System

Abstract

A system is provided.SOLUTION: A system comprising means for generating a virtual character based on specified characteristics using a generation algorithm, means for customizing characteristics of the virtual character in response to a user request, means for scheduling the virtual character based on the user request, means for collecting and analyzing activity data of the virtual character in real-time, means for presenting performance data of the virtual character to the user, means for performing an interaction between the virtual character and the user, and means for generating and providing an engagement report to the user based on the activity of the virtual character.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] There are many problems with the current influencer market. One issue is that talents lose their appeal or go on hiatus for various reasons. This leads to lower engagement rates, making it difficult for brands and companies to carry out stable promotional activities. Furthermore, when using human influencers, there are geographic and time constraints, making scheduling difficult. Furthermore, there is a risk that a brand's image could be damaged by scandals or other misconduct. It is necessary to solve these issues and provide a stable supply of influencers to bring a breath of fresh air to the entertainment industry. [Means for solving the problem]

[0005] The present invention provides a means for generating a virtual character (hereinafter referred to as an AI influencer) based on specified characteristics using a generation algorithm. This means that the AI ​​influencer is free from human constraints and can deliver stable, high-quality performance. It also provides a means for users to customize the characteristics of the AI ​​influencer, allowing for personalization according to the needs of brands and companies. It also includes a means for scheduling the AI ​​influencer based on user requests, eliminating time and geographic constraints. It also provides a means for collecting and analyzing activity data of the AI ​​influencer in real time, allowing for engagement rates and performance data to be presented to users. It also includes a means for users to give instructions to the AI ​​influencer in real time through a chat function, enabling direct interaction with the user. Finally, it incorporates a means for generating and providing user-provided engagement reports based on the virtual character's activities, supporting effective marketing strategies. These means solve the challenges in the current influencer market and achieve stable, highly efficient engagement.

[0006] A "generation algorithm" is a set of computational steps for generating a virtual character based on specified characteristics.

[0007] "Virtual characters" are digital agents created by generative algorithms, which are customizable and live on digital platforms.

[0008] A "user" is a brand, company, or individual who customizes, manages, or directs a virtual character.

[0009] "Customization Means" means the interfaces and functions provided to users to change and configure the characteristics and profile of their virtual characters.

[0010] The "schedule setting means" is a function for specifying the date and time of a virtual character's activities and for allowing the virtual character to operate in a planned manner.

[0011] "Activity Data" is information about a series of actions taken by a virtual character, including engagement rates, number of followers, comments, etc.

[0012] "Analytics" means algorithms or functions used to analyze engagement patterns and performance based on collected activity data.

[0013] "Performance Data" refers to indicators relating to the results of a virtual character's activities, including engagement rates and follower responses.

[0014] The "chat function" is a communication means that allows users to give instructions to virtual characters in real time, enabling direct interaction.

[0015] An "Engagement Report" is a detailed analytical report based on the activities of a virtual character and the results of those activities. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6]FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0017] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0019] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0020] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0021] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0022] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0023] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0024] [First embodiment]

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

[0026] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0027] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0028] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0031] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0033] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0034] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0036] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0037] The system of the present invention uses a generation algorithm to generate a virtual character (hereinafter referred to as an AI influencer) based on specified characteristics, and provides a set of means for users to customize and manage the character. Specific embodiments of the system are described below.

[0038] AI influencer generation

[0039] The server receives a user request and launches an AI influencer generation algorithm. This algorithm uses generative AI technology to create a realistic virtual character based on input characteristic data (e.g., gender, age, appearance, personality, etc.). For example, the algorithm determines the user's desired hairstyle, eye color, mouth shape, etc. based on the specified characteristics, and a deep learning model then creates a more realistic depiction.

[0040] Customization

[0041] The server stores information about the generated AI influencer on a dedicated web portal, which users can access. After logging in, users can fine-tune their virtual character through a customization screen. For example, they can fine-tune facial contours, select a tone of voice, and set personality attributes. This allows users to create a virtual character that best suits their brand or campaign.

[0042] Setting a schedule

[0043] Users will be provided with a schedule management screen where they can set the AI ​​influencer's activity dates and times, for example, they can schedule social media posts on a specific day of the week or a monthly live stream.

[0044] Managing Performance

[0045] The server collects and analyzes the AI ​​influencer's activity data in real time. For example, after posting on social media, it analyzes and accumulates data on engagement rates, increases or decreases in the number of followers, and the content of comments. The server analyzes this data and displays it visually to the user through a dashboard.

[0046] Interaction

[0047] A chat function is provided that allows users to directly interact with the AI ​​influencer using a dedicated device. For example, users can hold Q&A sessions during live streaming or give instructions on product introductions in real time. This enables two-way communication between the virtual character and the audience.

[0048] Generate engagement reports

[0049] The server generates engagement reports based on the collected activity data. These reports provide detailed information on campaign effectiveness, engagement trends, follower reactions, etc. Reports are generated automatically and provided to users via email or dashboard.

[0050] Specific examples

[0051] For example, a cosmetics brand user might create an AI influencer named "Luna" to promote a new product. The user can customize Luna's hairstyle, skin color, and voice tone to match the brand's image. Then, the user schedules her to post on social media every Friday and to promote the new product via live streaming once a month. The server manages this, collecting and analyzing engagement data in real time and displaying it on a dashboard. Through the dashboard, the user can monitor performance and receive engagement reports to support effective marketing activities.

[0052] In this way, the system according to the present invention solves the problems in the current influencer market and realizes stable and highly efficient engagement.

[0053] The processing flow will be explained below.

[0054] Step 1:

[0055] The user logs in to a dedicated web portal, then inputs a request to generate an AI influencer and specifies characteristic data (e.g., gender, age, appearance, personality, etc.).

[0056] Step 2:

[0057] The server receives the user's request and triggers a generation algorithm based on the specified characteristic data, which uses deep learning techniques to generate a base model of the AI ​​influencer.

[0058] Step 3:

[0059] The server then trains the generated base model to make it look and behave realistically, resulting in an AI influencer with realistic portrayals.

[0060] Step 4:

[0061] The server saves the generated AI influencer prototype on a dedicated web portal and notifies the user when the generation is complete. The user can then log in to the portal to view the generated character.

[0062] Step 5:

[0063] Users can access a customization screen to fine-tune appearance (e.g., hairstyle, eye color, mouth shape, etc.), voice tone, personality settings, etc. By adjusting these settings, users can create a virtual character that matches their brand image or individual needs.

[0064] Step 6:

[0065] The server reflects the customization data entered by the user and generates the final AI influencer, which is then stored on a dedicated web portal and presented to the user for review.

[0066] Step 7:

[0067] Users can access the schedule management screen and specify the days and times when the AI ​​influencer will be active. For example, users can set up social media posts on a specific day of the week and live stream once a month.

[0068] Step 8:

[0069] The server automatically saves the user-specified schedule in a database and creates an activity plan for the AI ​​influencer, ensuring that all actions are carried out as planned.

[0070] Step 9:

[0071] The server collects real-time activity data of AI influencers, including engagement rates, follower count increases and decreases, and comment content.

[0072] Step 10:

[0073] The server analyzes the collected data and presents it to the user visually through a dashboard, including engagement status and performance trends.

[0074] Step 11:

[0075] Users can access the chat function using a dedicated device and give instructions to the AI ​​influencer in real time. For example, users can instruct the AI ​​influencer to make comments or introduce products during live broadcasts.

[0076] Step 12:

[0077] The server analyzes the user's instructions and has the AI ​​influencer take appropriate action, enabling automatic and dynamic interactions.

[0078] Step 13:

[0079] The server generates an engagement report based on the collected activity data, which includes campaign effectiveness metrics and recommendations for improvement.

[0080] Step 14:

[0081] The server will provide the generated engagement report to the user via email or dashboard, allowing the user to review it and use it to improve their next marketing strategy.

[0082] Example 1

[0083] 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."

[0084] Conventional virtual character systems have had problems such as difficulty in achieving realistic depictions based on complex user-specified characteristics, customizing the generated characters, managing schedules, collecting and analyzing real-time performance data, interacting with users, and generating and providing detailed engagement reports. As a result, companies and individuals have had difficulty efficiently managing characters in marketing activities and brand promotions, preventing them from maximizing engagement effects.

[0085] 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.

[0086] In this invention, the server includes: means for generating a virtual character based on specified characteristics using a generation algorithm; means for saving the virtual character's characteristic data in a database and displaying it on a dedicated web portal; means for customizing the virtual character's characteristics according to a user's request; means for providing a dedicated schedule management screen and setting the virtual character's schedule according to a user's request; means for collecting and analyzing activity data in real time via the virtual character's social media API; means for visualizing the analyzed data on a dashboard and presenting it to the user; means for providing a chat function for the user to give instructions to the user in real time through interactions with the virtual character; and means for automatically generating an engagement report based on the virtual character's activity data and providing it to the user. This allows users to efficiently perform processes from virtual character generation and customization to activity management, data analysis, and report generation through a consistent system.

[0087] A "generation algorithm" is a computational method for generating a virtual character using specified characteristic data, and primarily utilizes deep learning technology.

[0088] "Characteristic data" is information including the gender, age, appearance, personality, etc. of a virtual character, and is an input value provided by a user.

[0089] "Database" means an information storage system for managing and storing information about generated and customized virtual characters.

[0090] "Web Portal" means an online platform for accessing, managing, and adjusting information about user-generated and customized virtual characters.

[0091] "Customization" refers to the act of a user adjusting the characteristics (e.g., appearance, tone of voice, personality attributes, etc.) of a generated virtual character to fine-tune it to their own needs.

[0092] The "schedule management screen" is an interface that allows the user to set the activity dates and times of the virtual character, and is provided in a calendar or list format.

[0093] "Social Media API" refers to the application programming interface provided by a social media platform and is the technology used to collect activity data (e.g., engagement data) of virtual characters.

[0094] A "dashboard" is an interface for visually displaying analyzed activity data, allowing users to check the performance of their virtual characters in real time.

[0095] "Chat function" refers to a means of communication that allows users to exchange messages with virtual characters in real time, and is primarily used for live streaming and question and answer sessions.

[0096] "Engagement Report" means a report detailing engagement results and trends based on virtual character activity data, provided to Users to assist them in effective campaign management.

[0097] The system of the present invention generates, customizes, schedules, manages and analyzes activity data, and generates and provides interaction and engagement reports for virtual characters (hereinafter referred to as AI influencers) through a dedicated server and user terminals. Detailed embodiments of the system are described below.

[0098] AI influencer generation

[0099] The server receives a request from the user. This request includes characteristic data of the virtual character (e.g., gender, age, appearance, personality, etc.). The server generates a virtual character based on these characteristics using, for example, a generative AI model running on Google Cloud Platform (GCP). The generated character is then detailed based on the characteristic data to achieve a realistic depiction using deep learning technology.

[0100] Customization

[0101] The server stores the generated AI influencer data in a database and displays it on a dedicated web portal. Users can access the customization screen by logging in to this web portal. On the customization screen, users can fine-tune their virtual character (e.g., adjust facial contours, select voice tone, and set personality attributes). The user interface (UI) is designed for intuitive operation, using sliders and drop-down menus.

[0102] Setting a schedule

[0103] Users can set the AI ​​influencer's activity dates and times by accessing a dedicated schedule management screen. Using the management screen, which is provided in calendar and list format, users can enter schedules for social media posts and live broadcasts on specific dates and times. For example, they can set specific schedules such as "posting new product introductions every Friday at 10:00 AM."

[0104] Managing Performance

[0105] The server uses social media APIs (e.g., Facebook Graph API, Twitter API) to collect real-time activity data on AI influencers. The acquired data includes engagement rates, increases or decreases in the number of followers, and comment content. This data is analyzed by the server and evaluated as engagement performance. Tools such as Google Analytics and IBM Watson Analytics are used for the analysis.

[0106] Dashboard visualization

[0107] The server displays the analyzed data on a dashboard, which is designed to allow users to intuitively view the data using visualization tools (e.g., D3.js, Chart.js). Through this dashboard, users can monitor the performance of their virtual characters in real time and make adjustments as needed.

[0108] Interaction

[0109] Using a dedicated device, users can access a chat function to interact with the virtual character, allowing them to answer questions and give instructions in real time during the live broadcast, enabling two-way communication between the virtual character and the viewer.

[0110] Generate engagement reports

[0111] The server automatically generates engagement reports based on the collected activity data. These reports include detailed records of campaign effectiveness, engagement trends, follower responses, etc. The generated reports are sent to users via email or a dashboard.

[0112] Specific examples

[0113] For example, a cosmetics brand user might create an AI influencer named "Luna" to promote a new product. The user can customize Luna's hairstyle, skin color, and voice tone to match the brand's image. Next, the user can schedule Luna to post on social media every Friday and to promote the new product via live streaming once a month. The server manages these schedules, collects and analyzes engagement data in real time, and displays it on a dashboard. Through the dashboard, the user can monitor performance and receive engagement reports to support effective marketing activities.

[0114] Example prompt sentence:

[0115] We want to generate an AI influencer called "Luna" to promote a new product. Luna's characteristics are as follows:

[0116] Gender: Female

[0117] Age: 25

[0118] Hairstyle: Long Straight

[0119] Eye color: Blue

[0120] Personality: Cheerful and energetic

[0121] Based on this, please generate a virtual character that is as realistic as possible, provide a customization screen for Luna, and once the setup is complete, schedule a social media post every Friday and a monthly live stream.

[0122] As described above, the system according to the present invention realizes efficient and sophisticated virtual character management and promotion in the marketing activities of companies and individuals.

[0123] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0124] Step 1:

[0125] The server receives the user request.

[0126] Input: The user enters the virtual character's characteristic data (e.g., gender, age, appearance, personality, etc.) in a form on the web portal.

[0127] Data processing and data calculation: The server receives the input characteristic data and converts it into an appropriate format, for example, converting text data into numerical data, to create an input dataset for the generative AI model.

[0128] Output: The characteristic data is the input dataset, properly formatted.

[0129] Step 2:

[0130] The server launches the generative AI model.

[0131] Input: The formatted input dataset.

[0132] Data processing and data calculation: The server uses a deep learning model built with TensorFlow or PyTorch, for example, to perform calculations to generate a virtual character based on the characteristic data.

[0133] Output: A 3D model or 2D image of the generated virtual character is output.

[0134] Step 3:

[0135] The server stores the generated AI influencer data in a database.

[0136] Input: 3D model and 2D image data of the generated virtual character.

[0137] Data processing and data calculation: The server processes the data to properly index and store it in the database.

[0138] Output: Virtual character information stored in a database.

[0139] Step 4:

[0140] The server displays the generated AI influencer data on a web portal.

[0141] Input: Generated virtual character information stored in a database.

[0142] Data processing and data calculation: The server processes the data to display it in the appropriate format on the web portal.

[0143] Output: The generated character is displayed in the customization screen of the web portal which the user can log in and view.

[0144] Step 5:

[0145] The user customizes the virtual character.

