System

Abstract

A system is provided.SOLUTION: The system includes a means for collecting answers to questions for a user to generate a persona reflecting the user's values, a generation AI means for generating a persona based on the collected answers, a prediction algorithm means for classifying the generated persona, and a means for disclosing the classified persona on a shared platform.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] In order to respond to today's diverse values ​​and rapidly changing consumer behavior, companies that produce marketing and advertising content need to accurately understand their target demographic. However, traditional research methods are costly and time-consuming, and there are limitations to the accuracy of the data they can obtain. As a result, it is difficult to efficiently and accurately understand targets and evaluate services. [Means for solving the problem]

[0005] This invention provides a means for users to answer questions to generate personas that reflect their personal values ​​and collect the response data, a generation AI means for generating personas based on the generated response data, a predictive algorithm means for classifying the generated personas, and a means for publishing the classified personas on a shared platform. This enables companies to accurately and efficiently understand their target demographic and develop appropriate marketing strategies and service evaluations. The addition of survey and point-awarding means can also encourage user engagement and improve the quality of the generated personas. Furthermore, the system includes a means for searching the generated personas based on their genre, selecting a persona appropriate for the purpose, and utilizing it in marketing strategies and service evaluations, thereby supporting accurate analysis of the target market and efficient strategy formulation.

[0006] A "user" is someone who uses the system to create a persona that reflects their own values ​​and share it with other users.

[0007] "Values" refer to the personal beliefs and standards that users hold as the basis for their beliefs, feelings, and decision-making.

[0008] "Persona" refers to virtual character data that reflects a user's values ​​and characteristics.

[0009] "Questions" refer to questions that allow users to input their own values.

[0010] "Response Data" refers to the information entered by a User in response to a Question.

[0011] "Generative AI" refers to artificial intelligence that generates personas based on user response data to questions.

[0012] "Predictive algorithm" refers to a data analysis method for classifying the generated personas.

[0013] "Sharing platform" refers to an online service system for sharing generated personas with other users.

[0014] "Publishing" refers to making the generated persona available for other users to view.

[0015] "Survey" refers to a survey in the form of questions to obtain additional information or feedback about a published persona.

[0016] "Points" refers to rewards given to users who answer surveys.

[0017] "Genre" refers to a group that classifies the generated personas into specific categories or uses.

[0018] "Search" refers to the act of a user searching for a persona based on a genre or specific criteria.

[0019] A "marketing strategy" refers to the plans and methods a company uses to efficiently deliver its products and services to the market.

[0020] "Service evaluation" refers to the act of evaluating the quality of the services provided and user satisfaction. [Brief explanation of the drawings]

[0021] [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

[0022] 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.

[0023] First, the terms used in the following description will be explained.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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."

[0029] [First embodiment]

[0030] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0031] 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.

[0032] 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).

[0033] 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.

[0034] 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.

[0035] 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.

[0036] 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.

[0037] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0038] 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.

[0039] 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.

[0040] 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.

[0041] 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."

[0042] The purpose of the PersonaNet system of the present invention is to allow users to create personas that reflect their own values ​​and share and use them with other users on a shared platform. This system includes the following program processing.

[0043] User Registration / Login

[0044] Users: New users create an account by entering the required information such as first name, last name, email address, and password. Existing users log in using their email address and password.

[0045] Terminal: Sends registration and login information to the server and receives responses from the server.

[0046] Server: Authenticates the received registration and login information by checking it against a database, and returns a success or error message to the device.

[0047] Persona creation

[0048] User: Once logged in, the user selects the "Create a Persona" option and answers a series of questions designed around psychology and statistics.

[0049] Device: The data answered by the user is temporarily stored and sent to the server once all questions have been answered.

[0050] Server: Passes the received response data to the generation AI to generate a persona that reflects the user's values. The generated persona is then saved in a database.

[0051] Persona genre classification

[0052] Server: The generated personas are passed through a predictive algorithm to classify them into appropriate genres. This genre information is also stored in a database.

[0053] Persona Publication

[0054] Server: Publishes the categorized personas on a shared platform, where they can be viewed and used by other users.

[0055] Using Personas

[0056] Users: Other users can search for personas based on specific genres or conditions on the platform and select them as needed. The selected personas can then be used for marketing strategies and service evaluations.

[0057] Terminal: Sends search criteria to the server, receives and displays the corresponding persona data.

[0058] Server: Retrieves personas that match the search criteria from the database and sends them to the device.

[0059] Survey implementation and point awarding

[0060] Server: Periodically conduct surveys about published personas and send notifications to users.

[0061] User: The user answers the survey and sends the data to the server.

[0062] Server: Stores the answer data in a database and awards points to users who answer. These points can be exchanged for various rewards within the system.

[0063] Customizing AI logic

[0064] Users: Specific users, such as companies, can purchase options to customize the logic of the generative AI. This customization allows them to generate personas tailored to specific uses.

[0065] Server: Verify purchase information and apply customization options to user accounts.

[0066] As a concrete example, consider the following scenario: A user creates a new account and creates a persona that reflects their values. This persona is categorized under the "youth marketing" category and published on a shared platform. Another user searches for personas in this category and uses them to develop a business plan. A business user also purchases the generator's customization option to generate a persona tailored to their specific needs, allowing them to develop a more refined marketing strategy.

[0067] This allows companies to efficiently and accurately understand their target demographic and reflect this in their marketing strategies and product development. Compared to traditional survey methods, this system can reduce costs and improve data accuracy.

[0068] The processing flow will be explained below.

[0069] Step 1:

[0070] A user visits the PersonaNet website and begins creating a new account.

[0071] Step 2:

[0072] The device makes an HTTP POST request to send the user's entered first name, last name, email address, and password to the server.

[0073] Step 3:

[0074] The server verifies the received registration information, stores it in a database, sends a confirmation email to the user that the registration was successful, and returns a success message to the terminal.

[0075] Step 4:

[0076] The user completes the email authentication, then proceeds to the login screen, enters their email address and password, and clicks the login button.

[0077] Step 5:

[0078] The terminal sends the login information to the server and makes an HTTP POST request to request authentication.

[0079] Step 6:

[0080] The server checks the received login information against a database, and if authentication is successful, generates session information and returns it to the terminal.

[0081] Step 7:

[0082] The user selects the "Create a Persona" option and begins answering a series of questions that are presented to them.

[0083] Step 8:

[0084] The terminal temporarily stores the data answered by the user sequentially, and when all questions have been answered, it makes an HTTP POST request to send it to the server.

[0085] Step 9:

[0086] The server passes the received response data to the generation AI, which generates a persona that reflects the user's values.

[0087] Step 10:

[0088] The server stores the generated persona data in a database, and uses predictive algorithms to classify the persona into an appropriate genre.

[0089] Step 11:

[0090] The server publishes the classified personas on a shared platform, making them available for other users to view.

[0091] Step 12:

[0092] Users can search for personas based on specific genres or conditions on the shared platform, and can select personas that meet specific needs, such as "marketing" or "product development."

[0093] Step 13:

[0094] The terminal sends the user's search criteria to the server and makes an HTTP GET request to obtain the corresponding persona.

[0095] Step 14:

[0096] The server retrieves personas that match the search criteria from the database and returns them to the terminal.

[0097] Step 15:

[0098] The terminal displays the acquired persona to the user.

[0099] Step 16:

[0100] The server periodically sends notifications to the users to conduct a survey about the published personas.

[0101] Step 17:

[0102] The user receives the survey notification, answers the survey, and makes an HTTP POST request to send the data from the terminal to the server.

[0103] Step 18:

[0104] The server stores the received survey responses in a database and assigns points to the accounts of the users who responded.

[0105] Step 19:

[0106] Users can confirm that points have been awarded on the shared platform, and the points can be exchanged for various rewards within the system.

[0107] Step 20:

[0108] To customize the logic of the generative AI, the user, a company representative, accesses a page to purchase options and makes a payment.

[0109] Step 21:

[0110] The terminal makes an HTTP POST request to send the purchase information to the server.

[0111] Step 22:

[0112] The server validates the received purchase information and applies the customization options to the user account.

[0113] Example 1

[0114] 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."

[0115] Conventional persona generation systems were inadequate in generating and classifying personas that reflected user values, limiting their use in marketing strategies and service evaluations. Furthermore, they lacked the means to temporarily store or search information related to persona generation, making them inconvenient for users and difficult to use efficiently. Another issue was the inability to flexibly generate personas tailored to specific uses.

[0116] 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.

[0117] In this invention, the server includes: a means for collecting response data from users who answer questions to generate a persona that reflects their own values; a generation AI means for generating a persona based on the generated response data; a means for temporarily storing the response data until the persona is generated and transmitting the data once all questions have been answered; a predictive algorithm means for classifying the generated persona; a means for publishing the classified persona on a shared platform; a means for searching persona information based on specific conditions; and a means for using the selected persona in marketing strategies and service evaluations. This enables efficient and flexible generation and classification of personas that reflect users' values, enabling them to be used in marketing strategies and service evaluations. Furthermore, the ability to temporarily store and transmit response data related to persona generation and the addition of a search means improves user convenience.

[0118] "Means for answering questions and collecting response data" refers to a function in which a user inputs answers to multiple questions provided, and the system saves and retains the input.

[0119] "Generative AI method for generating personas" is an artificial intelligence technology that generates virtual characters that reflect the user's values ​​and characteristics based on collected response data.

[0120] "Means for temporarily saving and sending data once all questions have been answered" is a function for temporarily saving the answer data entered by the user one by one, and sending the data to the server once all questions have been answered.

[0121] A "predictive algorithm means" is an algorithm that uses machine learning or statistical techniques to appropriately classify the generated personas.

[0122] "Means for publishing on a shared platform" is a function that allows the generated persona to be displayed in a public space on the Internet, making it accessible and usable by other users.

[0123] "Means for searching persona information based on specific conditions" is a function that searches for persona information in the database based on conditions specified by the user and finds the relevant persona.

[0124] "Means of using selected personas for marketing strategies and service evaluation" is a function that uses personas selected from search results to help develop marketing plans and evaluate services.

[0125] "Means for providing a questionnaire and collecting response data" refers to a function for presenting a questionnaire to users and collecting their responses.

[0126] The "means for awarding points" is a function for awarding points as a reward to users based on the collected response data.

[0127] The purpose of the PersonaNet system of this invention is to allow users to create personas that reflect their own values ​​and share and use them with other users on a shared platform. This system uses the functions of the server, terminal, and user, and includes the following program processing.

[0128] User Registration / Login

[0129] Servers, devices, and users:

[0130] New users create an account by entering required information such as first name, last name, email address, and password. Existing users log in using their email address and password. The registration and login information is sent from the device to the server, which then authenticates it by checking it against a database. If authentication is successful, a success message is returned to the device, allowing the user to access the system.

[0131] Persona creation

[0132] Servers, devices, and users:

[0133] Once logged in, users select the "Create Persona" option and answer a series of questions. The questions are designed based on psychology and statistics. The answer data is temporarily saved on the device and sent to the server once all questions have been answered. The server then passes the received answer data to a generation AI (e.g., OpenAI (registered trademark) GPT-4 (registered trademark)), which uses prompt text to generate a persona that reflects the user's values. The generated persona is then saved in a database.

[0134] Persona genre classification

[0135] server:

[0136] The generated personas are analyzed using a predictive algorithm (e.g., Random Forest or K-means clustering). Based on the analysis results, the personas are classified into appropriate genres. This genre information is also stored in the database.

[0137] Persona Publication

[0138] server:

[0139] The categorized personas are published on a sharing platform, where they can be viewed and used by other users.

[0140] Using Personas

[0141] Servers, devices, and users:

[0142] Other users can search for personas based on specific genres or conditions and select them as needed. The search conditions are sent from the device to the server, and the corresponding persona data is returned and displayed. The selected personas can be used for marketing strategies and service evaluations.

[0143] Survey implementation and point awarding

[0144] Server, User:

[0145] The server periodically conducts surveys about the published personas and sends notifications to users. Users respond to the surveys and send the data to the server. The server stores the received response data in a database and awards points to users who respond. These points can be exchanged for various rewards within the system.

[0146] Customizing AI logic

[0147] Server, User:

[0148] Certain users, such as companies, can purchase options to customize the logic of the generation AI. This customization allows the generation of personas tailored to specific uses. The server verifies the purchase information and applies the customization options to the user account.

[0149] Specific examples

[0150] For example, a user might create a new account and create a persona that reflects their values. This persona is then categorized under "youth marketing" and published on a shared platform. Another user might search for personas in this category and use them to develop a business plan. A business user might also purchase customization options for the AI ​​generator to generate personas tailored to their specific needs, allowing them to develop more sophisticated marketing strategies.

[0151] Prompt Sentence Examples

[0152] "I want to create a new persona specifically for the 'youth marketing' genre. Please answer the following questions:

[0153] 1. Age: 25

[0154] 2. Gender: Male

[0155] 3. Hobbies: Sports, listening to music

[0156] 4. Occupation: Salaryman

[0157] 5. Birthplace: Tokyo

[0158] 6. Values: Health-conscious, career-oriented

[0159] By selecting the "Generate Persona" option, users can answer the above questions and generate a detailed persona.

[0160] In this way, the PersonaNet system uses generative AI to generate detailed personas based on user input, allowing them to be shared and used by other users.

[0161] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0162] Program processing flow

[0163] User Registration / Login

[0164] Step 1: Enter your registration information

[0165] User: Enter the required information such as name, email address, and password, and click the "Register" button. Input data: name, email address, and password.

[0166] Step 2: Submit your registration information

[0167] Terminal: Sends the entered information to the server. Data sent: First name, last name, email address, password.

[0168] Step 3: Verify your registration details

[0169] Server: Compares the received information with the database and generates an authentication token if successful. Data verification: New data vs database.

[0170] Server: Saves the user account in the database based on the registration details and returns a success message to the terminal. It may also return an error message. Output: An authentication token.

[0171] Step 4: Enter your login details

[0172] User: Enter your registered email address and password and click the "Login" button. Input data: Email address, password.

[0173] Step 5: Submit login information

[0174] Terminal: Sends input information to the server. Data sent: Email address, password.

[0175] Step 6: Verify your login details

[0176] Server: Compares the entered information with the database and performs authentication. If it is correct, generates a token to start a user session and returns it to the terminal. It may also return an error message. Output: Authentication token.

[0177] Persona creation

[0178] Step 7: Select persona creation options

[0179] User: After logging in, select the "Create Persona" option. Input data: Select the Create Persona option.

[0180] Step 8: Answer questions

[0181] User: Answers a series of questions displayed. Example questions: age, gender, hobbies, values, etc. Input data: Questions and answers.

[0182] Step 9: Temporarily save your answers

[0183] Terminal: Temporarily saves the answer data. The saved data is sent to the server once all questions have been answered. Data processing: Temporarily saves and prepares the data for transmission.

[0184] Step 10: Submit your response data

[0185] Terminal: After answering all questions, send the data to the server. Data transmission: All answer data.

[0186] Step 11: Generate personas

[0187] Server: Passes the received answer data to a generation AI (e.g., OpenAI GPT-4) and generates a persona using the prompt. Data calculation: Persona generation by AI.

[0188] Server: Save the generated personas to the database. Output: The generated personas.

[0189] Persona genre classification

[0190] Step 12: Persona Analysis

[0191] Server: Analyzes the generated personas using predictive algorithms (e.g., Random Forest or K-means clustering). Data calculation: Persona analysis.

[0192] Step 13: Genre Classification

[0193] Server: Based on the analysis results, classify the persona into the appropriate genre. The classification information is also saved in the database. Output: Genre classification.

[0194] Persona Publication

[0195] Step 14: Prepare for publication

[0196] Server: Formats the categorized persona information for the shared platform. Data processing: Converts it into a publicly available format.

[0197] Step 15: Publishing

[0198] Server: Publishes personas onto a shared platform, making them visible and available to other users. Output: Published personas.

[0199] Using Personas

[0200] Step 16: Persona Search

[0201] User: Search for personas by specifying a specific genre or conditions. Input data: Search conditions.

[0202] Step 17: Submitting search criteria

[0203] Terminal: Sends search criteria to the server. Data transmission: Search criteria.

[0204] Step 18: Search based on criteria

[0205] Server: Retrieves personas that match the search criteria from the database and sends them to the device. Output: Personas from the search results.

[0206] Step 19: Persona Display

[0207] Terminal: Display persona information for search results. Data display: Search results.

[0208] Step 20: Use Personas

[0209] User: Use the selected persona for marketing strategies and service evaluation. Use the persona based on usage scenarios. Output: Analysis results based on usage scenarios.

[0210] Survey implementation and point awarding

[0211] Step 21: Survey Notification

[0212] Server: Periodically conducts surveys about published personas and sends notifications to users. Output: Survey notifications.

[0213] Step 22: Complete the survey

[0214] User: Answers the survey and sends the data to the server. Input data: Survey responses.

[0215] Step 23: Save the response data

[0216] Server: Saves the received response data in a database. Data storage: Response data.

[0217] Step 24: Points awarded

[0218] Server: Based on the answer data, points are awarded to the user who answered. These points can be exchanged for rewards within the system. Output: Awarded points.

[0219] Customizing AI logic

[0220] Step 25: Purchase customization options

[0221] User: Purchase logic customization options for the generation AI. Input data: Purchase information.

[0222] Step 26: Apply customizations

[0223] Server: Verify purchase information and apply customization options to user account. Output: Customization options.

[0224] (Application example 1)

[0225] 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."

[0226] In today's content distribution services, users have difficulty finding content that is optimized for their values ​​and interests, and there is a need for a system that can provide recommendations tailored to individual preferences. Furthermore, there is a lack of functionality that allows users to share personas with others and mutually recommend content. Therefore, there is an urgent need to develop a content recommendation system that accurately reflects user preferences.

[0227] 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.

[0228] In this invention, the server includes a means for allowing users to answer questions to generate a persona that reflects their own values ​​and collecting the answer data, a generation AI means for generating a persona based on the generated answer data, a means for recommending individual content based on the user's persona, and a means for publishing the classified persona on a sharing platform. This allows users to easily find content optimized for their preferences, and also enhances the function of sharing personas with other users and discovering content to recommend to each other.

[0229] "User" refers to an individual who uses this system, creates a persona that reflects their own values, and receives content recommendations.

[0230] "Values" refer to internal standards and guidelines that influence a user's behavior and preferences, such as personal beliefs, interests, and tastes.

[0231] A "persona" is a virtual character that reflects a user's values ​​and interests, and is used for content recommendations and marketing strategies.

[0232] "Means of answering questions and collecting response data" refers to the system's functionality of presenting questions to users to generate personas and collecting their responses as data.

[0233] "Generative AI means" refers to artificial intelligence technology for generating personas based on collected response data.

[0234] "Predictive algorithm means" refers to algorithm technology for analyzing personas generated by generative AI means and classifying them into appropriate genres.

[0235] "Means for recommending content" refers to a function that automatically selects and recommends content appropriate for a user based on the user's generated persona.

[0236] "Means for publishing" refers to the system's functionality for publishing classified personas on a shared platform so that they can be viewed and used by other users.

[0237] "Means for providing surveys and collecting response data" refers to the system's function of periodically presenting surveys to users and collecting their response data.

[0238] "Means for awarding points" refers to the system's function of awarding points to users based on collected response data and allowing them to exchange those points for rewards.

[0239] "Means for searching and selecting a persona" refers to the function of searching based on the genre of the generated personas and selecting a persona that suits a specific purpose.

[0240] "Means used for evaluating marketing strategies and services" refers to functions used to evaluate marketing strategies and services based on the selected personas.

[0241] "Genre" refers to the category into which personas are classified, and is divided based on hobbies, preferences, interests, etc.

[0242] This invention describes a system that allows a user to create a persona that reflects their own values ​​and recommends individual content based on that persona. Specific embodiments for realizing this system are described below.

[0243] 1. System Configuration

[0244] The system mainly consists of the following components:

[0245] User device: A device such as a smartphone, tablet, or computer.

[0246] Server: A remote server that processes and stores data.

[0247] AI model: Software that generates personas using generative AI technology.

[0248] 2. Program Processing

[0249] 2.1 User Registration / Login

[0250] To use the system, a user must first create an account. New users enter their name, email address, and password, and this information is sent from the user's device to the server. The server stores this information in a database and uses it as authentication information.

[0251] 2.2 Persona Creation

[0252] When a user logs in, they are presented with questions to generate a persona. The answers are temporarily stored on the user's device and then sent to the server, which then uses a generative AI model to generate a persona that reflects the user's values.

[0253] 2.3 Classifying and Publishing Personas

[0254] The generated personas are classified into genres by the server using a predictive algorithm, and the classified personas are published on a shared platform for other users to view and use.

[0255] 2.4 Content Recommendations

[0256] The server recommends content suitable for the user based on the generated persona, including movies, music, books, etc. The user's device receives and displays the recommendation list.

[0257] 2.5 Surveys and point allocation

[0258] Periodically, the server will provide users with surveys and collect their responses. Based on the collected data, users will be awarded points, which can be redeemed for rewards.

[0259] 2.6 Search and Selection

[0260] Users can search based on the genre of the generated personas and select the appropriate persona. The selected persona can be used for marketing strategies and service evaluation.

[0261] 3. Hardware and Software Used

[0262] Hardware: Smartphones, tablets, PCs, remote servers

[0263] Software: Generative AI models, predictive algorithms, database management systems, web server software (e.g., Flask)

[0264] Specific examples

[0265] Example prompts to generate personas based on user responses:

[0266] Generate personas based on your users' values ​​and interests using the following answers:

[0267] Answer 1: [User Answer 1]

[0268] Answer 2: [User Answer 2]

[0269] Answer 3: [User Answer 3]

[0270] ...

[0271] The generated results should be output in the following format:

[0272] {

[0273] "persona": {

[0274] "Personality": "〇〇",

[0275] "Interest": "〇〇",

[0276] "Values": "〇〇"

[0277] }

[0278] }

[0279] These technological elements allow users to easily find content that is optimized for their preferences, and also provide a comprehensive feature that allows users to share personas with other users and discover content that they can recommend to each other.

[0280] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0281] Step 1:

[0282] User Registration

[0283] The user enters their name, email address, and password and sends a registration request to the server, which stores this information in a database. The input data is name, email address, and password, and the output data is a message indicating whether registration was successful or not.

