is displayed on the user's device. The display format is text, graph, or list format, making it easy for users to understand. The input is the product proposal information sent from the server, and the output is the product information presented to the user.

[1431] 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.

[1432] The present invention is a system that supports important life decisions based on a user's personality, values, interests, skill set, and emotional information. This system is a platform that stores information provided by the user in a database and uses a generative AI model and an emotional engine to make optimal suggestions.

[1433] Program processing

[1434] Entering information

[1435] 1. Users: Enter information about their personality, values, interests, skill sets, and emotions.

[1436] Specifically, users answer questions and upload existing data (e.g., resumes, personality test results, emotion recognition data) using a web form or mobile app.

[1437] Sending information

[1438] 1. Terminal: Sends the entered information to the server.

[1439] Specifically, the terminal converts the input data into JSON format and sends it to the server via a secure API.

[1440] Information analysis and storage

[1441] 1. Server: Analyzes the received information and stores it in the personality value database and emotion database.

[1442] The server validates the input data, formats it appropriately, and then stores it in the database.

[1443] Extracting profile and emotion information

[1444] 1. Server: Extracts user profile information and emotion information from the personality value database and emotion database.

[1445] Specifically, the profile information and emotion information of a specified user are retrieved from a database using an SQL query.

[1446] Generating the Model

[1447] 1. Server: Generates a LifePath model based on the extracted profile information and emotion information.

[1448] Generative AI models are used to create predictive models using deep learning and reinforcement learning algorithms.

[1449] Selecting a field

[1450] 1. User: Select the area in which they would like support in making decisions such as further education, employment, and marriage.

[1451] Users are presented with options via a dashboard or conversational chatbot.

[1452] Proposal Generation

[1453] 1. Server: Integrates user profile information, sentiment information, and current information (such as labor market trends) to generate optimal recommendations.

[1454] Specific operation: Generate multiple scenarios and options based on generative AI models and emotion engines.

[1455] Adjusting the proposal

[1456] 1. Server: Adjusts suggestions based on the user's emotions recognized by the emotion engine.

[1457] Specific behavior: The suggestions are adjusted to adapt to the user's current emotional state.

[1458] Presenting the proposal

[1459] 1. Terminal: Presents the generated proposals to the user.

[1460] The proposals are displayed in an easy-to-understand format, including text, graphs, and lists.

[1461] Specific examples

[1462] In the case of continuing education

[1463] 1. User: Enter current grades, areas of interest, and recent emotional state (e.g., stress level).

[1464] 2. Terminal: Sends this information to the server.

[1465] 3. Server: Analyzes the received information and stores it in the personality value database and emotion database.

[1466] 4. Server: Extracts profile information and emotional information from the database and generates a LifePath model using a generative AI model.

[1467] 5. User: Select support for further education.

[1468] 6. Server: Integrates user profile information, education market data, and sentiment information to suggest the most suitable universities and courses.

[1469] 7. Server: Adjust the suggestions based on the user's recent stress level.

[1470] 8. Device: Display the following suggestion: "The best university for you is Department B at University A. This department is a field that is expected to grow in the future. Also, taking into account your current stress level, University A has a comprehensive stress management program."

[1471] In the case of employment

[1472] 1. User: Enters skill set, desired location, and emotional state (e.g., motivation level).

[1473] 2. Terminal: Sends this information to the server.

[1474] 3. Server: Analyzes user information and stores it in the personality value database and emotion database.

[1475] 4. Server: Extracts profile information and emotional information from the database and generates a LifePath model using a generative AI model.

[1476] 5. User: Select job-finding support.

[1477] 6. Server: Integrates user profile information, labor market data, and sentiment information to suggest optimal occupations and companies.

[1478] 7. Server: Adjust the suggestions based on the user's motivation level.

[1479] 8. Device: Display a suggestion: "The perfect job for you is Position D at Company C. This company is in a growth industry and offers a flexible work schedule that matches your current motivation level."

[1480] In the case of marriage

[1481] 1. User: Enters values, interests, and emotional state (e.g., happiness).

[1482] 2. Terminal: Sends this information to the server.

[1483] 3. Server: Analyzes user information and stores it in the personality value database and emotion database.

[1484] 4. Server: Extracts profile information and emotional information from the database and generates a LifePath model using a generative AI model.

[1485] 5. User: Select Marriage Support.

[1486] 6. Server: Integrates user profile information, matching algorithms, and emotional information to propose optimal partner candidates.

[1487] 7. Server: Adjust the suggestions based on the user's happiness.

[1488] 8. Device: Display a suggestion saying, "The ideal partner candidate for you is E. He or she has very similar values ​​and interests to you and has the potential to increase your current happiness."

[1489] By implementing the system of the present invention, users can make important life decisions more reliably and efficiently by taking emotional information into consideration, thereby maximizing the value of their decisions.

[1490] The processing flow will be explained below.

[1491] Program processing

[1492] Flow from inputting information to presenting proposals

[1493] Step 1:

[1494] Users: Enter information about their personality, values, interests, skill sets, and emotions.

[1495] What happens: Answer questions or upload resumes, personality assessments, or emotion recognition data using a web form or mobile app.

[1496] Step 2:

[1497] Terminal: Sends the entered information to the server.

[1498] Specific operation: Converts input data into JSON format and sends it to the server via a secure API.

[1499] Step 3:

[1500] Server: Analyzes the received information and stores it in a database.

[1501] Specific operation: Validate the input data and store it in the personality value database and emotion database in the appropriate format.

[1502] Step 4:

[1503] Server: Extracts user profile information and emotion information from the personality value database and emotion database.

[1504] Specific operation: Use SQL queries to retrieve the required profile and sentiment information from the respective databases.

[1505] Step 5:

[1506] Server: Generates a LifePath model based on the extracted profile information and emotion information.

[1507] Specific operation: Using a generative AI model, a predictive model is created using deep learning and reinforcement learning algorithms based on the acquired data.

[1508] Step 6:

[1509] User: Select the area in which they would like support in making decisions such as further education, employment, and marriage.

[1510] What it does: Select your preferred area of ​​support using the dashboard and conversational chatbot.

[1511] Step 7:

[1512] Terminal: Sends the user's selections to the server.

[1513] Specific operation: The selection is sent to the server via API.

[1514] Step 8:

[1515] Server: Integrates user profile information, sentiment information, and current information (such as labor market trends) to generate optimal proposals.

[1516] How it works: Using a generative AI model and sentiment engine, it combines user information with the latest market data to generate multiple scenarios and options.

[1517] Step 9:

[1518] Server: Adjusts the suggestions based on the user's emotions recognized by the emotion engine.

[1519] Specific behavior: The suggestions are adjusted to adapt to the user's current emotional state.

[1520] Step 10:

[1521] Terminal: Presents the generated suggestions to the user.

[1522] What it does: Display suggestions to the user in text, graph, and list format.

[1523] Example: Going on to higher education

[1524] Step 1:

[1525] User: Enter current grades, areas of interest, and recent emotional state (e.g., stress level).

[1526] What it does: Answer questions on a web form about grades, interests, and emotional state.

[1527] Step 2:

[1528] Terminal: Sends the entered information to the server.

[1529] What it does: Converts input data from a web form into JSON format and sends it through a secure API.

[1530] Step 3:

[1531] Server: Analyzes user information and stores it in a personality value database and an emotion database.

[1532] Specific operation: Validate and clean input data and save it in the database in the appropriate format.

[1533] Step 4:

[1534] Server: Extracts profile information and emotional information from the database and generates a LifePath model using a generative AI model.

[1535] Specific operations: Retrieves information from a database using SQL queries and generates a predictive model using machine learning algorithms.

[1536] Step 5:

[1537] User: Select support for further education.

[1538] Specific action: Select "Further education" on the dashboard or chatbot.

[1539] Step 6:

[1540] Server: Integrates user profile information, educational market data, and sentiment information to recommend the most suitable universities and courses.

[1541] How it works: It combines generative AI models with the latest education market data to generate optimal proposals by considering multiple scenarios.

[1542] Step 7:

[1543] Server: Adjusts the suggestions based on the user's recent stress level.

[1544] Specific operation: Based on data from the emotion engine, the suggestions are adjusted to correspond to the user's stress level.

[1545] Step 8:

[1546] Device: Display the following suggestion: "The best university for you is Department B at University A. This department is a field that is expected to grow in the future. Also, taking into account your current stress level, University A has a comprehensive stress management program."

[1547] What it does: Displays suggestions in text format and provides additional information on stress management.

[1548] Through these specific processing steps, the LifePath-AI system makes suggestions to the user to support optimal decision-making that takes emotional information into account.

[1549] Example 2

[1550] 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."

[1551] Conventional systems considered a user's personality, values, interests, and skill set, but did not consider emotional information. This made it difficult for them to generate optimal recommendations that took emotional factors into account when users made decisions. Furthermore, they lacked the ability to reflect labor market trends and other related data in real time, making it impossible to provide advice based on the latest information. To solve these problems, a system that can analyze multifaceted data, including emotional information, and make optimal recommendations is needed.