[0146] Input: Fine-tuning information for the virtual character entered by the user through the customization screen (e.g., adjusting facial contours, selecting voice tone, setting personality attributes).

[0147] Data Processing and Data Calculation: The server performs the data processing necessary to update the attributes of the virtual character based on the user's customization information.

[0148] Output: Final virtual character data based on user customized attributes.

[0149] Step 6:

[0150] The user accesses the schedule management screen and sets the activity date and time of the virtual character.

[0151] Input: User-generated scheduling information (e.g., scheduling a social media post or live stream on a specific date and time).

[0152] Data processing and data calculation: The server stores schedule information in a database and performs processing to properly manage it.

[0153] Output: Schedule information stored in the database.

[0154] Step 7:

[0155] The server collects and analyzes the activity data of the virtual characters in real time.

[0156] Input: Activity data obtained from social media APIs (e.g., engagement rate, follower count increase / decrease, comment content, etc.).

[0157] Data processing and data calculation: The server uses data analysis tools to analyze the acquired data and calculate the engagement performance.

[0158] Output: Parsed engagement data.

[0159] Step 8:

[0160] The server visualizes and displays the analytical data on a dashboard.

[0161] Input: Parsed engagement data.

[0162] Data processing and data calculations: The server generates graphs and charts of the data using visualization tools (e.g., D3.js, Chart.js).

[0163] Output: Visualized data that users can view in a dashboard.

[0164] Step 9:

[0165] Users interact with virtual characters in real time through a chat function.

[0166] Input: Real-time messages and instructions from the user.

[0167] Data processing and data calculation: The server processes chat messages and causes the virtual characters to react appropriately.

[0168] Output: The responses and actions of the virtual character.

[0169] Step 10:

[0170] The server generates an engagement report based on the collected activity data and provides it to the user.

[0171] Input: Parsed activity data and engagement information.

[0172] Data processing and data calculation: The server uses a report generation tool (e.g., Tableau, Microsoft Power BI) to automatically generate engagement reports.

[0173] Output: Engagement reports delivered to users (via emails and dashboards).

[0174] Through the above steps, the system according to the present invention enables users to efficiently create and manage virtual characters, as well as collect and analyze engagement data.

[0175] (Application example 1)

[0176] 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."

[0177] In recent years, advertising and marketing activities using virtual characters have become more common, but their operation requires high costs and specialized skills. Furthermore, collecting and analyzing engagement data in real time is difficult, making it time-consuming to measure effectiveness. Furthermore, there are limited ways for users to interact with virtual characters in real time, which creates the challenge of insufficient two-way communication.

[0178] 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.

[0179] In this invention, the server includes means for generating a virtual character based on specified characteristics using a generation algorithm, means for customizing the characteristics of the virtual character in response to a user request, means for setting a schedule for the virtual character in response to a user request, means for collecting and analyzing activity data of the virtual character in real time, means for presenting performance data of the virtual character to the user, means for executing an interaction between the virtual character and the user, means for generating an engagement report based on the activity of the virtual character and providing it to the user, means for providing a smartphone application, means for executing and managing an advertising campaign using the virtual character, and means for collecting engagement data using a social media API. This enables users to efficiently and effectively conduct advertising and marketing activities using virtual characters without requiring specialized skills.

[0180] A "generation algorithm" is an algorithm for automatically generating a virtual character based on specified characteristic data.

[0181] A "virtual character" is a digital character with specific characteristics created by a generative algorithm.

[0182] "Characteristics" refers to characteristic data used to create a virtual character, such as gender, age, appearance, and personality.

[0183] "Customization" refers to changing or adjusting the characteristics of a generated virtual character based on the user's requests.

[0184] "Schedule setting" is a function for setting the date, time and frequency of a virtual character's activities.

[0185] "Activity data" is data about all activities performed by a virtual character.

[0186] "Collecting and analyzing in real time" refers to a process in which activity data of a virtual character is collected at that moment and analyzed immediately.

[0187] "Performance Data" means data related to the results of a virtual character's activities, such as engagement rate and number of followers.

[0188] "Interaction" refers to real-time interaction between a virtual character and a user.

[0189] An "engagement report" is a report created based on data based on the activities of virtual characters.

[0190] A "smartphone application" is a software application that runs on a smartphone.

[0191] An "advertising campaign" is a planned advertising effort to promote a particular product or service.

[0192] A "social media API" is an externally accessible programmatic interface provided by a social media platform.

[0193] The system of the present invention uses a generation algorithm to generate a virtual character (hereinafter referred to as an AI influencer) based on specified characteristics, and provides a set of means for users to customize and manage the character. Specific embodiments of the system are described below.

[0194] 1. AI influencer generation

[0195] The server receives a user request and launches a generative algorithm, which uses generative AI technology to create a realistic virtual character based on the user's specified characteristics (e.g., gender, age, appearance, personality, etc.). For example, the algorithm determines the user's desired hairstyle, eye color, mouth shape, etc., based on the specified characteristics, and a deep learning model (e.g., TensorFlow or PyTorch) then creates a more realistic depiction.

[0196] 2. Customization

[0197] The server stores information about the generated AI influencer in a database (e.g., Amazon RDS) and makes it accessible to users. Users can fine-tune the virtual character through the customization screen of the smartphone application. For example, they can fine-tune the facial contours, select the tone of voice, and set personality attributes. This allows users to create an AI influencer that is best suited to their brand or campaign.

[0198] 3. Set a schedule

[0199] Users can access the smartphone application's schedule management screen and set the AI ​​influencer's activity dates and times. For example, they can set it to post on social media on a specific day of the week and to broadcast live once a month. The server automatically schedules the activities based on this information and carries them out at the specified dates and times.

[0200] 4. Performance Management

[0201] The server collects and analyzes activity data in real time using social media APIs (e.g., Twitter API, Instagram Graph API), collecting information such as engagement rates, increases or decreases in the number of followers, and comment content, and generates analytical results using Python's Pandas and Matplotlib.

[0202] 5. Interaction

[0203] Users can use chat features (e.g., Socket.IO) to interact with AI influencers in real time, for example, by answering questions during live streaming or giving instructions for product introductions in real time.

[0204] 6. Generate engagement reports

[0205] The server generates engagement reports based on the collected activity data, detailing campaign effectiveness, engagement trends, follower reactions, etc. Reports are generated automatically and provided to users via email (e.g., SendGrid) or an in-application dashboard.

[0206] Specific examples

[0207] Suppose a cosmetics brand user wants to generate an AI influencer to promote a new product. They can customize the generated character by entering a prompt such as "Generate a character that is female, 25 years old, with a short bob, blue eyes, and a lively personality." They then schedule a social media post every Friday and a monthly live stream to introduce the new product. The server manages this, collecting and analyzing engagement data in real time and displaying it on a dashboard. Users can monitor performance through the dashboard and receive engagement reports to support effective marketing activities.

[0208] In this way, the system according to the present invention enables users to easily and efficiently carry out advertising and marketing activities that utilize virtual characters.

[0209] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0210] Step 1:

[0211] The server receives a prompt from the user: "Generate a character that is female, 25 years old, with a short bob haircut, blue eyes, and a lively personality." Based on the input characteristic data, it generates a virtual character using a generative AI model (TensorFlow or PyTorch). This generative AI model uses deep learning technology to create a realistic depiction based on the input characteristic data. The resulting virtual character data is stored in a database (Amazon RDS).

[0212] Step 2:

[0213] The user accesses the customization screen via a smartphone application. The server retrieves information about the generated AI influencer from the database and presents it to the user. The user then fine-tunes characteristics such as hairstyle, eye color, facial contours, voice tone, and personality attributes. The adjusted data is sent to the server, which then uses the AI ​​model again to generate an updated virtual character and update the database.

[0214] Step 3:

[0215] The user accesses the schedule management screen and sets the activity schedule for the virtual character, for example, posting to social media every Friday, live streaming once a month, etc. The server saves this schedule data in a database and schedules tasks to be executed at the specified date and time.

[0216] Step 4:

[0217] The server executes the virtual character's posts at the specified date and time using social media APIs (Twitter API, Instagram Graph API), and if live streaming is set, starts streaming using the appropriate streaming API, so that the virtual character's activities are executed according to the schedule set by the user.

[0218] Step 5:

[0219] After a social media post or live stream is completed, the server collects engagement data (likes, comments, number of followers, etc.) in real time. The data obtained through the social media API is used to analyze the data using Python's Pandas and Matplotlib. The analysis results are displayed on a dashboard using visualization tools (D3.js, Chart.js).

[0220] Step 6:

[0221] Users use the chat function (Socket.IO) of a smartphone application to interact with the AI ​​influencer in real time. The server receives chat messages from users, and the AI ​​influencer reacts based on them.

[0222] Step 7:

[0223] The server generates engagement reports based on the collected engagement data, detailing campaign effectiveness, engagement trends, follower reactions, etc. Reports are generated automatically and provided to users via email (SendGrid) or an in-application dashboard.

[0224] Through the above steps, the system according to the present invention enables users to efficiently carry out advertising and marketing activities that utilize virtual characters.

[0225] 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.

[0226] The system of the present invention uses a generation algorithm to generate a virtual character (hereinafter referred to as an AI influencer) based on specified characteristics, and provides a set of means for users to customize, manage, and recognize emotions of the virtual character. Specific embodiments of the system are described below.

[0227] AI influencer generation

[0228] The server receives a user request and launches an AI influencer generation algorithm. This algorithm uses generative AI technology to create a realistic virtual character based on input characteristic data (e.g., gender, age, appearance, personality, etc.). For example, the algorithm determines the user's desired hairstyle, eye color, mouth shape, etc. based on the specified characteristics, and a deep learning model then creates a more realistic depiction.

[0229] Customization

[0230] The server stores information about the generated AI influencer on a dedicated web portal, which users can access. After logging in, users can fine-tune their virtual character through a customization screen. For example, they can fine-tune facial contours, select a tone of voice, and set personality attributes. This allows users to create a virtual character that best suits their brand or campaign.

[0231] Setting a schedule

[0232] Users will be provided with a schedule management screen where they can set the AI ​​influencer's activity dates and times, for example, they can schedule social media posts on a specific day of the week or a monthly live stream.

[0233] Managing Performance

[0234] The server collects and analyzes the AI ​​influencer's activity data in real time. For example, after posting on social media, it analyzes and accumulates data on engagement rates, increases or decreases in the number of followers, and the content of comments. The server analyzes this data and displays it visually to the user through a dashboard.

[0235] Interaction

[0236] A chat function is provided that allows users to directly interact with the AI ​​influencer using a dedicated device. For example, users can hold Q&A sessions during live streaming or give instructions on product introductions in real time. This enables two-way communication between the virtual character and the audience.

[0237] Generate engagement reports

[0238] The server generates engagement reports based on the collected activity data. These reports provide detailed information on campaign effectiveness, engagement trends, follower reactions, etc. Reports are generated automatically and provided to users via email or dashboard.

[0239] Emotion recognition implementation

[0240] The server uses an emotion engine to collect and analyze the user's emotional data. For example, it can grasp the user's emotional state in real time using techniques such as emotion analysis of text, emotion recognition from voice, and facial expression analysis.

[0241] Emotion-Based Interaction

[0242] The server automatically generates actions for the AI ​​influencer based on the analyzed emotional data. When a user gives instructions to the AI ​​influencer through the chat function, the emotional data is taken into consideration to generate optimal responses and actions. This function enables natural interactions that are in tune with the user's emotions.

[0243] Specific examples

[0244] For example, suppose a user creates an AI influencer named "Alex" to promote a new game character. The user customizes Alex's appearance, voice, and personality, and sets a schedule for introducing new game content every Wednesday. The server manages this activity, collects and analyzes engagement data, and displays it on a dashboard. During live broadcasts, users can also instruct the AI ​​influencer to answer questions in real time through the chat function. Furthermore, the server uses an emotion engine to analyze viewer reactions and automatically generate appropriate reactions and follow-ups based on the results. For example, if viewers are excited, the AI ​​influencer may show a "good-looking surprised reaction."

[0245] In this way, the system of the present invention solves the problems in the current influencer market and achieves stable, highly efficient engagement and natural interaction.

[0246] The processing flow will be explained below.

[0247] Step 1:

[0248] Users log in to a dedicated web portal and input a request to generate an AI influencer, specifying characteristic data (e.g., gender, age, appearance, personality, etc.).

[0249] Step 2:

[0250] The server receives the user's request and triggers a generation algorithm based on the specified characteristic data, which uses deep learning techniques to generate a base model of the AI ​​influencer.

[0251] Step 3:

[0252] The server then trains the generated base model to make it look and behave realistically, resulting in an AI influencer with realistic portrayals.

[0253] Step 4:

[0254] The server saves the generated AI influencer prototype on a dedicated web portal and notifies the user when the generation is complete. The user can then log in to the portal to view the generated character.

[0255] Step 5:

[0256] Users can access a customization screen to fine-tune appearance (e.g., hairstyle, eye color, mouth shape, etc.), voice tone, personality settings, etc. By adjusting these settings, users can create a virtual character that matches their brand image or individual needs.

[0257] Step 6:

[0258] The server reflects the customization data entered by the user and generates the final AI influencer, which is then stored on a dedicated web portal and presented to the user for review.

[0259] Step 7:

[0260] Users can access the schedule management screen and specify the days and times when the AI ​​influencer will be active. For example, users can set up social media posts on a specific day of the week and live stream once a month.

[0261] Step 8:

[0262] The server automatically saves the user-specified schedule in a database and creates an activity plan for the AI ​​influencer, ensuring that all actions are carried out as planned.

[0263] Step 9:

[0264] The server collects real-time activity data of AI influencers, including engagement rates, follower count increases and decreases, and comment content.

[0265] Step 10:

[0266] The server analyzes the collected data and presents it to the user visually through a dashboard, including engagement status and performance trends.

[0267] Step 11:

[0268] Users can access the chat function using a dedicated device and give instructions to the AI ​​influencer in real time. For example, users can instruct the AI ​​influencer to make comments or introduce products during live broadcasts.

[0269] Step 12:

[0270] The server analyzes the user's instructions and has the AI ​​influencer take appropriate action, enabling automatic and dynamic interactions.

[0271] Step 13:

[0272] The server uses an emotion engine to collect and analyze the user's emotional data. For example, it can grasp the user's emotional state in real time using techniques such as emotion analysis of text, emotion recognition from voice, and facial expression analysis.

[0273] Step 14:

[0274] The server automatically generates actions for the AI ​​influencer based on the analyzed emotional data. For example, if the user is excited, the AI ​​influencer will respond accordingly and show a facial expression.

[0275] Step 15:

[0276] The server generates an engagement report based on the collected activity data, which includes campaign effectiveness metrics and recommendations for improvement.

[0277] Step 16:

[0278] The server will provide the generated engagement report to the user via email or dashboard, allowing the user to review it and use it to improve their next marketing strategy.

[0279] Example 2

[0280] 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."

[0281] In conventional virtual character systems, the extent to which users can customize their characters is limited, and there is a lack of means to properly collect and analyze character activity data, making it difficult to achieve high levels of engagement and real-time interaction. Another problem is the lack of technology for natural interactions that take user emotions into account.