[0284] Step 2:

[0285] User Login

[0286] The user enters their email address and password and sends a login request to the server, which then checks the information in the database and authenticates them. The input data is the email address and password, and the output data is a message indicating whether authentication was successful or not.

[0287] Step 3:

[0288] Persona Generation

[0289] After logging in, users answer a series of questions presented by the system. These answers are temporarily stored on the device and then sent to the server. The server passes the answers to a generative AI model, which generates a persona that reflects the user's values. The input data is the answers, and the generated persona is returned as the output data.

[0290] Step 4:

[0291] Persona Classification

[0292] The server uses a predictive algorithm to classify the generated personas into genres. The input data is the generated persona, and the output data is the classified genre. The specific operation is to analyze the characteristics of the persona and classify it into the appropriate category.

[0293] Step 5:

[0294] Persona Announcement

[0295] The server publishes the classified personas on a shared platform. The input data is the classified personas, and the published personas are available as output data. The published personas can be viewed and used by other users.

[0296] Step 6:

[0297] Content Recommendation

[0298] The server recommends appropriate content to the user based on the generated persona. The recommendation list is sent to the device. The input data is the persona and a large amount of content data, and the output data is a list of recommended content. Specifically, the content is filtered based on the characteristics of the persona, and the most suitable items are selected.

[0299] Step 7:

[0300] Survey

[0301] The server periodically provides users with surveys and collects their responses. The input data is the survey responses, and the output data is the collected response data. This data is later used for analysis and point allocation.

[0302] Step 8:

[0303] Points awarded

[0304] The server awards points to users based on the collected response data. The input data is the survey response data, and the output data reflects the awarded points. Specifically, this is a process of adding points to the target user's account.

[0305] Step 9:

[0306] Persona Search

[0307] Users search based on persona genre and select a persona that suits their specific purpose. The input data are the search criteria, and the output data is a list of matching personas. This is the process of extracting appropriate personas from the database based on the search criteria.

[0308] Step 10:

[0309] Marketing Use

[0310] The server uses the selected persona for marketing strategies and service evaluation. Specific examples of use include formulating targeted advertising based on the persona and using it as a guide for product development. The input data is the selected persona, and the output data is a marketing report.

[0311] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0312] The purpose of the PersonaNet system of this invention is to allow users to create personas that reflect their own values ​​and emotions, and then share and use these with other users on a shared platform. This system incorporates an emotion engine that recognizes users' emotions and uses this information to generate and classify personas.

[0313] User Registration / Login

[0314] Users: New users create an account by entering the required information such as first name, last name, email address, and password. Existing users log in using their email address and password.

[0315] Terminal: Sends registration and login information to the server and receives responses from the server.

[0316] Server: Authenticates the received registration and login information by checking it against a database, and returns a success or error message to the device.

[0317] Persona creation

[0318] User: Once logged in, the user selects the "Create a Persona" option and begins answering a series of questions, which are designed based on psychology and statistics.

[0319] Device: The data provided by the user is temporarily stored and sent to the server once all questions have been answered. The data also includes elements for capturing the user's emotions.

[0320] Server: Passes the received response data to the emotion engine to recognize the user's emotion.

[0321] Server: The recognized emotional information is passed to the generation AI, which generates a persona that reflects the user's values ​​and emotions.

[0322] Persona genre classification

[0323] Server: The generated persona is run through a predictive algorithm to classify it into an appropriate genre based on the response data and emotional information. This genre information is also stored in a database.

[0324] Persona Publication

[0325] Server: Publish the categorized personas on a shared platform so that other users can view them.

[0326] Using Personas

[0327] Users: Other users can search for personas based on specific genres or conditions on the platform, select them as needed, and use the selected personas for marketing strategies and service evaluations.

[0328] Terminal: Sends search criteria to the server, receives and displays the corresponding persona data.

[0329] Server: Retrieves personas that match the search criteria from the database and returns them to the device.

[0330] Survey implementation and point awarding

[0331] Server: Send notifications to users to periodically survey them about their published personas.

[0332] User: The user receives a survey notification, answers the survey, and sends the data from the device to the server.

[0333] Server: Stores the received survey responses in a database and awards points to users who respond. These points can be exchanged for various rewards within the system.

[0334] Customizing AI logic

[0335] Users: Specific users, such as companies, can purchase options to customize the logic of the generative AI. This customization allows them to generate personas tailored to specific uses.

[0336] Server: Verify purchase information and apply customization options to user accounts.

[0337] For example, a user might create a new account and create a persona that reflects their values ​​and emotions. This persona is then categorized under the "youth marketing" category and published on a shared platform. Another user might search for personas in this category and use them to develop a business plan. A business user might also purchase customization options for the AI ​​generator, generating personas tailored to their specific needs and developing more sophisticated marketing strategies.

[0338] In this way, incorporating an emotion engine makes it possible to generate more accurate personas that take user emotions into account, allowing companies to develop more sophisticated marketing strategies. This system can reduce costs and improve data accuracy compared to traditional research methods.

[0339] The processing flow will be explained below.

[0340] Step 1:

[0341] A user visits the PersonaNet website and begins creating a new account.

[0342] Step 2:

[0343] The device makes an HTTP POST request to send the user's entered first name, last name, email address, and password to the server.

[0344] Step 3:

[0345] The server verifies the received registration information, stores it in a database, sends a confirmation email to the user that the registration was successful, and returns a success message to the terminal.

[0346] Step 4:

[0347] The user completes the email authentication, then proceeds to the login screen, enters their email address and password, and clicks the login button.

[0348] Step 5:

[0349] The terminal sends the login information to the server and makes an HTTP POST request to request authentication.

[0350] Step 6:

[0351] The server checks the received login information against a database, and if authentication is successful, generates session information and returns it to the terminal.

[0352] Step 7:

[0353] Users select the "Create a Persona" option and begin answering a series of questions, some of which are designed to capture the user's emotions.

[0354] Step 8:

[0355] The terminal temporarily stores the data answered by the user sequentially, and when all questions have been answered, it makes an HTTP POST request to send it to the server.

[0356] Step 9:

[0357] The server recognizes the user's emotions by passing the received response data to the emotion engine, which generates emotion information through linguistic and tone analysis of the response.

[0358] Step 10:

[0359] The server passes the generated emotional information to a generation AI, which generates a persona that reflects the user's values ​​and recognized emotions.

[0360] Step 11:

[0361] The server stores the generated persona data in a database, and uses predictive algorithms to classify the persona into the appropriate genre.

[0362] Step 12:

[0363] The server publishes the classified personas on a shared platform so that they can be viewed by users.

[0364] Step 13:

[0365] Users can search for personas based on specific genres or conditions on the shared platform, and can select personas that meet their needs, such as "marketing" or "product development."

[0366] Step 14:

[0367] The terminal sends the user's search criteria to the server and makes an HTTP GET request to obtain the corresponding persona.

[0368] Step 15:

[0369] The server retrieves personas that match the search criteria from the database and returns them to the terminal.

[0370] Step 16:

[0371] The terminal displays the acquired persona to the user.

[0372] Step 17:

[0373] The server periodically sends notifications to the relevant users to conduct a survey about the published personas.

[0374] Step 18:

[0375] The user receives a survey notification, answers the survey, and makes an HTTP POST request to send the data from the terminal to the server.

[0376] Step 19:

[0377] The server stores the received survey responses in a database and assigns points to the accounts of the users who responded.

[0378] Step 20:

[0379] The user can confirm that the points have been awarded on the shared platform and exchange the points for rewards within the system.

[0380] Step 21:

[0381] To customize the logic of the generative AI, the user, a company representative, accesses a page to purchase options and makes a payment.

[0382] Step 22:

[0383] The terminal makes an HTTP POST request to send the purchase information to the server.

[0384] Step 23:

[0385] The server validates the received purchase information and applies the customization options to the user account.

[0386] Example 2

[0387] 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."

[0388] In modern digital marketing and service evaluation, it is important to generate personas that accurately reflect users' values ​​and emotions. Conventional methods collect data without considering user emotions, which limits their accuracy. Furthermore, it is difficult to search, use, or customize the generated personas specifically for a company. This reduces the accuracy of marketing strategies and makes effective targeting difficult.

[0389] 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.

[0390] In this invention, the server includes: means for having users answer questions to generate a persona that reflects their values ​​and emotions and collecting the answer data; generation AI means for generating a persona based on the collected answer data and user emotion data; prediction algorithm means for classifying the generated personas; means for publishing the classified personas on a shared platform; means for users to search for and select personas based on specific genres or conditions; and means for viewing and using the selected personas. This enables the generation of highly accurate personas that take user emotions into consideration, allowing companies to effectively use the personas to improve targeting accuracy and optimize their marketing strategies.

[0391] A "user" is an entity that uses the system to generate, search, and use personas.

[0392] "Values" are the beliefs, opinions, and ways of thinking that form the basis for a user's actions.

[0393] "Emotion data" is information that indicates the user's emotional state and is recognized by the emotion engine.

[0394] "Answer data" refers to answer information to questions provided to the system by users.

[0395] A "persona" is a virtual character created based on a user's values ​​and emotional data.

[0396] The "generative AI means" is an artificial intelligence mechanism that generates personas based on collected response data and emotional data.

[0397] A "predictive algorithm" is an algorithm used to classify the generated personas.

[0398] The "sharing platform" is an online system for sharing generated personas with other users.

[0399] A "survey" is a survey question that is periodically provided to users and is a means of collecting response data.

[0400] The "point awarding means" is a mechanism for awarding points to users based on collected survey response data.

[0401] "Search tools" are functions that allow users to search for personas based on specific genres or conditions.

[0402] "Viewing means" is a function that allows users to display and check the personas selected by search.

[0403] "Corporate users" are specific users who have the authority to customize the logic of generative AI.

[0404] "Customization options" are settings that allow business users to tailor the logic of the generative AI to suit their specific needs.

[0405] The PersonaNet system of the present invention allows users to create personas that reflect their own values ​​and emotions, and then share and use those personas with other users on a shared platform. This system includes the following main elements:

[0406] 1. User Registration and Login

[0407] A user creates a new account by entering required information such as name, email address, and password. Existing users log in using their email address and password. The device sends this registration and login information to the server and receives a response from the server. The server compares the received information with a database and performs authentication. If authentication is successful, a success message is returned to the device; if it fails, an error message is returned.

[0408] 2. Persona Creation

[0409] After logging in, users select the "Create a Persona" option and answer a series of questions. These questions are designed based on psychology and statistics. The device temporarily stores the data the user has answered and sends it to the server once all questions have been answered. The answer data also includes elements to capture the user's emotions.

[0410] The server passes the received response data to an emotion engine (e.g., Microsoft® Azure® Text Analytics API) to recognize the user's emotion. It then passes the recognized emotion information and response data to a generative AI (e.g., OpenAI's GPT-3® model) to generate a persona. The generated persona is temporarily stored.

[0411] 3. Persona Genre Classification

[0412] The server then runs the generated personas through a predictive algorithm (e.g., a clustering algorithm using Scikit-learn) to classify them into appropriate genres. This classification information is then stored in a database.

[0413] 4. Publish your persona

[0414] The server publishes the categorized personas on a shared platform (e.g., a frontend using React) so that other users can view them.

[0415] 5. Use personas

[0416] Users can search for personas based on specific genres or conditions. The device sends the search conditions to the server, receives and displays the corresponding persona data. The server retrieves personas that match the search conditions from the database and returns them to the device.

[0417] 6. Survey implementation and point allocation

[0418] The server periodically conducts surveys regarding the published personas. The surveys are notified to the relevant users. The users receive the notification, answer the surveys, and send the data from their devices to the server. The server stores the received survey responses in a database and awards points to the users who answered. These points can be exchanged for various rewards within the system.

[0419] 7. Customizing AI logic

[0420] Corporate users can purchase options to customize the logic of the generative AI. The server verifies the purchase information and applies the customization options to the user account.

[0421] Specific examples

[0422] For example, a user can create a new account and answer questions to create a persona that reflects their values ​​and emotions. This persona is then categorized under the "youth marketing" category and published on a shared platform. Another user can search for personas in this category and use them to develop a business plan. A business user can also purchase customization options for the AI ​​generator to generate personas tailored to their specific needs, allowing them to develop more precise marketing strategies.

[0423] Prompt Sentence Examples

[0424] 1. Account creation prompt:

[0425] "You will now be registering with the PersonaNet system. Please enter your first and last name, email address, and password."

[0426] 2. Persona creation prompt:

[0427] "To create your persona, please answer the following questions. Please also include your emotional state."

[0428] 3. Persona search prompt:

[0429] "Search for personas in a specific genre. Enter the genre name."

[0430] This makes it possible to generate highly accurate personas that take user emotions into account, allowing companies to effectively utilize these personas to improve targeting accuracy and optimize their marketing strategies.

[0431] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0432] Step 1:

[0433] A user fills out a sign-up form with their name, email address, and password.

[0434] Enter your name, email address, and password.

[0435] Specific operation: The user enters the required information into the input fields and clicks the "Register" button.

[0436] Output: Input information is displayed on the terminal.

[0437] Step 2:

[0438] The device converts the entered registration information into JSON format and sends it to the server as an HTTPS POST request.

[0439] Input: Name, email address, and password entered by the user.

[0440] Specific behavior: Serializes input data and sends it to the server using a secure communication protocol.

[0441] Output: The server receives the request.

[0442] Step 3:

[0443] The server checks the received information against the database and performs authentication. If it is a new account, it is saved in the database. If an existing user logs in, authentication is performed.

[0444] Input: Name, email address, and password submitted by the user.

[0445] Specific behavior: Executes a database query to find user information, creates an account if new, or verifies information if existing.

[0446] Output: A success message or an error message is returned to the terminal as the authentication result.

[0447] Step 4:

[0448] After a user logs in, they select the "Create a Persona" option, which begins answering a series of questions.

[0449] Input: User selection, answer to question.

[0450] Specific operation: The user answers multiple questions presented by the system in sequence. Each answer is temporarily saved in local storage.

[0451] Output: All answer data for the question is saved.

[0452] Step 5:

[0453] The terminal temporarily stores the answer data to the questions and transmits it to the server when all questions have been answered.

[0454] Input: Answers to a series of questions.

[0455] Specific operation: When the user finishes inputting, the data stored in the local storage is converted to JSON format and sent to the server.

[0456] Output: The answer data is sent to the server.

[0457] Step 6:

[0458] The server passes the received response data to an emotion engine (e.g., Microsoft Azure's Text Analytics API) to recognize the user's emotion.

[0459] Input: Response data.

[0460] Specific operation: Sends response data to the emotion engine and receives an analyzed emotion score.

[0461] Output: A sentiment score is obtained.

[0462] Step 7:

[0463] The server passes the recognized emotional information to a generative AI (e.g., OpenAI's GPT-3 model) to generate a persona.

[0464] Input: Response data, sentiment scores.

[0465] Specific operations: Generate a prompt sentence, send it to the generative AI model, and receive the generated persona information.

[0466] Output: Generated persona information.

[0467] Step 8:

[0468] The server runs the generated personas through a predictive algorithm (e.g., a clustering algorithm using Scikit-learn) to classify them into categories. This information is then stored in a database.

[0469] Input: Generated persona information.

[0470] Specific operation: Apply a clustering algorithm to classify personas by genre.

[0471] Output: The classified persona information is stored in a database.

[0472] Step 9:

[0473] The server publishes the categorized persona data on a shared platform (e.g., a front-end using React).

[0474] Input: Classified persona information.

[0475] What it does: Retrieves classified persona information from the database and converts it into the appropriate format for display on a shared platform.

[0476] Output: The published persona information is displayed on a shared platform.

[0477] Step 10:

[0478] Users search for personas based on specific genres or criteria.

[0479] Input: Search criteria.

[0480] Specific behavior: The user enters search criteria into the search bar and clicks the search button.

[0481] Output: The search criteria is sent to the server.

[0482] Step 11:

[0483] The terminal sends the search criteria to the server, receives the corresponding persona data, and displays it.

[0484] Input: Search criteria.

[0485] What happens: A condition-based database query is executed, the relevant persona information is retrieved, and displayed to the user.

[0486] Output: The relevant persona information is displayed.

[0487] Step 12:

[0488] The server retrieves persona data that matches the search criteria from the database and returns it to the terminal.

[0489] Input: Search criteria.

[0490] Specific behavior: Executes a database query and returns the results to the device.

[0491] Output: Search results.

[0492] Step 13:

[0493] The server periodically sends notifications to users to survey them about their published personas.

[0494] Input: Survey trigger.

[0495] Specific actions: Define the survey content and send notifications to target users.

[0496] Output: Survey notification.

[0497] Step 14:

[0498] The user receives the survey notification, answers the survey, and transmits the data from the terminal to the server.

[0499] Input: Survey responses.

[0500] Specific action: Answer the survey and click the submit button.

[0501] Output: The answer data is sent to the server.

[0502] Step 15:

[0503] The server stores the received survey responses in a database and gives points to the users who responded.

[0504] Input: Survey response data.

[0505] What it does: Saves the answer data to a database and adds points to the user's account.

[0506] Output: Updated point data.

[0507] Step 16:

[0508] Enterprise users can purchase options to customize the logic of the generated AI.

[0509] Enter: Customization options.

[0510] Specific operation: After selecting the option and completing the purchase procedure, the server applies the option.

[0511] Output: The applied customization options.

[0512] Step 17:

[0513] The server verifies the purchase information and applies the customization options to the user account.

[0514] Input: Purchase information.

[0515] What it does: Verify purchase information and apply personalized settings to your user account.

[0516] Output: The user account whose settings were updated.

[0517] (Application example 2)

[0518] 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."

[0519] Modern advertising strategies require methods for detailed analysis of user values ​​and emotions and for targeting advertising based on those values. However, conventional systems are unable to fully utilize user emotional information, making it difficult to maximize advertising effectiveness. Furthermore, there is no established method for analyzing collected data in real time and dynamically optimizing advertising strategies. This makes it difficult to design and manage efficient and accurate advertising campaigns.

[0520] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0521] In this invention, the server includes: means for having users answer questions to generate a persona that reflects their values ​​and collecting the answer data; generation AI means for generating a persona based on the generated answer data; prediction algorithm means for classifying the generated persona; means for publishing the classified persona on a shared platform; emotion recognition means for analyzing the collected emotion data and utilizing the user's emotion information in persona generation; and means for integrating the persona based on the collected emotion data and values ​​into an advertising strategy. This enables highly accurate persona generation based on the user's values ​​and emotions, enabling efficient and dynamic design and management of advertising campaigns.

[0522] "Users" are users of the system who provide values ​​and emotional data to generate personas.

[0523] "Values" are fundamental beliefs and principles that influence a user's thoughts and actions.

[0524] A "persona" is a virtual character created based on a user's values ​​and emotional data, and is used as a target for marketing and advertising strategies.

[0525] A "question" is a query posed by the system to gather the user's values ​​and feelings.

[0526] "Response Data" means information provided by a user in response to a question.

[0527] "Generative AI means" is an artificial intelligence technology that analyzes response data and generates a persona that reflects the user's values ​​and emotions.

[0528] "Predictive algorithm means" is a data analysis technique for classifying the generated personas into appropriate genres.

[0529] The "sharing platform" is an online system for sharing generated and classified personas with other users.

[0530] "Emotion recognition means" is a technology that collects and analyzes user emotional data and uses it to generate personas.

[0531] An "advertising strategy" refers to a plan or method for effectively delivering advertisements to a target audience.

[0532] "Dynamic optimization" is a technique for instantly improving the content and delivery of advertising campaigns based on feedback collected in real time.

[0533] This invention is a system that allows users to create personas that reflect their own values ​​and emotions, and integrates and utilizes these personas in advertising campaigns. Detailed embodiments for realizing this system will be described below.

[0534] System Overview

[0535] 1. User Interface

[0536] The system is accessed by users through an application installed on a device such as a smartphone, smart glasses, or head-mounted display, and involves answering a series of questions that reflect their values.

[0537] 2. Emotional Data Collection

[0538] The device uses installed emotion recognition technology (e.g., Affectiva SDK, Kairos SDK) to collect emotional data from the user's facial expressions and voice, and analyzes the data in real time through the camera in the smart glasses or head-mounted display to identify the user's emotional state.

[0539] 3. Persona generation

[0540] The server generates a persona using a generative AI model (e.g., GPT-4) based on the collected response data and emotion data. The generative AI model generates a persona using the following prompt sentence:

[0541] Generate the following personas based on user sentiment data and values:

[0542] 1. Emotion data: [Happiness, Anger, Sadness, Surprise]

[0543] 2. Values: [Enterprising, Conservative, Sociable, Introverted]

[0544] For example, generate personas based on "enterprising and joyful" or "conservative and sensitive to surprises."

[0545] 4. Persona Classification

[0546] The generated personas are classified into appropriate genres by the server using a predictive algorithm (e.g., TENSORFLOW (registered trademark), PyTorch). The classified data is stored in a database.

[0547] 5. Share your personas

[0548] The classified personas are then published on a shared platform where they can be searched and selected by other users, who can then search for personas based on specific criteria and integrate them into their advertising campaigns.

[0549] 6. Integrate your advertising strategy

[0550] The server integrates the collected emotion data and generated personas into the advertising campaign management system, enabling highly accurate targeting of ads based on user emotions. It also monitors advertising effectiveness through real-time feedback and dynamically optimizes advertising strategies based on the feedback data.

[0551] Hardware and software used

[0552] Smart glasses / head-mounted displays: generic names (e.g., Google® Glass®, Microsoft HoloLens®)

[0553] Emotion Engine: Generic name (e.g., Affectiva SDK, Kairos SDK)

[0554] Generative AI model: Generic name (e.g., OpenAI GPT-4)

[0555] Machine learning framework: generic name (e.g., TensorFlow, PyTorch)

[0556] Database: Generic name (e.g., MySQL (registered trademark), MongoDB)

[0557] Cloud services: Generic names (e.g., Amazon Web Services (AWS (registered trademark)), Google Cloud Platform (GCP))

[0558] This approach makes it possible to effectively utilize user values ​​and emotional information, enabling highly accurate persona generation and dynamic optimization of advertising campaigns.

[0559] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0560] Step 1:

[0561] The user launches an application installed on a smartphone, smart glasses, or head-mounted display and logs in. First name, last name, email address, and password are required as input, and the server authenticates the user based on this information and returns a successful authentication message to the device.