[1552] 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.

[1553] In this invention, the server includes means for inputting personality information, values, interests, skill sets, and emotional information, means for transmitting the input information to the server, means for analyzing the received information and storing it in a database, means for extracting profile information and emotional information from the database, means for generating a model based on the extracted profile information and emotional information, means for making suggestions to the user using the generated model, and means for presenting the suggested information to the user. This enables the generation of optimal suggestions that take into account the user's emotional information, and real-time advice based on the latest labor market and related data.

[1554] "Personality information" refers to information such as the user's character, tendencies, behavioral patterns, and values.

[1555] "Values" is information about the beliefs, ethics, and standards of judgment that a user considers important.

[1556] "Interests" are information about areas or activities that interest a user.

[1557] A "skill set" is information about the skills, abilities, and expertise that a user possesses.

[1558] "Emotion information" is information that indicates the user's current emotional state and changes in emotions.

[1559] An "input method" is a method by which a user provides information to a system, including web forms, mobile applications, etc.

[1560] "Transmission means" refers to a method for transmitting information entered by a user to a server, and includes transmitting data through a secure API.

[1561] The "analysis means" is a method by which the server analyzes the information it receives, formats it appropriately, and stores it in the database.

[1562] "Storage means" refers to a method for storing the analyzed information in a database.

[1563] An "extraction means" is a method for extracting specific information from a database, such as using an SQL query.

[1564] "Generation means" refers to a method for creating a model based on extracted information, using deep learning or reinforcement learning algorithms.

[1565] The "proposal means" is a method for making optimal proposals to the user using the generated model.

[1566] A "presentation medium" is a method for visually displaying the proposed information to the user, including text, graphs, lists, and the like.

[1567] "Labor Market Trends" is the latest information on the labor market, including current employment conditions, job information by industry, and wage trends.

[1568] "Relevant data" is additional information that may influence a user's decision, such as education market data and trend information.

[1569] MODE FOR CARRYING OUT THE INVENTION

[1570] This invention is a system that supports important life decisions based on a user's personality, values, interests, skill set, and emotional information. This system is a platform that stores information provided by the user in a database and uses a generative AI model and an emotional engine to make optimal recommendations.

[1571] Entering information

[1572] 1. User: Enters information about personality, values, interests, skill sets, and emotions. Specifically, users answer questions or upload existing data (e.g., resumes, personality test results, emotion recognition data) using web forms or mobile apps. This allows for multifaceted information to be collected from users.

[1573] Sending information

[1574] 2. Terminal: The input information is sent to the server. Specifically, the terminal converts the input data into JSON format and sends it to the server via a secure API (HTTPS protocol). This transmission process uses encrypted communication to ensure the security of the information.

[1575] Information analysis and storage

[1576] 3. Server: Analyzes the received information and stores it in the personality value database and emotion database. Specifically, the server validates the input data, formats it appropriately, and then stores it in the database. This process is performed using Python scripts and a database management system (e.g., MySQL).

[1577] Extracting profile and emotion information

[1578] 4. Server: Extracts user profile information and emotion information from the personality value database and emotion database. Specifically, it uses SQL queries to retrieve the profile information and emotion information of a specified user from the database. During this process, it integrates related data using JOIN operations.

[1579] Generating the Model

[1580] 5. Server: Generates a LifePath model based on the extracted profile and emotional information. Using a generative AI model (e.g., TensorFlow, PyTorch), it creates a predictive model using deep learning and reinforcement learning algorithms. This model generates scenarios that help users make optimal decisions based on their profile and emotional information.

[1581] Proposal generation and refinement

[1582] 6. Server: Integrates user profile information, emotional information, and current information (such as labor market trends) to generate optimal proposals. Furthermore, the proposals are tailored based on the user's emotions as recognized by the emotion engine. By combining the generative AI model with the emotion engine, proposals optimized for individual needs are created.

[1583] Presenting the proposal

[1584] 7. Terminal: The generated suggestions are presented to the user in a visually appealing format, such as text, graphs, or lists. This is often done using front-end frameworks like React or Vue.js.

[1585] Specific examples

[1586] In the case of continuing education

[1587] Users input their current grades, areas of interest, and recent emotional state (e.g., stress level). This information is sent to the server, which analyzes and stores it in a database. Profile information and emotional information are then extracted from the database, and a LifePath model is generated using a generative AI model. When users select support for further education, the system integrates the user's profile information, educational market data, and emotional information to recommend the most suitable universities and courses. Finally, the system adjusts the recommendations based on the user's recent stress state, and finally presents the recommendations to the user.

[1588] Prompt Sentence Examples

[1589] The following text is presented as an example of how to select the best university.

[1590] "The best university for you is Department B at University A. This department is in a promising field, and given your current stress level, University A also has a strong stress management program."

[1591] In the case of employment

[1592] Users input their skill set, desired work location, and emotional state (e.g., motivation level). This information is sent to the server, which analyzes and stores the received information in a database. Profile information and emotional information are then extracted from the database, and a LifePath model is generated using a generative AI model. When a user selects job-hunting support, the system integrates the user's profile information, labor market data, and emotional information to suggest suitable occupations and companies. Finally, the system adjusts the suggestions based on the user's motivation level, and finally presents them to the user.

[1593] Prompt Sentence Examples

[1594] The following text is presented as an example of how to select the most suitable job.

[1595] "The perfect fit for you is Position D at Company C. This company is in a growth industry and offers a flexible work schedule that matches your current motivation level."

[1596] In the case of marriage

[1597] Users input their values, interests, and emotional state (e.g., happiness level). This information is sent to the server, which analyzes and stores it in a database. Profile information and emotional information are then extracted from the database, and a LifePath model is generated using a generative AI model. If marriage support is selected, the system integrates the user's profile information, matching algorithm, and emotional information to suggest suitable partner candidates. Finally, the system adjusts the suggestions based on the user's happiness level, and finally presents them to the user.

[1598] Prompt Sentence Examples

[1599] The following text is presented as an example of how to select the best partner:

[1600] "Your ideal partner candidate is Person E. Their values ​​and interests are very similar to yours, and they have the potential to increase your current happiness."

[1601] By implementing the system of the present invention, users can make important life decisions more reliably and efficiently by taking emotional information into consideration, thereby maximizing the value of their decisions.

[1602] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1603] Step 1:

[1604] Users input information about their personality, values, interests, skill sets, and emotions. Specifically, users use web forms or mobile apps to answer questions and upload existing data (e.g., resumes, personality test results, emotion recognition data). This input data is sent to the system.

[1605] Input: User-entered personality information, values, interests, skill sets, and emotional information.

[1606] Output: The input data is passed to the terminal.

[1607] Step 2:

[1608] Terminal: Sends the input information to the server. Specifically, the terminal converts the input data into JSON format and sends it to the server using a secure API (HTTPS protocol). During this process, encrypted communication is used to ensure the security of the data.

[1609] Input: Personality information, values, interests, skill sets, and emotional information provided by the user.

[1610] Output: JSON formatted data is sent to the server.

[1611] Step 3:

[1612] Server: Analyzes the received information and stores it in the personality value database and emotion database. Specifically, the server first validates the data to ensure that the format and content are appropriate. It then stores the prepared data in the database. This process uses Python scripts and a database management system (e.g., MySQL).

[1613] Input: Personality information, values, interests, skill sets, and emotional information sent from the device in JSON format.

[1614] Output: Parsed and validated data is stored in a database.

[1615] Step 4:

[1616] Server: Extracts user profile information and emotion information from the personality value database and emotion database. Specifically, it issues an SQL query to retrieve the profile information and emotion information of the specified user from the database. At this time, it integrates related data using a JOIN operation.

[1617] Input: User's personality information, values, interests, skill sets, and emotional information stored in a database.

[1618] Output: The extracted user profile information and emotion information are passed on to the next process.

[1619] Step 5:

[1620] Server: Generates a LifePath model based on the extracted profile and emotional information. Specifically, it uses a generative AI model (e.g., TensorFlow or PyTorch) to create a predictive model using deep learning and reinforcement learning algorithms. This model is designed to generate optimal scenarios based on the user's profile and emotional information.

[1621] Input: Extracted user profile information and sentiment information.

[1622] Output: The generated LifePath model is passed to the next step.

[1623] Step 6:

[1624] User: Selects the area in which they want support for decisions such as further education, employment, marriage, etc. Specifically, the user selects the area in which they need support through a dashboard or an interactive chatbot. The selected data is immediately sent to the server.

[1625] Input: User's choice of field such as further education, employment, marriage, etc.

[1626] Output: The selected field information is sent to the server.

[1627] Step 7:

[1628] Server: Integrates user profile information, sentiment information, and current information (such as labor market trends) to generate optimal recommendations. Specifically, the server retrieves real-time data (e.g., labor market trends and education market trends) from a database and generates multiple scenarios using a generative AI model and sentiment engine. These scenarios include multiple options for the field selected by the user.