[0282] 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.

[0283] In this invention, the server includes: means for generating a virtual character based on specified characteristics using a generation algorithm; means for customizing the characteristics of the virtual character in response to a user request; means for setting a schedule for the virtual character in response to a user request; means for collecting and analyzing activity data of the virtual character in real time; means for presenting performance data of the virtual character to the user; means for executing an interaction between the virtual character and the user; means for generating an engagement report based on the activity of the virtual character and providing it to the user; means for having an emotion recognition engine for collecting and analyzing emotion data of the user; and means for automatically generating actions for the virtual character based on the analyzed emotion data. This enables a user to highly customize a character, enabling real-time interaction and natural responses according to the user's emotions, and realizing a virtual character system.

[0284] A "generation algorithm" is an algorithm for generating a virtual character based on specified characteristic data.

[0285] "Characteristic data" refers to data including the gender, age, appearance, personality, etc. of a virtual character.

[0286] A "virtual character" is a character that is generated by a generation algorithm and drawn based on characteristic data.

[0287] "Customization" is the process of adjusting the characteristics of a virtual character according to the user's requirements.

[0288] "Schedule setting" is the act of scheduling the activities of a virtual character on dates and times specified by the user.

[0289] "Activity data" is data relating to activities performed by a virtual character.

[0290] "Real-time collection" refers to the process of collecting activity data of virtual characters in real time.

[0291] "Analysis" is the process of analyzing collected data and converting it into meaningful information.

[0292] "Performance data" is data that indicates the results of a virtual character's activities.

[0293] An "interaction" is an interaction that takes place between a user and a virtual character.

[0294] An "engagement report" is a report that summarizes the reactions and engagement of users and audience members based on the activities of virtual characters.

[0295] An "emotion recognition engine" is an engine for analyzing emotions from a user's text, voice, facial expressions, etc.

[0296] "Emotion data" refers to emotional information collected and analyzed by an emotion recognition engine.

[0297] "Automatic action generation" is the process of automatically generating the actions and responses of a virtual character based on analyzed emotional data.

[0298] The system of the present invention uses a generation algorithm to generate a virtual character (hereinafter referred to as an AI influencer) based on specified characteristics, and provides a set of means for users to customize, manage, and recognize emotions of the virtual character. Specific embodiments of the system are described below.

[0299] AI influencer generation

[0300] Upon receiving a request from a user, the server launches an algorithm to generate an AI influencer. This algorithm uses deep learning technologies such as "StyleGAN" and "DALL-E." The user inputs characteristic data such as gender, age, appearance, and personality, and the server uses that data to generate a realistic virtual character that matches the specified characteristics. For example, by specifying the user's desired hairstyle, eye color, mouth shape, etc., the generated AI influencer will have an appearance that reflects these characteristics.

[0301] Customization

[0302] The server stores the generated AI influencer data on a dedicated web portal, which users can access. Through the web portal, users can fine-tune the AI ​​influencer's facial features, tone of voice, personality attributes, and more, allowing them to create a virtual character that best suits their brand or campaign.

[0303] Setting a schedule

[0304] Users can access the schedule management screen on the web portal and set the AI ​​influencer's activity dates and times. For example, they can set a schedule to post on social media on a specific day of the week or a monthly live broadcast. The server stores this setting information and reflects it in the AI ​​influencer's activities.

[0305] Managing Performance

[0306] The server collects and analyzes the AI ​​influencer's activity data in real time, such as engagement rates after posting on social media, increases or decreases in the number of followers, and the content of comments, and visualizes this data and displays it on a dashboard, allowing users to check the AI ​​influencer's performance at a glance.

[0307] Interaction

[0308] Users can use a dedicated device (PC or smartphone) to use the chat function to interact with the AI ​​influencer. For example, they can hold a Q&A session during live streaming or instruct the AI ​​influencer to introduce a product in real time. By sending specific prompts, they can instruct the AI ​​influencer to take specific actions. For example, they can send a prompt such as, "Please explain the appeal of the new product to viewers."

[0309] Generate engagement reports

[0310] Based on the collected activity data, the server automatically generates engagement reports, which provide details such as campaign effectiveness measurements, engagement trends, and follower responses, and are delivered to users via email or dashboard.

[0311] Emotion recognition implementation

[0312] The server uses an emotion recognition engine to collect and analyze the user's emotional data. Using techniques such as text analysis, voice recognition, and facial expression analysis, the server can grasp the user's emotional state. For example, it can analyze the user's emotional state, such as "excited" or "sad," in real time.

[0313] Emotion-Based Interaction

[0314] The server automatically generates the AI ​​influencer's actions based on the analyzed emotional data. This means that when a user gives instructions to the AI ​​influencer through the chat function, the AI ​​influencer can generate optimal responses and actions taking into account the emotional data. For example, if a viewer is excited, the AI ​​influencer will show a "good-looking surprised reaction."

[0315] As a concrete example, a user can create an AI influencer named "Alex" to promote a new game character and customize its appearance, voice, and personality. The user sets Alex's activity date and time to introduce new game content every Wednesday. The server manages this activity, collects and analyzes engagement data, and displays it on a dashboard. During live broadcasts, users can also instruct the influencer to answer questions in real time through the chat function. Furthermore, the server uses an emotion recognition engine to analyze viewer reactions and automatically generate appropriate reactions and follow-ups based on the results.

[0316] In this way, the system of the present invention solves many of the problems in the current influencer market and enables natural, sophisticated interaction and highly efficient engagement.

[0317] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0318] Step 1:

[0319] The user logs in to a dedicated web portal and enters the AI ​​influencer's characteristic data (gender, age, appearance, personality, etc.). The input data is sent to the server and saved as characteristic data. Specifically, the user enters the characteristic data into the input form and presses the "Submit" button.

[0320] Step 2:

[0321] The server launches a generation algorithm based on the received characteristic data. A generation algorithm (e.g., StyleGAN or DALL-E) is used to generate a virtual character. The input characteristic data is fed into a deep learning model to generate realistic image data of the virtual character. The output image data is stored on the server.

[0322] Step 3:

[0323] The server stores the generated AI influencer data in a dedicated database and reflects it on a user-accessible web portal. Specifically, the server writes the generated character data to the database and calls an API to update the information on the web portal.

[0324] Step 4:

[0325] A user accesses the web portal and displays the customization screen, where the user fine-tunes the virtual character's characteristics (e.g., facial contours, hairstyle, tone of voice, etc.). When the user saves the changes, the input data is sent to the server and updated as the virtual character's characteristic data.

[0326] Step 5:

[0327] The server reflects the updated characteristic data and uses the deep learning model again to generate new image data. The new image data is saved in the database, and the character is regenerated with the user's customizations reflected.

[0328] Step 6:

[0329] The user accesses the schedule management screen on the web portal and sets the AI ​​influencer's activity dates and times. For example, they enter a posting schedule to introduce new gaming content every Wednesday. The input data is sent to the server and saved as a schedule.

[0330] Step 7:

[0331] The server receives the schedule information and automatically executes the AI ​​influencer's activities based on the specified date and time. For example, it posts to social media according to the schedule. It also stores data related to the activities (e.g., post content, posting date and time) in a database.

[0332] Step 8:

[0333] The server collects and analyzes the AI ​​influencer's activity data in real time. For example, it collects and stores data such as engagement rates, follower count changes, and comment content after social media posts in a database. The collected data is analyzed using analysis software (e.g., Python libraries such as Pandas and Matplotlib), and the results are displayed on a dashboard.

[0334] Step 9:

[0335] Users can use a dedicated device to use the chat function to interact with the AI ​​influencer. For example, during live streaming, the user can send a prompt such as, "Please explain the appeal of the new product to the viewers." The chat contents are sent to the server and saved.

[0336] Step 10:

[0337] The server analyzes the chat content and generates the AI ​​influencer's actions based on the specified instructions. For example, it analyzes the prompt text and uses a generative AI model to generate appropriate videos and text. The generated content is then instantly sent to the user's device.

[0338] Step 11:

[0339] The server generates an engagement report based on the collected activity data. The report includes campaign effectiveness measurement, engagement trends, follower reactions, etc. The generated report is provided to the user via email or dashboard.

[0340] Step 12:

[0341] The server uses an emotion recognition engine to collect and analyze user emotion data. For example, it performs text analysis, voice recognition, and facial expression analysis of viewers during live streaming. The collected emotion data is stored in a database.

[0342] Step 13:

[0343] The server automatically generates the AI ​​influencer's actions based on the analyzed emotional data. For example, if the viewer is excited, the AI ​​influencer will generate an action that shows a "good-looking surprised reaction." The generated content is instantly sent to the user's device.

[0344] As described above, by performing specific actions, inputs, data processing, and outputs at each step, users can use highly customized virtual characters to achieve natural, advanced interactions and highly efficient engagement.

[0345] (Application example 2)

[0346] 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."

[0347] The problem that this invention aims to solve is to provide a system for content distribution using virtual characters (AI influencers) that allows users to easily create and customize virtual characters and smoothly realize two-way communication with users. In particular, the aim is to enable appropriate reactions based on viewer emotions in real time in a format that can be used on devices such as smartphones. In addition, the aim is to maximize the effect of engagement by efficiently collecting and analyzing character performance data and visually presenting it.

[0348] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for generating a virtual character based on specified characteristics using a generation algorithm; means for customizing the characteristics of the virtual character in response to a user request; means for setting a schedule for the virtual character based on a user request; means for collecting and analyzing activity data of the virtual character in real time; means for presenting performance data of the virtual character to the user; means for executing an interaction between the virtual character and the user; means for generating an engagement report based on the activity of the virtual character and providing it to the user; means for live streaming the virtual character on a smartphone terminal; means for responding in real time to comments from users and viewers via a chat function and analyzing their emotions; and means for automatically generating a reaction of the virtual character based on the emotion data. This allows users to easily and effectively deliver content using virtual characters. Furthermore, by reacting in response to the viewer's emotions, interaction with the viewer can be deepened and engagement can be improved.

[0349] A "generation algorithm" is an algorithm for generating a virtual character based on characteristic data input by a user.

[0350] A "virtual character" is a character that has user-specified characteristics and is generated by a generation algorithm.

[0351] "Means for customization" refers to means by which a user can fine-tune the appearance, personality, voice, etc. of a generated virtual character.

[0352] The "means for setting a schedule" is a means for a user to set the date, time, and frequency of a virtual character's activities.

[0353] "Means for collecting and analyzing activity data in real time" refers to means for collecting and analyzing data on the behavior and performance of a virtual character in real time.

[0354] "Means for presenting performance data" means means for presenting collected and analyzed information about the performance of a virtual character to a user.

[0355] "Means for performing interaction" refers to means by which a user can have two-way communication with a virtual character in real time.

[0356] The "means for generating and providing an engagement report" refers to a means for generating an engagement report based on the activities of a virtual character and providing it to a user.

[0357] "Means for live streaming on a smartphone terminal" refers to means for live streaming of a virtual character using a smartphone terminal.

[0358] The "chat function" is a function that allows users and viewers to exchange messages in real time.

[0359] "Means for analyzing emotions" refers to the means of analyzing viewers' comments and reactions and understanding their emotions.

[0360] The "means for automatically generating a reaction" is a means for automatically generating an appropriate reaction of a virtual character based on the analyzed emotion data.

[0361] The system of the present invention generates a virtual character (hereinafter referred to as an AI influencer) based on specified characteristics and provides a set of means for users to customize, manage, and recognize emotions of the virtual character. Furthermore, the present invention uses a smartphone as a platform, allowing users to easily perform live streaming using the virtual character.

[0362] System configuration

[0363] The system uses the following hardware and software:

[0364] Hardware: Smartphone (iOS, Android)

[0365] Software: Python, TensorFlow, Flutter, RestAPI, Firebase

[0366] Implementation of the generation algorithm

[0367] The server receives requests from users and generates virtual characters using a generation algorithm based on deep learning technology (TensorFlow) to generate realistic virtual characters based on input characteristic data.

[0368] Customization features

[0369] The generated virtual character data is stored in Firebase, and users can access it through a dedicated web portal or app to freely customize the character's appearance, voice, personality, etc. This allows users to create a character that best suits their brand or campaign.

[0370] Schedule management

[0371] Users can set the dates and times for their virtual characters to be active within the app, and the schedule is managed by Firebase, making it easy to schedule posts and live streams at specific times.

[0372] Performance Analysis

[0373] The activity data of the virtual characters is collected in real time and analyzed by the server. The analysis results are provided to users via a Rest API and displayed visually on a dashboard, including engagement rates, follower counts, and comment content.

[0374] Interaction Features

[0375] Users can use the chat feature to have two-way communication with the virtual character, which is particularly useful during live broadcasts, allowing users to direct answers to viewer questions in real time.

[0376] Emotion Recognition and Response Generation

[0377] The server uses emotion analysis technology (TextBlob) to analyze viewers' comments and reactions and understand their emotions. Based on the analysis results, the virtual character's reaction is automatically generated. For example, if a viewer is excited, the character will respond appropriately, such as showing a happy expression.

[0378] Specific examples

[0379] Consider a case where a user creates an AI influencer named "Yuki" to introduce a game.

[0380] The user inputs characteristic data to generate a character named "Yuki," which includes gender: female, age: 20, appearance: black hair, blue eyes, and personality: cheerful.

[0381] Customize your character's appearance, voice, and personality, and set a schedule that introduces new game content every Wednesday.

[0382] Viewer comment: "This game is so much fun!"

[0383] TextBlob analyzes the "positive" emotion, and the AI ​​influencer "Yuki" responds with a "happy!" expression, saying, "I'm glad everyone is having fun!"

[0384] Prompt Sentence Examples

[0385] Users can create a new AI influencer named "Yuki." The user's characteristics are: gender: female, age: 20, physical features: black hair, blue eyes, personality: cheerful. Yuki will introduce new game content and hold live streams every Wednesday. Users can customize Yuki's appearance, voice, and personality, and instruct her to answer questions in real time during the live stream. At the same time, the system analyzes emotions from viewer comments and generates appropriate reactions.

[0386] In this way, by using the system according to the present invention, users can achieve high engagement while also achieving natural interaction with viewers.

[0387] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0388] Step 1: User input of characteristic data

[0389] The user enters the virtual character's name, gender, age, appearance, personality, and other characteristic data into the smartphone app's input form, which is then sent to the server.

[0390] Input: Characteristic data (name, gender, age, appearance, personality, etc.)

[0391] Output: Attribute data sent to the server

[0392] Step 2: Generate a virtual character

[0393] The server then launches a generation algorithm based on the received characteristic data, using deep learning technology (TensorFlow) to generate a realistic virtual character.

[0394] Input: characteristic data

[0395] Output: Generated virtual character

[0396] Step 3: Customizing your virtual character

[0397] Users can customize the generated character through a dedicated web portal or app, fine-tuning appearance, voice, personality, etc., and then finalize the character. The customized character data is stored in Firebase.