[0562] Step 2:

[0563] After logging in, the user selects the "Create Persona" option and answers the provided questions, which include multiple psychological and statistical questions. The device temporarily stores the answer data and sends it to the server once the user has completed answering all the questions. The input contains the user's answer data, and the output is the data to be transmitted to the server.

[0564] Step 3:

[0565] The server passes the received response data to an emotion engine to analyze the user's emotion. The emotion recognition means analyzes the user's emotion data. The emotion data is input and emotion information is output.

[0566] Step 4:

[0567] The server generates a persona using a generative AI model based on the analyzed emotional information. Specifically, the server inputs the following prompt sentence into the generative AI model:

[0568] Generate the following personas based on user sentiment data and values:

[0569] 1. Emotion data: [Happiness, Anger, Sadness, Surprise]

[0570] 2. Values: [Enterprising, Conservative, Sociable, Introverted]

[0571] For example, generate personas based on "enterprising and joyful" or "conservative and sensitive to surprises."

[0572] The input includes emotional and value data, and the output is a generated persona.

[0573] Step 5:

[0574] The server classifies the generated personas into appropriate genres using a predictive algorithm. The classified personas are stored in a database, which contains the generated personas as input and provides the classification results as output.

[0575] Step 6:

[0576] The server publishes the classified personas on a shared platform, where other users can search and select personas based on specific criteria. The input contains the classified personas, and the output is the published persona information.

[0577] Step 7:

[0578] The user enters search criteria and selects an appropriate persona on the shared platform. The terminal sends the search criteria to the server, which receives and displays the corresponding persona data. The search criteria are included as input, and the corresponding persona information is obtained as output.

[0579] Step 8:

[0580] The server integrates the selected personas into an advertising campaign management system, monitors advertising effectiveness based on real-time feedback, and dynamically optimizes advertising strategies based on the feedback data. The inputs include the selected personas and real-time feedback data, and the output is an optimized advertising strategy.

[0581] 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.

[0582] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0583] 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.

[0584] [Second embodiment]

[0585] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0586] 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.

[0587] 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).

[0588] 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.

[0589] 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.

[0590] 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).

[0591] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0592] 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.

[0593] 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.

[0594] 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.

[0595] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0596] 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."

[0597] The purpose of the PersonaNet system of the present invention is to allow users to create personas that reflect their own values ​​and share and use them with other users on a shared platform. This system includes the following program processing.

[0598] User Registration / Login

[0599] Users: New users create an account by entering the required information such as first name, last name, email address, and password. Existing users log in using their email address and password.

[0600] Terminal: Sends registration and login information to the server and receives responses from the server.

[0601] Server: Authenticates the received registration and login information by checking it against a database, and returns a success or error message to the device.

[0602] Persona creation

[0603] User: Once logged in, the user selects the "Create a Persona" option and answers a series of questions designed around psychology and statistics.

[0604] Device: The data answered by the user is temporarily stored and sent to the server once all questions have been answered.

[0605] Server: Passes the received response data to the generation AI to generate a persona that reflects the user's values. The generated persona is then saved in a database.

[0606] Persona genre classification

[0607] Server: The generated personas are passed through a predictive algorithm to classify them into appropriate genres. This genre information is also stored in a database.

[0608] Persona Publication

[0609] Server: Publishes the categorized personas on a shared platform, where they can be viewed and used by other users.

[0610] Using Personas

[0611] Users: Other users can search for personas based on specific genres or conditions on the platform and select them as needed. The selected personas can then be used for marketing strategies and service evaluations.

[0612] Terminal: Sends search criteria to the server, receives and displays the corresponding persona data.

[0613] Server: Retrieves personas that match the search criteria from the database and sends them to the device.

[0614] Survey implementation and point awarding

[0615] Server: Periodically conduct surveys about published personas and send notifications to users.

[0616] User: The user answers the survey and sends the data to the server.

[0617] Server: Stores the answer data in a database and awards points to users who answer. These points can be exchanged for various rewards within the system.

[0618] Customizing AI logic

[0619] Users: Specific users, such as companies, can purchase options to customize the logic of the generative AI. This customization allows them to generate personas tailored to specific uses.

[0620] Server: Verify purchase information and apply customization options to user accounts.

[0621] As a concrete example, consider the following scenario: A user creates a new account and creates a persona that reflects their values. This persona is categorized under the "youth marketing" category and published on a shared platform. Another user searches for personas in this category and uses them to develop a business plan. A business user also purchases the generator's customization option to generate a persona tailored to their specific needs, allowing them to develop a more refined marketing strategy.

[0622] This allows companies to efficiently and accurately understand their target demographic and reflect this in their marketing strategies and product development. Compared to traditional survey methods, this system can reduce costs and improve data accuracy.

[0623] The processing flow will be explained below.

[0624] Step 1:

[0625] A user visits the PersonaNet website and begins creating a new account.

[0626] Step 2:

[0627] The device makes an HTTP POST request to send the user's entered first name, last name, email address, and password to the server.

[0628] Step 3:

[0629] The server verifies the received registration information, stores it in a database, sends a confirmation email to the user that the registration was successful, and returns a success message to the terminal.

[0630] Step 4:

[0631] The user completes the email authentication, then proceeds to the login screen, enters their email address and password, and clicks the login button.

[0632] Step 5:

[0633] The terminal sends the login information to the server and makes an HTTP POST request to request authentication.

[0634] Step 6:

[0635] The server checks the received login information against a database, and if authentication is successful, generates session information and returns it to the terminal.

[0636] Step 7:

[0637] The user selects the "Create a Persona" option and begins answering a series of questions that are presented to them.

[0638] Step 8:

[0639] The terminal temporarily stores the data answered by the user sequentially, and when all questions have been answered, it makes an HTTP POST request to send it to the server.

[0640] Step 9:

[0641] The server passes the received response data to the generation AI, which generates a persona that reflects the user's values.

[0642] Step 10:

[0643] The server stores the generated persona data in a database, and uses predictive algorithms to classify the persona into an appropriate genre.

[0644] Step 11:

[0645] The server publishes the classified personas on a shared platform, making them available for other users to view.

[0646] Step 12:

[0647] Users can search for personas based on specific genres or conditions on the shared platform, and can select personas that meet specific needs, such as "marketing" or "product development."

[0648] Step 13:

[0649] The terminal sends the user's search criteria to the server and makes an HTTP GET request to obtain the corresponding persona.

[0650] Step 14:

[0651] The server retrieves personas that match the search criteria from the database and returns them to the terminal.

[0652] Step 15:

[0653] The terminal displays the acquired persona to the user.

[0654] Step 16:

[0655] The server periodically sends notifications to the users to conduct a survey about the published personas.

[0656] Step 17:

[0657] The user receives the survey notification, answers the survey, and makes an HTTP POST request to send the data from the terminal to the server.

[0658] Step 18:

[0659] The server stores the received survey responses in a database and assigns points to the accounts of the users who responded.

[0660] Step 19:

[0661] Users can confirm that points have been awarded on the shared platform, and the points can be exchanged for various rewards within the system.

[0662] Step 20:

[0663] To customize the logic of the generative AI, the user, a company representative, accesses a page to purchase options and makes a payment.

[0664] Step 21:

[0665] The terminal makes an HTTP POST request to send the purchase information to the server.

[0666] Step 22:

[0667] The server validates the received purchase information and applies the customization options to the user account.

[0668] Example 1

[0669] 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."

[0670] Conventional persona generation systems were inadequate in generating and classifying personas that reflected user values, limiting their use in marketing strategies and service evaluations. Furthermore, they lacked the means to temporarily store or search information related to persona generation, making them inconvenient for users and difficult to use efficiently. Another issue was the inability to flexibly generate personas tailored to specific uses.

[0671] 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.

[0672] In this invention, the server includes: a means for collecting response data from users who answer questions to generate a persona that reflects their own values; a generation AI means for generating a persona based on the generated response data; a means for temporarily storing the response data until the persona is generated and transmitting the data once all questions have been answered; a predictive algorithm means for classifying the generated persona; a means for publishing the classified persona on a shared platform; a means for searching persona information based on specific conditions; and a means for using the selected persona in marketing strategies and service evaluations. This enables efficient and flexible generation and classification of personas that reflect users' values, enabling them to be used in marketing strategies and service evaluations. Furthermore, the ability to temporarily store and transmit response data related to persona generation and the addition of a search means improves user convenience.

[0673] "Means for answering questions and collecting response data" refers to a function in which a user inputs answers to multiple questions provided, and the system saves and retains the input.

[0674] "Generative AI method for generating personas" is an artificial intelligence technology that generates virtual characters that reflect the user's values ​​and characteristics based on collected response data.

[0675] "Means for temporarily saving and sending data once all questions have been answered" is a function for temporarily saving the answer data entered by the user one by one, and sending the data to the server once all questions have been answered.

[0676] A "predictive algorithm means" is an algorithm that uses machine learning or statistical techniques to appropriately classify the generated personas.

[0677] "Means for publishing on a shared platform" is a function that allows the generated persona to be displayed in a public space on the Internet, making it accessible and usable by other users.

[0678] "Means for searching persona information based on specific conditions" is a function that searches for persona information in the database based on conditions specified by the user and finds the relevant persona.

[0679] "Means of using selected personas for marketing strategies and service evaluation" is a function that uses personas selected from search results to help develop marketing plans and evaluate services.

[0680] "Means for providing a questionnaire and collecting response data" refers to a function for presenting a questionnaire to users and collecting their responses.

[0681] The "means for awarding points" is a function for awarding points as a reward to users based on the collected response data.

[0682] The purpose of the PersonaNet system of this invention is to allow users to create personas that reflect their own values ​​and share and use them with other users on a shared platform. This system uses the functions of the server, terminal, and user, and includes the following program processing.

[0683] User Registration / Login

[0684] Servers, devices, and users:

[0685] New users create an account by entering required information such as first name, last name, email address, and password. Existing users log in using their email address and password. The registration and login information is sent from the device to the server, which then authenticates it by checking it against a database. If authentication is successful, a success message is returned to the device, allowing the user to access the system.

[0686] Persona creation

[0687] Servers, devices, and users:

[0688] Once logged in, users select the "Create Persona" option and answer a series of questions. The questions are designed based on psychology and statistics. The answer data is temporarily saved on the device and sent to the server once all questions have been answered. The server then passes the received answer data to a generation AI (e.g., OpenAI GPT-4), which uses prompts to generate a persona that reflects the user's values. The generated persona is then saved in a database.

[0689] Persona genre classification

[0690] server:

[0691] The generated personas are analyzed using a predictive algorithm (e.g., Random Forest or K-means clustering). Based on the analysis results, the personas are classified into appropriate genres. This genre information is also stored in the database.

[0692] Persona Publication

[0693] server:

[0694] The categorized personas are published on a sharing platform, where they can be viewed and used by other users.

[0695] Using Personas

[0696] Servers, devices, and users:

[0697] Other users can search for personas based on specific genres or conditions and select them as needed. The search conditions are sent from the device to the server, and the corresponding persona data is returned and displayed. The selected personas can be used for marketing strategies and service evaluations.

[0698] Survey implementation and point awarding

[0699] Server, User:

[0700] The server periodically conducts surveys about the published personas and sends notifications to users. Users respond to the surveys and send the data to the server. The server stores the received response data in a database and awards points to users who respond. These points can be exchanged for various rewards within the system.

[0701] Customizing AI logic

[0702] Server, User:

[0703] Certain users, such as companies, can purchase options to customize the logic of the generation AI. This customization allows the generation of personas tailored to specific uses. The server verifies the purchase information and applies the customization options to the user account.

[0704] Specific examples

[0705] For example, a user might create a new account and create a persona that reflects their values. This persona is then categorized under "youth marketing" and published on a shared platform. Another user might search for personas in this category and use them to develop a business plan. A business user might also purchase customization options for the AI ​​generator to generate personas tailored to their specific needs, allowing them to develop more sophisticated marketing strategies.

[0706] Prompt Sentence Examples

[0707] "I want to create a new persona specifically for the 'youth marketing' genre. Please answer the following questions:

[0708] 1. Age: 25

[0709] 2. Gender: Male

[0710] 3. Hobbies: Sports, listening to music

[0711] 4. Occupation: Salaryman

[0712] 5. Birthplace: Tokyo

[0713] 6. Values: Health-conscious, career-oriented

[0714] By selecting the "Generate Persona" option, users can answer the above questions and generate a detailed persona.

[0715] In this way, the PersonaNet system uses generative AI to generate detailed personas based on user input, allowing them to be shared and used by other users.

[0716] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0717] Program processing flow

[0718] User Registration / Login

[0719] Step 1: Enter your registration information

[0720] User: Enter the required information such as name, email address, and password, and click the "Register" button. Input data: name, email address, and password.

[0721] Step 2: Submit your registration information

[0722] Terminal: Sends the entered information to the server. Data sent: First name, last name, email address, password.

[0723] Step 3: Verify your registration details

[0724] Server: Compares the received information with the database and generates an authentication token if successful. Data verification: New data vs database.

[0725] Server: Saves the user account in the database based on the registration details and returns a success message to the terminal. It may also return an error message. Output: An authentication token.

[0726] Step 4: Enter your login details

[0727] User: Enter your registered email address and password and click the "Login" button. Input data: Email address, password.

[0728] Step 5: Submit login information

[0729] Terminal: Sends input information to the server. Data sent: Email address, password.

[0730] Step 6: Verify your login details

[0731] Server: Compares the entered information with the database and performs authentication. If it is correct, generates a token to start a user session and returns it to the terminal. It may also return an error message. Output: Authentication token.

[0732] Persona creation

[0733] Step 7: Select persona creation options

[0734] User: After logging in, select the "Create Persona" option. Input data: Select the Create Persona option.

[0735] Step 8: Answer questions

[0736] User: Answers a series of questions displayed. Example questions: age, gender, hobbies, values, etc. Input data: Questions and answers.

[0737] Step 9: Temporarily save your answers

[0738] Terminal: Temporarily saves the answer data. The saved data is sent to the server once all questions have been answered. Data processing: Temporarily saves and prepares the data for transmission.

[0739] Step 10: Submit your response data

[0740] Terminal: After answering all questions, send the data to the server. Data transmission: All answer data.

[0741] Step 11: Generate personas

[0742] Server: Passes the received answer data to a generation AI (e.g., OpenAI GPT-4) and generates a persona using the prompt. Data calculation: Persona generation by AI.

[0743] Server: Save the generated personas to the database. Output: The generated personas.

[0744] Persona genre classification

[0745] Step 12: Persona Analysis

[0746] Server: Analyzes the generated personas using predictive algorithms (e.g., Random Forest or K-means clustering). Data calculation: Persona analysis.

[0747] Step 13: Genre Classification

[0748] Server: Based on the analysis results, classify the persona into the appropriate genre. The classification information is also saved in the database. Output: Genre classification.

[0749] Persona Publication

[0750] Step 14: Prepare for publication

[0751] Server: Formats the categorized persona information for the shared platform. Data processing: Converts it into a publicly available format.

[0752] Step 15: Publishing

[0753] Server: Publishes personas onto a shared platform, making them visible and available to other users. Output: Published personas.

[0754] Using Personas

[0755] Step 16: Persona Search

[0756] User: Search for personas by specifying a specific genre or conditions. Input data: Search conditions.

[0757] Step 17: Submitting search criteria

[0758] Terminal: Sends search criteria to the server. Data transmission: Search criteria.

[0759] Step 18: Search based on criteria

[0760] Server: Retrieves personas that match the search criteria from the database and sends them to the device. Output: Personas from the search results.

[0761] Step 19: Persona Display

[0762] Terminal: Display persona information for search results. Data display: Search results.

[0763] Step 20: Use Personas

[0764] User: Use the selected persona for marketing strategies and service evaluation. Use the persona based on usage scenarios. Output: Analysis results based on usage scenarios.

[0765] Survey implementation and point awarding

[0766] Step 21: Survey Notification

[0767] Server: Periodically conducts surveys about published personas and sends notifications to users. Output: Survey notifications.

[0768] Step 22: Complete the survey

[0769] User: Answers the survey and sends the data to the server. Input data: Survey responses.

[0770] Step 23: Save the response data

[0771] Server: Saves the received response data in a database. Data storage: Response data.

[0772] Step 24: Points awarded

[0773] Server: Based on the answer data, points are awarded to the user who answered. These points can be exchanged for rewards within the system. Output: Awarded points.

[0774] Customizing AI logic

[0775] Step 25: Purchase customization options

[0776] User: Purchase logic customization options for the generation AI. Input data: Purchase information.

[0777] Step 26: Apply customizations

[0778] Server: Verify purchase information and apply customization options to user account. Output: Customization options.

[0779] (Application example 1)

[0780] 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."

[0781] In today's content distribution services, users have difficulty finding content that is optimized for their values ​​and interests, and there is a need for a system that can provide recommendations tailored to individual preferences. Furthermore, there is a lack of functionality that allows users to share personas with others and mutually recommend content. Therefore, there is an urgent need to develop a content recommendation system that accurately reflects user preferences.

[0782] 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.

[0783] In this invention, the server includes a means for allowing users to answer questions to generate a persona that reflects their own values ​​and collecting the answer data, a generation AI means for generating a persona based on the generated answer data, a means for recommending individual content based on the user's persona, and a means for publishing the classified persona on a sharing platform. This allows users to easily find content optimized for their preferences, and also enhances the function of sharing personas with other users and discovering content to recommend to each other.

[0784] "User" refers to an individual who uses this system, creates a persona that reflects their own values, and receives content recommendations.

[0785] "Values" refer to internal standards and guidelines that influence a user's behavior and preferences, such as personal beliefs, interests, and tastes.

[0786] A "persona" is a virtual character that reflects a user's values ​​and interests, and is used for content recommendations and marketing strategies.

[0787] "Means of answering questions and collecting response data" refers to the system's functionality of presenting questions to users to generate personas and collecting their responses as data.

[0788] "Generative AI means" refers to artificial intelligence technology for generating personas based on collected response data.

[0789] "Predictive algorithm means" refers to algorithm technology for analyzing personas generated by generative AI means and classifying them into appropriate genres.

[0790] "Means for recommending content" refers to a function that automatically selects and recommends content appropriate for a user based on the user's generated persona.

[0791] "Means for publishing" refers to the system's functionality for publishing classified personas on a shared platform so that they can be viewed and used by other users.

[0792] "Means for providing surveys and collecting response data" refers to the system's function of periodically presenting surveys to users and collecting their response data.

[0793] "Means for awarding points" refers to the system's function of awarding points to users based on collected response data and allowing them to exchange those points for rewards.

[0794] "Means for searching and selecting a persona" refers to the function of searching based on the genre of the generated personas and selecting a persona that suits a specific purpose.

[0795] "Means used for evaluating marketing strategies and services" refers to functions used to evaluate marketing strategies and services based on the selected personas.

[0796] "Genre" refers to the category into which personas are classified, and is divided based on hobbies, preferences, interests, etc.

[0797] This invention describes a system that allows a user to create a persona that reflects their own values ​​and recommends individual content based on that persona. Specific embodiments for realizing this system are described below.

[0798] 1. System Configuration

[0799] The system mainly consists of the following components:

[0800] User device: A device such as a smartphone, tablet, or computer.

[0801] Server: A remote server that processes and stores data.

[0802] AI model: Software that generates personas using generative AI technology.

[0803] 2. Program Processing

[0804] 2.1 User Registration / Login

[0805] To use the system, a user must first create an account. New users enter their name, email address, and password, and this information is sent from the user's device to the server. The server stores this information in a database and uses it as authentication information.

[0806] 2.2 Persona Creation

[0807] When a user logs in, they are presented with questions to generate a persona. The answers are temporarily stored on the user's device and then sent to the server, which then uses a generative AI model to generate a persona that reflects the user's values.

[0808] 2.3 Classifying and Publishing Personas

[0809] The generated personas are classified into genres by the server using a predictive algorithm, and the classified personas are published on a shared platform for other users to view and use.

[0810] 2.4 Content Recommendations

[0811] The server recommends content suitable for the user based on the generated persona, including movies, music, books, etc. The user's device receives and displays the recommendation list.

[0812] 2.5 Surveys and point allocation

[0813] Periodically, the server will provide users with surveys and collect their responses. Based on the collected data, users will be awarded points, which can be redeemed for rewards.

[0814] 2.6 Search and Selection

[0815] Users can search based on the genre of the generated personas and select the appropriate persona. The selected persona can be used for marketing strategies and service evaluation.

[0816] 3. Hardware and Software Used

[0817] Hardware: Smartphones, tablets, PCs, remote servers

[0818] Software: Generative AI models, predictive algorithms, database management systems, web server software (e.g., Flask)

[0819] Specific examples

[0820] Example prompts to generate personas based on user responses:

[0821] Generate personas based on your users' values ​​and interests using the following answers:

[0822] Answer 1: [User Answer 1]

[0823] Answer 2: [User Answer 2]

[0824] Answer 3: [User Answer 3]

[0825] ...

[0826] The generated results should be output in the following format:

[0827] {

[0828] "persona": {

[0829] "Personality": "〇〇",

[0830] "Interest": "〇〇",

[0831] "Values": "〇〇"

[0832] }

[0833] }

[0834] These technological elements allow users to easily find content that is optimized for their preferences, and also provide a comprehensive feature that allows users to share personas with other users and discover content that they can recommend to each other.

[0835] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0836] Step 1:

[0837] User Registration

[0838] The user enters their name, email address, and password and sends a registration request to the server, which stores this information in a database. The input data is name, email address, and password, and the output data is a message indicating whether registration was successful or not.

[0839] Step 2:

[0840] User Login

[0841] The user enters their email address and password and sends a login request to the server, which then checks the information in the database and authenticates them. The input data is the email address and password, and the output data is a message indicating whether authentication was successful or not.

[0842] Step 3:

[0843] Persona Generation

[0844] After logging in, users answer a series of questions presented by the system. These answers are temporarily stored on the device and then sent to the server. The server passes the answers to a generative AI model, which generates a persona that reflects the user's values. The input data is the answers, and the generated persona is returned as the output data.

[0845] Step 4:

[0846] Persona Classification

[0847] The server uses a predictive algorithm to classify the generated personas into genres. The input data is the generated persona, and the output data is the classified genre. The specific operation is to analyze the characteristics of the persona and classify it into the appropriate category.

[0848] Step 5:

[0849] Persona Announcement

[0850] The server publishes the classified personas on a shared platform. The input data is the classified personas, and the published personas are available as output data. The published personas can be viewed and used by other users.