[1629] Inputs: Generated LifePath model, user choices, real-time labor and education market data.

[1630] Output: The generated proposals are passed on to the next step.

[1631] Step 8:

[1632] Server: Adjusts suggestions based on the user's emotions recognized by the emotion engine. Specifically, the emotion engine analyzes the user's emotional state in real time and adjusts the suggestions to optimize them for the user's current emotional state. NLP (natural language processing) technology is heavily used in this process.

[1633] Input: Generated suggestions, user's real-time sentiment information.

[1634] Output: The adjusted proposal is sent to the device.

[1635] Step 9:

[1636] Terminal: Presents the generated suggestions to the user. Specifically, the terminal displays the suggestions received from the server in a visually easy-to-understand format, such as text, graphs, or lists. Front-end frameworks such as React or Vue.js are often used for this.

[1637] Input: adjusted proposal.

[1638] Output: The suggestions presented to the user.

[1639] (Application example 2)

[1640] 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."

[1641] In today's world, when users make a wide range of important decisions, such as furthering their education, finding employment, or getting married, they need support that takes into account their emotional state and personality, rather than simply providing information. However, conventional systems have difficulty responding to each user in detail and individually, and they do not adjust their suggestions in real time. This makes it difficult to provide optimal suggestions for users.

[1642] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting personality information, values, interests, and skill sets, means for transmitting the input information to the server, means for analyzing the received information and storing it in a database, means for extracting profile information from the database, means for generating a model using a generative AI model based on the extracted profile information, means for making suggestions to the user using the generated model, means for presenting the suggested information to the user, means for adjusting the content of the suggestions in real time in conjunction with a smart device, and means for overlaying the content of the suggestions in the user's field of view. This enables more accurate and efficient support by making personalized suggestions based on the user's emotional state and personality in real time.

[1643] "Personality information" is information that indicates the user's character traits and behavior patterns.

[1644] "Values" are information that indicates the beliefs, principles, and priorities that a user holds dear.

[1645] "Interests" is information about the subjects or fields in which a user is interested or concerned.

[1646] A "skill set" is information that indicates a collection of specific skills and abilities that a user possesses.

[1647] A "server" is a computer system that processes information submitted by users, stores it in a database, and generates recommendations.

[1648] A "database" is a structured storage scheme on a computer system for storing personality information, values, interests, skill sets, and other related information.

[1649] A "generative AI model" is an artificial intelligence algorithm that generates optimal suggestions based on user input information.

[1650] The "means of generation" is the process of using an AI model to create specific proposals based on specified profile information.

[1651] "Smart devices" are devices including wearable and mobile devices that allow users to visually obtain information and check proposal content in real time.

[1652] "Means for real-time adjustment" refers to a function that monitors the user's emotional state and profile information in real time and dynamically adjusts the content of suggestions.

[1653] "Overlay display" is a technology that displays information directly overlaid on the user's field of vision on the display of a smart device.

[1654] These definitions provide a clear understanding of the specific functions and features of the system provided by the invention.

[1655] The present invention relates to a system that provides a personalized shopping experience in a physical store based on a user's personality information, values, interests, skill set, and emotional information. The system collects information provided by the user and makes optimal suggestions using a generative AI model and an emotional engine. Specific embodiments for implementing the present invention are described below.

[1656] First, the user puts on a smart device (e.g., smart glasses). The smart glasses have a built-in camera and display, and are capable of recognizing the user's face and emotions. The user first registers their personality information, values, interests, skill set, and emotional information in the smart glasses application. This information is sent to the server, analyzed, and stored in a database.

[1657] The server analyzes the received information and stores it in a database. The database contains personality information, values, interests, skill sets, and emotional information. Based on this information, a generative AI model is used to extract user profile information and generate optimal recommendations. The generated model is created using a deep learning framework (e.g., TensorFlow or PyTorch).

[1658] The smart glasses' camera then performs real-time facial and emotional recognition of the user and sends the data to a server. The server then analyzes the data and adjusts the recommendations in real time based on a generative AI model and emotion engine. The recommendations are overlaid on the user's field of view, allowing the user to receive optimal shopping recommendations based on their emotional state and personality.

[1659] For example, if a user is stressed in a store, the server can analyze that information and provide information about stores that offer a relaxing environment or suggest products that have a relaxing effect. Also, if a user is looking for products specialized in a particular field, the server can provide specialized suggestions for that field. Below are some example prompts for inputting data into the generative AI model.

[1660] Specific examples

[1661] "User 12345's profile information states that their personality traits are INTJ, their values ​​emphasize creativity and innovation, and their interests are technology and fashion. Their skill set excels in programming and design. Their current emotional state is neutral, and their past emotional states are happy and neutral."

[1662] In this way, the system of the present invention can provide real-time personalized shopping suggestions that take into account the user's emotional state and personality information, thereby enabling the user to enjoy a more comfortable and satisfying shopping experience.

[1663] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1664] Step 1:

[1665] A user puts on a smart device, specifically smart glasses, and inputs their personality information, values, interests, skill sets, and emotional information, which are registered in advance in the smart glasses application. The input information is converted into JSON format and sent to the server.

[1666] Input: personality information, values, interests, skill sets, emotional information

[1667] Output: User data in JSON format

[1668] Step 2:

[1669] The device sends the information entered by the user to the server. Specifically, the smart glasses application uses a secure API to send the data to the server. The data is sent over an encrypted connection using protocols such as SSL / TLS.

[1670] Input: User data in JSON format

[1671] Output: User data sent to the server

[1672] Step 3:

[1673] The server parses the received information and stores it in a database. First, it validates the received data and then stores it in an SQL database (e.g., PostgreSQL). Validation involves checking whether all required fields are present, whether the format is correct, etc. Before saving it to the database, the information is properly formatted according to a specified schema.

[1674] Input: User data sent to the server

[1675] Output: User data stored in the database

[1676] Step 4:

[1677] The server extracts user profile information from the database. It uses an SQL query to retrieve the profile information of a specified user. This profile information includes personality information, values, interests, skill sets, and emotional information. The extracted information is used as input for the generative AI model.

[1678] Input: User data stored in the database

[1679] Output: Input data for the generative AI model

[1680] Step 5:

[1681] The server uses a generative AI model based on the extracted profile information to generate a model. Using a deep learning framework (e.g., TensorFlow or PyTorch), the extracted data is input and a model is generated that predicts the best suggestions for the user. This generative model is then used to generate personalized suggestions for each individual user.

[1682] Input: Input data for the generative AI model

[1683] Output: Generated proposed content model

[1684] Step 6:

[1685] The server uses the generated model to generate appropriate suggestions based on the user's profile information, taking into account the user's current emotional data. The generated suggestions are then tailored to best suit the user's needs.

[1686] Input: The generated proposal content model and the user's current emotion data.

[1687] Output: Generated proposals

[1688] Step 7:

[1689] The device displays the recommendations to the user in real time, overlaid on the smart glasses screen and displayed directly in the user's field of vision, allowing the user to check the optimal product recommendations and promotions at any time in the physical store.

[1690] Input: Generated proposal

[1691] Output: Proposal content overlaid on smart glasses

[1692] Step 8:

[1693] The smart device monitors the user's emotional state in real time and transmits the data to the server. The server analyzes the received emotional data and adjusts the suggestions to suit the user's current emotional state. This process optimizes the suggestions in real time.

[1694] Input: Real-time user emotion data

[1695] Output: Adjusted proposal

[1696] 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.

[1697] 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.

[1698] 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.

[1699] [Fourth embodiment]

[1700] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1701] 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.

[1702] 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).

[1703] 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.

[1704] 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.

[1705] 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).

[1706] 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. 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.

[1707] 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.

[1708] 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.

[1709] 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.

[1710] 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.

[1711] 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.

[1712] 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."

[1713] This invention is a system that supports important life decisions based on input information such as a user's personality, values, interests, and skill set. This system is a platform that stores the information provided by the user in a database and makes optimal suggestions using a generative AI model.

[1714] Program processing

[1715] Entering information

[1716] 1. User: Enter information about your personality, values, interests, skill set, etc.

[1717] Specifically, users use a web form or mobile app to answer questions or upload existing data (e.g., resume, personality test results).

[1718] Sending information

[1719] 1. Terminal: Sends the entered information to the server.

[1720] Specifically, the terminal converts the input data into JSON format and sends it to the server via a secure API.

[1721] Information analysis and storage

[1722] 1. Server: Analyzes the received information and stores it in a database.

[1723] The server validates the input data, formats it appropriately, and then stores it in the personality value database.

[1724] Extracting Profile Information

[1725] 1. Server: Extracts user profile information from the personality value database.

[1726] Specifically, the server uses an SQL query to retrieve profile information for the specified user from a database.