[0398] Input: User customization data

[0399] Output: Customized character data

[0400] Step 4: Set a schedule

[0401] Users can set the dates and times for their virtual characters to be active within the app, and posts and live streams are scheduled based on the specified dates and times. Schedule data is also stored in Firebase.

[0402] Input: Schedule information

[0403] Output: Saved schedule data

[0404] Step 5: Go Live

[0405] At the specified date and time, a live broadcast using a virtual character will begin on a smartphone device, and users can monitor the broadcast in real time and manage interactions with viewers.

[0406] Input: Scheduled date and time

[0407] Output:Start live streaming

[0408] Step 6: Interact with your audience

[0409] During the live broadcast, users and viewers can exchange messages using the chat function, and questions can be sent in real time, which the virtual characters will respond to.

[0410] Input: Viewer comments and questions

[0411] Output: Response message from virtual character

[0412] Step 7: Emotion Recognition and Response Generation

[0413] The server uses TextBlob to perform sentiment analysis based on the viewers' comments, and automatically generates appropriate responses for the virtual character based on the sentiment data.

[0414] Input: Viewer comments

[0415] Output: Auto-generated responses of virtual characters

[0416] Step 8: Collect and analyze performance data

[0417] The server collects and analyzes real-time activity data of the virtual characters during live broadcasts, including engagement rates, increases or decreases in the number of followers, and the content of comments.

[0418] Input: Activity data during live streaming

[0419] Output: Parsed performance data

[0420] Step 9: Presenting the results

[0421] The server analyzes performance data and visually presents it to the user through a dashboard, allowing the user to decide on the next action to take.

[0422] Input: Parsed performance data

[0423] Output: Data presented to the user

[0424] Step 10: Generate and deliver engagement reports

[0425] The server generates and provides to the user an engagement report based on the collected and analyzed activity data, which details campaign effectiveness measurements, engagement trends, and follower responses.

[0426] Input: Collected and analyzed activity data

[0427] Output: Generated engagement report

[0428] Through these steps, users can create and customize virtual characters to achieve effective live streaming and high engagement.

[0429] 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.

[0430] 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.

[0431] 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.

[0432] [Second embodiment]

[0433] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0434] 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.

[0435] 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).

[0436] 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.

[0437] 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.

[0438] 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).

[0439] 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.

[0440] 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.

[0441] 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.

[0442] 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.

[0443] 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.

[0444] 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."

[0445] The system of the present invention uses a generation algorithm to generate a virtual character (hereinafter referred to as an AI influencer) based on specified characteristics, and provides a set of means for users to customize and manage the character. Specific embodiments of the system are described below.

[0446] AI influencer generation

[0447] The server receives a user request and launches an AI influencer generation algorithm. This algorithm uses generative AI technology to create a realistic virtual character based on input characteristic data (e.g., gender, age, appearance, personality, etc.). For example, the algorithm determines the user's desired hairstyle, eye color, mouth shape, etc. based on the specified characteristics, and a deep learning model then creates a more realistic depiction.

[0448] Customization

[0449] The server stores information about the generated AI influencer on a dedicated web portal, which users can access. After logging in, users can fine-tune their virtual character through a customization screen. For example, they can fine-tune facial contours, select a tone of voice, and set personality attributes. This allows users to create a virtual character that best suits their brand or campaign.

[0450] Setting a schedule

[0451] Users will be provided with a schedule management screen where they can set the AI ​​influencer's activity dates and times, for example, they can schedule social media posts on a specific day of the week or a monthly live stream.

[0452] Managing Performance

[0453] The server collects and analyzes the AI ​​influencer's activity data in real time. For example, after posting on social media, it analyzes and accumulates data on engagement rates, increases or decreases in the number of followers, and the content of comments. The server analyzes this data and displays it visually to the user through a dashboard.

[0454] Interaction

[0455] A chat function is provided that allows users to directly interact with the AI ​​influencer using a dedicated device. For example, users can hold Q&A sessions during live streaming or give instructions on product introductions in real time. This enables two-way communication between the virtual character and the audience.

[0456] Generate engagement reports

[0457] The server generates engagement reports based on the collected activity data. These reports provide detailed information on campaign effectiveness, engagement trends, follower reactions, etc. Reports are generated automatically and provided to users via email or dashboard.

[0458] Specific examples

[0459] For example, a cosmetics brand user might create an AI influencer named "Luna" to promote a new product. The user can customize Luna's hairstyle, skin color, and voice tone to match the brand's image. Then, the user schedules her to post on social media every Friday and to promote the new product via live streaming once a month. The server manages this, collecting and analyzing engagement data in real time and displaying it on a dashboard. Through the dashboard, the user can monitor performance and receive engagement reports to support effective marketing activities.

[0460] In this way, the system according to the present invention solves the problems in the current influencer market and realizes stable and highly efficient engagement.

[0461] The processing flow will be explained below.

[0462] Step 1:

[0463] The user logs in to a dedicated web portal, then inputs a request to generate an AI influencer and specifies characteristic data (e.g., gender, age, appearance, personality, etc.).

[0464] Step 2:

[0465] The server receives the user's request and triggers a generation algorithm based on the specified characteristic data, which uses deep learning techniques to generate a base model of the AI ​​influencer.

[0466] Step 3:

[0467] The server then trains the generated base model to make it look and behave realistically, resulting in an AI influencer with realistic portrayals.

[0468] Step 4:

[0469] The server saves the generated AI influencer prototype on a dedicated web portal and notifies the user when the generation is complete. The user can then log in to the portal to view the generated character.

[0470] Step 5:

[0471] Users can access a customization screen to fine-tune appearance (e.g., hairstyle, eye color, mouth shape, etc.), voice tone, personality settings, etc. By adjusting these settings, users can create a virtual character that matches their brand image or individual needs.

[0472] Step 6:

[0473] The server reflects the customization data entered by the user and generates the final AI influencer, which is then stored on a dedicated web portal and presented to the user for review.

[0474] Step 7:

[0475] Users can access the schedule management screen and specify the days and times when the AI ​​influencer will be active. For example, users can set up social media posts on a specific day of the week and live stream once a month.

[0476] Step 8:

[0477] The server automatically saves the user-specified schedule in a database and creates an activity plan for the AI ​​influencer, ensuring that all actions are carried out as planned.

[0478] Step 9:

[0479] The server collects real-time activity data of AI influencers, including engagement rates, follower count increases and decreases, and comment content.

[0480] Step 10:

[0481] The server analyzes the collected data and presents it to the user visually through a dashboard, including engagement status and performance trends.

[0482] Step 11:

[0483] Users can access the chat function using a dedicated device and give instructions to the AI ​​influencer in real time. For example, users can instruct the AI ​​influencer to make comments or introduce products during live broadcasts.

[0484] Step 12:

[0485] The server analyzes the user's instructions and has the AI ​​influencer take appropriate action, enabling automatic and dynamic interactions.

[0486] Step 13:

[0487] The server generates an engagement report based on the collected activity data, which includes campaign effectiveness metrics and recommendations for improvement.

[0488] Step 14:

[0489] The server will provide the generated engagement report to the user via email or dashboard, allowing the user to review it and use it to improve their next marketing strategy.

[0490] Example 1

[0491] 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."

[0492] Conventional virtual character systems have had problems such as difficulty in achieving realistic depictions based on complex user-specified characteristics, customizing the generated characters, managing schedules, collecting and analyzing real-time performance data, interacting with users, and generating and providing detailed engagement reports. As a result, companies and individuals have had difficulty efficiently managing characters in marketing activities and brand promotions, preventing them from maximizing engagement effects.

[0493] 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.

[0494] In this invention, the server includes: means for generating a virtual character based on specified characteristics using a generation algorithm; means for saving the virtual character's characteristic data in a database and displaying it on a dedicated web portal; means for customizing the virtual character's characteristics according to a user's request; means for providing a dedicated schedule management screen and setting the virtual character's schedule according to a user's request; means for collecting and analyzing activity data in real time via the virtual character's social media API; means for visualizing the analyzed data on a dashboard and presenting it to the user; means for providing a chat function for the user to give instructions to the user in real time through interactions with the virtual character; and means for automatically generating an engagement report based on the virtual character's activity data and providing it to the user. This allows users to efficiently perform processes from virtual character generation and customization to activity management, data analysis, and report generation through a consistent system.

[0495] A "generation algorithm" is a computational method for generating a virtual character using specified characteristic data, and primarily utilizes deep learning technology.

[0496] "Characteristic data" is information including the gender, age, appearance, personality, etc. of a virtual character, and is an input value provided by a user.

[0497] "Database" means an information storage system for managing and storing information about generated and customized virtual characters.

[0498] "Web Portal" means an online platform for accessing, managing, and adjusting information about user-generated and customized virtual characters.

[0499] "Customization" refers to the act of a user adjusting the characteristics (e.g., appearance, tone of voice, personality attributes, etc.) of a generated virtual character to fine-tune it to their own needs.

[0500] The "schedule management screen" is an interface that allows the user to set the activity dates and times of the virtual character, and is provided in a calendar or list format.

[0501] "Social Media API" refers to the application programming interface provided by a social media platform and is the technology used to collect activity data (e.g., engagement data) of virtual characters.

[0502] A "dashboard" is an interface for visually displaying analyzed activity data, allowing users to check the performance of their virtual characters in real time.

[0503] "Chat function" refers to a means of communication that allows users to exchange messages with virtual characters in real time, and is primarily used for live streaming and question and answer sessions.

[0504] "Engagement Report" means a report detailing engagement results and trends based on virtual character activity data, provided to Users to assist them in effective campaign management.

[0505] The system of the present invention generates, customizes, schedules, manages and analyzes activity data, and generates and provides interaction and engagement reports for virtual characters (hereinafter referred to as AI influencers) through a dedicated server and user terminals. Detailed embodiments of the system are described below.

[0506] AI influencer generation

[0507] The server receives a request from the user. This request includes characteristic data of the virtual character (e.g., gender, age, appearance, personality, etc.). The server generates a virtual character based on these characteristics using, for example, a generative AI model running on Google Cloud Platform (GCP). The generated character is then detailed based on the characteristic data to achieve a realistic depiction using deep learning technology.

[0508] Customization

[0509] The server stores the generated AI influencer data in a database and displays it on a dedicated web portal. Users can access the customization screen by logging in to this web portal. On the customization screen, users can fine-tune their virtual character (e.g., adjust facial contours, select voice tone, and set personality attributes). The user interface (UI) is designed for intuitive operation, using sliders and drop-down menus.

[0510] Setting a schedule

[0511] Users can set the AI ​​influencer's activity dates and times by accessing a dedicated schedule management screen. Using the management screen, which is provided in calendar and list format, users can enter schedules for social media posts and live broadcasts on specific dates and times. For example, they can set specific schedules such as "posting new product introductions every Friday at 10:00 AM."

[0512] Managing Performance

[0513] The server uses social media APIs (e.g., Facebook Graph API, Twitter API) to collect real-time activity data on AI influencers. The acquired data includes engagement rates, increases or decreases in the number of followers, and comment content. This data is analyzed by the server and evaluated as engagement performance. Tools such as Google Analytics and IBM Watson Analytics are used for the analysis.

[0514] Dashboard visualization

[0515] The server displays the analyzed data on a dashboard, which is designed to allow users to intuitively view the data using visualization tools (e.g., D3.js, Chart.js). Through this dashboard, users can monitor the performance of their virtual characters in real time and make adjustments as needed.

[0516] Interaction

[0517] Using a dedicated device, users can access a chat function to interact with the virtual character, allowing them to answer questions and give instructions in real time during the live broadcast, enabling two-way communication between the virtual character and the viewer.

[0518] Generate engagement reports

[0519] The server automatically generates engagement reports based on the collected activity data. These reports include detailed records of campaign effectiveness, engagement trends, follower responses, etc. The generated reports are sent to users via email or a dashboard.

[0520] Specific examples

[0521] For example, a cosmetics brand user might create an AI influencer named "Luna" to promote a new product. The user can customize Luna's hairstyle, skin color, and voice tone to match the brand's image. Next, the user can schedule Luna to post on social media every Friday and to promote the new product via live streaming once a month. The server manages these schedules, collects and analyzes engagement data in real time, and displays it on a dashboard. Through the dashboard, the user can monitor performance and receive engagement reports to support effective marketing activities.

[0522] Example prompt sentence:

[0523] We want to generate an AI influencer called "Luna" to promote a new product. Luna's characteristics are as follows:

[0524] Gender: Female

[0525] Age: 25

[0526] Hairstyle: Long Straight

[0527] Eye color: Blue

[0528] Personality: Cheerful and energetic

[0529] Based on this, please generate a virtual character that is as realistic as possible, provide a customization screen for Luna, and once the setup is complete, schedule a social media post every Friday and a monthly live stream.

[0530] As described above, the system according to the present invention realizes efficient and sophisticated virtual character management and promotion in the marketing activities of companies and individuals.

[0531] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0532] Step 1:

[0533] The server receives the user request.

[0534] Input: The user enters the virtual character's characteristic data (e.g., gender, age, appearance, personality, etc.) in a form on the web portal.

[0535] Data processing and data calculation: The server receives the input characteristic data and converts it into an appropriate format, for example, converting text data into numerical data, to create an input dataset for the generative AI model.

[0536] Output: The characteristic data is the input dataset, properly formatted.

[0537] Step 2:

[0538] The server launches the generative AI model.

[0539] Input: The formatted input dataset.

[0540] Data processing and data calculation: The server uses a deep learning model built with TensorFlow or PyTorch, for example, to perform calculations to generate a virtual character based on the characteristic data.

[0541] Output: A 3D model or 2D image of the generated virtual character is output.

[0542] Step 3:

[0543] The server stores the generated AI influencer data in a database.

[0544] Input: 3D model and 2D image data of the generated virtual character.

[0545] Data processing and data calculation: The server processes the data to properly index and store it in the database.

[0546] Output: Virtual character information stored in a database.

[0547] Step 4:

[0548] The server displays the generated AI influencer data on a web portal.

[0549] Input: Generated virtual character information stored in a database.

[0550] Data processing and data calculation: The server processes the data to display it in the appropriate format on the web portal.

[0551] Output: The generated character is displayed in the customization screen of the web portal which the user can log in and view.

[0552] Step 5:

[0553] The user customizes the virtual character.

[0554] Input: Fine-tuning information for the virtual character entered by the user through the customization screen (e.g., adjusting facial contours, selecting voice tone, setting personality attributes).

[0555] Data Processing and Data Calculation: The server performs the data processing necessary to update the attributes of the virtual character based on the user's customization information.

[0556] Output: Final virtual character data based on user customized attributes.

[0557] Step 6:

[0558] The user accesses the schedule management screen and sets the activity date and time of the virtual character.

[0559] Input: User-generated scheduling information (e.g., scheduling a social media post or live stream on a specific date and time).

[0560] Data processing and data calculation: The server stores schedule information in a database and performs processing to properly manage it.

[0561] Output: Schedule information stored in the database.

[0562] Step 7:

[0563] The server collects and analyzes the activity data of the virtual characters in real time.