[0851] Step 6:

[0852] Content Recommendation

[0853] The server recommends appropriate content to the user based on the generated persona. The recommendation list is sent to the device. The input data is the persona and a large amount of content data, and the output data is a list of recommended content. Specifically, the content is filtered based on the characteristics of the persona, and the most suitable items are selected.

[0854] Step 7:

[0855] Survey

[0856] The server periodically provides users with surveys and collects their responses. The input data is the survey responses, and the output data is the collected response data. This data is later used for analysis and point allocation.

[0857] Step 8:

[0858] Points awarded

[0859] The server awards points to users based on the collected response data. The input data is the survey response data, and the output data reflects the awarded points. Specifically, this is a process of adding points to the target user's account.

[0860] Step 9:

[0861] Persona Search

[0862] Users search based on persona genre and select a persona that suits their specific purpose. The input data are the search criteria, and the output data is a list of matching personas. This is the process of extracting appropriate personas from the database based on the search criteria.

[0863] Step 10:

[0864] Marketing Use

[0865] The server uses the selected persona for marketing strategies and service evaluation. Specific examples of use include formulating targeted advertising based on the persona and using it as a guide for product development. The input data is the selected persona, and the output data is a marketing report.

[0866] 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.

[0867] The purpose of the PersonaNet system of this invention is to allow users to create personas that reflect their own values ​​and emotions, and then share and use these with other users on a shared platform. This system incorporates an emotion engine that recognizes users' emotions and uses this information to generate and classify personas.

[0868] User Registration / Login

[0869] Users: New users create an account by entering the required information such as first name, last name, email address, and password. Existing users log in using their email address and password.

[0870] Terminal: Sends registration and login information to the server and receives responses from the server.

[0871] Server: Authenticates the received registration and login information by checking it against a database, and returns a success or error message to the device.

[0872] Persona creation

[0873] User: Once logged in, the user selects the "Create a Persona" option and begins answering a series of questions, which are designed based on psychology and statistics.

[0874] Device: The data provided by the user is temporarily stored and sent to the server once all questions have been answered. The data also includes elements for capturing the user's emotions.

[0875] Server: Passes the received response data to the emotion engine to recognize the user's emotion.

[0876] Server: The recognized emotional information is passed to the generation AI, which generates a persona that reflects the user's values ​​and emotions.

[0877] Persona genre classification

[0878] Server: The generated persona is run through a predictive algorithm to classify it into an appropriate genre based on the response data and emotional information. This genre information is also stored in a database.

[0879] Persona Publication

[0880] Server: Publish the categorized personas on a shared platform so that other users can view them.

[0881] Using Personas

[0882] Users: Other users can search for personas based on specific genres or conditions on the platform, select them as needed, and use the selected personas for marketing strategies and service evaluations.

[0883] Terminal: Sends search criteria to the server, receives and displays the corresponding persona data.

[0884] Server: Retrieves personas that match the search criteria from the database and returns them to the device.

[0885] Survey implementation and point awarding

[0886] Server: Send notifications to users to periodically survey them about their published personas.

[0887] User: The user receives a survey notification, answers the survey, and sends the data from the device to the server.

[0888] Server: Stores the received survey responses in a database and awards points to users who respond. These points can be exchanged for various rewards within the system.

[0889] Customizing AI logic

[0890] Users: Specific users, such as companies, can purchase options to customize the logic of the generative AI. This customization allows them to generate personas tailored to specific uses.

[0891] Server: Verify purchase information and apply customization options to user accounts.

[0892] For example, a user might create a new account and create a persona that reflects their values ​​and emotions. This persona is then categorized under the "youth marketing" category and published on a shared platform. Another user might search for personas in this category and use them to develop a business plan. A business user might also purchase customization options for the AI ​​generator, generating personas tailored to their specific needs and developing more sophisticated marketing strategies.

[0893] In this way, incorporating an emotion engine makes it possible to generate more accurate personas that take user emotions into account, allowing companies to develop more sophisticated marketing strategies. This system can reduce costs and improve data accuracy compared to traditional research methods.

[0894] The processing flow will be explained below.

[0895] Step 1:

[0896] A user visits the PersonaNet website and begins creating a new account.

[0897] Step 2:

[0898] The device makes an HTTP POST request to send the user's entered first name, last name, email address, and password to the server.

[0899] Step 3:

[0900] The server verifies the received registration information, stores it in a database, sends a confirmation email to the user that the registration was successful, and returns a success message to the terminal.

[0901] Step 4:

[0902] The user completes the email authentication, then proceeds to the login screen, enters their email address and password, and clicks the login button.

[0903] Step 5:

[0904] The terminal sends the login information to the server and makes an HTTP POST request to request authentication.

[0905] Step 6:

[0906] The server checks the received login information against a database, and if authentication is successful, generates session information and returns it to the terminal.

[0907] Step 7:

[0908] Users select the "Create a Persona" option and begin answering a series of questions, some of which are designed to capture the user's emotions.

[0909] Step 8:

[0910] The terminal temporarily stores the data answered by the user sequentially, and when all questions have been answered, it makes an HTTP POST request to send it to the server.

[0911] Step 9:

[0912] The server recognizes the user's emotions by passing the received response data to the emotion engine, which generates emotion information through linguistic and tone analysis of the response.

[0913] Step 10:

[0914] The server passes the generated emotional information to a generation AI, which generates a persona that reflects the user's values ​​and recognized emotions.

[0915] Step 11:

[0916] The server stores the generated persona data in a database, and uses predictive algorithms to classify the persona into the appropriate genre.

[0917] Step 12:

[0918] The server publishes the classified personas on a shared platform so that they can be viewed by users.

[0919] Step 13:

[0920] Users can search for personas based on specific genres or conditions on the shared platform, and can select personas that meet their needs, such as "marketing" or "product development."

[0921] Step 14:

[0922] The terminal sends the user's search criteria to the server and makes an HTTP GET request to obtain the corresponding persona.

[0923] Step 15:

[0924] The server retrieves personas that match the search criteria from the database and returns them to the terminal.

[0925] Step 16:

[0926] The terminal displays the acquired persona to the user.

[0927] Step 17:

[0928] The server periodically sends notifications to the relevant users to conduct a survey about the published personas.

[0929] Step 18:

[0930] The user receives a survey notification, answers the survey, and makes an HTTP POST request to send the data from the terminal to the server.

[0931] Step 19:

[0932] The server stores the received survey responses in a database and assigns points to the accounts of the users who responded.

[0933] Step 20:

[0934] The user can confirm that the points have been awarded on the shared platform and exchange the points for rewards within the system.

[0935] Step 21:

[0936] To customize the logic of the generative AI, the user, a company representative, accesses a page to purchase options and makes a payment.

[0937] Step 22:

[0938] The terminal makes an HTTP POST request to send the purchase information to the server.

[0939] Step 23:

[0940] The server validates the received purchase information and applies the customization options to the user account.

[0941] Example 2

[0942] 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."

[0943] In modern digital marketing and service evaluation, it is important to generate personas that accurately reflect users' values ​​and emotions. Conventional methods collect data without considering user emotions, which limits their accuracy. Furthermore, it is difficult to search, use, or customize the generated personas specifically for a company. This reduces the accuracy of marketing strategies and makes effective targeting difficult.

[0944] 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.

[0945] In this invention, the server includes: means for having users answer questions to generate a persona that reflects their values ​​and emotions and collecting the answer data; generation AI means for generating a persona based on the collected answer data and user emotion data; prediction algorithm means for classifying the generated personas; means for publishing the classified personas on a shared platform; means for users to search for and select personas based on specific genres or conditions; and means for viewing and using the selected personas. This enables the generation of highly accurate personas that take user emotions into consideration, allowing companies to effectively use the personas to improve targeting accuracy and optimize their marketing strategies.

[0946] A "user" is an entity that uses the system to generate, search, and use personas.

[0947] "Values" are the beliefs, opinions, and ways of thinking that form the basis for a user's actions.

[0948] "Emotion data" is information that indicates the user's emotional state and is recognized by the emotion engine.

[0949] "Answer data" refers to answer information to questions provided to the system by users.

[0950] A "persona" is a virtual character created based on a user's values ​​and emotional data.

[0951] The "generative AI means" is an artificial intelligence mechanism that generates personas based on collected response data and emotional data.

[0952] A "predictive algorithm" is an algorithm used to classify the generated personas.

[0953] The "sharing platform" is an online system for sharing generated personas with other users.

[0954] A "survey" is a survey question that is periodically provided to users and is a means of collecting response data.

[0955] The "point awarding means" is a mechanism for awarding points to users based on collected survey response data.

[0956] "Search tools" are functions that allow users to search for personas based on specific genres or conditions.

[0957] "Viewing means" is a function that allows users to display and check the personas selected by search.

[0958] "Corporate users" are specific users who have the authority to customize the logic of generative AI.

[0959] "Customization options" are settings that allow business users to tailor the logic of the generative AI to suit their specific needs.

[0960] The PersonaNet system of the present invention allows users to create personas that reflect their own values ​​and emotions, and then share and use those personas with other users on a shared platform. This system includes the following main elements:

[0961] 1. User Registration and Login

[0962] A user creates a new account by entering required information such as name, email address, and password. Existing users log in using their email address and password. The device sends this registration and login information to the server and receives a response from the server. The server compares the received information with a database and performs authentication. If authentication is successful, a success message is returned to the device; if it fails, an error message is returned.

[0963] 2. Persona Creation

[0964] After logging in, users select the "Create a Persona" option and answer a series of questions. These questions are designed based on psychology and statistics. The device temporarily stores the data the user has answered and sends it to the server once all questions have been answered. The answer data also includes elements to capture the user's emotions.

[0965] The server passes the received response data to an emotion engine (e.g., Microsoft Azure's Text Analytics API) to recognize the user's emotion. It then passes the recognized emotion information and response data to a generative AI (e.g., OpenAI's GPT-3 model) to generate a persona. The generated persona is temporarily stored.

[0966] 3. Persona Genre Classification

[0967] The server then runs the generated personas through a predictive algorithm (e.g., a clustering algorithm using Scikit-learn) to classify them into appropriate genres. This classification information is then stored in a database.

[0968] 4. Publish your persona

[0969] The server publishes the categorized personas on a shared platform (e.g., a frontend using React) so that other users can view them.

[0970] 5. Use personas

[0971] Users can search for personas based on specific genres or conditions. The device sends the search conditions to the server, receives and displays the corresponding persona data. The server retrieves personas that match the search conditions from the database and returns them to the device.

[0972] 6. Survey implementation and point allocation

[0973] The server periodically conducts surveys regarding the published personas. The surveys are notified to the relevant users. The users receive the notification, answer the surveys, and send the data from their devices to the server. The server stores the received survey responses in a database and awards points to the users who answered. These points can be exchanged for various rewards within the system.

[0974] 7. Customizing AI logic

[0975] Corporate users can purchase options to customize the logic of the generative AI. The server verifies the purchase information and applies the customization options to the user account.

[0976] Specific examples

[0977] For example, a user can create a new account and answer questions to create a persona that reflects their values ​​and emotions. This persona is then categorized under the "youth marketing" category and published on a shared platform. Another user can search for personas in this category and use them to develop a business plan. A business user can also purchase customization options for the AI ​​generator to generate personas tailored to their specific needs, allowing them to develop more precise marketing strategies.

[0978] Prompt Sentence Examples

[0979] 1. Account creation prompt:

[0980] "You will now be registering with the PersonaNet system. Please enter your first and last name, email address, and password."

[0981] 2. Persona creation prompt:

[0982] "To create your persona, please answer the following questions. Please also include your emotional state."

[0983] 3. Persona search prompt:

[0984] "Search for personas in a specific genre. Enter the genre name."

[0985] This makes it possible to generate highly accurate personas that take user emotions into account, allowing companies to effectively utilize these personas to improve targeting accuracy and optimize their marketing strategies.

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

[0987] Step 1:

[0988] A user fills out a sign-up form with their name, email address, and password.

[0989] Enter your name, email address, and password.

[0990] Specific operation: The user enters the required information into the input fields and clicks the "Register" button.

[0991] Output: Input information is displayed on the terminal.

[0992] Step 2:

[0993] The device converts the entered registration information into JSON format and sends it to the server as an HTTPS POST request.

[0994] Input: Name, email address, and password entered by the user.

[0995] Specific behavior: Serializes input data and sends it to the server using a secure communication protocol.

[0996] Output: The server receives the request.

[0997] Step 3:

[0998] The server checks the received information against the database and performs authentication. If it is a new account, it is saved in the database. If an existing user logs in, authentication is performed.

[0999] Input: Name, email address, and password submitted by the user.

[1000] Specific behavior: Executes a database query to find user information, creates an account if new, or verifies information if existing.

[1001] Output: A success message or an error message is returned to the terminal as the authentication result.

[1002] Step 4:

[1003] After a user logs in, they select the "Create a Persona" option, which begins answering a series of questions.

[1004] Input: User selection, answer to question.

[1005] Specific operation: The user answers multiple questions presented by the system in sequence. Each answer is temporarily saved in local storage.

[1006] Output: All answer data for the question is saved.

[1007] Step 5:

[1008] The terminal temporarily stores the answer data to the questions and transmits it to the server when all questions have been answered.

[1009] Input: Answers to a series of questions.

[1010] Specific operation: When the user finishes inputting, the data stored in the local storage is converted to JSON format and sent to the server.

[1011] Output: The answer data is sent to the server.

[1012] Step 6:

[1013] The server passes the received response data to an emotion engine (e.g., Microsoft Azure's Text Analytics API) to recognize the user's emotion.

[1014] Input: Response data.

[1015] Specific operation: Sends response data to the emotion engine and receives an analyzed emotion score.

[1016] Output: A sentiment score is obtained.

[1017] Step 7:

[1018] The server passes the recognized emotional information to a generative AI (e.g., OpenAI's GPT-3 model) to generate a persona.

[1019] Input: Response data, sentiment scores.

[1020] Specific operations: Generate a prompt sentence, send it to the generative AI model, and receive the generated persona information.

[1021] Output: Generated persona information.

[1022] Step 8:

[1023] The server runs the generated personas through a predictive algorithm (e.g., a clustering algorithm using Scikit-learn) to classify them into categories. This information is then stored in a database.

[1024] Input: Generated persona information.

[1025] Specific operation: Apply a clustering algorithm to classify personas by genre.

[1026] Output: The classified persona information is stored in a database.

[1027] Step 9:

[1028] The server publishes the categorized persona data on a shared platform (e.g., a front-end using React).

[1029] Input: Classified persona information.

[1030] What it does: Retrieves classified persona information from the database and converts it into the appropriate format for display on a shared platform.

[1031] Output: The published persona information is displayed on a shared platform.

[1032] Step 10:

[1033] Users search for personas based on specific genres or criteria.

[1034] Input: Search criteria.

[1035] Specific behavior: The user enters search criteria into the search bar and clicks the search button.

[1036] Output: The search criteria is sent to the server.

[1037] Step 11:

[1038] The terminal sends the search criteria to the server, receives the corresponding persona data, and displays it.

[1039] Input: Search criteria.

[1040] What happens: A condition-based database query is executed, the relevant persona information is retrieved, and displayed to the user.

[1041] Output: The relevant persona information is displayed.

[1042] Step 12:

[1043] The server retrieves persona data that matches the search criteria from the database and returns it to the terminal.

[1044] Input: Search criteria.

[1045] Specific behavior: Executes a database query and returns the results to the device.

[1046] Output: Search results.

[1047] Step 13:

[1048] The server periodically sends notifications to users to survey them about their published personas.

[1049] Input: Survey trigger.

[1050] Specific actions: Define the survey content and send notifications to target users.

[1051] Output: Survey notification.

[1052] Step 14:

[1053] The user receives the survey notification, answers the survey, and transmits the data from the terminal to the server.

[1054] Input: Survey responses.

[1055] Specific action: Answer the survey and click the submit button.

[1056] Output: The answer data is sent to the server.

[1057] Step 15:

[1058] The server stores the received survey responses in a database and gives points to the users who responded.

[1059] Input: Survey response data.

[1060] What it does: Saves the answer data to a database and adds points to the user's account.

[1061] Output: Updated point data.

[1062] Step 16:

[1063] Enterprise users can purchase options to customize the logic of the generated AI.

[1064] Enter: Customization options.

[1065] Specific operation: After selecting the option and completing the purchase procedure, the server applies the option.

[1066] Output: The applied customization options.

[1067] Step 17:

[1068] The server verifies the purchase information and applies the customization options to the user account.

[1069] Input: Purchase information.

[1070] What it does: Verify purchase information and apply personalized settings to your user account.

[1071] Output: The user account whose settings were updated.

[1072] (Application example 2)

[1073] 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."

[1074] Modern advertising strategies require methods for detailed analysis of user values ​​and emotions and for targeting advertising based on those values. However, conventional systems are unable to fully utilize user emotional information, making it difficult to maximize advertising effectiveness. Furthermore, there is no established method for analyzing collected data in real time and dynamically optimizing advertising strategies. This makes it difficult to design and manage efficient and accurate advertising campaigns.

[1075] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1076] In this invention, the server includes: means for having users answer questions to generate a persona that reflects their values ​​and collecting the answer data; generation AI means for generating a persona based on the generated answer data; prediction algorithm means for classifying the generated persona; means for publishing the classified persona on a shared platform; emotion recognition means for analyzing the collected emotion data and utilizing the user's emotion information in persona generation; and means for integrating the persona based on the collected emotion data and values ​​into an advertising strategy. This enables highly accurate persona generation based on the user's values ​​and emotions, enabling efficient and dynamic design and management of advertising campaigns.

[1077] "Users" are users of the system who provide values ​​and emotional data to generate personas.

[1078] "Values" are fundamental beliefs and principles that influence a user's thoughts and actions.

[1079] A "persona" is a virtual character created based on a user's values ​​and emotional data, and is used as a target for marketing and advertising strategies.

[1080] A "question" is a query posed by the system to gather the user's values ​​and feelings.

[1081] "Response Data" means information provided by a user in response to a question.

[1082] "Generative AI means" is an artificial intelligence technology that analyzes response data and generates a persona that reflects the user's values ​​and emotions.

[1083] "Predictive algorithm means" is a data analysis technique for classifying the generated personas into appropriate genres.

[1084] The "sharing platform" is an online system for sharing generated and classified personas with other users.

[1085] "Emotion recognition means" is a technology that collects and analyzes user emotional data and uses it to generate personas.

[1086] An "advertising strategy" refers to a plan or method for effectively delivering advertisements to a target audience.

[1087] "Dynamic optimization" is a technique for instantly improving the content and delivery of advertising campaigns based on feedback collected in real time.

[1088] This invention is a system that allows users to create personas that reflect their own values ​​and emotions, and integrates and utilizes these personas in advertising campaigns. Detailed embodiments for realizing this system will be described below.

[1089] System Overview

[1090] 1. User Interface

[1091] The system is accessed by users through an application installed on a device such as a smartphone, smart glasses, or head-mounted display, and involves answering a series of questions that reflect their values.

[1092] 2. Emotional Data Collection

[1093] The device uses installed emotion recognition technology (e.g., Affectiva SDK, Kairos SDK) to collect emotional data from the user's facial expressions and voice, and analyzes the data in real time through the camera in the smart glasses or head-mounted display to identify the user's emotional state.

[1094] 3. Persona generation

[1095] The server generates a persona using a generative AI model (e.g., GPT-4) based on the collected response data and emotion data. The generative AI model generates a persona using the following prompt sentence:

[1096] Generate the following personas based on user sentiment data and values:

[1097] 1. Emotion data: [Happiness, Anger, Sadness, Surprise]

[1098] 2. Values: [Enterprising, Conservative, Sociable, Introverted]

[1099] For example, generate personas based on "enterprising and joyful" or "conservative and sensitive to surprises."

[1100] 4. Persona Classification

[1101] The generated personas are classified into appropriate genres by the server using a predictive algorithm (e.g., TensorFlow, PyTorch). The classified data is stored in a database.

[1102] 5. Share your personas

[1103] The classified personas are then published on a shared platform where they can be searched and selected by other users, who can then search for personas based on specific criteria and integrate them into their advertising campaigns.

[1104] 6. Integrate your advertising strategy

[1105] The server integrates the collected emotion data and generated personas into the advertising campaign management system, enabling highly accurate targeting of ads based on user emotions. It also monitors advertising effectiveness through real-time feedback and dynamically optimizes advertising strategies based on the feedback data.

[1106] Hardware and software used

[1107] Smart glasses / head-mounted displays: generic names (e.g., Google Glass, Microsoft HoloLens)

[1108] Emotion Engine: Generic name (e.g., Affectiva SDK, Kairos SDK)

[1109] Generative AI model: Generic name (e.g., OpenAI GPT-4)

[1110] Machine learning framework: generic name (e.g., TensorFlow, PyTorch)

[1111] Database: Generic name (e.g. MySQL, MongoDB)

[1112] Cloud services: generic names (e.g., Amazon Web Services (AWS), Google Cloud Platform (GCP))

[1113] This approach makes it possible to effectively utilize user values ​​and emotional information, enabling highly accurate persona generation and dynamic optimization of advertising campaigns.

[1114] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1115] Step 1:

[1116] The user launches an application installed on a smartphone, smart glasses, or head-mounted display and logs in. First name, last name, email address, and password are required as input, and the server authenticates the user based on this information and returns a successful authentication message to the device.

[1117] Step 2:

[1118] After logging in, the user selects the "Create Persona" option and answers the provided questions, which include multiple psychological and statistical questions. The device temporarily stores the answer data and sends it to the server once the user has completed answering all the questions. The input contains the user's answer data, and the output is the data to be transmitted to the server.

[1119] Step 3:

[1120] The server passes the received response data to an emotion engine to analyze the user's emotion. The emotion recognition means analyzes the user's emotion data. The emotion data is input and emotion information is output.

[1121] Step 4:

[1122] The server generates a persona using a generative AI model based on the analyzed emotional information. Specifically, the server inputs the following prompt sentence into the generative AI model:

[1123] Generate the following personas based on user sentiment data and values:

[1124] 1. Emotion data: [Happiness, Anger, Sadness, Surprise]

[1125] 2. Values: [Enterprising, Conservative, Sociable, Introverted]

[1126] For example, generate personas based on "enterprising and joyful" or "conservative and sensitive to surprises."