[1727] Generating the Model

[1728] 1. Server: Generates a LifePath model based on the extracted profile information.

[1729] Generative AI models are used to create predictive models based on user characteristics and past data, leveraging machine learning algorithms such as deep learning and reinforcement learning.

[1730] Selecting a field

[1731] 1. User: Select the area in which they would like support in making decisions such as further education, employment, and marriage.

[1732] Users are presented with options via a dashboard or chatbot.

[1733] Proposal Generation

[1734] 1. Server: Integrates user profile information with current information (e.g., labor market trends) to generate optimal recommendations.

[1735] Here, based on the user's profile information, the system considers multiple scenarios to determine optimal options for further education, career, partner, etc.

[1736] Presenting the proposal

[1737] 1. Terminal: Presents the generated proposals to the user.

[1738] The proposals are displayed in an easy-to-understand format, including text, graphs, and lists.

[1739] Specific examples

[1740] In the case of continuing education

[1741] 1. User: Enter your current grades and areas of interest.

[1742] 2. Terminal: Sends this information to the server.

[1743] 3. Server: Analyzes the user's information and stores it in a personality value database.

[1744] 4. Server: Extracts profile information from the database and generates a LifePath model using a generative AI model.

[1745] 5. User: Select further education.

[1746] 6. Server: Integrates user profiles with education market data to recommend the most suitable universities and courses.

[1747] 7. Terminal: Display a suggestion saying, "The best university for the user is Faculty B at University A. This faculty is a field that is expected to grow in the future."

[1748] In the case of employment

[1749] 1. User: Enter your skill set and desired location.

[1750] 2. Terminal: Sends this information to the server.

[1751] 3. Server: Analyzes the user's information and stores it in a personality value database.

[1752] 4. Server: Extracts profile information from the database and generates a LifePath model using a generative AI model.

[1753] 5. User: Selects employment.

[1754] 6. Server: Integrates user profiles with labor market data to suggest the most suitable jobs and companies.

[1755] 7. Terminal: Display a suggestion saying, "The best job for you is Position D at Company C. This company is in an industry that is expected to continue to grow."

[1756] In the case of marriage

[1757] 1. User: Enter your values ​​and interests.

[1758] 2. Terminal: Sends this information to the server.

[1759] 3. Server: Analyzes the user's information and stores it in a personality value database.

[1760] 4. Server: Extracts profile information from the database and generates a LifePath model using a generative AI model.

[1761] 5. User: Selects marriage.

[1762] 6. Server: Integrates user profiles and matching algorithms to suggest optimal partner candidates.

[1763] 7. Device: Display a suggestion saying, "The ideal partner candidate for you is E. He / she has very similar values ​​and interests to you."

[1764] By implementing the system of the present invention, users can make important life decisions more reliably and efficiently, maximizing their value.

[1765] The processing flow will be explained below.

[1766] Program processing

[1767] Flow from inputting information to presenting proposals

[1768] Step 1:

[1769] Users: Enter information about their personality, values, interests, skill sets, etc.

[1770] What it does: Answer questions or upload your resume or personality assessment using a web form or mobile app.

[1771] Step 2:

[1772] Terminal: Sends the entered information to the server.

[1773] Specific operation: Converts input data into JSON format and sends it to the server via a secure API.

[1774] Step 3:

[1775] Server: Analyzes the received information and stores it in a database.

[1776] Specific operation: Validates input data and stores it in the personality value database in the appropriate format.

[1777] Step 4:

[1778] Server: Extracts user profile information from the personality value database.

[1779] What it does: Retrieves the profile information for the specified user from the database using an SQL query.

[1780] Step 5:

[1781] Server: Generates a LifePath model based on the extracted profile information.

[1782] What it does: Use generative AI models to create predictive models using deep learning and reinforcement learning algorithms.

[1783] Step 6:

[1784] User: Select the area in which they would like support in making decisions such as further education, employment, and marriage.

[1785] Specific Action: Select an option on a dashboard or conversational chatbot.

[1786] Step 7:

[1787] Terminal: Sends the user's selections to the server.

[1788] Specific operation: The selected fields are sent to the server via API.

[1789] Step 8:

[1790] Server: Integrates user profile information with current information (such as labor market trends) to generate optimal proposals.

[1791] How it works: Uses generative AI models and up-to-date market data to generate multiple scenarios and options.

[1792] Step 9:

[1793] Terminal: Presents the generated suggestions to the user.

[1794] Specific behavior: Display suggestions to the user in text, graph, and list format.

[1795] Specific examples of procedures for continuing education

[1796] Step 1:

[1797] User: Enter current grades and areas of interest.

[1798] What it does: Answer questions about grades and interests in a web form.

[1799] Step 2:

[1800] Terminal: Sends the entered information to the server.

[1801] What it does: Converts input data from a web form into JSON format and sends it through a secure API.

[1802] Step 3:

[1803] Server: Analyzes user information and stores it in a personality value database.

[1804] Specific operation: Validate and clean input data and save it in the database in the appropriate format.

[1805] Step 4:

[1806] Server: Extracts profile information from the database and generates a LifePath model using a generative AI model.

[1807] Specific operations: Retrieves information from a database using SQL queries and generates a predictive model using machine learning algorithms.

[1808] Step 5:

[1809] User: Select support for further education.

[1810] Specific action: Select "Further education" on the dashboard or chatbot.

[1811] Step 6:

[1812] Server: Integrates user profile information with education market data to recommend the most suitable universities and courses.

[1813] Specific operation: Based on the generative AI model and education market data, proposals are generated taking into account multiple scenarios.

[1814] Step 7:

[1815] Terminal: Display a suggestion saying, "The best university for the user is Faculty B at University A. This faculty is a field that is expected to grow in the future."

[1816] What it does: Display the suggestions to the user in text format.

[1817] Through these specific processing steps, the LifePath-AI system makes suggestions to the user to support optimal decision-making.

[1818] Example 1

[1819] 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."

[1820] Previous systems supporting important life decisions were unable to properly utilize users' personality information, values, interests, skill sets, etc., and had difficulty making proposals that took real-time market trends into account. As a result, they were unable to provide optimal proposals to users, and their decision-making support functions were inadequate.

[1821] 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.

[1822] In this invention, the server includes means for inputting personality information, values, interests, and skill sets, means for converting the input information into a data format and transmitting it, means for validating the received information and storing it in a database, means for extracting profile information from the database, means for generating a predictive model using a generative AI model based on the extracted profile information, means for generating proposals suitable for the user for each field, and means for visualizing the generated proposals and presenting them to the user, thereby making it possible to provide appropriate and dynamic proposals to the user.

[1823] "Personality information" is information about the user's character and behavioral characteristics.

[1824] "Values" is information about the beliefs and ethics that users consider important.

[1825] "Interests" is information about areas or activities that interest a user.

[1826] A "skill set" is a collection of skills, knowledge, and abilities possessed by a user.

[1827] "Transmitting means" refers to the method or process for sending the input information to the server.

[1828] "Validation" is the process of verifying received data to ensure it is accurate and complete.

[1829] A "database" is a system for storing and managing structured data.

[1830] "Profile information" is comprehensive information including a user's personal information, past data, and history.

[1831] A "generative AI model" is a model that uses machine learning algorithms to make predictions and classifications.

[1832] A "predictive model" is a model that predicts future trends and options based on specific data.

[1833] A "means for generating suggestions" is a method or process for creating optimal options or suggested actions based on user information.

[1834] "Visualization" refers to the presentation of data or information in an easy-to-understand format, such as a graph, text, or list.

[1835] "Real-time market trend data" means the latest data on current market conditions and trends.

[1836] This invention is a system that supports important life decisions based on input of a user's personality information, values, interests, skill set, etc. This system uses a generative AI model to make optimal recommendations taking into account real-time market trends.

[1837] Hardware and Software Configuration

[1838] Hardware

[1839] Device: The device through which a user enters information (e.g., PC, smartphone, tablet).

[1840] Server: A computer system for analyzing information, storing it in a database, and running generative AI models.

[1841] software

[1842] Web form or mobile application: An interface where users enter their personality information, values, interests, and skill sets.

[1843] Database: A relational database (e.g., MySQL, PostgreSQL) to store user information.

[1844] Generative AI models: Machine learning algorithms (e.g., TensorFlow, PyTorch) that generate predictive models based on user profile information.

[1845] Secure API: A communication protocol (e.g. HTTPS) for sending data from a device to a server.

[1846] Program processing overview

[1847] Entering information

[1848] Users use web forms or mobile apps to enter information such as personality, values, interests, and skill sets. For example, users may answer questions about their personality, work history, and areas of interest.

[1849] Sending information

[1850] The device converts the input information into JSON format and sends it to the server via a secure API endpoint, for example, by sending a POST request to " / api / userdata."

[1851] Information analysis and storage

[1852] The server validates the information it receives, formats it appropriately, and stores it in a relational database, for example by checking the format of the input data and adding default values ​​if any fields are missing.