[0564] Input: Activity data obtained from social media APIs (e.g., engagement rate, follower count increase / decrease, comment content, etc.).

[0565] Data processing and data calculation: The server uses data analysis tools to analyze the acquired data and calculate the engagement performance.

[0566] Output: Parsed engagement data.

[0567] Step 8:

[0568] The server visualizes and displays the analytical data on a dashboard.

[0569] Input: Parsed engagement data.

[0570] Data processing and data calculations: The server generates graphs and charts of the data using visualization tools (e.g., D3.js, Chart.js).

[0571] Output: Visualized data that users can view in a dashboard.

[0572] Step 9:

[0573] Users interact with virtual characters in real time through a chat function.

[0574] Input: Real-time messages and instructions from the user.

[0575] Data processing and data calculation: The server processes chat messages and causes the virtual characters to react appropriately.

[0576] Output: The responses and actions of the virtual character.

[0577] Step 10:

[0578] The server generates an engagement report based on the collected activity data and provides it to the user.

[0579] Input: Parsed activity data and engagement information.

[0580] Data processing and data calculation: The server uses a report generation tool (e.g., Tableau, Microsoft Power BI) to automatically generate engagement reports.

[0581] Output: Engagement reports delivered to users (via emails and dashboards).

[0582] Through the above steps, the system according to the present invention enables users to efficiently create and manage virtual characters, as well as collect and analyze engagement data.

[0583] (Application example 1)

[0584] 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."

[0585] In recent years, advertising and marketing activities using virtual characters have become more common, but their operation requires high costs and specialized skills. Furthermore, collecting and analyzing engagement data in real time is difficult, making it time-consuming to measure effectiveness. Furthermore, there are limited ways for users to interact with virtual characters in real time, which creates the challenge of insufficient two-way communication.

[0586] 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.

[0587] In this invention, the server includes means for generating a virtual character based on specified characteristics using a generation algorithm, means for customizing the characteristics of the virtual character in response to a user request, means for setting a schedule for the virtual character in response to a user request, means for collecting and analyzing activity data of the virtual character in real time, means for presenting performance data of the virtual character to the user, means for executing an interaction between the virtual character and the user, means for generating an engagement report based on the activity of the virtual character and providing it to the user, means for providing a smartphone application, means for executing and managing an advertising campaign using the virtual character, and means for collecting engagement data using a social media API. This enables users to efficiently and effectively conduct advertising and marketing activities using virtual characters without requiring specialized skills.

[0588] A "generation algorithm" is an algorithm for automatically generating a virtual character based on specified characteristic data.

[0589] A "virtual character" is a digital character with specific characteristics created by a generative algorithm.

[0590] "Characteristics" refers to characteristic data used to create a virtual character, such as gender, age, appearance, and personality.

[0591] "Customization" refers to changing or adjusting the characteristics of a generated virtual character based on the user's requests.

[0592] "Schedule setting" is a function for setting the date, time and frequency of a virtual character's activities.

[0593] "Activity data" is data about all activities performed by a virtual character.

[0594] "Collecting and analyzing in real time" refers to a process in which activity data of a virtual character is collected at that moment and analyzed immediately.

[0595] "Performance Data" means data related to the results of a virtual character's activities, such as engagement rate and number of followers.

[0596] "Interaction" refers to real-time interaction between a virtual character and a user.

[0597] An "engagement report" is a report created based on data based on the activities of virtual characters.

[0598] A "smartphone application" is a software application that runs on a smartphone.

[0599] An "advertising campaign" is a planned advertising effort to promote a particular product or service.

[0600] A "social media API" is an externally accessible programmatic interface provided by a social media platform.

[0601] The system of the present invention uses a generation algorithm to generate a virtual character (hereinafter referred to as an AI influencer) based on specified characteristics, and provides a set of means for users to customize and manage the character. Specific embodiments of the system are described below.

[0602] 1. AI influencer generation

[0603] The server receives a user request and launches a generative algorithm, which uses generative AI technology to create a realistic virtual character based on the user's specified characteristics (e.g., gender, age, appearance, personality, etc.). For example, the algorithm determines the user's desired hairstyle, eye color, mouth shape, etc., based on the specified characteristics, and a deep learning model (e.g., TensorFlow or PyTorch) then creates a more realistic depiction.

[0604] 2. Customization

[0605] The server stores information about the generated AI influencer in a database (e.g., Amazon RDS) and makes it accessible to users. Users can fine-tune the virtual character through the customization screen of the smartphone application. For example, they can fine-tune the facial contours, select the tone of voice, and set personality attributes. This allows users to create an AI influencer that is best suited to their brand or campaign.

[0606] 3. Set a schedule

[0607] Users can access the smartphone application's schedule management screen and set the AI ​​influencer's activity dates and times. For example, they can set it to post on social media on a specific day of the week and to broadcast live once a month. The server automatically schedules the activities based on this information and carries them out at the specified dates and times.

[0608] 4. Performance Management

[0609] The server collects and analyzes activity data in real time using social media APIs (e.g., Twitter API, Instagram Graph API), collecting information such as engagement rates, increases or decreases in the number of followers, and comment content, and generates analytical results using Python's Pandas and Matplotlib.

[0610] 5. Interaction

[0611] Users can use chat features (e.g., Socket.IO) to interact with AI influencers in real time, for example, by answering questions during live streaming or giving instructions for product introductions in real time.

[0612] 6. Generate engagement reports

[0613] The server generates engagement reports based on the collected activity data, detailing campaign effectiveness, engagement trends, follower reactions, etc. Reports are generated automatically and provided to users via email (e.g., SendGrid) or an in-application dashboard.

[0614] Specific examples

[0615] Suppose a cosmetics brand user wants to generate an AI influencer to promote a new product. They can customize the generated character by entering a prompt such as "Generate a character that is female, 25 years old, with a short bob, blue eyes, and a lively personality." They then schedule a social media post every Friday and a monthly live stream to introduce the new product. The server manages this, collecting and analyzing engagement data in real time and displaying it on a dashboard. Users can monitor performance through the dashboard and receive engagement reports to support effective marketing activities.

[0616] In this way, the system according to the present invention enables users to easily and efficiently carry out advertising and marketing activities that utilize virtual characters.

[0617] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0618] Step 1:

[0619] The server receives a prompt from the user: "Generate a character that is female, 25 years old, with a short bob haircut, blue eyes, and a lively personality." Based on the input characteristic data, it generates a virtual character using a generative AI model (TensorFlow or PyTorch). This generative AI model uses deep learning technology to create a realistic depiction based on the input characteristic data. The resulting virtual character data is stored in a database (Amazon RDS).

[0620] Step 2:

[0621] The user accesses the customization screen via a smartphone application. The server retrieves information about the generated AI influencer from the database and presents it to the user. The user then fine-tunes characteristics such as hairstyle, eye color, facial contours, voice tone, and personality attributes. The adjusted data is sent to the server, which then uses the AI ​​model again to generate an updated virtual character and update the database.

[0622] Step 3:

[0623] The user accesses the schedule management screen and sets the activity schedule for the virtual character, for example, posting to social media every Friday, live streaming once a month, etc. The server saves this schedule data in a database and schedules tasks to be executed at the specified date and time.

[0624] Step 4:

[0625] The server executes the virtual character's posts at the specified date and time using social media APIs (Twitter API, Instagram Graph API), and if live streaming is set, starts streaming using the appropriate streaming API, so that the virtual character's activities are executed according to the schedule set by the user.

[0626] Step 5:

[0627] After a social media post or live stream is completed, the server collects engagement data (likes, comments, number of followers, etc.) in real time. The data obtained through the social media API is used to analyze the data using Python's Pandas and Matplotlib. The analysis results are displayed on a dashboard using visualization tools (D3.js, Chart.js).

[0628] Step 6:

[0629] Users use the chat function (Socket.IO) of a smartphone application to interact with the AI ​​influencer in real time. The server receives chat messages from users, and the AI ​​influencer reacts based on them.

[0630] Step 7:

[0631] The server generates engagement reports based on the collected engagement data, detailing campaign effectiveness, engagement trends, follower reactions, etc. Reports are generated automatically and provided to users via email (SendGrid) or an in-application dashboard.

[0632] Through the above steps, the system according to the present invention enables users to efficiently carry out advertising and marketing activities that utilize virtual characters.

[0633] 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.

[0634] The system of the present invention uses a generation algorithm to generate a virtual character (hereinafter referred to as an AI influencer) based on specified characteristics, and provides a set of means for users to customize, manage, and recognize emotions of the virtual character. Specific embodiments of the system are described below.

[0635] AI influencer generation

[0636] The server receives a user request and launches an AI influencer generation algorithm. This algorithm uses generative AI technology to create a realistic virtual character based on input characteristic data (e.g., gender, age, appearance, personality, etc.). For example, the algorithm determines the user's desired hairstyle, eye color, mouth shape, etc. based on the specified characteristics, and a deep learning model then creates a more realistic depiction.

[0637] Customization

[0638] The server stores information about the generated AI influencer on a dedicated web portal, which users can access. After logging in, users can fine-tune their virtual character through a customization screen. For example, they can fine-tune facial contours, select a tone of voice, and set personality attributes. This allows users to create a virtual character that best suits their brand or campaign.

[0639] Setting a schedule

[0640] Users will be provided with a schedule management screen where they can set the AI ​​influencer's activity dates and times, for example, they can schedule social media posts on a specific day of the week or a monthly live stream.

[0641] Managing Performance

[0642] The server collects and analyzes the AI ​​influencer's activity data in real time. For example, after posting on social media, it analyzes and accumulates data on engagement rates, increases or decreases in the number of followers, and the content of comments. The server analyzes this data and displays it visually to the user through a dashboard.

[0643] Interaction

[0644] A chat function is provided that allows users to directly interact with the AI ​​influencer using a dedicated device. For example, users can hold Q&A sessions during live streaming or give instructions on product introductions in real time. This enables two-way communication between the virtual character and the audience.

[0645] Generate engagement reports

[0646] The server generates engagement reports based on the collected activity data. These reports provide detailed information on campaign effectiveness, engagement trends, follower reactions, etc. Reports are generated automatically and provided to users via email or dashboard.

[0647] Emotion recognition implementation

[0648] The server uses an emotion engine to collect and analyze the user's emotional data. For example, it can grasp the user's emotional state in real time using techniques such as emotion analysis of text, emotion recognition from voice, and facial expression analysis.

[0649] Emotion-Based Interaction

[0650] The server automatically generates actions for the AI ​​influencer based on the analyzed emotional data. When a user gives instructions to the AI ​​influencer through the chat function, the emotional data is taken into consideration to generate optimal responses and actions. This function enables natural interactions that are in tune with the user's emotions.

[0651] Specific examples

[0652] For example, suppose a user creates an AI influencer named "Alex" to promote a new game character. The user customizes Alex's appearance, voice, and personality, and sets a schedule for introducing new game content every Wednesday. The server manages this activity, collects and analyzes engagement data, and displays it on a dashboard. During live broadcasts, users can also instruct the AI ​​influencer to answer questions in real time through the chat function. Furthermore, the server uses an emotion engine to analyze viewer reactions and automatically generate appropriate reactions and follow-ups based on the results. For example, if viewers are excited, the AI ​​influencer may show a "good-looking surprised reaction."

[0653] In this way, the system of the present invention solves the problems in the current influencer market and achieves stable, highly efficient engagement and natural interaction.

[0654] The processing flow will be explained below.

[0655] Step 1:

[0656] Users log in to a dedicated web portal and input a request to generate an AI influencer, specifying characteristic data (e.g., gender, age, appearance, personality, etc.).

[0657] Step 2:

[0658] The server receives the user's request and triggers a generation algorithm based on the specified characteristic data, which uses deep learning techniques to generate a base model of the AI ​​influencer.

[0659] Step 3:

[0660] The server then trains the generated base model to make it look and behave realistically, resulting in an AI influencer with realistic portrayals.

[0661] Step 4:

[0662] The server saves the generated AI influencer prototype on a dedicated web portal and notifies the user when the generation is complete. The user can then log in to the portal to view the generated character.

[0663] Step 5:

[0664] Users can access a customization screen to fine-tune appearance (e.g., hairstyle, eye color, mouth shape, etc.), voice tone, personality settings, etc. By adjusting these settings, users can create a virtual character that matches their brand image or individual needs.

[0665] Step 6:

[0666] The server reflects the customization data entered by the user and generates the final AI influencer, which is then stored on a dedicated web portal and presented to the user for review.

[0667] Step 7:

[0668] Users can access the schedule management screen and specify the days and times when the AI ​​influencer will be active. For example, users can set up social media posts on a specific day of the week and live stream once a month.

[0669] Step 8:

[0670] The server automatically saves the user-specified schedule in a database and creates an activity plan for the AI ​​influencer, ensuring that all actions are carried out as planned.

[0671] Step 9:

[0672] The server collects real-time activity data of AI influencers, including engagement rates, follower count increases and decreases, and comment content.

[0673] Step 10:

[0674] The server analyzes the collected data and presents it to the user visually through a dashboard, including engagement status and performance trends.

[0675] Step 11:

[0676] Users can access the chat function using a dedicated device and give instructions to the AI ​​influencer in real time. For example, users can instruct the AI ​​influencer to make comments or introduce products during live broadcasts.

[0677] Step 12:

[0678] The server analyzes the user's instructions and has the AI ​​influencer take appropriate action, enabling automatic and dynamic interactions.

[0679] Step 13:

[0680] The server uses an emotion engine to collect and analyze the user's emotional data. For example, it can grasp the user's emotional state in real time using techniques such as emotion analysis of text, emotion recognition from voice, and facial expression analysis.

[0681] Step 14:

[0682] The server automatically generates actions for the AI ​​influencer based on the analyzed emotional data. For example, if the user is excited, the AI ​​influencer will respond accordingly and show a facial expression.

[0683] Step 15:

[0684] The server generates an engagement report based on the collected activity data, which includes campaign effectiveness metrics and recommendations for improvement.

[0685] Step 16:

[0686] The server will provide the generated engagement report to the user via email or dashboard, allowing the user to review it and use it to improve their next marketing strategy.

[0687] Example 2

[0688] 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."

[0689] In conventional virtual character systems, the extent to which users can customize their characters is limited, and there is a lack of means to properly collect and analyze character activity data, making it difficult to achieve high levels of engagement and real-time interaction. Another problem is the lack of technology for natural interactions that take user emotions into account.

[0690] 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.

[0691] In this invention, the server includes: means for generating a virtual character based on specified characteristics using a generation algorithm; means for customizing the characteristics of the virtual character in response to a user request; means for setting a schedule for the virtual character in response to a user request; means for collecting and analyzing activity data of the virtual character in real time; means for presenting performance data of the virtual character to the user; means for executing an interaction between the virtual character and the user; means for generating an engagement report based on the activity of the virtual character and providing it to the user; means for having an emotion recognition engine for collecting and analyzing emotion data of the user; and means for automatically generating actions for the virtual character based on the analyzed emotion data. This enables a user to highly customize a character, enabling real-time interaction and natural responses according to the user's emotions, and realizing a virtual character system.