[1127] The input includes emotional and value data, and the output is a generated persona.

[1128] Step 5:

[1129] The server classifies the generated personas into appropriate genres using a predictive algorithm. The classified personas are stored in a database, which contains the generated personas as input and provides the classification results as output.

[1130] Step 6:

[1131] The server publishes the classified personas on a shared platform, where other users can search and select personas based on specific criteria. The input contains the classified personas, and the output is the published persona information.

[1132] Step 7:

[1133] The user enters search criteria and selects an appropriate persona on the shared platform. The terminal sends the search criteria to the server, which receives and displays the corresponding persona data. The search criteria are included as input, and the corresponding persona information is obtained as output.

[1134] Step 8:

[1135] The server integrates the selected personas into an advertising campaign management system, monitors advertising effectiveness based on real-time feedback, and dynamically optimizes advertising strategies based on the feedback data. The inputs include the selected personas and real-time feedback data, and the output is an optimized advertising strategy.

[1136] 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.

[1137] 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.

[1138] 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.

[1139] [Third embodiment]

[1140] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1141] 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.

[1142] 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).

[1143] 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.

[1144] 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.

[1145] 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).

[1146] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1147] 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.

[1148] 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.

[1149] 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.

[1150] 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.

[1151] 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."

[1152] The purpose of the PersonaNet system of the present invention is to allow users to create personas that reflect their own values ​​and share and use them with other users on a shared platform. This system includes the following program processing.

[1153] User Registration / Login

[1154] Users: New users create an account by entering the required information such as first name, last name, email address, and password. Existing users log in using their email address and password.

[1155] Terminal: Sends registration and login information to the server and receives responses from the server.

[1156] Server: Authenticates the received registration and login information by checking it against a database, and returns a success or error message to the device.

[1157] Persona creation

[1158] User: Once logged in, the user selects the "Create a Persona" option and answers a series of questions designed around psychology and statistics.

[1159] Device: The data answered by the user is temporarily stored and sent to the server once all questions have been answered.

[1160] Server: Passes the received response data to the generation AI to generate a persona that reflects the user's values. The generated persona is then saved in a database.

[1161] Persona genre classification

[1162] Server: The generated personas are passed through a predictive algorithm to classify them into appropriate genres. This genre information is also stored in a database.

[1163] Persona Publication

[1164] Server: Publishes the categorized personas on a shared platform, where they can be viewed and used by other users.

[1165] Using Personas

[1166] Users: Other users can search for personas based on specific genres or conditions on the platform and select them as needed. The selected personas can then be used for marketing strategies and service evaluations.

[1167] Terminal: Sends search criteria to the server, receives and displays the corresponding persona data.

[1168] Server: Retrieves personas that match the search criteria from the database and sends them to the device.

[1169] Survey implementation and point awarding

[1170] Server: Periodically conduct surveys about published personas and send notifications to users.

[1171] User: The user answers the survey and sends the data to the server.

[1172] Server: Stores the answer data in a database and awards points to users who answer. These points can be exchanged for various rewards within the system.

[1173] Customizing AI logic

[1174] Users: Specific users, such as companies, can purchase options to customize the logic of the generative AI. This customization allows them to generate personas tailored to specific uses.

[1175] Server: Verify purchase information and apply customization options to user accounts.

[1176] As a concrete example, consider the following scenario: A user creates a new account and creates a persona that reflects their values. This persona is categorized under the "youth marketing" category and published on a shared platform. Another user searches for personas in this category and uses them to develop a business plan. A business user also purchases the generator's customization option to generate a persona tailored to their specific needs, allowing them to develop a more refined marketing strategy.

[1177] This allows companies to efficiently and accurately understand their target demographic and reflect this in their marketing strategies and product development. Compared to traditional survey methods, this system can reduce costs and improve data accuracy.

[1178] The processing flow will be explained below.

[1179] Step 1:

[1180] A user visits the PersonaNet website and begins creating a new account.

[1181] Step 2:

[1182] The device makes an HTTP POST request to send the user's entered first name, last name, email address, and password to the server.

[1183] Step 3:

[1184] The server verifies the received registration information, stores it in a database, sends a confirmation email to the user that the registration was successful, and returns a success message to the terminal.

[1185] Step 4:

[1186] The user completes the email authentication, then proceeds to the login screen, enters their email address and password, and clicks the login button.

[1187] Step 5:

[1188] The terminal sends the login information to the server and makes an HTTP POST request to request authentication.

[1189] Step 6:

[1190] The server checks the received login information against a database, and if authentication is successful, generates session information and returns it to the terminal.

[1191] Step 7:

[1192] The user selects the "Create a Persona" option and begins answering a series of questions that are presented to them.

[1193] Step 8:

[1194] The terminal temporarily stores the data answered by the user sequentially, and when all questions have been answered, it makes an HTTP POST request to send it to the server.

[1195] Step 9:

[1196] The server passes the received response data to the generation AI, which generates a persona that reflects the user's values.

[1197] Step 10:

[1198] The server stores the generated persona data in a database, and uses predictive algorithms to classify the persona into an appropriate genre.

[1199] Step 11:

[1200] The server publishes the classified personas on a shared platform, making them available for other users to view.

[1201] Step 12:

[1202] Users can search for personas based on specific genres or conditions on the shared platform, and can select personas that meet specific needs, such as "marketing" or "product development."

[1203] Step 13:

[1204] The terminal sends the user's search criteria to the server and makes an HTTP GET request to obtain the corresponding persona.

[1205] Step 14:

[1206] The server retrieves personas that match the search criteria from the database and returns them to the terminal.

[1207] Step 15:

[1208] The terminal displays the acquired persona to the user.

[1209] Step 16:

[1210] The server periodically sends notifications to the users to conduct a survey about the published personas.

[1211] Step 17:

[1212] The user receives the survey notification, answers the survey, and makes an HTTP POST request to send the data from the terminal to the server.

[1213] Step 18:

[1214] The server stores the received survey responses in a database and assigns points to the accounts of the users who responded.

[1215] Step 19:

[1216] Users can confirm that points have been awarded on the shared platform, and the points can be exchanged for various rewards within the system.

[1217] Step 20:

[1218] To customize the logic of the generative AI, the user, a company representative, accesses a page to purchase options and makes a payment.

[1219] Step 21:

[1220] The terminal makes an HTTP POST request to send the purchase information to the server.

[1221] Step 22:

[1222] The server validates the received purchase information and applies the customization options to the user account.

[1223] Example 1

[1224] 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."

[1225] Conventional persona generation systems were inadequate in generating and classifying personas that reflected user values, limiting their use in marketing strategies and service evaluations. Furthermore, they lacked the means to temporarily store or search information related to persona generation, making them inconvenient for users and difficult to use efficiently. Another issue was the inability to flexibly generate personas tailored to specific uses.

[1226] 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.

[1227] In this invention, the server includes: a means for collecting response data from users who answer questions to generate a persona that reflects their own values; a generation AI means for generating a persona based on the generated response data; a means for temporarily storing the response data until the persona is generated and transmitting the data once all questions have been answered; a predictive algorithm means for classifying the generated persona; a means for publishing the classified persona on a shared platform; a means for searching persona information based on specific conditions; and a means for using the selected persona in marketing strategies and service evaluations. This enables efficient and flexible generation and classification of personas that reflect users' values, enabling them to be used in marketing strategies and service evaluations. Furthermore, the ability to temporarily store and transmit response data related to persona generation and the addition of a search means improves user convenience.

[1228] "Means for answering questions and collecting response data" refers to a function in which a user inputs answers to multiple questions provided, and the system saves and retains the input.

[1229] "Generative AI method for generating personas" is an artificial intelligence technology that generates virtual characters that reflect the user's values ​​and characteristics based on collected response data.

[1230] "Means for temporarily saving and sending data once all questions have been answered" is a function for temporarily saving the answer data entered by the user one by one, and sending the data to the server once all questions have been answered.

[1231] A "predictive algorithm means" is an algorithm that uses machine learning or statistical techniques to appropriately classify the generated personas.

[1232] "Means for publishing on a shared platform" is a function that allows the generated persona to be displayed in a public space on the Internet, making it accessible and usable by other users.

[1233] "Means for searching persona information based on specific conditions" is a function that searches for persona information in the database based on conditions specified by the user and finds the relevant persona.

[1234] "Means of using selected personas for marketing strategies and service evaluation" is a function that uses personas selected from search results to help develop marketing plans and evaluate services.

[1235] "Means for providing a questionnaire and collecting response data" refers to a function for presenting a questionnaire to users and collecting their responses.

[1236] The "means for awarding points" is a function for awarding points as a reward to users based on the collected response data.

[1237] The purpose of the PersonaNet system of this invention is to allow users to create personas that reflect their own values ​​and share and use them with other users on a shared platform. This system uses the functions of the server, terminal, and user, and includes the following program processing.

[1238] User Registration / Login

[1239] Servers, devices, and users:

[1240] New users create an account by entering required information such as first name, last name, email address, and password. Existing users log in using their email address and password. The registration and login information is sent from the device to the server, which then authenticates it by checking it against a database. If authentication is successful, a success message is returned to the device, allowing the user to access the system.

[1241] Persona creation

[1242] Servers, devices, and users:

[1243] Once logged in, users select the "Create Persona" option and answer a series of questions. The questions are designed based on psychology and statistics. The answer data is temporarily saved on the device and sent to the server once all questions have been answered. The server then passes the received answer data to a generation AI (e.g., OpenAI GPT-4), which uses prompts to generate a persona that reflects the user's values. The generated persona is then saved in a database.

[1244] Persona genre classification

[1245] server:

[1246] The generated personas are analyzed using a predictive algorithm (e.g., Random Forest or K-means clustering). Based on the analysis results, the personas are classified into appropriate genres. This genre information is also stored in the database.

[1247] Persona Publication

[1248] server:

[1249] The categorized personas are published on a sharing platform, where they can be viewed and used by other users.

[1250] Using Personas

[1251] Servers, devices, and users:

[1252] Other users can search for personas based on specific genres or conditions and select them as needed. The search conditions are sent from the device to the server, and the corresponding persona data is returned and displayed. The selected personas can be used for marketing strategies and service evaluations.

[1253] Survey implementation and point awarding

[1254] Server, User:

[1255] The server periodically conducts surveys about the published personas and sends notifications to users. Users respond to the surveys and send the data to the server. The server stores the received response data in a database and awards points to users who respond. These points can be exchanged for various rewards within the system.

[1256] Customizing AI logic

[1257] Server, User:

[1258] Certain users, such as companies, can purchase options to customize the logic of the generation AI. This customization allows the generation of personas tailored to specific uses. The server verifies the purchase information and applies the customization options to the user account.

[1259] Specific examples

[1260] For example, a user might create a new account and create a persona that reflects their values. This persona is then categorized under "youth marketing" and published on a shared platform. Another user might search for personas in this category and use them to develop a business plan. A business user might also purchase customization options for the AI ​​generator to generate personas tailored to their specific needs, allowing them to develop more sophisticated marketing strategies.

[1261] Prompt Sentence Examples

[1262] "I want to create a new persona specifically for the 'youth marketing' genre. Please answer the following questions:

[1263] 1. Age: 25

[1264] 2. Gender: Male

[1265] 3. Hobbies: Sports, listening to music

[1266] 4. Occupation: Salaryman

[1267] 5. Birthplace: Tokyo

[1268] 6. Values: Health-conscious, career-oriented

[1269] By selecting the "Generate Persona" option, users can answer the above questions and generate a detailed persona.

[1270] In this way, the PersonaNet system uses generative AI to generate detailed personas based on user input, allowing them to be shared and used by other users.

[1271] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1272] Program processing flow

[1273] User Registration / Login

[1274] Step 1: Enter your registration information

[1275] User: Enter the required information such as name, email address, and password, and click the "Register" button. Input data: name, email address, and password.

[1276] Step 2: Submit your registration information

[1277] Terminal: Sends the entered information to the server. Data sent: First name, last name, email address, password.

[1278] Step 3: Verify your registration details

[1279] Server: Compares the received information with the database and generates an authentication token if successful. Data verification: New data vs database.

[1280] Server: Saves the user account in the database based on the registration details and returns a success message to the terminal. It may also return an error message. Output: An authentication token.

[1281] Step 4: Enter your login details

[1282] User: Enter your registered email address and password and click the "Login" button. Input data: Email address, password.

[1283] Step 5: Submit login information

[1284] Terminal: Sends input information to the server. Data sent: Email address, password.

[1285] Step 6: Verify your login details

[1286] Server: Compares the entered information with the database and performs authentication. If it is correct, generates a token to start a user session and returns it to the terminal. It may also return an error message. Output: Authentication token.

[1287] Persona creation

[1288] Step 7: Select persona creation options

[1289] User: After logging in, select the "Create Persona" option. Input data: Select the Create Persona option.

[1290] Step 8: Answer questions

[1291] User: Answers a series of questions displayed. Example questions: age, gender, hobbies, values, etc. Input data: Questions and answers.

[1292] Step 9: Temporarily save your answers

[1293] Terminal: Temporarily saves the answer data. The saved data is sent to the server once all questions have been answered. Data processing: Temporarily saves and prepares the data for transmission.

[1294] Step 10: Submit your response data

[1295] Terminal: After answering all questions, send the data to the server. Data transmission: All answer data.

[1296] Step 11: Generate personas

[1297] Server: Passes the received answer data to a generation AI (e.g., OpenAI GPT-4) and generates a persona using the prompt. Data calculation: Persona generation by AI.

[1298] Server: Save the generated personas to the database. Output: The generated personas.

[1299] Persona genre classification

[1300] Step 12: Persona Analysis

[1301] Server: Analyzes the generated personas using predictive algorithms (e.g., Random Forest or K-means clustering). Data calculation: Persona analysis.

[1302] Step 13: Genre Classification

[1303] Server: Based on the analysis results, classify the persona into the appropriate genre. The classification information is also saved in the database. Output: Genre classification.

[1304] Persona Publication

[1305] Step 14: Prepare for publication

[1306] Server: Formats the categorized persona information for the shared platform. Data processing: Converts it into a publicly available format.

[1307] Step 15: Publishing

[1308] Server: Publishes personas onto a shared platform, making them visible and available to other users. Output: Published personas.

[1309] Using Personas

[1310] Step 16: Persona Search

[1311] User: Search for personas by specifying a specific genre or conditions. Input data: Search conditions.

[1312] Step 17: Submitting search criteria

[1313] Terminal: Sends search criteria to the server. Data transmission: Search criteria.

[1314] Step 18: Search based on criteria

[1315] Server: Retrieves personas that match the search criteria from the database and sends them to the device. Output: Personas from the search results.

[1316] Step 19: Persona Display

[1317] Terminal: Display persona information for search results. Data display: Search results.

[1318] Step 20: Use Personas

[1319] User: Use the selected persona for marketing strategies and service evaluation. Use the persona based on usage scenarios. Output: Analysis results based on usage scenarios.

[1320] Survey implementation and point awarding

[1321] Step 21: Survey Notification

[1322] Server: Periodically conducts surveys about published personas and sends notifications to users. Output: Survey notifications.

[1323] Step 22: Complete the survey

[1324] User: Answers the survey and sends the data to the server. Input data: Survey responses.

[1325] Step 23: Save the response data

[1326] Server: Saves the received response data in a database. Data storage: Response data.

[1327] Step 24: Points awarded

[1328] Server: Based on the answer data, points are awarded to the user who answered. These points can be exchanged for rewards within the system. Output: Awarded points.

[1329] Customizing AI logic

[1330] Step 25: Purchase customization options

[1331] User: Purchase logic customization options for the generation AI. Input data: Purchase information.

[1332] Step 26: Apply customizations

[1333] Server: Verify purchase information and apply customization options to user account. Output: Customization options.

[1334] (Application example 1)

[1335] 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."

[1336] In today's content distribution services, users have difficulty finding content that is optimized for their values ​​and interests, and there is a need for a system that can provide recommendations tailored to individual preferences. Furthermore, there is a lack of functionality that allows users to share personas with others and mutually recommend content. Therefore, there is an urgent need to develop a content recommendation system that accurately reflects user preferences.

[1337] 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.

[1338] In this invention, the server includes a means for allowing users to answer questions to generate a persona that reflects their own values ​​and collecting the answer data, a generation AI means for generating a persona based on the generated answer data, a means for recommending individual content based on the user's persona, and a means for publishing the classified persona on a sharing platform. This allows users to easily find content optimized for their preferences, and also enhances the function of sharing personas with other users and discovering content to recommend to each other.

[1339] "User" refers to an individual who uses this system, creates a persona that reflects their own values, and receives content recommendations.

[1340] "Values" refer to internal standards and guidelines that influence a user's behavior and preferences, such as personal beliefs, interests, and tastes.

[1341] A "persona" is a virtual character that reflects a user's values ​​and interests, and is used for content recommendations and marketing strategies.

[1342] "Means of answering questions and collecting response data" refers to the system's functionality of presenting questions to users to generate personas and collecting their responses as data.

[1343] "Generative AI means" refers to artificial intelligence technology for generating personas based on collected response data.

[1344] "Predictive algorithm means" refers to algorithm technology for analyzing personas generated by generative AI means and classifying them into appropriate genres.

[1345] "Means for recommending content" refers to a function that automatically selects and recommends content appropriate for a user based on the user's generated persona.

[1346] "Means for publishing" refers to the system's functionality for publishing classified personas on a shared platform so that they can be viewed and used by other users.

[1347] "Means for providing surveys and collecting response data" refers to the system's function of periodically presenting surveys to users and collecting their response data.

[1348] "Means for awarding points" refers to the system's function of awarding points to users based on collected response data and allowing them to exchange those points for rewards.

[1349] "Means for searching and selecting a persona" refers to the function of searching based on the genre of the generated personas and selecting a persona that suits a specific purpose.

[1350] "Means used for evaluating marketing strategies and services" refers to functions used to evaluate marketing strategies and services based on the selected personas.

[1351] "Genre" refers to the category into which personas are classified, and is divided based on hobbies, preferences, interests, etc.

[1352] This invention describes a system that allows a user to create a persona that reflects their own values ​​and recommends individual content based on that persona. Specific embodiments for realizing this system are described below.

[1353] 1. System Configuration

[1354] The system mainly consists of the following components:

[1355] User device: A device such as a smartphone, tablet, or computer.

[1356] Server: A remote server that processes and stores data.

[1357] AI model: Software that generates personas using generative AI technology.

[1358] 2. Program Processing

[1359] 2.1 User Registration / Login

[1360] To use the system, a user must first create an account. New users enter their name, email address, and password, and this information is sent from the user's device to the server. The server stores this information in a database and uses it as authentication information.

[1361] 2.2 Persona Creation

[1362] When a user logs in, they are presented with questions to generate a persona. The answers are temporarily stored on the user's device and then sent to the server, which then uses a generative AI model to generate a persona that reflects the user's values.

[1363] 2.3 Classifying and Publishing Personas

[1364] The generated personas are classified into genres by the server using a predictive algorithm, and the classified personas are published on a shared platform for other users to view and use.

[1365] 2.4 Content Recommendations

[1366] The server recommends content suitable for the user based on the generated persona, including movies, music, books, etc. The user's device receives and displays the recommendation list.

[1367] 2.5 Surveys and point allocation

[1368] Periodically, the server will provide users with surveys and collect their responses. Based on the collected data, users will be awarded points, which can be redeemed for rewards.

[1369] 2.6 Search and Selection

[1370] Users can search based on the genre of the generated personas and select the appropriate persona. The selected persona can be used for marketing strategies and service evaluation.

[1371] 3. Hardware and Software Used

[1372] Hardware: Smartphones, tablets, PCs, remote servers

[1373] Software: Generative AI models, predictive algorithms, database management systems, web server software (e.g., Flask)

[1374] Specific examples

[1375] Example prompts to generate personas based on user responses:

[1376] Generate personas based on your users' values ​​and interests using the following answers:

[1377] Answer 1: [User Answer 1]

[1378] Answer 2: [User Answer 2]

[1379] Answer 3: [User Answer 3]

[1380] ...

[1381] The generated results should be output in the following format:

[1382] {

[1383] "persona": {

[1384] "Personality": "〇〇",

[1385] "Interest": "〇〇",

[1386] "Values": "〇〇"

[1387] }

[1388] }

[1389] These technological elements allow users to easily find content that is optimized for their preferences, and also provide a comprehensive feature that allows users to share personas with other users and discover content that they can recommend to each other.

[1390] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1391] Step 1:

[1392] User Registration

[1393] The user enters their name, email address, and password and sends a registration request to the server, which stores this information in a database. The input data is name, email address, and password, and the output data is a message indicating whether registration was successful or not.

[1394] Step 2:

[1395] User Login

[1396] The user enters their email address and password and sends a login request to the server, which then checks the information in the database and authenticates them. The input data is the email address and password, and the output data is a message indicating whether authentication was successful or not.

[1397] Step 3:

[1398] Persona Generation

[1399] After logging in, users answer a series of questions presented by the system. These answers are temporarily stored on the device and then sent to the server. The server passes the answers to a generative AI model, which generates a persona that reflects the user's values. The input data is the answers, and the generated persona is returned as the output data.

[1400] Step 4:

[1401] Persona Classification

[1402] The server uses a predictive algorithm to classify the generated personas into genres. The input data is the generated persona, and the output data is the classified genre. The specific operation is to analyze the characteristics of the persona and classify it into the appropriate category.

[1403] Step 5:

[1404] Persona Announcement

[1405] The server publishes the classified personas on a shared platform. The input data is the classified personas, and the published personas are available as output data. The published personas can be viewed and used by other users.

[1406] Step 6:

[1407] Content Recommendation

[1408] The server recommends appropriate content to the user based on the generated persona. The recommendation list is sent to the device. The input data is the persona and a large amount of content data, and the output data is a list of recommended content. Specifically, the content is filtered based on the characteristics of the persona, and the most suitable items are selected.

[1409] Step 7:

[1410] Survey

[1411] The server periodically provides users with surveys and collects their responses. The input data is the survey responses, and the output data is the collected response data. This data is later used for analysis and point allocation.

[1412] Step 8:

[1413] Points awarded

[1414] The server awards points to users based on the collected response data. The input data is the survey response data, and the output data reflects the awarded points. Specifically, this is a process of adding points to the target user's account.

[1415] Step 9:

[1416] Persona Search

[1417] Users search based on persona genre and select a persona that suits their specific purpose. The input data are the search criteria, and the output data is a list of matching personas. This is the process of extracting appropriate personas from the database based on the search criteria.