[1853] Extracting Profile Information

[1854] The server extracts the user's profile information from the personality value database, specifically, by using an SQL query to retrieve data based on the specified user ID.

[1855] Generating the Model

[1856] The server runs a generative AI model based on the extracted profile information to generate an individual predictive model, for example, using TensorFlow to predict educational and career options suited to the user's attributes.

[1857] Proposal Generation

[1858] The server integrates user profile information with real-time market trend data to generate optimal suggestions for each field, for example, suggesting the most suitable career options for users while taking into account the latest trends in the labor market.

[1859] Presenting the proposal

[1860] The terminal visualizes the suggestions received from the server and presents them to the user in the form of text, graphs, or lists.

[1861] Specific examples

[1862] Prompt Sentence Examples

[1863] "I'm good at math and interested in biology. I'd like to pursue a career in biotechnology. Can you suggest some educational options for me?"

[1864] Processing flow

[1865] 1. User: Enter your field of interest and grade information

[1866] "I'm good at math and interested in biology."

[1867] 2. Terminal: Sends input information to the server in JSON format

[1868] Example: Send a POST request to " / api / userdata"

[1869] 3. Server: Validate the information and store it in the database

[1870] Example: Saving data using an SQL query

[1871] 4. Server: Extracts user profile information and runs generative AI models

[1872] Example: Generating a predictive model with TensorFlow

[1873] 5. Server: Proposing the best school to go to

[1874] For example: "Faculty B at University A is suitable."

[1875] 6. Terminal: Visualize the proposal and present it to the user

[1876] Example: "The best university for the user is University A, Department B."

[1877] In this way, by utilizing generative AI models based on detailed user information and making specific and optimal suggestions that take real-time market trends into account, the system can effectively support users in making important life decisions.

[1878] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1879] Step 1:

[1880] A user enters information such as personality information, values, interests, and skill sets using a web form or mobile app.

[1881] Input: Information that a user enters into a form (e.g., personality test results, resume, areas of interest).

[1882] Output: The input information is saved on the form.

[1883] Specific behavior: The user answers each question and uploads existing data if necessary.

[1884] Step 2:

[1885] The device converts the input information into JSON format and sends it to the server via a secure API.

[1886] Input: User information saved in the form.

[1887] Output: The data is converted to JSON format and sent through a secure API.

[1888] Specific operation: The terminal serializes the input data in JSON format and sends a POST request to the server's API endpoint using the HTTPS protocol.

[1889] Step 3:

[1890] The server validates the received information and stores it in the database.

[1891] Input: JSON data sent from the terminal.

[1892] Output: Accurate and complete data stored in the database.

[1893] Specific operation: The server validates the received data, checks for incompleteness or inconsistencies, and then saves it to the database using an SQL query.

[1894] Step 4:

[1895] The server extracts the user's profile information from the database.

[1896] Input: User data in the database.

[1897] Output: The extracted profile information.

[1898] What happens: The server executes an SQL query to retrieve the profile based on the specified user ID.

[1899] Step 5:

[1900] The server runs a generative AI model based on the extracted profile information to generate a predictive model.

[1901] Input: Extracted profile information.

[1902] Output: The generated predictive model.

[1903] Specific operation: The server uses a machine learning framework such as TensorFlow or PyTorch to train and generate a predictive model using the profile information as input data.

[1904] Step 6:

[1905] Users select the areas in which they would like support, such as further education, employment, or marriage.

[1906] Input: User's choice.

[1907] Output: Selected field information.

[1908] Specific behavior: The user uses the dashboard or chatbot to select the desired field from the options displayed.

[1909] Step 7:

[1910] The server integrates the user's profile information with real-time market trend data to generate optimal offers.

[1911] Inputs: Profile information, real-time market trend data.

[1912] Output: The generated proposals.

[1913] How it works: The server uses a reinforcement learning algorithm to combine profiles and market data to run simulations and generate the most appropriate proposals.

[1914] Step 8:

[1915] The terminal visualizes the generated suggestions and presents them to the user.

[1916] Input: Proposal data from the server.

[1917] Output: A visualized proposal.

[1918] Specific operation: The device displays the suggestions received from the server in graph, list, and text format, making it easy for the user to understand.

[1919] (Application example 1)

[1920] 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."

[1921] Conventional personalization systems have been unable to fully utilize a wide range of information, such as a user's personality, values, interests, and skill set, to make optimal product recommendations. It has also been difficult for users to efficiently find products that best fit their lifestyles and purchasing behavior. Furthermore, it has been difficult to reflect market trends in real time and keep recommendations up to date.

[1922] 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.

[1923] In this invention, the server includes means for inputting personality information, values, interests, and skill sets, means for transmitting the input information to the server, means for analyzing the received information and storing it in a database, means for extracting profile information from the database, means for creating a generative AI model based on the extracted profile information, means for suggesting optimal products to the user using the generated generative AI model, and means for presenting the suggested product information to the user. This allows the user to efficiently receive suggestions of products optimal for their lifestyle, support their purchasing behavior, and receive the latest suggestions that reflect market trends.

[1924] "Personality information" is information that indicates the user's character and behavioral characteristics.

[1925] "Values" is information that indicates the beliefs and ethics that a user considers important.

[1926] "Interests" is information that indicates the fields and activities in which a user is interested.

[1927] A "skill set" is information that indicates the skills and abilities that a user possesses.

[1928] The "server" is a computer system that receives, analyzes, and stores data and makes recommendations using generative AI models.

[1929] A "generative AI model" is an artificial intelligence model that is generated based on a user's profile information and is used to make optimal suggestions to the user.

[1930] "Profile information" is a collective term for data about a user, such as personality information, values, interests, and skill sets.

[1931] A "product" is a good or service related to a user's lifestyle or purchasing behavior.

[1932] "Input means" is an interface through which a user provides personality information, values, interests, and skill sets to the system.

[1933] The "transmitting means" is a communication function for transferring the input information to the server.

[1934] The "analyzing means" refers to the algorithms or programs that process the received information and store it in a database.

[1935] "Storing means" is a function for saving analyzed information in a database.

[1936] The "means for extracting" is a function for extracting specific profile information from the database.

[1937] The "means of creation" is the process of forming a generative AI model based on the extracted information.

[1938] The "means of suggestion" is a function that uses a generative AI model to determine the best product for the user and generate that information.

[1939] The "presentation means" is an interface for displaying the generated proposal content in an easy-to-understand manner to the user.

[1940] "Purchasing behavior" refers to the actions and decisions a user makes when purchasing a product or service.

[1941] "Market trends" refer to current supply, demand, trends, and tendencies in the commercial market.

[1942] The present invention relates to a system that inputs information such as a user's personality, values, interests, and skill set, and then uses this information to suggest products that are optimal for the user's lifestyle and purchasing behavior. This system is composed of multiple components, which are described in detail below.

[1943] Entering information

[1944] Users use an interface to input their personality information, values, interests, and skill sets. This interface can be provided through a smartphone application, a web form, or a dedicated terminal. Users can enter information by answering questions.

[1945] Sending information

[1946] The terminal transmits the input information to the server. To do this, the terminal converts the input data into JSON format and transmits it to the server via a secure API. HTTPS is used as the communication protocol.

[1947] Information analysis and storage

[1948] The server analyzes the received information, processes the data as needed, and stores it in a database. Python and Flask are used for analyzing the information, and PostgreSQL is used as the database. The server also validates the input data and formats it appropriately.

[1949] Extracting profile information and creating a generative AI model

[1950] The server extracts user profile information from a database using SQL queries. It then creates a generative AI model based on the extracted profile information. This process is performed using a deep learning framework (e.g., TensorFlow or PyTorch).

[1951] Product proposals

[1952] The server uses the generated generative AI model to suggest optimal products to users. The suggestions are updated taking into account real-time market trends, ensuring that users are always provided with the best products.

[1953] Presenting the proposal

[1954] The device then presents the suggested product information to the user, which is displayed in an easy-to-understand format, including text, graphs, and lists, allowing the user to select and purchase the products.

[1955] Specific examples

[1956] For example, let's say an "extroverted, outdoorsy user" enters the following information:

[1957] Personality information: Extrovert

[1958] Values: Adventure

[1959] Interests: Outdoors

[1960] Skill Set: Rock Climbing

[1961] The server uses this information to create a generative AI model, which, taking into account the latest market trends, suggests products such as:

[1962] Latest hiking shoes

[1963] waterproof jacket

[1964] Users receive these suggestions through the app, with example prompts such as:

[1965] "Recommend the best products for users based on their personality, values, interests, and skill sets."

[1966] By implementing the system of the present invention, users can efficiently find products that best suit their lifestyles, supporting their purchasing behavior. In addition, they can receive the latest proposals that reflect market trends in real time.

[1967] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1968] Step 1:

[1969] Users enter personality information, values, interests, and skill sets.