[0692] A "generation algorithm" is an algorithm for generating a virtual character based on specified characteristic data.

[0693] "Characteristic data" refers to data including the gender, age, appearance, personality, etc. of a virtual character.

[0694] A "virtual character" is a character that is generated by a generation algorithm and drawn based on characteristic data.

[0695] "Customization" is the process of adjusting the characteristics of a virtual character according to the user's requirements.

[0696] "Schedule setting" is the act of scheduling the activities of a virtual character on dates and times specified by the user.

[0697] "Activity data" is data relating to activities performed by a virtual character.

[0698] "Real-time collection" refers to the process of collecting activity data of virtual characters in real time.

[0699] "Analysis" is the process of analyzing collected data and converting it into meaningful information.

[0700] "Performance data" is data that indicates the results of a virtual character's activities.

[0701] An "interaction" is an interaction that takes place between a user and a virtual character.

[0702] An "engagement report" is a report that summarizes the reactions and engagement of users and audience members based on the activities of virtual characters.

[0703] An "emotion recognition engine" is an engine for analyzing emotions from a user's text, voice, facial expressions, etc.

[0704] "Emotion data" refers to emotional information collected and analyzed by an emotion recognition engine.

[0705] "Automatic action generation" is the process of automatically generating the actions and responses of a virtual character based on analyzed emotional data.

[0706] The system of the present invention uses a generation algorithm to generate a virtual character (hereinafter referred to as an AI influencer) based on specified characteristics, and provides a set of means for users to customize, manage, and recognize emotions of the virtual character. Specific embodiments of the system are described below.

[0707] AI influencer generation

[0708] Upon receiving a request from a user, the server launches an algorithm to generate an AI influencer. This algorithm uses deep learning technologies such as "StyleGAN" and "DALL-E." The user inputs characteristic data such as gender, age, appearance, and personality, and the server uses that data to generate a realistic virtual character that matches the specified characteristics. For example, by specifying the user's desired hairstyle, eye color, mouth shape, etc., the generated AI influencer will have an appearance that reflects these characteristics.

[0709] Customization

[0710] The server stores the generated AI influencer data on a dedicated web portal, which users can access. Through the web portal, users can fine-tune the AI ​​influencer's facial features, tone of voice, personality attributes, and more, allowing them to create a virtual character that best suits their brand or campaign.

[0711] Setting a schedule

[0712] Users can access the schedule management screen on the web portal and set the AI ​​influencer's activity dates and times. For example, they can set a schedule to post on social media on a specific day of the week or a monthly live broadcast. The server stores this setting information and reflects it in the AI ​​influencer's activities.

[0713] Managing Performance

[0714] The server collects and analyzes the AI ​​influencer's activity data in real time, such as engagement rates after posting on social media, increases or decreases in the number of followers, and the content of comments, and visualizes this data and displays it on a dashboard, allowing users to check the AI ​​influencer's performance at a glance.

[0715] Interaction

[0716] Users can use a dedicated device (PC or smartphone) to use the chat function to interact with the AI ​​influencer. For example, they can hold a Q&A session during live streaming or instruct the AI ​​influencer to introduce a product in real time. By sending specific prompts, they can instruct the AI ​​influencer to take specific actions. For example, they can send a prompt such as, "Please explain the appeal of the new product to viewers."

[0717] Generate engagement reports

[0718] Based on the collected activity data, the server automatically generates engagement reports, which provide details such as campaign effectiveness measurements, engagement trends, and follower responses, and are delivered to users via email or dashboard.

[0719] Emotion recognition implementation

[0720] The server uses an emotion recognition engine to collect and analyze the user's emotional data. Using techniques such as text analysis, voice recognition, and facial expression analysis, the server can grasp the user's emotional state. For example, it can analyze the user's emotional state, such as "excited" or "sad," in real time.

[0721] Emotion-Based Interaction

[0722] The server automatically generates the AI ​​influencer's actions based on the analyzed emotional data. This means that when a user gives instructions to the AI ​​influencer through the chat function, the AI ​​influencer can generate optimal responses and actions taking into account the emotional data. For example, if a viewer is excited, the AI ​​influencer will show a "good-looking surprised reaction."

[0723] As a concrete example, a user can create an AI influencer named "Alex" to promote a new game character and customize its appearance, voice, and personality. The user sets Alex's activity date and time to introduce new game content every Wednesday. The server manages this activity, collects and analyzes engagement data, and displays it on a dashboard. During live broadcasts, users can also instruct the influencer to answer questions in real time through the chat function. Furthermore, the server uses an emotion recognition engine to analyze viewer reactions and automatically generate appropriate reactions and follow-ups based on the results.

[0724] In this way, the system of the present invention solves many of the problems in the current influencer market and enables natural, sophisticated interaction and highly efficient engagement.

[0725] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0726] Step 1:

[0727] The user logs in to a dedicated web portal and enters the AI ​​influencer's characteristic data (gender, age, appearance, personality, etc.). The input data is sent to the server and saved as characteristic data. Specifically, the user enters the characteristic data into the input form and presses the "Submit" button.

[0728] Step 2:

[0729] The server launches a generation algorithm based on the received characteristic data. A generation algorithm (e.g., StyleGAN or DALL-E) is used to generate a virtual character. The input characteristic data is fed into a deep learning model to generate realistic image data of the virtual character. The output image data is stored on the server.

[0730] Step 3:

[0731] The server stores the generated AI influencer data in a dedicated database and reflects it on a user-accessible web portal. Specifically, the server writes the generated character data to the database and calls an API to update the information on the web portal.

[0732] Step 4:

[0733] A user accesses the web portal and displays the customization screen, where the user fine-tunes the virtual character's characteristics (e.g., facial contours, hairstyle, tone of voice, etc.). When the user saves the changes, the input data is sent to the server and updated as the virtual character's characteristic data.

[0734] Step 5:

[0735] The server reflects the updated characteristic data and uses the deep learning model again to generate new image data. The new image data is saved in the database, and the character is regenerated with the user's customizations reflected.

[0736] Step 6:

[0737] The user accesses the schedule management screen on the web portal and sets the AI ​​influencer's activity dates and times. For example, they enter a posting schedule to introduce new gaming content every Wednesday. The input data is sent to the server and saved as a schedule.

[0738] Step 7:

[0739] The server receives the schedule information and automatically executes the AI ​​influencer's activities based on the specified date and time. For example, it posts to social media according to the schedule. It also stores data related to the activities (e.g., post content, posting date and time) in a database.

[0740] Step 8:

[0741] The server collects and analyzes the AI ​​influencer's activity data in real time. For example, it collects and stores data such as engagement rates, follower count changes, and comment content after social media posts in a database. The collected data is analyzed using analysis software (e.g., Python libraries such as Pandas and Matplotlib), and the results are displayed on a dashboard.

[0742] Step 9:

[0743] Users can use a dedicated device to use the chat function to interact with the AI ​​influencer. For example, during live streaming, the user can send a prompt such as, "Please explain the appeal of the new product to the viewers." The chat contents are sent to the server and saved.

[0744] Step 10:

[0745] The server analyzes the chat content and generates the AI ​​influencer's actions based on the specified instructions. For example, it analyzes the prompt text and uses a generative AI model to generate appropriate videos and text. The generated content is then instantly sent to the user's device.

[0746] Step 11:

[0747] The server generates an engagement report based on the collected activity data. The report includes campaign effectiveness measurement, engagement trends, follower reactions, etc. The generated report is provided to the user via email or dashboard.

[0748] Step 12:

[0749] The server uses an emotion recognition engine to collect and analyze user emotion data. For example, it performs text analysis, voice recognition, and facial expression analysis of viewers during live streaming. The collected emotion data is stored in a database.

[0750] Step 13:

[0751] The server automatically generates the AI ​​influencer's actions based on the analyzed emotional data. For example, if the viewer is excited, the AI ​​influencer will generate an action that shows a "good-looking surprised reaction." The generated content is instantly sent to the user's device.

[0752] As described above, by performing specific actions, inputs, data processing, and outputs at each step, users can use highly customized virtual characters to achieve natural, advanced interactions and highly efficient engagement.

[0753] (Application example 2)

[0754] 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."

[0755] The problem that this invention aims to solve is to provide a system for content distribution using virtual characters (AI influencers) that allows users to easily create and customize virtual characters and smoothly realize two-way communication with users. In particular, the aim is to enable appropriate reactions based on viewer emotions in real time in a format that can be used on devices such as smartphones. In addition, the aim is to maximize the effect of engagement by efficiently collecting and analyzing character performance data and visually presenting it.

[0756] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for generating a virtual character based on specified characteristics using a generation algorithm; means for customizing the characteristics of the virtual character in response to a user request; means for setting a schedule for the virtual character based on a user request; means for collecting and analyzing activity data of the virtual character in real time; means for presenting performance data of the virtual character to the user; means for executing an interaction between the virtual character and the user; means for generating an engagement report based on the activity of the virtual character and providing it to the user; means for live streaming the virtual character on a smartphone terminal; means for responding in real time to comments from users and viewers via a chat function and analyzing their emotions; and means for automatically generating a reaction of the virtual character based on the emotion data. This allows users to easily and effectively deliver content using virtual characters. Furthermore, by reacting in response to the viewer's emotions, interaction with the viewer can be deepened and engagement can be improved.

[0757] A "generation algorithm" is an algorithm for generating a virtual character based on characteristic data input by a user.

[0758] A "virtual character" is a character that has user-specified characteristics and is generated by a generation algorithm.

[0759] "Means for customization" refers to means by which a user can fine-tune the appearance, personality, voice, etc. of a generated virtual character.

[0760] The "means for setting a schedule" is a means for a user to set the date, time, and frequency of a virtual character's activities.

[0761] "Means for collecting and analyzing activity data in real time" refers to means for collecting and analyzing data on the behavior and performance of a virtual character in real time.

[0762] "Means for presenting performance data" means means for presenting collected and analyzed information about the performance of a virtual character to a user.

[0763] "Means for performing interaction" refers to means by which a user can have two-way communication with a virtual character in real time.

[0764] The "means for generating and providing an engagement report" refers to a means for generating an engagement report based on the activities of a virtual character and providing it to a user.

[0765] "Means for live streaming on a smartphone terminal" refers to means for live streaming of a virtual character using a smartphone terminal.

[0766] The "chat function" is a function that allows users and viewers to exchange messages in real time.

[0767] "Means for analyzing emotions" refers to the means of analyzing viewers' comments and reactions and understanding their emotions.

[0768] The "means for automatically generating a reaction" is a means for automatically generating an appropriate reaction of a virtual character based on the analyzed emotion data.

[0769] The system of the present invention generates a virtual character (hereinafter referred to as an AI influencer) based on specified characteristics and provides a set of means for users to customize, manage, and recognize emotions of the virtual character. Furthermore, the present invention uses a smartphone as a platform, allowing users to easily perform live streaming using the virtual character.

[0770] System configuration

[0771] The system uses the following hardware and software:

[0772] Hardware: Smartphone (iOS, Android)

[0773] Software: Python, TensorFlow, Flutter, RestAPI, Firebase

[0774] Implementation of the generation algorithm

[0775] The server receives requests from users and generates virtual characters using a generation algorithm based on deep learning technology (TensorFlow) to generate realistic virtual characters based on input characteristic data.

[0776] Customization features

[0777] The generated virtual character data is stored in Firebase, and users can access it through a dedicated web portal or app to freely customize the character's appearance, voice, personality, etc. This allows users to create a character that best suits their brand or campaign.

[0778] Schedule management

[0779] Users can set the dates and times for their virtual characters to be active within the app, and the schedule is managed by Firebase, making it easy to schedule posts and live streams at specific times.

[0780] Performance Analysis

[0781] The activity data of the virtual characters is collected in real time and analyzed by the server. The analysis results are provided to users via a Rest API and displayed visually on a dashboard, including engagement rates, follower counts, and comment content.

[0782] Interaction Features

[0783] Users can use the chat feature to have two-way communication with the virtual character, which is particularly useful during live broadcasts, allowing users to direct answers to viewer questions in real time.

[0784] Emotion Recognition and Response Generation

[0785] The server uses emotion analysis technology (TextBlob) to analyze viewers' comments and reactions and understand their emotions. Based on the analysis results, the virtual character's reaction is automatically generated. For example, if a viewer is excited, the character will respond appropriately, such as showing a happy expression.

[0786] Specific examples

[0787] Consider a case where a user creates an AI influencer named "Yuki" to introduce a game.

[0788] The user inputs characteristic data to generate a character named "Yuki," which includes gender: female, age: 20, appearance: black hair, blue eyes, and personality: cheerful.

[0789] Customize your character's appearance, voice, and personality, and set a schedule that introduces new game content every Wednesday.

[0790] Viewer comment: "This game is so much fun!"

[0791] TextBlob analyzes the "positive" emotion, and the AI ​​influencer "Yuki" responds with a "happy!" expression, saying, "I'm glad everyone is having fun!"

[0792] Prompt Sentence Examples

[0793] Users can create a new AI influencer named "Yuki." The user's characteristics are: gender: female, age: 20, physical features: black hair, blue eyes, personality: cheerful. Yuki will introduce new game content and hold live streams every Wednesday. Users can customize Yuki's appearance, voice, and personality, and instruct her to answer questions in real time during the live stream. At the same time, the system analyzes emotions from viewer comments and generates appropriate reactions.

[0794] In this way, by using the system according to the present invention, users can achieve high engagement while also achieving natural interaction with viewers.

[0795] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0796] Step 1: User input of characteristic data

[0797] The user enters the virtual character's name, gender, age, appearance, personality, and other characteristic data into the smartphone app's input form, which is then sent to the server.

[0798] Input: Characteristic data (name, gender, age, appearance, personality, etc.)

[0799] Output: Attribute data sent to the server

[0800] Step 2: Generate a virtual character

[0801] The server then launches a generation algorithm based on the received characteristic data, using deep learning technology (TensorFlow) to generate a realistic virtual character.

[0802] Input: characteristic data

[0803] Output: Generated virtual character

[0804] Step 3: Customizing your virtual character

[0805] Users can customize the generated character through a dedicated web portal or app, fine-tuning appearance, voice, personality, etc., and then finalize the character. The customized character data is stored in Firebase.

[0806] Input: User customization data

[0807] Output: Customized character data

[0808] Step 4: Set a schedule

[0809] Users can set the dates and times for their virtual characters to be active within the app, and posts and live streams are scheduled based on the specified dates and times. Schedule data is also stored in Firebase.

[0810] Input: Schedule information

[0811] Output: Saved schedule data

[0812] Step 5: Go Live

[0813] At the specified date and time, a live broadcast using a virtual character will begin on a smartphone device, and users can monitor the broadcast in real time and manage interactions with viewers.

[0814] Input: Scheduled date and time

[0815] Output:Start live streaming

[0816] Step 6: Interact with your audience

[0817] During the live broadcast, users and viewers can exchange messages using the chat function, and questions can be sent in real time, which the virtual characters will respond to.