[1418] Step 10:

[1419] Marketing Use

[1420] The server uses the selected persona for marketing strategies and service evaluation. Specific examples of use include formulating targeted advertising based on the persona and using it as a guide for product development. The input data is the selected persona, and the output data is a marketing report.

[1421] 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.

[1422] The purpose of the PersonaNet system of this invention is to allow users to create personas that reflect their own values ​​and emotions, and then share and use these with other users on a shared platform. This system incorporates an emotion engine that recognizes users' emotions and uses this information to generate and classify personas.

[1423] User Registration / Login

[1424] Users: New users create an account by entering the required information such as first name, last name, email address, and password. Existing users log in using their email address and password.

[1425] Terminal: Sends registration and login information to the server and receives responses from the server.

[1426] Server: Authenticates the received registration and login information by checking it against a database, and returns a success or error message to the device.

[1427] Persona creation

[1428] User: Once logged in, the user selects the "Create a Persona" option and begins answering a series of questions, which are designed based on psychology and statistics.

[1429] Device: The data provided by the user is temporarily stored and sent to the server once all questions have been answered. The data also includes elements for capturing the user's emotions.

[1430] Server: Passes the received response data to the emotion engine to recognize the user's emotion.

[1431] Server: The recognized emotional information is passed to the generation AI, which generates a persona that reflects the user's values ​​and emotions.

[1432] Persona genre classification

[1433] Server: The generated persona is run through a predictive algorithm to classify it into an appropriate genre based on the response data and emotional information. This genre information is also stored in a database.

[1434] Persona Publication

[1435] Server: Publish the categorized personas on a shared platform so that other users can view them.

[1436] Using Personas

[1437] Users: Other users can search for personas based on specific genres or conditions on the platform, select them as needed, and use the selected personas for marketing strategies and service evaluations.

[1438] Terminal: Sends search criteria to the server, receives and displays the corresponding persona data.

[1439] Server: Retrieves personas that match the search criteria from the database and returns them to the device.

[1440] Survey implementation and point awarding

[1441] Server: Send notifications to users to periodically survey them about their published personas.

[1442] User: The user receives a survey notification, answers the survey, and sends the data from the device to the server.

[1443] Server: Stores the received survey responses in a database and awards points to users who respond. These points can be exchanged for various rewards within the system.

[1444] Customizing AI logic

[1445] Users: Specific users, such as companies, can purchase options to customize the logic of the generative AI. This customization allows them to generate personas tailored to specific uses.

[1446] Server: Verify purchase information and apply customization options to user accounts.

[1447] For example, a user might create a new account and create a persona that reflects their values ​​and emotions. This persona is then categorized under the "youth marketing" category and published on a shared platform. Another user might search for personas in this category and use them to develop a business plan. A business user might also purchase customization options for the AI ​​generator, generating personas tailored to their specific needs and developing more sophisticated marketing strategies.

[1448] In this way, incorporating an emotion engine makes it possible to generate more accurate personas that take user emotions into account, allowing companies to develop more sophisticated marketing strategies. This system can reduce costs and improve data accuracy compared to traditional research methods.

[1449] The processing flow will be explained below.

[1450] Step 1:

[1451] A user visits the PersonaNet website and begins creating a new account.

[1452] Step 2:

[1453] The device makes an HTTP POST request to send the user's entered first name, last name, email address, and password to the server.

[1454] Step 3:

[1455] The server verifies the received registration information, stores it in a database, sends a confirmation email to the user that the registration was successful, and returns a success message to the terminal.

[1456] Step 4:

[1457] The user completes the email authentication, then proceeds to the login screen, enters their email address and password, and clicks the login button.

[1458] Step 5:

[1459] The terminal sends the login information to the server and makes an HTTP POST request to request authentication.

[1460] Step 6:

[1461] The server checks the received login information against a database, and if authentication is successful, generates session information and returns it to the terminal.

[1462] Step 7:

[1463] Users select the "Create a Persona" option and begin answering a series of questions, some of which are designed to capture the user's emotions.

[1464] Step 8:

[1465] The terminal temporarily stores the data answered by the user sequentially, and when all questions have been answered, it makes an HTTP POST request to send it to the server.

[1466] Step 9:

[1467] The server recognizes the user's emotions by passing the received response data to the emotion engine, which generates emotion information through linguistic and tone analysis of the response.

[1468] Step 10:

[1469] The server passes the generated emotional information to a generation AI, which generates a persona that reflects the user's values ​​and recognized emotions.

[1470] Step 11:

[1471] The server stores the generated persona data in a database, and uses predictive algorithms to classify the persona into the appropriate genre.

[1472] Step 12:

[1473] The server publishes the classified personas on a shared platform so that they can be viewed by users.

[1474] Step 13:

[1475] Users can search for personas based on specific genres or conditions on the shared platform, and can select personas that meet their needs, such as "marketing" or "product development."

[1476] Step 14:

[1477] The terminal sends the user's search criteria to the server and makes an HTTP GET request to obtain the corresponding persona.

[1478] Step 15:

[1479] The server retrieves personas that match the search criteria from the database and returns them to the terminal.

[1480] Step 16:

[1481] The terminal displays the acquired persona to the user.

[1482] Step 17:

[1483] The server periodically sends notifications to the relevant users to conduct a survey about the published personas.

[1484] Step 18:

[1485] The user receives a survey notification, answers the survey, and makes an HTTP POST request to send the data from the terminal to the server.

[1486] Step 19:

[1487] The server stores the received survey responses in a database and assigns points to the accounts of the users who responded.

[1488] Step 20:

[1489] The user can confirm that the points have been awarded on the shared platform and exchange the points for rewards within the system.

[1490] Step 21:

[1491] To customize the logic of the generative AI, the user, a company representative, accesses a page to purchase options and makes a payment.

[1492] Step 22:

[1493] The terminal makes an HTTP POST request to send the purchase information to the server.

[1494] Step 23:

[1495] The server validates the received purchase information and applies the customization options to the user account.

[1496] Example 2

[1497] 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."

[1498] In modern digital marketing and service evaluation, it is important to generate personas that accurately reflect users' values ​​and emotions. Conventional methods collect data without considering user emotions, which limits their accuracy. Furthermore, it is difficult to search, use, or customize the generated personas specifically for a company. This reduces the accuracy of marketing strategies and makes effective targeting difficult.

[1499] 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.

[1500] In this invention, the server includes: means for having users answer questions to generate a persona that reflects their values ​​and emotions and collecting the answer data; generation AI means for generating a persona based on the collected answer data and user emotion data; prediction algorithm means for classifying the generated personas; means for publishing the classified personas on a shared platform; means for users to search for and select personas based on specific genres or conditions; and means for viewing and using the selected personas. This enables the generation of highly accurate personas that take user emotions into consideration, allowing companies to effectively use the personas to improve targeting accuracy and optimize their marketing strategies.

[1501] A "user" is an entity that uses the system to generate, search, and use personas.

[1502] "Values" are the beliefs, opinions, and ways of thinking that form the basis for a user's actions.

[1503] "Emotion data" is information that indicates the user's emotional state and is recognized by the emotion engine.

[1504] "Answer data" refers to answer information to questions provided to the system by users.

[1505] A "persona" is a virtual character created based on a user's values ​​and emotional data.

[1506] The "generative AI means" is an artificial intelligence mechanism that generates personas based on collected response data and emotional data.

[1507] A "predictive algorithm" is an algorithm used to classify the generated personas.

[1508] The "sharing platform" is an online system for sharing generated personas with other users.

[1509] A "survey" is a survey question that is periodically provided to users and is a means of collecting response data.

[1510] The "point awarding means" is a mechanism for awarding points to users based on collected survey response data.

[1511] "Search tools" are functions that allow users to search for personas based on specific genres or conditions.

[1512] "Viewing means" is a function that allows users to display and check the personas selected by search.

[1513] "Corporate users" are specific users who have the authority to customize the logic of generative AI.

[1514] "Customization options" are settings that allow business users to tailor the logic of the generative AI to suit their specific needs.

[1515] The PersonaNet system of the present invention allows users to create personas that reflect their own values ​​and emotions, and then share and use those personas with other users on a shared platform. This system includes the following main elements:

[1516] 1. User Registration and Login

[1517] A user creates a new account by entering required information such as name, email address, and password. Existing users log in using their email address and password. The device sends this registration and login information to the server and receives a response from the server. The server compares the received information with a database and performs authentication. If authentication is successful, a success message is returned to the device; if it fails, an error message is returned.

[1518] 2. Persona Creation

[1519] After logging in, users select the "Create a Persona" option and answer a series of questions. These questions are designed based on psychology and statistics. The device temporarily stores the data the user has answered and sends it to the server once all questions have been answered. The answer data also includes elements to capture the user's emotions.

[1520] The server passes the received response data to an emotion engine (e.g., Microsoft Azure's Text Analytics API) to recognize the user's emotion. It then passes the recognized emotion information and response data to a generative AI (e.g., OpenAI's GPT-3 model) to generate a persona. The generated persona is temporarily stored.

[1521] 3. Persona Genre Classification

[1522] The server then runs the generated personas through a predictive algorithm (e.g., a clustering algorithm using Scikit-learn) to classify them into appropriate genres. This classification information is then stored in a database.

[1523] 4. Publish your persona

[1524] The server publishes the categorized personas on a shared platform (e.g., a frontend using React) so that other users can view them.

[1525] 5. Use personas

[1526] Users can search for personas based on specific genres or conditions. The device sends the search conditions to the server, receives and displays the corresponding persona data. The server retrieves personas that match the search conditions from the database and returns them to the device.

[1527] 6. Survey implementation and point allocation

[1528] The server periodically conducts surveys regarding the published personas. The surveys are notified to the relevant users. The users receive the notification, answer the surveys, and send the data from their devices to the server. The server stores the received survey responses in a database and awards points to the users who answered. These points can be exchanged for various rewards within the system.

[1529] 7. Customizing AI logic

[1530] Corporate users can purchase options to customize the logic of the generative AI. The server verifies the purchase information and applies the customization options to the user account.

[1531] Specific examples

[1532] For example, a user can create a new account and answer questions to create a persona that reflects their values ​​and emotions. This persona is then categorized under the "youth marketing" category and published on a shared platform. Another user can search for personas in this category and use them to develop a business plan. A business user can also purchase customization options for the AI ​​generator to generate personas tailored to their specific needs, allowing them to develop more precise marketing strategies.

[1533] Prompt Sentence Examples

[1534] 1. Account creation prompt:

[1535] "You will now be registering with the PersonaNet system. Please enter your first and last name, email address, and password."

[1536] 2. Persona creation prompt:

[1537] "To create your persona, please answer the following questions. Please also include your emotional state."

[1538] 3. Persona search prompt:

[1539] "Search for personas in a specific genre. Enter the genre name."

[1540] This makes it possible to generate highly accurate personas that take user emotions into account, allowing companies to effectively utilize these personas to improve targeting accuracy and optimize their marketing strategies.

[1541] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1542] Step 1:

[1543] A user fills out a sign-up form with their name, email address, and password.

[1544] Enter your name, email address, and password.

[1545] Specific operation: The user enters the required information into the input fields and clicks the "Register" button.

[1546] Output: Input information is displayed on the terminal.

[1547] Step 2:

[1548] The device converts the entered registration information into JSON format and sends it to the server as an HTTPS POST request.

[1549] Input: Name, email address, and password entered by the user.

[1550] Specific behavior: Serializes input data and sends it to the server using a secure communication protocol.

[1551] Output: The server receives the request.

[1552] Step 3:

[1553] The server checks the received information against the database and performs authentication. If it is a new account, it is saved in the database. If an existing user logs in, authentication is performed.

[1554] Input: Name, email address, and password submitted by the user.

[1555] Specific behavior: Executes a database query to find user information, creates an account if new, or verifies information if existing.

[1556] Output: A success message or an error message is returned to the terminal as the authentication result.

[1557] Step 4:

[1558] After a user logs in, they select the "Create a Persona" option, which begins answering a series of questions.

[1559] Input: User selection, answer to question.

[1560] Specific operation: The user answers multiple questions presented by the system in sequence. Each answer is temporarily saved in local storage.

[1561] Output: All answer data for the question is saved.

[1562] Step 5:

[1563] The terminal temporarily stores the answer data to the questions and transmits it to the server when all questions have been answered.

[1564] Input: Answers to a series of questions.

[1565] Specific operation: When the user finishes inputting, the data stored in the local storage is converted to JSON format and sent to the server.

[1566] Output: The answer data is sent to the server.

[1567] Step 6:

[1568] The server passes the received response data to an emotion engine (e.g., Microsoft Azure's Text Analytics API) to recognize the user's emotion.

[1569] Input: Response data.

[1570] Specific operation: Sends response data to the emotion engine and receives an analyzed emotion score.

[1571] Output: A sentiment score is obtained.

[1572] Step 7:

[1573] The server passes the recognized emotional information to a generative AI (e.g., OpenAI's GPT-3 model) to generate a persona.

[1574] Input: Response data, sentiment scores.

[1575] Specific operations: Generate a prompt sentence, send it to the generative AI model, and receive the generated persona information.

[1576] Output: Generated persona information.

[1577] Step 8:

[1578] The server runs the generated personas through a predictive algorithm (e.g., a clustering algorithm using Scikit-learn) to classify them into categories. This information is then stored in a database.

[1579] Input: Generated persona information.

[1580] Specific operation: Apply a clustering algorithm to classify personas by genre.

[1581] Output: The classified persona information is stored in a database.

[1582] Step 9:

[1583] The server publishes the categorized persona data on a shared platform (e.g., a front-end using React).

[1584] Input: Classified persona information.

[1585] What it does: Retrieves classified persona information from the database and converts it into the appropriate format for display on a shared platform.

[1586] Output: The published persona information is displayed on a shared platform.

[1587] Step 10:

[1588] Users search for personas based on specific genres or criteria.

[1589] Input: Search criteria.

[1590] Specific behavior: The user enters search criteria into the search bar and clicks the search button.

[1591] Output: The search criteria is sent to the server.

[1592] Step 11:

[1593] The terminal sends the search criteria to the server, receives the corresponding persona data, and displays it.

[1594] Input: Search criteria.

[1595] What happens: A condition-based database query is executed, the relevant persona information is retrieved, and displayed to the user.

[1596] Output: The relevant persona information is displayed.

[1597] Step 12:

[1598] The server retrieves persona data that matches the search criteria from the database and returns it to the terminal.

[1599] Input: Search criteria.

[1600] Specific behavior: Executes a database query and returns the results to the device.

[1601] Output: Search results.

[1602] Step 13:

[1603] The server periodically sends notifications to users to survey them about their published personas.

[1604] Input: Survey trigger.

[1605] Specific actions: Define the survey content and send notifications to target users.

[1606] Output: Survey notification.

[1607] Step 14:

[1608] The user receives the survey notification, answers the survey, and transmits the data from the terminal to the server.

[1609] Input: Survey responses.

[1610] Specific action: Answer the survey and click the submit button.

[1611] Output: The answer data is sent to the server.

[1612] Step 15:

[1613] The server stores the received survey responses in a database and gives points to the users who responded.

[1614] Input: Survey response data.

[1615] What it does: Saves the answer data to a database and adds points to the user's account.

[1616] Output: Updated point data.

[1617] Step 16:

[1618] Enterprise users can purchase options to customize the logic of the generated AI.

[1619] Enter: Customization options.

[1620] Specific operation: After selecting the option and completing the purchase procedure, the server applies the option.

[1621] Output: The applied customization options.

[1622] Step 17:

[1623] The server verifies the purchase information and applies the customization options to the user account.

[1624] Input: Purchase information.

[1625] What it does: Verify purchase information and apply personalized settings to your user account.

[1626] Output: The user account whose settings were updated.

[1627] (Application example 2)

[1628] 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."

[1629] Modern advertising strategies require methods for detailed analysis of user values ​​and emotions and for targeting advertising based on those values. However, conventional systems are unable to fully utilize user emotional information, making it difficult to maximize advertising effectiveness. Furthermore, there is no established method for analyzing collected data in real time and dynamically optimizing advertising strategies. This makes it difficult to design and manage efficient and accurate advertising campaigns.

[1630] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1631] In this invention, the server includes: means for having users answer questions to generate a persona that reflects their values ​​and collecting the answer data; generation AI means for generating a persona based on the generated answer data; prediction algorithm means for classifying the generated persona; means for publishing the classified persona on a shared platform; emotion recognition means for analyzing the collected emotion data and utilizing the user's emotion information in persona generation; and means for integrating the persona based on the collected emotion data and values ​​into an advertising strategy. This enables highly accurate persona generation based on the user's values ​​and emotions, enabling efficient and dynamic design and management of advertising campaigns.

[1632] "Users" are users of the system who provide values ​​and emotional data to generate personas.

[1633] "Values" are fundamental beliefs and principles that influence a user's thoughts and actions.

[1634] A "persona" is a virtual character created based on a user's values ​​and emotional data, and is used as a target for marketing and advertising strategies.

[1635] A "question" is a query posed by the system to gather the user's values ​​and feelings.

[1636] "Response Data" means information provided by a user in response to a question.

[1637] "Generative AI means" is an artificial intelligence technology that analyzes response data and generates a persona that reflects the user's values ​​and emotions.

[1638] "Predictive algorithm means" is a data analysis technique for classifying the generated personas into appropriate genres.

[1639] The "sharing platform" is an online system for sharing generated and classified personas with other users.

[1640] "Emotion recognition means" is a technology that collects and analyzes user emotional data and uses it to generate personas.

[1641] An "advertising strategy" refers to a plan or method for effectively delivering advertisements to a target audience.

[1642] "Dynamic optimization" is a technique for instantly improving the content and delivery of advertising campaigns based on feedback collected in real time.

[1643] This invention is a system that allows users to create personas that reflect their own values ​​and emotions, and integrates and utilizes these personas in advertising campaigns. Detailed embodiments for realizing this system will be described below.

[1644] System Overview

[1645] 1. User Interface

[1646] The system is accessed by users through an application installed on a device such as a smartphone, smart glasses, or head-mounted display, and involves answering a series of questions that reflect their values.

[1647] 2. Emotional Data Collection

[1648] The device uses installed emotion recognition technology (e.g., Affectiva SDK, Kairos SDK) to collect emotional data from the user's facial expressions and voice, and analyzes the data in real time through the camera in the smart glasses or head-mounted display to identify the user's emotional state.

[1649] 3. Persona generation

[1650] The server generates a persona using a generative AI model (e.g., GPT-4) based on the collected response data and emotion data. The generative AI model generates a persona using the following prompt sentence:

[1651] Generate the following personas based on user sentiment data and values:

[1652] 1. Emotion data: [Happiness, Anger, Sadness, Surprise]

[1653] 2. Values: [Enterprising, Conservative, Sociable, Introverted]

[1654] For example, generate personas based on "enterprising and joyful" or "conservative and sensitive to surprises."

[1655] 4. Persona Classification

[1656] The generated personas are classified into appropriate genres by the server using a predictive algorithm (e.g., TensorFlow, PyTorch). The classified data is stored in a database.

[1657] 5. Share your personas

[1658] The classified personas are then published on a shared platform where they can be searched and selected by other users, who can then search for personas based on specific criteria and integrate them into their advertising campaigns.

[1659] 6. Integrate your advertising strategy

[1660] The server integrates the collected emotion data and generated personas into the advertising campaign management system, enabling highly accurate targeting of ads based on user emotions. It also monitors advertising effectiveness through real-time feedback and dynamically optimizes advertising strategies based on the feedback data.

[1661] Hardware and software used

[1662] Smart glasses / head-mounted displays: generic names (e.g., Google Glass, Microsoft HoloLens)

[1663] Emotion Engine: Generic name (e.g., Affectiva SDK, Kairos SDK)

[1664] Generative AI model: Generic name (e.g., OpenAI GPT-4)

[1665] Machine learning framework: generic name (e.g., TensorFlow, PyTorch)

[1666] Database: Generic name (e.g. MySQL, MongoDB)

[1667] Cloud services: generic names (e.g., Amazon Web Services (AWS), Google Cloud Platform (GCP))

[1668] This approach makes it possible to effectively utilize user values ​​and emotional information, enabling highly accurate persona generation and dynamic optimization of advertising campaigns.

[1669] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1670] Step 1:

[1671] The user launches an application installed on a smartphone, smart glasses, or head-mounted display and logs in. First name, last name, email address, and password are required as input, and the server authenticates the user based on this information and returns a successful authentication message to the device.

[1672] Step 2:

[1673] After logging in, the user selects the "Create Persona" option and answers the provided questions, which include multiple psychological and statistical questions. The device temporarily stores the answer data and sends it to the server once the user has completed answering all the questions. The input contains the user's answer data, and the output is the data to be transmitted to the server.

[1674] Step 3:

[1675] The server passes the received response data to an emotion engine to analyze the user's emotion. The emotion recognition means analyzes the user's emotion data. The emotion data is input and emotion information is output.

[1676] Step 4:

[1677] The server generates a persona using a generative AI model based on the analyzed emotional information. Specifically, the server inputs the following prompt sentence into the generative AI model:

[1678] Generate the following personas based on user sentiment data and values:

[1679] 1. Emotion data: [Happiness, Anger, Sadness, Surprise]

[1680] 2. Values: [Enterprising, Conservative, Sociable, Introverted]

[1681] For example, generate personas based on "enterprising and joyful" or "conservative and sensitive to surprises."

[1682] The input includes emotional and value data, and the output is a generated persona.

[1683] Step 5:

[1684] The server classifies the generated personas into appropriate genres using a predictive algorithm. The classified personas are stored in a database, which contains the generated personas as input and provides the classification results as output.

[1685] Step 6:

[1686] The server publishes the classified personas on a shared platform, where other users can search and select personas based on specific criteria. The input contains the classified personas, and the output is the published persona information.

[1687] Step 7:

[1688] The user enters search criteria and selects an appropriate persona on the shared platform. The terminal sends the search criteria to the server, which receives and displays the corresponding persona data. The search criteria are included as input, and the corresponding persona information is obtained as output.

[1689] Step 8:

[1690] The server integrates the selected personas into an advertising campaign management system, monitors advertising effectiveness based on real-time feedback, and dynamically optimizes advertising strategies based on the feedback data. The inputs include the selected personas and real-time feedback data, and the output is an optimized advertising strategy.