[1970] Enter information through an input device, use a smartphone application, or use a web form. At this stage, the input data is in text format. For example, the user enters information such as "extroverted," "adventure," "outdoors," and "rock climbing."

[1971] Step 2:

[1972] The terminal transmits the input information to the server.

[1973] The input data is converted to JSON format and sent to the server via a secure API using the HTTPS protocol. Here, the input is JSON format data and the output is the data sent to the server.

[1974] Step 3:

[1975] The server analyzes the received information and stores it in a database.

[1976] The server validates the JSON data received and checks items such as personality information, values, interests, and skill sets. After checking, it converts it into the required format for database storage and saves it in the PostgreSQL database. At this point, the input is the JSON data sent to the server, and the output is the user information stored in the database.

[1977] Step 4:

[1978] The server extracts the profile information from the database.

[1979] User information stored in the database is extracted using an SQL query. Specifically, the target user's profile is obtained based on information such as "extroverted," "adventure," "outdoors," and "rock climbing." The input is the extraction query from the database, and the output is the extracted profile information.

[1980] Step 5:

[1981] A generative AI model is created based on the profile information extracted by the server.

[1982] The extracted information is processed using a deep learning framework (e.g., TensorFlow or PyTorch) to create a generative AI model that suggests optimal products to users. For example, a model is generated that suggests appropriate outdoor gear for "extrovert," "adventure," "outdoors," and "rock climbing." The input is the extracted profile information, and the output is the generated generative AI model.

[1983] Step 6:

[1984] The server uses the generated generative AI model to suggest the best products to the user.

[1985] Using a generative AI model and incorporating additional data such as market trends and purchasing history, the system selects the best products for a user. Specifically, it generates suggestions such as "latest hiking shoes" or "waterproof jacket." The input is the generative AI model and additional data, and the output is a list of product suggestions.

[1986] Step 7:

[1987] The terminal presents the suggested product information to the user.

[1988] The product proposal information sent from the server is displayed on the user's device. The display format is text, graph, or list format, making it easy for users to understand. The input is the product proposal information sent from the server, and the output is the product information presented to the user.

[1989] 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.

[1990] The present invention is a system that supports important life decisions based on a user's personality, values, interests, skill set, and emotional information. This system is a platform that stores information provided by the user in a database and uses a generative AI model and an emotional engine to make optimal suggestions.

[1991] Program processing

[1992] Entering information

[1993] 1. Users: Enter information about their personality, values, interests, skill sets, and emotions.

[1994] Specifically, users answer questions and upload existing data (e.g., resumes, personality test results, emotion recognition data) using a web form or mobile app.

[1995] Sending information

[1996] 1. Terminal: Sends the entered information to the server.

[1997] Specifically, the terminal converts the input data into JSON format and sends it to the server via a secure API.

[1998] Information analysis and storage

[1999] 1. Server: Analyzes the received information and stores it in the personality value database and emotion database.

[2000] The server validates the input data, formats it appropriately, and then stores it in the database.

[2001] Extracting profile and emotion information

[2002] 1. Server: Extracts user profile information and emotion information from the personality value database and emotion database.

[2003] Specifically, the profile information and emotion information of a specified user are retrieved from a database using an SQL query.

[2004] Generating the Model

[2005] 1. Server: Generates a LifePath model based on the extracted profile information and emotion information.

[2006] Generative AI models are used to create predictive models using deep learning and reinforcement learning algorithms.

[2007] Selecting a field

[2008] 1. User: Select the area in which they would like support in making decisions such as further education, employment, and marriage.

[2009] Users are presented with options via a dashboard or conversational chatbot.

[2010] Proposal Generation

[2011] 1. Server: Integrates user profile information, sentiment information, and current information (such as labor market trends) to generate optimal recommendations.

[2012] Specific operation: Generate multiple scenarios and options based on generative AI models and emotion engines.

[2013] Adjusting the proposal

[2014] 1. Server: Adjusts suggestions based on the user's emotions recognized by the emotion engine.

[2015] Specific behavior: The suggestions are adjusted to adapt to the user's current emotional state.

[2016] Presenting the proposal

[2017] 1. Terminal: Presents the generated proposals to the user.

[2018] The proposals are displayed in an easy-to-understand format, including text, graphs, and lists.

[2019] Specific examples

[2020] In the case of continuing education

[2021] 1. User: Enter current grades, areas of interest, and recent emotional state (e.g., stress level).

[2022] 2. Terminal: Sends this information to the server.

[2023] 3. Server: Analyzes the received information and stores it in the personality value database and emotion database.

[2024] 4. Server: Extracts profile information and emotional information from the database and generates a LifePath model using a generative AI model.

[2025] 5. User: Select support for further education.

[2026] 6. Server: Integrates user profile information, education market data, and sentiment information to suggest the most suitable universities and courses.

[2027] 7. Server: Adjust the suggestions based on the user's recent stress level.

[2028] 8. Device: Display the following suggestion: "The best university for you is Department B at University A. This department is a field that is expected to grow in the future. Also, taking into account your current stress level, University A has a comprehensive stress management program."

[2029] In the case of employment

[2030] 1. User: Enters skill set, desired location, and emotional state (e.g., motivation level).

[2031] 2. Terminal: Sends this information to the server.

[2032] 3. Server: Analyzes user information and stores it in the personality value database and emotion database.

[2033] 4. Server: Extracts profile information and emotional information from the database and generates a LifePath model using a generative AI model.

[2034] 5. User: Select job-finding support.

[2035] 6. Server: Integrates user profile information, labor market data, and sentiment information to suggest optimal occupations and companies.

[2036] 7. Server: Adjust the suggestions based on the user's motivation level.

[2037] 8. Device: Display a suggestion: "The perfect job for you is Position D at Company C. This company is in a growth industry and offers a flexible work schedule that matches your current motivation level."

[2038] In the case of marriage

[2039] 1. User: Enters values, interests, and emotional state (e.g., happiness).

[2040] 2. Terminal: Sends this information to the server.

[2041] 3. Server: Analyzes user information and stores it in the personality value database and emotion database.

[2042] 4. Server: Extracts profile information and emotional information from the database and generates a LifePath model using a generative AI model.

[2043] 5. User: Select Marriage Support.

[2044] 6. Server: Integrates user profile information, matching algorithms, and emotional information to propose optimal partner candidates.

[2045] 7. Server: Adjust the suggestions based on the user's happiness.

[2046] 8. Device: Display a suggestion saying, "The ideal partner candidate for you is E. He or she has very similar values ​​and interests to you and has the potential to increase your current happiness."

[2047] By implementing the system of the present invention, users can make important life decisions more reliably and efficiently by taking emotional information into consideration, thereby maximizing the value of their decisions.

[2048] The processing flow will be explained below.

[2049] Program processing

[2050] Flow from inputting information to presenting proposals

[2051] Step 1:

[2052] Users: Enter information about their personality, values, interests, skill sets, and emotions.

[2053] What happens: Answer questions or upload resumes, personality assessments, or emotion recognition data using a web form or mobile app.

[2054] Step 2:

[2055] Terminal: Sends the entered information to the server.

[2056] Specific operation: Converts input data into JSON format and sends it to the server via a secure API.

[2057] Step 3:

[2058] Server: Analyzes the received information and stores it in a database.

[2059] Specific operation: Validate the input data and store it in the personality value database and emotion database in the appropriate format.

[2060] Step 4:

[2061] Server: Extracts user profile information and emotion information from the personality value database and emotion database.

[2062] Specific operation: Use SQL queries to retrieve the required profile and sentiment information from the respective databases.

[2063] Step 5:

[2064] Server: Generates a LifePath model based on the extracted profile information and emotion information.

[2065] Specific operation: Using a generative AI model, a predictive model is created using deep learning and reinforcement learning algorithms based on the acquired data.

[2066] Step 6:

[2067] User: Select the area in which they would like support in making decisions such as further education, employment, and marriage.

[2068] What it does: Select your preferred area of ​​support using the dashboard and conversational chatbot.

[2069] Step 7:

[2070] Terminal: Sends the user's selections to the server.

[2071] Specific operation: The selection is sent to the server via API.

[2072] Step 8:

[2073] Server: Integrates user profile information, sentiment information, and current information (such as labor market trends) to generate optimal proposals.

[2074] How it works: Using a generative AI model and sentiment engine, it combines user information with the latest market data to generate multiple scenarios and options.

[2075] Step 9:

[2076] Server: Adjusts the suggestions based on the user's emotions recognized by the emotion engine.

[2077] Specific behavior: The suggestions are adjusted to adapt to the user's current emotional state.

[2078] Step 10:

[2079] Terminal: Presents the generated suggestions to the user.

[2080] What it does: Display suggestions to the user in text, graph, and list format.

[2081] Example: Going on to higher education

[2082] Step 1:

[2083] User: Enter current grades, areas of interest, and recent emotional state (e.g., stress level).

[2084] What it does: Answer questions on a web form about grades, interests, and emotional state.