[0818] Input: Viewer comments and questions

[0819] Output: Response message from virtual character

[0820] Step 7: Emotion Recognition and Response Generation

[0821] The server uses TextBlob to perform sentiment analysis based on the viewers' comments, and automatically generates appropriate responses for the virtual character based on the sentiment data.

[0822] Input: Viewer comments

[0823] Output: Auto-generated responses of virtual characters

[0824] Step 8: Collect and analyze performance data

[0825] The server collects and analyzes real-time activity data of the virtual characters during live broadcasts, including engagement rates, increases or decreases in the number of followers, and the content of comments.

[0826] Input: Activity data during live streaming

[0827] Output: Parsed performance data

[0828] Step 9: Presenting the results

[0829] The server analyzes performance data and visually presents it to the user through a dashboard, allowing the user to decide on the next action to take.

[0830] Input: Parsed performance data

[0831] Output: Data presented to the user

[0832] Step 10: Generate and deliver engagement reports

[0833] The server generates and provides to the user an engagement report based on the collected and analyzed activity data, which details campaign effectiveness measurements, engagement trends, and follower responses.

[0834] Input: Collected and analyzed activity data

[0835] Output: Generated engagement report

[0836] Through these steps, users can create and customize virtual characters to achieve effective live streaming and high engagement.

[0837] 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.

[0838] 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.

[0839] 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.

[0840] [Third embodiment]

[0841] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0842] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0843] 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).

[0844] 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.

[0845] 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.

[0846] 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).

[0847] 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.

[0848] 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.

[0849] 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.

[0850] 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.

[0851] 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.

[0852] 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."

[0853] The system of the present invention uses a generation algorithm to generate a virtual character (hereinafter referred to as an AI influencer) based on specified characteristics, and provides a set of means for users to customize and manage the character. Specific embodiments of the system are described below.

[0854] AI influencer generation

[0855] The server receives a user request and launches an AI influencer generation algorithm. This algorithm uses generative AI technology to create a realistic virtual character based on input characteristic data (e.g., gender, age, appearance, personality, etc.). For example, the algorithm determines the user's desired hairstyle, eye color, mouth shape, etc. based on the specified characteristics, and a deep learning model then creates a more realistic depiction.

[0856] Customization

[0857] The server stores information about the generated AI influencer on a dedicated web portal, which users can access. After logging in, users can fine-tune their virtual character through a customization screen. For example, they can fine-tune facial contours, select a tone of voice, and set personality attributes. This allows users to create a virtual character that best suits their brand or campaign.

[0858] Setting a schedule

[0859] Users will be provided with a schedule management screen where they can set the AI ​​influencer's activity dates and times, for example, they can schedule social media posts on a specific day of the week or a monthly live stream.

[0860] Managing Performance

[0861] The server collects and analyzes the AI ​​influencer's activity data in real time. For example, after posting on social media, it analyzes and accumulates data on engagement rates, increases or decreases in the number of followers, and the content of comments. The server analyzes this data and displays it visually to the user through a dashboard.

[0862] Interaction

[0863] A chat function is provided that allows users to directly interact with the AI ​​influencer using a dedicated device. For example, users can hold Q&A sessions during live streaming or give instructions on product introductions in real time. This enables two-way communication between the virtual character and the audience.

[0864] Generate engagement reports

[0865] The server generates engagement reports based on the collected activity data. These reports provide detailed information on campaign effectiveness, engagement trends, follower reactions, etc. Reports are generated automatically and provided to users via email or dashboard.

[0866] Specific examples

[0867] For example, a cosmetics brand user might create an AI influencer named "Luna" to promote a new product. The user can customize Luna's hairstyle, skin color, and voice tone to match the brand's image. Then, the user schedules her to post on social media every Friday and to promote the new product via live streaming once a month. The server manages this, collecting and analyzing engagement data in real time and displaying it on a dashboard. Through the dashboard, the user can monitor performance and receive engagement reports to support effective marketing activities.

[0868] In this way, the system according to the present invention solves the problems in the current influencer market and realizes stable and highly efficient engagement.

[0869] The processing flow will be explained below.

[0870] Step 1:

[0871] The user logs in to a dedicated web portal, then inputs a request to generate an AI influencer and specifies characteristic data (e.g., gender, age, appearance, personality, etc.).

[0872] Step 2:

[0873] The server receives the user's request and triggers a generation algorithm based on the specified characteristic data, which uses deep learning techniques to generate a base model of the AI ​​influencer.

[0874] Step 3:

[0875] The server then trains the generated base model to make it look and behave realistically, resulting in an AI influencer with realistic portrayals.

[0876] Step 4:

[0877] The server saves the generated AI influencer prototype on a dedicated web portal and notifies the user when the generation is complete. The user can then log in to the portal to view the generated character.

[0878] Step 5:

[0879] Users can access a customization screen to fine-tune appearance (e.g., hairstyle, eye color, mouth shape, etc.), voice tone, personality settings, etc. By adjusting these settings, users can create a virtual character that matches their brand image or individual needs.

[0880] Step 6:

[0881] The server reflects the customization data entered by the user and generates the final AI influencer, which is then stored on a dedicated web portal and presented to the user for review.

[0882] Step 7:

[0883] Users can access the schedule management screen and specify the days and times when the AI ​​influencer will be active. For example, users can set up social media posts on a specific day of the week and live stream once a month.

[0884] Step 8:

[0885] The server automatically saves the user-specified schedule in a database and creates an activity plan for the AI ​​influencer, ensuring that all actions are carried out as planned.

[0886] Step 9:

[0887] The server collects real-time activity data of AI influencers, including engagement rates, follower count increases and decreases, and comment content.

[0888] Step 10:

[0889] The server analyzes the collected data and presents it to the user visually through a dashboard, including engagement status and performance trends.

[0890] Step 11:

[0891] Users can access the chat function using a dedicated device and give instructions to the AI ​​influencer in real time. For example, users can instruct the AI ​​influencer to make comments or introduce products during live broadcasts.

[0892] Step 12:

[0893] The server analyzes the user's instructions and has the AI ​​influencer take appropriate action, enabling automatic and dynamic interactions.

[0894] Step 13:

[0895] The server generates an engagement report based on the collected activity data, which includes campaign effectiveness metrics and recommendations for improvement.

[0896] Step 14:

[0897] The server will provide the generated engagement report to the user via email or dashboard, allowing the user to review it and use it to improve their next marketing strategy.

[0898] Example 1

[0899] 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."

[0900] Conventional virtual character systems have had problems such as difficulty in achieving realistic depictions based on complex user-specified characteristics, customizing the generated characters, managing schedules, collecting and analyzing real-time performance data, interacting with users, and generating and providing detailed engagement reports. As a result, companies and individuals have had difficulty efficiently managing characters in marketing activities and brand promotions, preventing them from maximizing engagement effects.

[0901] 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.

[0902] In this invention, the server includes: means for generating a virtual character based on specified characteristics using a generation algorithm; means for saving the virtual character's characteristic data in a database and displaying it on a dedicated web portal; means for customizing the virtual character's characteristics according to a user's request; means for providing a dedicated schedule management screen and setting the virtual character's schedule according to a user's request; means for collecting and analyzing activity data in real time via the virtual character's social media API; means for visualizing the analyzed data on a dashboard and presenting it to the user; means for providing a chat function for the user to give instructions to the user in real time through interactions with the virtual character; and means for automatically generating an engagement report based on the virtual character's activity data and providing it to the user. This allows users to efficiently perform processes from virtual character generation and customization to activity management, data analysis, and report generation through a consistent system.

[0903] A "generation algorithm" is a computational method for generating a virtual character using specified characteristic data, and primarily utilizes deep learning technology.

[0904] "Characteristic data" is information including the gender, age, appearance, personality, etc. of a virtual character, and is an input value provided by a user.

[0905] "Database" means an information storage system for managing and storing information about generated and customized virtual characters.

[0906] "Web Portal" means an online platform for accessing, managing, and adjusting information about user-generated and customized virtual characters.

[0907] "Customization" refers to the act of a user adjusting the characteristics (e.g., appearance, tone of voice, personality attributes, etc.) of a generated virtual character to fine-tune it to their own needs.

[0908] The "schedule management screen" is an interface that allows the user to set the activity dates and times of the virtual character, and is provided in a calendar or list format.

[0909] "Social Media API" refers to the application programming interface provided by a social media platform and is the technology used to collect activity data (e.g., engagement data) of virtual characters.

[0910] A "dashboard" is an interface for visually displaying analyzed activity data, allowing users to check the performance of their virtual characters in real time.

[0911] "Chat function" refers to a means of communication that allows users to exchange messages with virtual characters in real time, and is primarily used for live streaming and question and answer sessions.

[0912] "Engagement Report" means a report detailing engagement results and trends based on virtual character activity data, provided to Users to assist them in effective campaign management.

[0913] The system of the present invention generates, customizes, schedules, manages and analyzes activity data, and generates and provides interaction and engagement reports for virtual characters (hereinafter referred to as AI influencers) through a dedicated server and user terminals. Detailed embodiments of the system are described below.

[0914] AI influencer generation

[0915] The server receives a request from the user. This request includes characteristic data of the virtual character (e.g., gender, age, appearance, personality, etc.). The server generates a virtual character based on these characteristics using, for example, a generative AI model running on Google Cloud Platform (GCP). The generated character is then detailed based on the characteristic data to achieve a realistic depiction using deep learning technology.

[0916] Customization

[0917] The server stores the generated AI influencer data in a database and displays it on a dedicated web portal. Users can access the customization screen by logging in to this web portal. On the customization screen, users can fine-tune their virtual character (e.g., adjust facial contours, select voice tone, and set personality attributes). The user interface (UI) is designed for intuitive operation, using sliders and drop-down menus.

[0918] Setting a schedule

[0919] Users can set the AI ​​influencer's activity dates and times by accessing a dedicated schedule management screen. Using the management screen, which is provided in calendar and list format, users can enter schedules for social media posts and live broadcasts on specific dates and times. For example, they can set specific schedules such as "posting new product introductions every Friday at 10:00 AM."

[0920] Managing Performance

[0921] The server uses social media APIs (e.g., Facebook Graph API, Twitter API) to collect real-time activity data on AI influencers. The acquired data includes engagement rates, increases or decreases in the number of followers, and comment content. This data is analyzed by the server and evaluated as engagement performance. Tools such as Google Analytics and IBM Watson Analytics are used for the analysis.

[0922] Dashboard visualization

[0923] The server displays the analyzed data on a dashboard, which is designed to allow users to intuitively view the data using visualization tools (e.g., D3.js, Chart.js). Through this dashboard, users can monitor the performance of their virtual characters in real time and make adjustments as needed.

[0924] Interaction

[0925] Using a dedicated device, users can access a chat function to interact with the virtual character, allowing them to answer questions and give instructions in real time during the live broadcast, enabling two-way communication between the virtual character and the viewer.

[0926] Generate engagement reports

[0927] The server automatically generates engagement reports based on the collected activity data. These reports include detailed records of campaign effectiveness, engagement trends, follower responses, etc. The generated reports are sent to users via email or a dashboard.

[0928] Specific examples

[0929] For example, a cosmetics brand user might create an AI influencer named "Luna" to promote a new product. The user can customize Luna's hairstyle, skin color, and voice tone to match the brand's image. Next, the user can schedule Luna to post on social media every Friday and to promote the new product via live streaming once a month. The server manages these schedules, collects and analyzes engagement data in real time, and displays it on a dashboard. Through the dashboard, the user can monitor performance and receive engagement reports to support effective marketing activities.

[0930] Example prompt sentence:

[0931] We want to generate an AI influencer called "Luna" to promote a new product. Luna's characteristics are as follows:

[0932] Gender: Female

[0933] Age: 25

[0934] Hairstyle: Long Straight

[0935] Eye color: Blue

[0936] Personality: Cheerful and energetic

[0937] Based on this, please generate a virtual character that is as realistic as possible, provide a customization screen for Luna, and once the setup is complete, schedule a social media post every Friday and a monthly live stream.

[0938] As described above, the system according to the present invention realizes efficient and sophisticated virtual character management and promotion in the marketing activities of companies and individuals.

[0939] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0940] Step 1:

[0941] The server receives the user request.

[0942] Input: The user enters the virtual character's characteristic data (e.g., gender, age, appearance, personality, etc.) in a form on the web portal.

[0943] Data processing and data calculation: The server receives the input characteristic data and converts it into an appropriate format, for example, converting text data into numerical data, to create an input dataset for the generative AI model.

[0944] Output: The characteristic data is the input dataset, properly formatted.

[0945] Step 2:

[0946] The server launches the generative AI model.

[0947] Input: The formatted input dataset.

[0948] Data processing and data calculation: The server uses a deep learning model built with TensorFlow or PyTorch, for example, to perform calculations to generate a virtual character based on the characteristic data.

[0949] Output: A 3D model or 2D image of the generated virtual character is output.

[0950] Step 3:

[0951] The server stores the generated AI influencer data in a database.

[0952] Input: 3D model and 2D image data of the generated virtual character.

[0953] Data processing and data calculation: The server processes the data to properly index and store it in the database.

[0954] Output: Virtual character information stored in a database.

[0955] Step 4:

[0956] The server displays the generated AI influencer data on a web portal.

[0957] Input: Generated virtual character information stored in a database.

[0958] Data processing and data calculation: The server processes the data to display it in the appropriate format on the web portal.

[0959] Output: The generated character is displayed in the customization screen of the web portal which the user can log in and view.

[0960] Step 5:

[0961] The user customizes the virtual character.

[0962] Input: Fine-tuning information for the virtual character entered by the user through the customization screen (e.g., adjusting facial contours, selecting voice tone, setting personality attributes).

[0963] Data Processing and Data Calculation: The server performs the data processing necessary to update the attributes of the virtual character based on the user's customization information.

[0964] Output: Final virtual character data based on user customized attributes.

[0965] Step 6:

[0966] The user accesses the schedule management screen and sets the activity date and time of the virtual character.

[0967] Input: User-generated scheduling information (e.g., scheduling a social media post or live stream on a specific date and time).

[0968] Data processing and data calculation: The server stores schedule information in a database and performs processing to properly manage it.

[0969] Output: Schedule information stored in the database.

[0970] Step 7:

[0971] The server collects and analyzes the activity data of the virtual characters in real time.

[0972] Input: Activity data obtained from social media APIs (e.g., engagement rate, follower count increase / decrease, comment content, etc.).

[0973] Data processing and data calculation: The server uses data analysis tools to analyze the acquired data and calculate the engagement performance.

[0974] Output: Parsed engagement data.

[0975] Step 8:

[0976] The server visualizes and displays the analytical data on a dashboard.

[0977] Input: Parsed engagement data.

[0978] Data processing and data calculations: The server generates graphs and charts of the data using visualization tools (e.g., D3.js, Chart.js).

[0979] Output: Visualized data that users can view in a dashboard.

[0980] Step 9:

[0981] Users interact with virtual characters in real time through a chat function.

[0982] Input: Real-time messages and instructions from the user.

[0983] Data processing and data calculation: The server processes chat messages and causes the virtual characters to react appropriately.