[1691] 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.

[1692] 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.

[1693] 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.

[1694] [Fourth embodiment]

[1695] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1696] 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.

[1697] 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).

[1698] 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.

[1699] 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.

[1700] 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).

[1701] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1702] 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.

[1703] 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.

[1704] 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.

[1705] 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.

[1706] 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.

[1707] 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."

[1708] The purpose of the PersonaNet system of the present invention is to allow users to create personas that reflect their own values ​​and share and use them with other users on a shared platform. This system includes the following program processing.

[1709] User Registration / Login

[1710] Users: New users create an account by entering the required information such as first name, last name, email address, and password. Existing users log in using their email address and password.

[1711] Terminal: Sends registration and login information to the server and receives responses from the server.

[1712] Server: Authenticates the received registration and login information by checking it against a database, and returns a success or error message to the device.

[1713] Persona creation

[1714] User: Once logged in, the user selects the "Create a Persona" option and answers a series of questions designed around psychology and statistics.

[1715] Device: The data answered by the user is temporarily stored and sent to the server once all questions have been answered.

[1716] Server: Passes the received response data to the generation AI to generate a persona that reflects the user's values. The generated persona is then saved in a database.

[1717] Persona genre classification

[1718] Server: The generated personas are passed through a predictive algorithm to classify them into appropriate genres. This genre information is also stored in a database.

[1719] Persona Publication

[1720] Server: Publishes the categorized personas on a shared platform, where they can be viewed and used by other users.

[1721] Using Personas

[1722] Users: Other users can search for personas based on specific genres or conditions on the platform and select them as needed. The selected personas can then be used for marketing strategies and service evaluations.

[1723] Terminal: Sends search criteria to the server, receives and displays the corresponding persona data.

[1724] Server: Retrieves personas that match the search criteria from the database and sends them to the device.

[1725] Survey implementation and point awarding

[1726] Server: Periodically conduct surveys about published personas and send notifications to users.

[1727] User: The user answers the survey and sends the data to the server.

[1728] Server: Stores the answer data in a database and awards points to users who answer. These points can be exchanged for various rewards within the system.

[1729] Customizing AI logic

[1730] Users: Specific users, such as companies, can purchase options to customize the logic of the generative AI. This customization allows them to generate personas tailored to specific uses.

[1731] Server: Verify purchase information and apply customization options to user accounts.

[1732] As a concrete example, consider the following scenario: A user creates a new account and creates a persona that reflects their values. This persona is categorized under the "youth marketing" category and published on a shared platform. Another user searches for personas in this category and uses them to develop a business plan. A business user also purchases the generator's customization option to generate a persona tailored to their specific needs, allowing them to develop a more refined marketing strategy.

[1733] This allows companies to efficiently and accurately understand their target demographic and reflect this in their marketing strategies and product development. Compared to traditional survey methods, this system can reduce costs and improve data accuracy.

[1734] The processing flow will be explained below.

[1735] Step 1:

[1736] A user visits the PersonaNet website and begins creating a new account.

[1737] Step 2:

[1738] The device makes an HTTP POST request to send the user's entered first name, last name, email address, and password to the server.

[1739] Step 3:

[1740] The server verifies the received registration information, stores it in a database, sends a confirmation email to the user that the registration was successful, and returns a success message to the terminal.

[1741] Step 4:

[1742] The user completes the email authentication, then proceeds to the login screen, enters their email address and password, and clicks the login button.

[1743] Step 5:

[1744] The terminal sends the login information to the server and makes an HTTP POST request to request authentication.

[1745] Step 6:

[1746] The server checks the received login information against a database, and if authentication is successful, generates session information and returns it to the terminal.

[1747] Step 7:

[1748] The user selects the "Create a Persona" option and begins answering a series of questions that are presented to them.

[1749] Step 8:

[1750] The terminal temporarily stores the data answered by the user sequentially, and when all questions have been answered, it makes an HTTP POST request to send it to the server.

[1751] Step 9:

[1752] The server passes the received response data to the generation AI, which generates a persona that reflects the user's values.

[1753] Step 10:

[1754] The server stores the generated persona data in a database, and uses predictive algorithms to classify the persona into an appropriate genre.

[1755] Step 11:

[1756] The server publishes the classified personas on a shared platform, making them available for other users to view.

[1757] Step 12:

[1758] Users can search for personas based on specific genres or conditions on the shared platform, and can select personas that meet specific needs, such as "marketing" or "product development."

[1759] Step 13:

[1760] The terminal sends the user's search criteria to the server and makes an HTTP GET request to obtain the corresponding persona.

[1761] Step 14:

[1762] The server retrieves personas that match the search criteria from the database and returns them to the terminal.

[1763] Step 15:

[1764] The terminal displays the acquired persona to the user.

[1765] Step 16:

[1766] The server periodically sends notifications to the users to conduct a survey about the published personas.

[1767] Step 17:

[1768] The user receives the survey notification, answers the survey, and makes an HTTP POST request to send the data from the terminal to the server.

[1769] Step 18:

[1770] The server stores the received survey responses in a database and assigns points to the accounts of the users who responded.

[1771] Step 19:

[1772] Users can confirm that points have been awarded on the shared platform, and the points can be exchanged for various rewards within the system.

[1773] Step 20:

[1774] To customize the logic of the generative AI, the user, a company representative, accesses a page to purchase options and makes a payment.

[1775] Step 21:

[1776] The terminal makes an HTTP POST request to send the purchase information to the server.

[1777] Step 22:

[1778] The server validates the received purchase information and applies the customization options to the user account.

[1779] Example 1

[1780] 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."

[1781] Conventional persona generation systems were inadequate in generating and classifying personas that reflected user values, limiting their use in marketing strategies and service evaluations. Furthermore, they lacked the means to temporarily store or search information related to persona generation, making them inconvenient for users and difficult to use efficiently. Another issue was the inability to flexibly generate personas tailored to specific uses.

[1782] 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.

[1783] In this invention, the server includes: a means for collecting response data from users who answer questions to generate a persona that reflects their own values; a generation AI means for generating a persona based on the generated response data; a means for temporarily storing the response data until the persona is generated and transmitting the data once all questions have been answered; a predictive algorithm means for classifying the generated persona; a means for publishing the classified persona on a shared platform; a means for searching persona information based on specific conditions; and a means for using the selected persona in marketing strategies and service evaluations. This enables efficient and flexible generation and classification of personas that reflect users' values, enabling them to be used in marketing strategies and service evaluations. Furthermore, the ability to temporarily store and transmit response data related to persona generation and the addition of a search means improves user convenience.

[1784] "Means for answering questions and collecting response data" refers to a function in which a user inputs answers to multiple questions provided, and the system saves and retains the input.

[1785] "Generative AI method for generating personas" is an artificial intelligence technology that generates virtual characters that reflect the user's values ​​and characteristics based on collected response data.

[1786] "Means for temporarily saving and sending data once all questions have been answered" is a function for temporarily saving the answer data entered by the user one by one, and sending the data to the server once all questions have been answered.

[1787] A "predictive algorithm means" is an algorithm that uses machine learning or statistical techniques to appropriately classify the generated personas.

[1788] "Means for publishing on a shared platform" is a function that allows the generated persona to be displayed in a public space on the Internet, making it accessible and usable by other users.

[1789] "Means for searching persona information based on specific conditions" is a function that searches for persona information in the database based on conditions specified by the user and finds the relevant persona.

[1790] "Means of using selected personas for marketing strategies and service evaluation" is a function that uses personas selected from search results to help develop marketing plans and evaluate services.

[1791] "Means for providing a questionnaire and collecting response data" refers to a function for presenting a questionnaire to users and collecting their responses.

[1792] The "means for awarding points" is a function for awarding points as a reward to users based on the collected response data.

[1793] The purpose of the PersonaNet system of this invention is to allow users to create personas that reflect their own values ​​and share and use them with other users on a shared platform. This system uses the functions of the server, terminal, and user, and includes the following program processing.

[1794] User Registration / Login

[1795] Servers, devices, and users:

[1796] New users create an account by entering required information such as first name, last name, email address, and password. Existing users log in using their email address and password. The registration and login information is sent from the device to the server, which then authenticates it by checking it against a database. If authentication is successful, a success message is returned to the device, allowing the user to access the system.

[1797] Persona creation

[1798] Servers, devices, and users:

[1799] Once logged in, users select the "Create Persona" option and answer a series of questions. The questions are designed based on psychology and statistics. The answer data is temporarily saved on the device and sent to the server once all questions have been answered. The server then passes the received answer data to a generation AI (e.g., OpenAI GPT-4), which uses prompts to generate a persona that reflects the user's values. The generated persona is then saved in a database.

[1800] Persona genre classification

[1801] server:

[1802] The generated personas are analyzed using a predictive algorithm (e.g., Random Forest or K-means clustering). Based on the analysis results, the personas are classified into appropriate genres. This genre information is also stored in the database.

[1803] Persona Publication

[1804] server:

[1805] The categorized personas are published on a sharing platform, where they can be viewed and used by other users.

[1806] Using Personas

[1807] Servers, devices, and users:

[1808] Other users can search for personas based on specific genres or conditions and select them as needed. The search conditions are sent from the device to the server, and the corresponding persona data is returned and displayed. The selected personas can be used for marketing strategies and service evaluations.

[1809] Survey implementation and point awarding

[1810] Server, User:

[1811] The server periodically conducts surveys about the published personas and sends notifications to users. Users respond to the surveys and send the data to the server. The server stores the received response data in a database and awards points to users who respond. These points can be exchanged for various rewards within the system.

[1812] Customizing AI logic

[1813] Server, User:

[1814] Certain users, such as companies, can purchase options to customize the logic of the generation AI. This customization allows the generation of personas tailored to specific uses. The server verifies the purchase information and applies the customization options to the user account.

[1815] Specific examples

[1816] For example, a user might create a new account and create a persona that reflects their values. This persona is then categorized under "youth marketing" and published on a shared platform. Another user might search for personas in this category and use them to develop a business plan. A business user might also purchase customization options for the AI ​​generator to generate personas tailored to their specific needs, allowing them to develop more sophisticated marketing strategies.

[1817] Prompt Sentence Examples

[1818] "I want to create a new persona specifically for the 'youth marketing' genre. Please answer the following questions:

[1819] 1. Age: 25

[1820] 2. Gender: Male

[1821] 3. Hobbies: Sports, listening to music

[1822] 4. Occupation: Salaryman

[1823] 5. Birthplace: Tokyo

[1824] 6. Values: Health-conscious, career-oriented

[1825] By selecting the "Generate Persona" option, users can answer the above questions and generate a detailed persona.

[1826] In this way, the PersonaNet system uses generative AI to generate detailed personas based on user input, allowing them to be shared and used by other users.

[1827] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1828] Program processing flow

[1829] User Registration / Login

[1830] Step 1: Enter your registration information

[1831] User: Enter the required information such as name, email address, and password, and click the "Register" button. Input data: name, email address, and password.

[1832] Step 2: Submit your registration information

[1833] Terminal: Sends the entered information to the server. Data sent: First name, last name, email address, password.

[1834] Step 3: Verify your registration details

[1835] Server: Compares the received information with the database and generates an authentication token if successful. Data verification: New data vs database.

[1836] Server: Saves the user account in the database based on the registration details and returns a success message to the terminal. It may also return an error message. Output: An authentication token.

[1837] Step 4: Enter your login details

[1838] User: Enter your registered email address and password and click the "Login" button. Input data: Email address, password.

[1839] Step 5: Submit login information

[1840] Terminal: Sends input information to the server. Data sent: Email address, password.

[1841] Step 6: Verify your login details

[1842] Server: Compares the entered information with the database and performs authentication. If it is correct, generates a token to start a user session and returns it to the terminal. It may also return an error message. Output: Authentication token.

[1843] Persona creation

[1844] Step 7: Select persona creation options

[1845] User: After logging in, select the "Create Persona" option. Input data: Select the Create Persona option.

[1846] Step 8: Answer questions

[1847] User: Answers a series of questions displayed. Example questions: age, gender, hobbies, values, etc. Input data: Questions and answers.

[1848] Step 9: Temporarily save your answers

[1849] Terminal: Temporarily saves the answer data. The saved data is sent to the server once all questions have been answered. Data processing: Temporarily saves and prepares the data for transmission.

[1850] Step 10: Submit your response data

[1851] Terminal: After answering all questions, send the data to the server. Data transmission: All answer data.

[1852] Step 11: Generate personas

[1853] Server: Passes the received answer data to a generation AI (e.g., OpenAI GPT-4) and generates a persona using the prompt. Data calculation: Persona generation by AI.

[1854] Server: Save the generated personas to the database. Output: The generated personas.

[1855] Persona genre classification

[1856] Step 12: Persona Analysis

[1857] Server: Analyzes the generated personas using predictive algorithms (e.g., Random Forest or K-means clustering). Data calculation: Persona analysis.

[1858] Step 13: Genre Classification

[1859] Server: Based on the analysis results, classify the persona into the appropriate genre. The classification information is also saved in the database. Output: Genre classification.

[1860] Persona Publication

[1861] Step 14: Prepare for publication

[1862] Server: Formats the categorized persona information for the shared platform. Data processing: Converts it into a publicly available format.

[1863] Step 15: Publishing

[1864] Server: Publishes personas onto a shared platform, making them visible and available to other users. Output: Published personas.

[1865] Using Personas

[1866] Step 16: Persona Search

[1867] User: Search for personas by specifying a specific genre or conditions. Input data: Search conditions.

[1868] Step 17: Submitting search criteria

[1869] Terminal: Sends search criteria to the server. Data transmission: Search criteria.

[1870] Step 18: Search based on criteria

[1871] Server: Retrieves personas that match the search criteria from the database and sends them to the device. Output: Personas from the search results.

[1872] Step 19: Persona Display

[1873] Terminal: Display persona information for search results. Data display: Search results.

[1874] Step 20: Use Personas

[1875] User: Use the selected persona for marketing strategies and service evaluation. Use the persona based on usage scenarios. Output: Analysis results based on usage scenarios.

[1876] Survey implementation and point awarding

[1877] Step 21: Survey Notification

[1878] Server: Periodically conducts surveys about published personas and sends notifications to users. Output: Survey notifications.

[1879] Step 22: Complete the survey

[1880] User: Answers the survey and sends the data to the server. Input data: Survey responses.

[1881] Step 23: Save the response data

[1882] Server: Saves the received response data in a database. Data storage: Response data.

[1883] Step 24: Points awarded

[1884] Server: Based on the answer data, points are awarded to the user who answered. These points can be exchanged for rewards within the system. Output: Awarded points.

[1885] Customizing AI logic

[1886] Step 25: Purchase customization options

[1887] User: Purchase logic customization options for the generation AI. Input data: Purchase information.

[1888] Step 26: Apply customizations

[1889] Server: Verify purchase information and apply customization options to user account. Output: Customization options.

[1890] (Application example 1)

[1891] 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."

[1892] In today's content distribution services, users have difficulty finding content that is optimized for their values ​​and interests, and there is a need for a system that can provide recommendations tailored to individual preferences. Furthermore, there is a lack of functionality that allows users to share personas with others and mutually recommend content. Therefore, there is an urgent need to develop a content recommendation system that accurately reflects user preferences.

[1893] 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.

[1894] In this invention, the server includes a means for allowing users to answer questions to generate a persona that reflects their own values ​​and collecting the answer data, a generation AI means for generating a persona based on the generated answer data, a means for recommending individual content based on the user's persona, and a means for publishing the classified persona on a sharing platform. This allows users to easily find content optimized for their preferences, and also enhances the function of sharing personas with other users and discovering content to recommend to each other.

[1895] "User" refers to an individual who uses this system, creates a persona that reflects their own values, and receives content recommendations.

[1896] "Values" refer to internal standards and guidelines that influence a user's behavior and preferences, such as personal beliefs, interests, and tastes.

[1897] A "persona" is a virtual character that reflects a user's values ​​and interests, and is used for content recommendations and marketing strategies.

[1898] "Means of answering questions and collecting response data" refers to the system's functionality of presenting questions to users to generate personas and collecting their responses as data.

[1899] "Generative AI means" refers to artificial intelligence technology for generating personas based on collected response data.

[1900] "Predictive algorithm means" refers to algorithm technology for analyzing personas generated by generative AI means and classifying them into appropriate genres.

[1901] "Means for recommending content" refers to a function that automatically selects and recommends content appropriate for a user based on the user's generated persona.

[1902] "Means for publishing" refers to the system's functionality for publishing classified personas on a shared platform so that they can be viewed and used by other users.

[1903] "Means for providing surveys and collecting response data" refers to the system's function of periodically presenting surveys to users and collecting their response data.

[1904] "Means for awarding points" refers to the system's function of awarding points to users based on collected response data and allowing them to exchange those points for rewards.

[1905] "Means for searching and selecting a persona" refers to the function of searching based on the genre of the generated personas and selecting a persona that suits a specific purpose.

[1906] "Means used for evaluating marketing strategies and services" refers to functions used to evaluate marketing strategies and services based on the selected personas.

[1907] "Genre" refers to the category into which personas are classified, and is divided based on hobbies, preferences, interests, etc.

[1908] This invention describes a system that allows a user to create a persona that reflects their own values ​​and recommends individual content based on that persona. Specific embodiments for realizing this system are described below.

[1909] 1. System Configuration

[1910] The system mainly consists of the following components:

[1911] User device: A device such as a smartphone, tablet, or computer.

[1912] Server: A remote server that processes and stores data.

[1913] AI model: Software that generates personas using generative AI technology.

[1914] 2. Program Processing

[1915] 2.1 User Registration / Login

[1916] To use the system, a user must first create an account. New users enter their name, email address, and password, and this information is sent from the user's device to the server. The server stores this information in a database and uses it as authentication information.

[1917] 2.2 Persona Creation

[1918] When a user logs in, they are presented with questions to generate a persona. The answers are temporarily stored on the user's device and then sent to the server, which then uses a generative AI model to generate a persona that reflects the user's values.

[1919] 2.3 Classifying and Publishing Personas

[1920] The generated personas are classified into genres by the server using a predictive algorithm, and the classified personas are published on a shared platform for other users to view and use.

[1921] 2.4 Content Recommendations

[1922] The server recommends content suitable for the user based on the generated persona, including movies, music, books, etc. The user's device receives and displays the recommendation list.

[1923] 2.5 Surveys and point allocation

[1924] Periodically, the server will provide users with surveys and collect their responses. Based on the collected data, users will be awarded points, which can be redeemed for rewards.

[1925] 2.6 Search and Selection

[1926] Users can search based on the genre of the generated personas and select the appropriate persona. The selected persona can be used for marketing strategies and service evaluation.

[1927] 3. Hardware and Software Used

[1928] Hardware: Smartphones, tablets, PCs, remote servers

[1929] Software: Generative AI models, predictive algorithms, database management systems, web server software (e.g., Flask)

[1930] Specific examples

[1931] Example prompts to generate personas based on user responses:

[1932] Generate personas based on your users' values ​​and interests using the following answers:

[1933] Answer 1: [User Answer 1]

[1934] Answer 2: [User Answer 2]

[1935] Answer 3: [User Answer 3]

[1936] ...

[1937] The generated results should be output in the following format:

[1938] {

[1939] "persona": {

[1940] "Personality": "〇〇",

[1941] "Interest": "〇〇",

[1942] "Values": "〇〇"

[1943] }

[1944] }

[1945] These technological elements allow users to easily find content that is optimized for their preferences, and also provide a comprehensive feature that allows users to share personas with other users and discover content that they can recommend to each other.

[1946] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1947] Step 1:

[1948] User Registration

[1949] The user enters their name, email address, and password and sends a registration request to the server, which stores this information in a database. The input data is name, email address, and password, and the output data is a message indicating whether registration was successful or not.

[1950] Step 2:

[1951] User Login

[1952] The user enters their email address and password and sends a login request to the server, which then checks the information in the database and authenticates them. The input data is the email address and password, and the output data is a message indicating whether authentication was successful or not.

[1953] Step 3:

[1954] Persona Generation

[1955] After logging in, users answer a series of questions presented by the system. These answers are temporarily stored on the device and then sent to the server. The server passes the answers to a generative AI model, which generates a persona that reflects the user's values. The input data is the answers, and the generated persona is returned as the output data.

[1956] Step 4:

[1957] Persona Classification

[1958] The server uses a predictive algorithm to classify the generated personas into genres. The input data is the generated persona, and the output data is the classified genre. The specific operation is to analyze the characteristics of the persona and classify it into the appropriate category.

[1959] Step 5:

[1960] Persona Announcement

[1961] The server publishes the classified personas on a shared platform. The input data is the classified personas, and the published personas are available as output data. The published personas can be viewed and used by other users.

[1962] Step 6:

[1963] Content Recommendation

[1964] The server recommends appropriate content to the user based on the generated persona. The recommendation list is sent to the device. The input data is the persona and a large amount of content data, and the output data is a list of recommended content. Specifically, the content is filtered based on the characteristics of the persona, and the most suitable items are selected.

[1965] Step 7:

[1966] Survey

[1967] The server periodically provides users with surveys and collects their responses. The input data is the survey responses, and the output data is the collected response data. This data is later used for analysis and point allocation.

[1968] Step 8:

[1969] Points awarded

[1970] The server awards points to users based on the collected response data. The input data is the survey response data, and the output data reflects the awarded points. Specifically, this is a process of adding points to the target user's account.

[1971] Step 9:

[1972] Persona Search

[1973] Users search based on persona genre and select a persona that suits their specific purpose. The input data are the search criteria, and the output data is a list of matching personas. This is the process of extracting appropriate personas from the database based on the search criteria.

[1974] Step 10:

[1975] Marketing Use

[1976] The server uses the selected persona for marketing strategies and service evaluation. Specific examples of use include formulating targeted advertising based on the persona and using it as a guide for product development. The input data is the selected persona, and the output data is a marketing report.

[1977] 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.

[1978] The purpose of the PersonaNet system of this invention is to allow users to create personas that reflect their own values ​​and emotions, and then share and use these with other users on a shared platform. This system incorporates an emotion engine that recognizes users' emotions and uses this information to generate and classify personas.

[1979] User Registration / Login

[1980] Users: New users create an account by entering the required information such as first name, last name, email address, and password. Existing users log in using their email address and password.

[1981] Terminal: Sends registration and login information to the server and receives responses from the server.