[2085] Step 2:

[2086] Terminal: Sends the entered information to the server.

[2087] What it does: Converts input data from a web form into JSON format and sends it through a secure API.

[2088] Step 3:

[2089] Server: Analyzes user information and stores it in a personality value database and an emotion database.

[2090] Specific operation: Validate and clean input data and save it in the database in the appropriate format.

[2091] Step 4:

[2092] Server: Extracts profile information and emotional information from the database and generates a LifePath model using a generative AI model.

[2093] Specific operations: Retrieves information from a database using SQL queries and generates a predictive model using machine learning algorithms.

[2094] Step 5:

[2095] User: Select support for further education.

[2096] Specific action: Select "Further education" on the dashboard or chatbot.

[2097] Step 6:

[2098] Server: Integrates user profile information, educational market data, and sentiment information to recommend the most suitable universities and courses.

[2099] How it works: It combines generative AI models with the latest education market data to generate optimal proposals by considering multiple scenarios.

[2100] Step 7:

[2101] Server: Adjusts the suggestions based on the user's recent stress level.

[2102] Specific operation: Based on data from the emotion engine, the suggestions are adjusted to correspond to the user's stress level.

[2103] Step 8:

[2104] Device: Display the following suggestion: "The best university for you is Department B at University A. This department is a field that is expected to grow in the future. Also, taking into account your current stress level, University A has a comprehensive stress management program."

[2105] What it does: Displays suggestions in text format and provides additional information on stress management.

[2106] Through these specific processing steps, the LifePath-AI system makes suggestions to the user to support optimal decision-making that takes emotional information into account.

[2107] Example 2

[2108] 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."

[2109] Conventional systems considered a user's personality, values, interests, and skill set, but did not consider emotional information. This made it difficult for them to generate optimal recommendations that took emotional factors into account when users made decisions. Furthermore, they lacked the ability to reflect labor market trends and other related data in real time, making it impossible to provide advice based on the latest information. To solve these problems, a system that can analyze multifaceted data, including emotional information, and make optimal recommendations is needed.

[2110] 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.

[2111] In this invention, the server includes means for inputting personality information, values, interests, skill sets, and emotional information, means for transmitting the input information to the server, means for analyzing the received information and storing it in a database, means for extracting profile information and emotional information from the database, means for generating a model based on the extracted profile information and emotional information, means for making suggestions to the user using the generated model, and means for presenting the suggested information to the user. This enables the generation of optimal suggestions that take into account the user's emotional information, and real-time advice based on the latest labor market and related data.

[2112] "Personality information" refers to information such as the user's character, tendencies, behavioral patterns, and values.

[2113] "Values" is information about the beliefs, ethics, and standards of judgment that a user considers important.

[2114] "Interests" are information about areas or activities that interest a user.

[2115] A "skill set" is information about the skills, abilities, and expertise that a user possesses.

[2116] "Emotion information" is information that indicates the user's current emotional state and changes in emotions.

[2117] An "input method" is a method by which a user provides information to a system, including web forms, mobile applications, etc.

[2118] "Transmission means" refers to a method for transmitting information entered by a user to a server, and includes transmitting data through a secure API.

[2119] The "analysis means" is a method by which the server analyzes the information it receives, formats it appropriately, and stores it in the database.

[2120] "Storage means" refers to a method for storing the analyzed information in a database.

[2121] An "extraction means" is a method for extracting specific information from a database, such as using an SQL query.

[2122] "Generation means" refers to a method for creating a model based on extracted information, using deep learning or reinforcement learning algorithms.

[2123] The "proposal means" is a method for making optimal proposals to the user using the generated model.

[2124] A "presentation medium" is a method for visually displaying the proposed information to the user, including text, graphs, lists, and the like.

[2125] "Labor Market Trends" is the latest information on the labor market, including current employment conditions, job information by industry, and wage trends.

[2126] "Relevant data" is additional information that may influence a user's decision, such as education market data and trend information.

[2127] MODE FOR CARRYING OUT THE INVENTION

[2128] This invention is a system that supports important life decisions based on a user's personality, values, interests, skill set, and emotional information. This system is a platform that stores information provided by the user in a database and uses a generative AI model and an emotional engine to make optimal recommendations.

[2129] Entering information

[2130] 1. User: Enters information about personality, values, interests, skill sets, and emotions. Specifically, users answer questions or upload existing data (e.g., resumes, personality test results, emotion recognition data) using web forms or mobile apps. This allows for multifaceted information to be collected from users.

[2131] Sending information

[2132] 2. Terminal: The input information is sent to the server. Specifically, the terminal converts the input data into JSON format and sends it to the server via a secure API (HTTPS protocol). This transmission process uses encrypted communication to ensure the security of the information.

[2133] Information analysis and storage

[2134] 3. Server: Analyzes the received information and stores it in the personality value database and emotion database. Specifically, the server validates the input data, formats it appropriately, and then stores it in the database. This process is performed using Python scripts and a database management system (e.g., MySQL).

[2135] Extracting profile and emotion information

[2136] 4. Server: Extracts user profile information and emotion information from the personality value database and emotion database. Specifically, it uses SQL queries to retrieve the profile information and emotion information of a specified user from the database. During this process, it integrates related data using JOIN operations.

[2137] Generating the Model

[2138] 5. Server: Generates a LifePath model based on the extracted profile and emotional information. Using a generative AI model (e.g., TensorFlow, PyTorch), it creates a predictive model using deep learning and reinforcement learning algorithms. This model generates scenarios that help users make optimal decisions based on their profile and emotional information.

[2139] Proposal generation and refinement

[2140] 6. Server: Integrates user profile information, emotional information, and current information (such as labor market trends) to generate optimal proposals. Furthermore, the proposals are tailored based on the user's emotions as recognized by the emotion engine. By combining the generative AI model with the emotion engine, proposals optimized for individual needs are created.

[2141] Presenting the proposal

[2142] 7. Terminal: The generated suggestions are presented to the user in a visually appealing format, such as text, graphs, or lists. This is often done using front-end frameworks like React or Vue.js.

[2143] Specific examples

[2144] In the case of continuing education

[2145] Users input their current grades, areas of interest, and recent emotional state (e.g., stress level). This information is sent to the server, which analyzes and stores it in a database. Profile information and emotional information are then extracted from the database, and a LifePath model is generated using a generative AI model. When users select support for further education, the system integrates the user's profile information, educational market data, and emotional information to recommend the most suitable universities and courses. Finally, the system adjusts the recommendations based on the user's recent stress state, and finally presents the recommendations to the user.

[2146] Prompt Sentence Examples

[2147] The following text is presented as an example of how to select the best university.

[2148] "The best university for you is Department B at University A. This department is in a promising field, and given your current stress level, University A also has a strong stress management program."

[2149] In the case of employment

[2150] Users input their skill set, desired work location, and emotional state (e.g., motivation level). This information is sent to the server, which analyzes and stores the received information in a database. Profile information and emotional information are then extracted from the database, and a LifePath model is generated using a generative AI model. When a user selects job-hunting support, the system integrates the user's profile information, labor market data, and emotional information to suggest suitable occupations and companies. Finally, the system adjusts the suggestions based on the user's motivation level, and finally presents them to the user.

[2151] Prompt Sentence Examples

[2152] The following text is presented as an example of how to select the most suitable job.

[2153] "The perfect fit for you is Position D at Company C. This company is in a growth industry and offers a flexible work schedule that matches your current motivation level."

[2154] In the case of marriage

[2155] Users input their values, interests, and emotional state (e.g., happiness level). This information is sent to the server, which analyzes and stores it in a database. Profile information and emotional information are then extracted from the database, and a LifePath model is generated using a generative AI model. If marriage support is selected, the system integrates the user's profile information, matching algorithm, and emotional information to suggest suitable partner candidates. Finally, the system adjusts the suggestions based on the user's happiness level, and finally presents them to the user.

[2156] Prompt Sentence Examples

[2157] The following text is presented as an example of how to select the best partner:

[2158] "Your ideal partner candidate is Person E. Their values ​​and interests are very similar to yours, and they have the potential to increase your current happiness."

[2159] By implementing the system of the present invention, users can make important life decisions more reliably and efficiently by taking emotional information into consideration, thereby maximizing the value of their decisions.

[2160] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2161] Step 1:

[2162] Users input information about their personality, values, interests, skill sets, and emotions. Specifically, users use web forms or mobile apps to answer questions and upload existing data (e.g., resumes, personality test results, emotion recognition data). This input data is sent to the system.

[2163] Input: User-entered personality information, values, interests, skill sets, and emotional information.

[2164] Output: The input data is passed to the terminal.

[2165] Step 2:

[2166] Terminal: Sends the input information to the server. Specifically, the terminal converts the input data into JSON format and sends it to the server using a secure API (HTTPS protocol). During this process, encrypted communication is used to ensure the security of the data.

[2167] Input: Personality information, values, interests, skill sets, and emotional information provided by the user.