[1982] Server: Authenticates the received registration and login information by checking it against a database, and returns a success or error message to the device.

[1983] Persona creation

[1984] User: Once logged in, the user selects the "Create a Persona" option and begins answering a series of questions, which are designed based on psychology and statistics.

[1985] Device: The data provided by the user is temporarily stored and sent to the server once all questions have been answered. The data also includes elements for capturing the user's emotions.

[1986] Server: Passes the received response data to the emotion engine to recognize the user's emotion.

[1987] Server: The recognized emotional information is passed to the generation AI, which generates a persona that reflects the user's values ​​and emotions.

[1988] Persona genre classification

[1989] Server: The generated persona is run through a predictive algorithm to classify it into an appropriate genre based on the response data and emotional information. This genre information is also stored in a database.

[1990] Persona Publication

[1991] Server: Publish the categorized personas on a shared platform so that other users can view them.

[1992] Using Personas

[1993] Users: Other users can search for personas based on specific genres or conditions on the platform, select them as needed, and use the selected personas for marketing strategies and service evaluations.

[1994] Terminal: Sends search criteria to the server, receives and displays the corresponding persona data.

[1995] Server: Retrieves personas that match the search criteria from the database and returns them to the device.

[1996] Survey implementation and point awarding

[1997] Server: Send notifications to users to periodically survey them about their published personas.

[1998] User: The user receives a survey notification, answers the survey, and sends the data from the device to the server.

[1999] Server: Stores the received survey responses in a database and awards points to users who respond. These points can be exchanged for various rewards within the system.

[2000] Customizing AI logic

[2001] Users: Specific users, such as companies, can purchase options to customize the logic of the generative AI. This customization allows them to generate personas tailored to specific uses.

[2002] Server: Verify purchase information and apply customization options to user accounts.

[2003] For example, a user might create a new account and create a persona that reflects their values ​​and emotions. This persona is then categorized under the "youth marketing" category and published on a shared platform. Another user might search for personas in this category and use them to develop a business plan. A business user might also purchase customization options for the AI ​​generator, generating personas tailored to their specific needs and developing more sophisticated marketing strategies.

[2004] In this way, incorporating an emotion engine makes it possible to generate more accurate personas that take user emotions into account, allowing companies to develop more sophisticated marketing strategies. This system can reduce costs and improve data accuracy compared to traditional research methods.

[2005] The processing flow will be explained below.

[2006] Step 1:

[2007] A user visits the PersonaNet website and begins creating a new account.

[2008] Step 2:

[2009] The device makes an HTTP POST request to send the user's entered first name, last name, email address, and password to the server.

[2010] Step 3:

[2011] The server verifies the received registration information, stores it in a database, sends a confirmation email to the user that the registration was successful, and returns a success message to the terminal.

[2012] Step 4:

[2013] The user completes the email authentication, then proceeds to the login screen, enters their email address and password, and clicks the login button.

[2014] Step 5:

[2015] The terminal sends the login information to the server and makes an HTTP POST request to request authentication.

[2016] Step 6:

[2017] The server checks the received login information against a database, and if authentication is successful, generates session information and returns it to the terminal.

[2018] Step 7:

[2019] Users select the "Create a Persona" option and begin answering a series of questions, some of which are designed to capture the user's emotions.

[2020] Step 8:

[2021] The terminal temporarily stores the data answered by the user sequentially, and when all questions have been answered, it makes an HTTP POST request to send it to the server.

[2022] Step 9:

[2023] The server recognizes the user's emotions by passing the received response data to the emotion engine, which generates emotion information through linguistic and tone analysis of the response.

[2024] Step 10:

[2025] The server passes the generated emotional information to a generation AI, which generates a persona that reflects the user's values ​​and recognized emotions.

[2026] Step 11:

[2027] The server stores the generated persona data in a database, and uses predictive algorithms to classify the persona into the appropriate genre.

[2028] Step 12:

[2029] The server publishes the classified personas on a shared platform so that they can be viewed by users.

[2030] Step 13:

[2031] Users can search for personas based on specific genres or conditions on the shared platform, and can select personas that meet their needs, such as "marketing" or "product development."

[2032] Step 14:

[2033] The terminal sends the user's search criteria to the server and makes an HTTP GET request to obtain the corresponding persona.

[2034] Step 15:

[2035] The server retrieves personas that match the search criteria from the database and returns them to the terminal.

[2036] Step 16:

[2037] The terminal displays the acquired persona to the user.

[2038] Step 17:

[2039] The server periodically sends notifications to the relevant users to conduct a survey about the published personas.

[2040] Step 18:

[2041] The user receives a survey notification, answers the survey, and makes an HTTP POST request to send the data from the terminal to the server.

[2042] Step 19:

[2043] The server stores the received survey responses in a database and assigns points to the accounts of the users who responded.

[2044] Step 20:

[2045] The user can confirm that the points have been awarded on the shared platform and exchange the points for rewards within the system.

[2046] Step 21:

[2047] To customize the logic of the generative AI, the user, a company representative, accesses a page to purchase options and makes a payment.

[2048] Step 22:

[2049] The terminal makes an HTTP POST request to send the purchase information to the server.

[2050] Step 23:

[2051] The server validates the received purchase information and applies the customization options to the user account.

[2052] Example 2

[2053] 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."

[2054] In modern digital marketing and service evaluation, it is important to generate personas that accurately reflect users' values ​​and emotions. Conventional methods collect data without considering user emotions, which limits their accuracy. Furthermore, it is difficult to search, use, or customize the generated personas specifically for a company. This reduces the accuracy of marketing strategies and makes effective targeting difficult.

[2055] 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.

[2056] In this invention, the server includes: means for having users answer questions to generate a persona that reflects their values ​​and emotions and collecting the answer data; generation AI means for generating a persona based on the collected answer data and user emotion data; prediction algorithm means for classifying the generated personas; means for publishing the classified personas on a shared platform; means for users to search for and select personas based on specific genres or conditions; and means for viewing and using the selected personas. This enables the generation of highly accurate personas that take user emotions into consideration, allowing companies to effectively use the personas to improve targeting accuracy and optimize their marketing strategies.

[2057] A "user" is an entity that uses the system to generate, search, and use personas.

[2058] "Values" are the beliefs, opinions, and ways of thinking that form the basis for a user's actions.

[2059] "Emotion data" is information that indicates the user's emotional state and is recognized by the emotion engine.

[2060] "Answer data" refers to answer information to questions provided to the system by users.

[2061] A "persona" is a virtual character created based on a user's values ​​and emotional data.

[2062] The "generative AI means" is an artificial intelligence mechanism that generates personas based on collected response data and emotional data.

[2063] A "predictive algorithm" is an algorithm used to classify the generated personas.

[2064] The "sharing platform" is an online system for sharing generated personas with other users.

[2065] A "survey" is a survey question that is periodically provided to users and is a means of collecting response data.

[2066] The "point awarding means" is a mechanism for awarding points to users based on collected survey response data.

[2067] "Search tools" are functions that allow users to search for personas based on specific genres or conditions.

[2068] "Viewing means" is a function that allows users to display and check the personas selected by search.

[2069] "Corporate users" are specific users who have the authority to customize the logic of generative AI.

[2070] "Customization options" are settings that allow business users to tailor the logic of the generative AI to suit their specific needs.

[2071] The PersonaNet system of the present invention allows users to create personas that reflect their own values ​​and emotions, and then share and use those personas with other users on a shared platform. This system includes the following main elements:

[2072] 1. User Registration and Login

[2073] A user creates a new account by entering required information such as name, email address, and password. Existing users log in using their email address and password. The device sends this registration and login information to the server and receives a response from the server. The server compares the received information with a database and performs authentication. If authentication is successful, a success message is returned to the device; if it fails, an error message is returned.

[2074] 2. Persona Creation

[2075] After logging in, users select the "Create a Persona" option and answer a series of questions. These questions are designed based on psychology and statistics. The device temporarily stores the data the user has answered and sends it to the server once all questions have been answered. The answer data also includes elements to capture the user's emotions.

[2076] The server passes the received response data to an emotion engine (e.g., Microsoft Azure's Text Analytics API) to recognize the user's emotion. It then passes the recognized emotion information and response data to a generative AI (e.g., OpenAI's GPT-3 model) to generate a persona. The generated persona is temporarily stored.

[2077] 3. Persona Genre Classification

[2078] The server then runs the generated personas through a predictive algorithm (e.g., a clustering algorithm using Scikit-learn) to classify them into appropriate genres. This classification information is then stored in a database.

[2079] 4. Publish your persona

[2080] The server publishes the categorized personas on a shared platform (e.g., a frontend using React) so that other users can view them.

[2081] 5. Use personas

[2082] Users can search for personas based on specific genres or conditions. The device sends the search conditions to the server, receives and displays the corresponding persona data. The server retrieves personas that match the search conditions from the database and returns them to the device.

[2083] 6. Survey implementation and point allocation

[2084] The server periodically conducts surveys regarding the published personas. The surveys are notified to the relevant users. The users receive the notification, answer the surveys, and send the data from their devices to the server. The server stores the received survey responses in a database and awards points to the users who answered. These points can be exchanged for various rewards within the system.

[2085] 7. Customizing AI logic

[2086] Corporate users can purchase options to customize the logic of the generative AI. The server verifies the purchase information and applies the customization options to the user account.

[2087] Specific examples

[2088] For example, a user can create a new account and answer questions to create a persona that reflects their values ​​and emotions. This persona is then categorized under the "youth marketing" category and published on a shared platform. Another user can search for personas in this category and use them to develop a business plan. A business user can also purchase customization options for the AI ​​generator to generate personas tailored to their specific needs, allowing them to develop more precise marketing strategies.

[2089] Prompt Sentence Examples

[2090] 1. Account creation prompt:

[2091] "You will now be registering with the PersonaNet system. Please enter your first and last name, email address, and password."

[2092] 2. Persona creation prompt:

[2093] "To create your persona, please answer the following questions. Please also include your emotional state."

[2094] 3. Persona search prompt:

[2095] "Search for personas in a specific genre. Enter the genre name."

[2096] This makes it possible to generate highly accurate personas that take user emotions into account, allowing companies to effectively utilize these personas to improve targeting accuracy and optimize their marketing strategies.

[2097] The flow of the identification process in the second embodiment will be described with reference to FIG.

[2098] Step 1:

[2099] A user fills out a sign-up form with their name, email address, and password.

[2100] Enter your name, email address, and password.

[2101] Specific operation: The user enters the required information into the input fields and clicks the "Register" button.

[2102] Output: Input information is displayed on the terminal.

[2103] Step 2:

[2104] The device converts the entered registration information into JSON format and sends it to the server as an HTTPS POST request.

[2105] Input: Name, email address, and password entered by the user.

[2106] Specific behavior: Serializes input data and sends it to the server using a secure communication protocol.

[2107] Output: The server receives the request.

[2108] Step 3:

[2109] The server checks the received information against the database and performs authentication. If it is a new account, it is saved in the database. If an existing user logs in, authentication is performed.

[2110] Input: Name, email address, and password submitted by the user.

[2111] Specific behavior: Executes a database query to find user information, creates an account if new, or verifies information if existing.

[2112] Output: A success message or an error message is returned to the terminal as the authentication result.

[2113] Step 4:

[2114] After a user logs in, they select the "Create a Persona" option, which begins answering a series of questions.

[2115] Input: User selection, answer to question.

[2116] Specific operation: The user answers multiple questions presented by the system in sequence. Each answer is temporarily saved in local storage.

[2117] Output: All answer data for the question is saved.

[2118] Step 5:

[2119] The terminal temporarily stores the answer data to the questions and transmits it to the server when all questions have been answered.

[2120] Input: Answers to a series of questions.

[2121] Specific operation: When the user finishes inputting, the data stored in the local storage is converted to JSON format and sent to the server.

[2122] Output: The answer data is sent to the server.

[2123] Step 6:

[2124] The server passes the received response data to an emotion engine (e.g., Microsoft Azure's Text Analytics API) to recognize the user's emotion.

[2125] Input: Response data.

[2126] Specific operation: Sends response data to the emotion engine and receives an analyzed emotion score.

[2127] Output: A sentiment score is obtained.

[2128] Step 7:

[2129] The server passes the recognized emotional information to a generative AI (e.g., OpenAI's GPT-3 model) to generate a persona.

[2130] Input: Response data, sentiment scores.

[2131] Specific operations: Generate a prompt sentence, send it to the generative AI model, and receive the generated persona information.

[2132] Output: Generated persona information.

[2133] Step 8:

[2134] The server runs the generated personas through a predictive algorithm (e.g., a clustering algorithm using Scikit-learn) to classify them into categories. This information is then stored in a database.

[2135] Input: Generated persona information.

[2136] Specific operation: Apply a clustering algorithm to classify personas by genre.

[2137] Output: The classified persona information is stored in a database.

[2138] Step 9:

[2139] The server publishes the categorized persona data on a shared platform (e.g., a front-end using React).

[2140] Input: Classified persona information.

[2141] What it does: Retrieves classified persona information from the database and converts it into the appropriate format for display on a shared platform.

[2142] Output: The published persona information is displayed on a shared platform.

[2143] Step 10:

[2144] Users search for personas based on specific genres or criteria.

[2145] Input: Search criteria.

[2146] Specific behavior: The user enters search criteria into the search bar and clicks the search button.

[2147] Output: The search criteria is sent to the server.

[2148] Step 11:

[2149] The terminal sends the search criteria to the server, receives the corresponding persona data, and displays it.

[2150] Input: Search criteria.

[2151] What happens: A condition-based database query is executed, the relevant persona information is retrieved, and displayed to the user.

[2152] Output: The relevant persona information is displayed.

[2153] Step 12:

[2154] The server retrieves persona data that matches the search criteria from the database and returns it to the terminal.

[2155] Input: Search criteria.

[2156] Specific behavior: Executes a database query and returns the results to the device.

[2157] Output: Search results.

[2158] Step 13:

[2159] The server periodically sends notifications to users to survey them about their published personas.

[2160] Input: Survey trigger.

[2161] Specific actions: Define the survey content and send notifications to target users.

[2162] Output: Survey notification.

[2163] Step 14:

[2164] The user receives the survey notification, answers the survey, and transmits the data from the terminal to the server.

[2165] Input: Survey responses.

[2166] Specific action: Answer the survey and click the submit button.

[2167] Output: The answer data is sent to the server.

[2168] Step 15:

[2169] The server stores the received survey responses in a database and gives points to the users who responded.

[2170] Input: Survey response data.

[2171] What it does: Saves the answer data to a database and adds points to the user's account.

[2172] Output: Updated point data.

[2173] Step 16:

[2174] Enterprise users can purchase options to customize the logic of the generated AI.

[2175] Enter: Customization options.

[2176] Specific operation: After selecting the option and completing the purchase procedure, the server applies the option.

[2177] Output: The applied customization options.

[2178] Step 17:

[2179] The server verifies the purchase information and applies the customization options to the user account.

[2180] Input: Purchase information.

[2181] What it does: Verify purchase information and apply personalized settings to your user account.

[2182] Output: The user account whose settings were updated.

[2183] (Application example 2)

[2184] 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."

[2185] Modern advertising strategies require methods for detailed analysis of user values ​​and emotions and for targeting advertising based on those values. However, conventional systems are unable to fully utilize user emotional information, making it difficult to maximize advertising effectiveness. Furthermore, there is no established method for analyzing collected data in real time and dynamically optimizing advertising strategies. This makes it difficult to design and manage efficient and accurate advertising campaigns.

[2186] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[2187] In this invention, the server includes: means for having users answer questions to generate a persona that reflects their values ​​and collecting the answer data; generation AI means for generating a persona based on the generated answer data; prediction algorithm means for classifying the generated persona; means for publishing the classified persona on a shared platform; emotion recognition means for analyzing the collected emotion data and utilizing the user's emotion information in persona generation; and means for integrating the persona based on the collected emotion data and values ​​into an advertising strategy. This enables highly accurate persona generation based on the user's values ​​and emotions, enabling efficient and dynamic design and management of advertising campaigns.

[2188] "Users" are users of the system who provide values ​​and emotional data to generate personas.

[2189] "Values" are fundamental beliefs and principles that influence a user's thoughts and actions.

[2190] A "persona" is a virtual character created based on a user's values ​​and emotional data, and is used as a target for marketing and advertising strategies.

[2191] A "question" is a query posed by the system to gather the user's values ​​and feelings.

[2192] "Response Data" means information provided by a user in response to a question.

[2193] "Generative AI means" is an artificial intelligence technology that analyzes response data and generates a persona that reflects the user's values ​​and emotions.

[2194] "Predictive algorithm means" is a data analysis technique for classifying the generated personas into appropriate genres.

[2195] The "sharing platform" is an online system for sharing generated and classified personas with other users.

[2196] "Emotion recognition means" is a technology that collects and analyzes user emotional data and uses it to generate personas.

[2197] An "advertising strategy" refers to a plan or method for effectively delivering advertisements to a target audience.

[2198] "Dynamic optimization" is a technique for instantly improving the content and delivery of advertising campaigns based on feedback collected in real time.

[2199] This invention is a system that allows users to create personas that reflect their own values ​​and emotions, and integrates and utilizes these personas in advertising campaigns. Detailed embodiments for realizing this system will be described below.

[2200] System Overview

[2201] 1. User Interface

[2202] The system is accessed by users through an application installed on a device such as a smartphone, smart glasses, or head-mounted display, and involves answering a series of questions that reflect their values.

[2203] 2. Emotional Data Collection

[2204] The device uses installed emotion recognition technology (e.g., Affectiva SDK, Kairos SDK) to collect emotional data from the user's facial expressions and voice, and analyzes the data in real time through the camera in the smart glasses or head-mounted display to identify the user's emotional state.

[2205] 3. Persona generation

[2206] The server generates a persona using a generative AI model (e.g., GPT-4) based on the collected response data and emotion data. The generative AI model generates a persona using the following prompt sentence:

[2207] Generate the following personas based on user sentiment data and values:

[2208] 1. Emotion data: [Happiness, Anger, Sadness, Surprise]

[2209] 2. Values: [Enterprising, Conservative, Sociable, Introverted]

[2210] For example, generate personas based on "enterprising and joyful" or "conservative and sensitive to surprises."

[2211] 4. Persona Classification

[2212] The generated personas are classified into appropriate genres by the server using a predictive algorithm (e.g., TensorFlow, PyTorch). The classified data is stored in a database.

[2213] 5. Share your personas

[2214] The classified personas are then published on a shared platform where they can be searched and selected by other users, who can then search for personas based on specific criteria and integrate them into their advertising campaigns.

[2215] 6. Integrate your advertising strategy

[2216] The server integrates the collected emotion data and generated personas into the advertising campaign management system, enabling highly accurate targeting of ads based on user emotions. It also monitors advertising effectiveness through real-time feedback and dynamically optimizes advertising strategies based on the feedback data.

[2217] Hardware and software used

[2218] Smart glasses / head-mounted displays: generic names (e.g., Google Glass, Microsoft HoloLens)

[2219] Emotion Engine: Generic name (e.g., Affectiva SDK, Kairos SDK)

[2220] Generative AI model: Generic name (e.g., OpenAI GPT-4)

[2221] Machine learning framework: generic name (e.g., TensorFlow, PyTorch)

[2222] Database: Generic name (e.g. MySQL, MongoDB)

[2223] Cloud services: generic names (e.g., Amazon Web Services (AWS), Google Cloud Platform (GCP))

[2224] This approach makes it possible to effectively utilize user values ​​and emotional information, enabling highly accurate persona generation and dynamic optimization of advertising campaigns.

[2225] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[2226] Step 1:

[2227] The user launches an application installed on a smartphone, smart glasses, or head-mounted display and logs in. First name, last name, email address, and password are required as input, and the server authenticates the user based on this information and returns a successful authentication message to the device.

[2228] Step 2:

[2229] After logging in, the user selects the "Create Persona" option and answers the provided questions, which include multiple psychological and statistical questions. The device temporarily stores the answer data and sends it to the server once the user has completed answering all the questions. The input contains the user's answer data, and the output is the data to be transmitted to the server.

[2230] Step 3:

[2231] The server passes the received response data to an emotion engine to analyze the user's emotion. The emotion recognition means analyzes the user's emotion data. The emotion data is input and emotion information is output.

[2232] Step 4:

[2233] The server generates a persona using a generative AI model based on the analyzed emotional information. Specifically, the server inputs the following prompt sentence into the generative AI model:

[2234] Generate the following personas based on user sentiment data and values:

[2235] 1. Emotion data: [Happiness, Anger, Sadness, Surprise]

[2236] 2. Values: [Enterprising, Conservative, Sociable, Introverted]

[2237] For example, generate personas based on "enterprising and joyful" or "conservative and sensitive to surprises."

[2238] The input includes emotional and value data, and the output is a generated persona.

[2239] Step 5:

[2240] The server classifies the generated personas into appropriate genres using a predictive algorithm. The classified personas are stored in a database, which contains the generated personas as input and provides the classification results as output.

[2241] Step 6:

[2242] The server publishes the classified personas on a shared platform, where other users can search and select personas based on specific criteria. The input contains the classified personas, and the output is the published persona information.

[2243] Step 7:

[2244] The user enters search criteria and selects an appropriate persona on the shared platform. The terminal sends the search criteria to the server, which receives and displays the corresponding persona data. The search criteria are included as input, and the corresponding persona information is obtained as output.

[2245] Step 8:

[2246] The server integrates the selected personas into an advertising campaign management system, monitors advertising effectiveness based on real-time feedback, and dynamically optimizes advertising strategies based on the feedback data. The inputs include the selected personas and real-time feedback data, and the output is an optimized advertising strategy.

[2247] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[2248] 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.

[2249] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[2250] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[2251] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2252] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[2253] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[2254] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[2255] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[2256] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[2257] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[2258] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[2259] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[2260] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[2261] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[2262] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-P...

Claims

1. A means for users to answer questions to generate personas that reflect their own values ​​and collect the response data; A generation AI means for generating personas based on the generated response data; a predictive algorithmic means for classifying the generated personas; A means of publishing the classified personas on a shared platform; A system including:

2. A means of periodically providing surveys to users and collecting user response data; A means for awarding points based on the collected response data; The system of claim 1 further comprising:

3. A means to search based on the genre of the generated personas and select a persona that suits the purpose, How to use the selected personas for marketing strategies and service evaluations; The system of claim 1 further comprising:

Images