[2168] Output: JSON formatted data is sent to the server.

[2169] Step 3:

[2170] Server: Analyzes the received information and stores it in the personality value database and emotion database. Specifically, the server first validates the data to ensure that the format and content are appropriate. It then stores the prepared data in the database. This process uses Python scripts and a database management system (e.g., MySQL).

[2171] Input: Personality information, values, interests, skill sets, and emotional information sent from the device in JSON format.

[2172] Output: Parsed and validated data is stored in a database.

[2173] Step 4:

[2174] Server: Extracts user profile information and emotion information from the personality value database and emotion database. Specifically, it issues an SQL query to retrieve the profile information and emotion information of the specified user from the database. At this time, it integrates related data using a JOIN operation.

[2175] Input: User's personality information, values, interests, skill sets, and emotional information stored in a database.

[2176] Output: The extracted user profile information and emotion information are passed on to the next process.

[2177] Step 5:

[2178] Server: Generates a LifePath model based on the extracted profile and emotional information. Specifically, it uses a generative AI model (e.g., TensorFlow or PyTorch) to create a predictive model using deep learning and reinforcement learning algorithms. This model is designed to generate optimal scenarios based on the user's profile and emotional information.

[2179] Input: Extracted user profile information and sentiment information.

[2180] Output: The generated LifePath model is passed to the next step.

[2181] Step 6:

[2182] User: Selects the area in which they want support for decisions such as further education, employment, marriage, etc. Specifically, the user selects the area in which they need support through a dashboard or an interactive chatbot. The selected data is immediately sent to the server.

[2183] Input: User's choice of field such as further education, employment, marriage, etc.

[2184] Output: The selected field information is sent to the server.

[2185] Step 7:

[2186] Server: Integrates user profile information, sentiment information, and current information (such as labor market trends) to generate optimal recommendations. Specifically, the server retrieves real-time data (e.g., labor market trends and education market trends) from a database and generates multiple scenarios using a generative AI model and sentiment engine. These scenarios include multiple options for the field selected by the user.

[2187] Inputs: Generated LifePath model, user choices, real-time labor and education market data.

[2188] Output: The generated proposals are passed on to the next step.

[2189] Step 8:

[2190] Server: Adjusts suggestions based on the user's emotions recognized by the emotion engine. Specifically, the emotion engine analyzes the user's emotional state in real time and adjusts the suggestions to optimize them for the user's current emotional state. NLP (natural language processing) technology is heavily used in this process.

[2191] Input: Generated suggestions, user's real-time sentiment information.

[2192] Output: The adjusted proposal is sent to the device.

[2193] Step 9:

[2194] Terminal: Presents the generated suggestions to the user. Specifically, the terminal displays the suggestions received from the server in a visually easy-to-understand format, such as text, graphs, or lists. Front-end frameworks such as React or Vue.js are often used for this.

[2195] Input: adjusted proposal.

[2196] Output: The suggestions presented to the user.

[2197] (Application example 2)

[2198] 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."

[2199] In today's world, when users make a wide range of important decisions, such as furthering their education, finding employment, or getting married, they need support that takes into account their emotional state and personality, rather than simply providing information. However, conventional systems have difficulty responding to each user in detail and individually, and they do not adjust their suggestions in real time. This makes it difficult to provide optimal suggestions for users.

[2200] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for inputting personality information, values, interests, and skill sets, means for transmitting the input information to the server, means for analyzing the received information and storing it in a database, means for extracting profile information from the database, means for generating a model using a generative AI model based on the extracted profile information, means for making suggestions to the user using the generated model, means for presenting the suggested information to the user, means for adjusting the content of the suggestions in real time in conjunction with a smart device, and means for overlaying the content of the suggestions in the user's field of view. This enables more accurate and efficient support by making personalized suggestions based on the user's emotional state and personality in real time.

[2201] "Personality information" is information that indicates the user's character traits and behavior patterns.

[2202] "Values" are information that indicates the beliefs, principles, and priorities that a user holds dear.

[2203] "Interests" is information about the subjects or fields in which a user is interested or concerned.

[2204] A "skill set" is information that indicates a collection of specific skills and abilities that a user possesses.

[2205] A "server" is a computer system that processes information submitted by users, stores it in a database, and generates recommendations.

[2206] A "database" is a structured storage scheme on a computer system for storing personality information, values, interests, skill sets, and other related information.

[2207] A "generative AI model" is an artificial intelligence algorithm that generates optimal suggestions based on user input information.

[2208] The "means of generation" is the process of using an AI model to create specific proposals based on specified profile information.

[2209] "Smart devices" are devices including wearable and mobile terminals that allow users to visually obtain information and check proposal content in real time.

[2210] "Means for real-time adjustment" refers to a function that monitors the user's emotional state and profile information in real time and dynamically adjusts the content of suggestions.

[2211] "Overlay display" is a technology that displays information directly overlaid on the user's field of vision on the display of a smart device.

[2212] These definitions provide a clear understanding of the specific functions and features of the system provided by the invention.

[2213] The present invention relates to a system that provides a personalized shopping experience in a physical store based on a user's personality information, values, interests, skill set, and emotional information. The system collects information provided by the user and makes optimal suggestions using a generative AI model and an emotional engine. Specific embodiments for implementing the present invention are described below.

[2214] First, the user puts on a smart device (e.g., smart glasses). The smart glasses have a built-in camera and display, and are capable of recognizing the user's face and emotions. The user first registers their personality information, values, interests, skill set, and emotional information in the smart glasses application. This information is sent to the server, analyzed, and stored in a database.

[2215] The server analyzes the received information and stores it in a database. The database contains personality information, values, interests, skill sets, and emotional information. Based on this information, a generative AI model is used to extract user profile information and generate optimal recommendations. The generated model is created using a deep learning framework (e.g., TensorFlow or PyTorch).

[2216] The smart glasses' camera then performs real-time facial and emotional recognition of the user and sends the data to a server. The server then analyzes the data and adjusts the recommendations in real time based on a generative AI model and emotion engine. The recommendations are overlaid on the user's field of view, allowing the user to receive optimal shopping recommendations based on their emotional state and personality.

[2217] For example, if a user is stressed in a store, the server can analyze that information and provide information about stores that offer a relaxing environment or suggest products that have a relaxing effect. Also, if a user is looking for products specialized in a particular field, the server can provide specialized suggestions for that field. Below are some example prompts for inputting data into the generative AI model.

[2218] Specific examples

[2219] "User 12345's profile information states that their personality traits are INTJ, their values ​​emphasize creativity and innovation, and their interests are technology and fashion. Their skill set excels in programming and design. Their current emotional state is neutral, and their past emotional states are happy and neutral."

[2220] In this way, the system of the present invention can provide real-time personalized shopping suggestions that take into account the user's emotional state and personality information, thereby enabling the user to enjoy a more comfortable and satisfying shopping experience.

[2221] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2222] Step 1:

[2223] A user puts on a smart device, specifically smart glasses, and inputs their personality information, values, interests, skill sets, and emotional information, which are registered in advance in the smart glasses application. The input information is converted into JSON format and sent to the server.

[2224] Input: personality information, values, interests, skill sets, emotional information

[2225] Output: User data in JSON format

[2226] Step 2:

[2227] The device sends the information entered by the user to the server. Specifically, the smart glasses application uses a secure API to send the data to the server. The data is sent over an encrypted connection using protocols such as SSL / TLS.

[2228] Input: User data in JSON format

[2229] Output: User data sent to the server

[2230] Step 3:

[2231] The server parses the received information and stores it in a database. First, it validates the received data and then stores it in an SQL database (e.g., PostgreSQL). Validation involves checking whether all required fields are present, whether the format is correct, etc. Before saving it to the database, the information is properly formatted according to a specified schema.

[2232] Input: User data sent to the server

[2233] Output: User data stored in the database

[2234] Step 4:

[2235] The server extracts user profile information from the database. It uses an SQL query to retrieve the profile information of a specified user. This profile information includes personality information, values, interests, skill sets, and emotional information. The extracted information is used as input for the generative AI model.

[2236] Input: User data stored in the database

[2237] Output: Input data for the generative AI model

[2238] Step 5:

[2239] The server uses a generative AI model based on the extracted profile information to generate a model. Using a deep learning framework (e.g., TensorFlow or PyTorch), the extracted data is input and a model is generated that predicts the best suggestions for the user. This generative model is then used to generate personalized suggestions fo...

Claims

1. a means of inputting personality information, values, interests, and skill sets; means for transmitting the input information to a server; means for analyzing the received information and storing it in a database; means for extracting profile information from the database; a means for generating a model based on the extracted profile information; a means for making suggestions to a user using the generated model; means for presenting the suggested information to a user; A system including:

2. The system of claim 1 further comprising means for supporting user selection of a field.

3. 10. The system of claim 1, further comprising means for updating the recommendations to take into account labor market trends.

Images