[0984] Output: The responses and actions of the virtual character.

[0985] Step 10:

[0986] The server generates an engagement report based on the collected activity data and provides it to the user.

[0987] Input: Parsed activity data and engagement information.

[0988] Data processing and data calculation: The server uses a report generation tool (e.g., Tableau, Microsoft Power BI) to automatically generate engagement reports.

[0989] Output: Engagement reports delivered to users (via emails and dashboards).

[0990] Through the above steps, the system according to the present invention enables users to efficiently create and manage virtual characters, as well as collect and analyze engagement data.

[0991] (Application example 1)

[0992] 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."

[0993] In recent years, advertising and marketing activities using virtual characters have become more common, but their operation requires high costs and specialized skills. Furthermore, collecting and analyzing engagement data in real time is difficult, making it time-consuming to measure effectiveness. Furthermore, there are limited ways for users to interact with virtual characters in real time, which creates the challenge of insufficient two-way communication.

[0994] 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.

[0995] In this invention, the server includes means for generating a virtual character based on specified characteristics using a generation algorithm, means for customizing the characteristics of the virtual character in response to a user request, means for setting a schedule for the virtual character in response to a user request, means for collecting and analyzing activity data of the virtual character in real time, means for presenting performance data of the virtual character to the user, means for executing an interaction between the virtual character and the user, means for generating an engagement report based on the activity of the virtual character and providing it to the user, means for providing a smartphone application, means for executing and managing an advertising campaign using the virtual character, and means for collecting engagement data using a social media API. This enables users to efficiently and effectively conduct advertising and marketing activities using virtual characters without requiring specialized skills.

[0996] A "generation algorithm" is an algorithm for automatically generating a virtual character based on specified characteristic data.

[0997] A "virtual character" is a digital character with specific characteristics created by a generative algorithm.

[0998] "Characteristics" refers to characteristic data used to create a virtual character, such as gender, age, appearance, and personality.

[0999] "Customization" refers to changing or adjusting the characteristics of a generated virtual character based on the user's requests.

[1000] "Schedule setting" is a function for setting the date, time and frequency of a virtual character's activities.

[1001] "Activity data" is data about all activities performed by a virtual character.

[1002] "Collecting and analyzing in real time" refers to a process in which activity data of a virtual character is collected at that moment and analyzed immediately.

[1003] "Performance Data" means data related to the results of a virtual character's activities, such as engagement rate and number of followers.

[1004] "Interaction" refers to real-time interaction between a virtual character and a user.

[1005] An "engagement report" is a report created based on data based on the activities of virtual characters.

[1006] A "smartphone application" is a software application that runs on a smartphone.

[1007] An "advertising campaign" is a planned advertising effort to promote a particular product or service.

[1008] A "social media API" is an externally accessible programmatic interface provided by a social media platform.

[1009] The system of the present invention uses a generation algorithm to generate a virtual character (hereinafter referred to as an AI influencer) based on specified characteristics, and provides a set of means for users to customize and manage the character. Specific embodiments of the system are described below.

[1010] 1. AI influencer generation

[1011] The server receives a user request and launches a generative algorithm, which uses generative AI technology to create a realistic virtual character based on the user's specified characteristics (e.g., gender, age, appearance, personality, etc.). For example, the algorithm determines the user's desired hairstyle, eye color, mouth shape, etc., based on the specified characteristics, and a deep learning model (e.g., TensorFlow or PyTorch) then creates a more realistic depiction.

[1012] 2. Customization

[1013] The server stores information about the generated AI influencer in a database (e.g., Amazon RDS) and makes it accessible to users. Users can fine-tune the virtual character through the customization screen of the smartphone application. For example, they can fine-tune the facial contours, select the tone of voice, and set personality attributes. This allows users to create an AI influencer that is best suited to their brand or campaign.

[1014] 3. Set a schedule

[1015] Users can access the smartphone application's schedule management screen and set the AI ​​influencer's activity dates and times. For example, they can set it to post on social media on a specific day of the week and to broadcast live once a month. The server automatically schedules the activities based on this information and carries them out at the specified dates and times.

[1016] 4. Performance Management

[1017] The server collects and analyzes activity data in real time using social media APIs (e.g., Twitter API, Instagram Graph API), collecting information such as engagement rates, increases or decreases in the number of followers, and comment content, and generates analytical results using Python's Pandas and Matplotlib.

[1018] 5. Interaction

[1019] Users can use chat features (e.g., Socket.IO) to interact with AI influencers in real time, for example, by answering questions during live streaming or giving instructions for product introductions in real time.

[1020] 6. Generate engagement reports

[1021] The server generates engagement reports based on the collected activity data, detailing campaign effectiveness, engagement trends, follower reactions, etc. Reports are generated automatically and provided to users via email (e.g., SendGrid) or an in-application dashboard.

[1022] Specific examples

[1023] Suppose a cosmetics brand user wants to generate an AI influencer to promote a new product. They can customize the generated character by entering a prompt such as "Generate a character that is female, 25 years old, with a short bob, blue eyes, and a lively personality." They then schedule a social media post every Friday and a monthly live stream to introduce the new product. The server manages this, collecting and analyzing engagement data in real time and displaying it on a dashboard. Users can monitor performance through the dashboard and receive engagement reports to support effective marketing activities.

[1024] In this way, the system according to the present invention enables users to easily and efficiently carry out advertising and marketing activities that utilize virtual characters.

[1025] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1026] Step 1:

[1027] The server receives a prompt from the user: "Generate a character that is female, 25 years old, with a short bob haircut, blue eyes, and a lively personality." Based on the input characteristic data, it generates a virtual character using a generative AI model (TensorFlow or PyTorch). This generative AI model uses deep learning technology to create a realistic depiction based on the input characteristic data. The resulting virtual character data is stored in a database (Amazon RDS).

[1028] Step 2:

[1029] The user accesses the customization screen via a smartphone application. The server retrieves information about the generated AI influencer from the database and presents it to the user. The user then fine-tunes characteristics such as hairstyle, eye color, facial contours, voice tone, and personality attributes. The adjusted data is sent to the server, which then uses the AI ​​model again to generate an updated virtual character and update the database.

[1030] Step 3:

[1031] The user accesses the schedule management screen and sets the activity schedule for the virtual character, for example, posting to social media every Friday, live streaming once a month, etc. The server saves this schedule data in a database and schedules tasks to be executed at the specified date and time.

[1032] Step 4:

[1033] The server executes the virtual character's posts at the specified date and time using social media APIs (Twitter API, Instagram Graph API), and if live streaming is set, starts streaming using the appropriate streaming API, so that the virtual character's activities are executed according to the schedule set by the user.

[1034] Step 5:

[1035] After a social media post or live stream is completed, the server collects engagement data (likes, comments, number of followers, etc.) in real time. The data obtained through the social media API is used to analyze the data using Python's Pandas and Matplotlib. The analysis results are displayed on a dashboard using visualization tools (D3.js, Chart.js).

[1036] Step 6:

[1037] Users use the chat function (Socket.IO) of a smartphone application to interact with the AI ​​influencer in real time. The server receives chat messages from users, and the AI ​​influencer reacts based on them.

[1038] Step 7:

[1039] The server generates engagement reports based on the collected engagement data, detailing campaign effectiveness, engagement trends, follower reactions, etc. Reports are generated automatically and provided to users via email (SendGrid) or an in-application dashboard.

[1040] Through the above steps, the system according to the present invention enables users to efficiently carry out advertising and marketing activities that utilize virtual characters.

[1041] 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.

[1042] The system of the present invention uses a generation algorithm to generate a virtual character (hereinafter referred to as an AI influencer) based on specified characteristics, and provides a set of means for users to customize, manage, and recognize emotions of the virtual character. Specific embodiments of the system are described below.

[1043] AI influencer generation

[1044] The server receives a user request and launches an AI influencer generation algorithm. This algorithm uses generative AI technology to create a realistic virtual character based on input characteristic data (e.g., gender, age, appearance, personality, etc.). For example, the algorithm determines the user's desired hairstyle, eye color, mouth shape, etc. based on the specified characteristics, and a deep learning model then creates a more realistic depiction.

[1045] Customization

[1046] The server stores information about the generated AI influencer on a dedicated web portal, which users can access. After logging in, users can fine-tune their virtual character through a customization screen. For example, they can fine-tune facial contours, select a tone of voice, and set personality attributes. This allows users to create a virtual character that best suits their brand or campaign.

[1047] Setting a schedule

[1048] Users will be provided with a schedule management screen where they can set the AI ​​influencer's activity dates and times, for example, they can schedule social media posts on a specific day of the week or a monthly live stream.

[1049] Managing Performance

[1050] The server collects and analyzes the AI ​​influencer's activity data in real time. For example, after posting on social media, it analyzes and accumulates data on engagement rates, increases or decreases in the number of followers, and the content of comments. The server analyzes this data and displays it visually to the user through a dashboard.

[1051] Interaction

[1052] A chat function is provided that allows users to directly interact with the AI ​​influencer using a dedicated device. For example, users can hold Q&A sessions during live streaming or give instructions on product introductions in real time. This enables two-way communication between the virtual character and the audience.

[1053] Generate engagement reports

[1054] The server generates engagement reports based on the collected activity data. These reports provide detailed information on campaign effectiveness, engagement trends, follower reactions, etc. Reports are generated automatically and provided to users via email or dashboard.

[1055] Emotion recognition implementation

[1056] The server uses an emotion engine to collect and analyze the user's emotional data. For example, it can grasp the user's emotional state in real time using techniques such as emotion analysis of text, emotion recognition from voice, and facial expression analysis.

[1057] Emotion-Based Interaction

[1058] The server automatically generates actions for the AI ​​influencer based on the analyzed emotional data. When a user gives instructions to the AI ​​influencer through the chat function, the emotional data is taken into consideration to generate optimal responses and actions. This function enables natural interactions that are in tune with the user's emotions.

[1059] Specific examples

[1060] For example, suppose a user creates an AI influencer named "Alex" to promote a new game character. The user customizes Alex's appearance, voice, and personality, and sets a schedule for introducing new game content every Wednesday. The server manages this activity, collects and analyzes engagement data, and displays it on a dashboard. During live broadcasts, users can also instruct the AI ​​influencer to answer questions in real time through the chat function. Furthermore, the server uses an emotion engine to analyze viewer reactions and automatically generate appropriate reactions and follow-ups based on the results. For example, if viewers are excited, the AI ​​influencer may show a "good-looking surprised reaction."

[1061] In this way, the system of the present invention solves the problems in the current influencer market and achieves stable, highly efficient engagement and natural interaction.

[1062] The processing flow will be explained below.

[1063] Step 1:

[1064] Users log in to a dedicated web portal and input a request to generate an AI influencer, specifying characteristic data (e.g., gender, age, appearance, personality, etc.).

[1065] Step 2:

[1066] The server receives the user's request and triggers a generation algorithm based on the specified characteristic data, which uses deep learning techniques to generate a base model of the AI ​​influencer.

[1067] Step 3:

[1068] The server then trains the generated base model to make it look and behave realistically, resulting in an AI influencer with realistic portrayals.

[1069] Step 4:

[1070] The server saves the generated AI influencer prototype on a dedicated web portal and notifies the user when the generation is complete. The user can then log in to the portal to view the generated character.

[1071] Step 5:

[1072] Users can access a customization screen to fine-tune appearance (e.g., hairstyle, eye color, mouth shape, etc.), voice tone, personality settings, etc. By adjusting these settings, users can create a virtual character that matches their brand image or individual needs.

[1073] Step 6:

[1074] The server reflects the customization data entered by the user and generates the final AI influencer, which is then stored on a dedicated web portal and presented to the user for review.

[1075] Step 7:

[1076] Users can access the schedule management screen and specify the days and times when the AI ​​influencer will be active. For example, users can set up social media posts on a specific day of the week and live stream once a month.

[1077] Step 8:

[1078] The server automatically saves the user-specified schedule in a database and creates an activity plan for the AI ​​influencer, ensuring that all actions are carried out as planned.

[1079] Step 9:

[1080] The server collects real-time activity data of AI influencers, including engagement rates, follower count increases and decreases, and comment content.

[1081] Step 10:

[1082] The server analyzes the collected data and presents it to the user visually through a dashboard, including engagement status and performance trends.

[1083] Step 11:

[1084] Users can access the chat function using a dedicated device and give instructions to the AI ​​influencer in real time. For example, users can instruct the AI ​​influencer to make comments or introduce products during live broadcasts.

[1085] Step 12:

[1086] The server analyzes the user's instructions and has the AI ​​influencer take appropriate action, enabling automatic and dynamic interactions.

[1087] Step 13:

[1088] The server uses an emotion engine to collect and analyze the user's emotional data. For example, it can grasp the user's emotional state in real time using techniques such as emotion analysis of text, emotion recognition from voice, and facial expression analysis.

[1089] Step 14:

[1090] The server automatically generates actions for the AI ​​influencer based on the analyzed emotional data. For example, if the user is excited, the AI ​​influencer will respond accordingly and show a facial expression.

[1091] Step 15:

[1092] The server generates an engagement report based on the collected activity data, which includes campaign effectiveness metrics and recommendations for improvement.

[1093] Step 16:

[1094] The server will provide the generated engagement report to the user via email or dashboard, allowing the user to review it and use it to improve their next marketing strategy.

[1095] Example 2

[1096] 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."

[1097] In conventional virtual character systems, the extent to which users can customize their characters is limited, and there is a lack of means to properly collect and analyze character activity data, making it difficult to achieve high levels of engagement and real-time interaction. Another problem is the lack of technology for natural interactions that take user emotions into account.

[1098] 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.

[1099] In this invention, the server includes: means for generating a virtual character based on specified characteristics using a generation algorithm; means for customizing the characteristics of the virtual character in response to a user request; means for setting a schedule for the virtual character in response to a user request; means for collecting and analyzing activity data of the virtual character in real time; means for presenting performance data of the virtual character to the user; means for executing an interaction between the virtual character and the user; means for generating an engagement report based on the activity of the virtual character and providing it to the user; means for having an emotion recognition engine for collecting and analyzing emotion data of the user; and means for automatically generating actions for the virtual character based on the analyzed emotion data. This enables a user to highly customize a character, enabling real-time interaction and natural responses according to the user's emotions, and realizing a virtual character system.

[1100] A "generation algorithm" is an algorithm for generating a virtual character based on specified characteristic data.

[1101] "Characteristic data" refers to data including the gender, age, appearance, personality, etc. of a virtual character.

[1102] A "virtual character" is a character that is generated by a generation algorithm and drawn based on characteristic data.

[1103] "Customization" is the process of adjusting the characteristics of a virtual character according to the user's requirements.

[1104] "Schedule setting" is the act of scheduling the activities of a virtual character on dates and times specified by the user.

[1105] "Activity data" is data relating to activities performed by a virtual character.

[1106] "Real-time collection" refers to the process of collecting activity data of virtual characters in real time.

[1107] "Analysis" is the process of analyzing collected data and converting it into meaningful information.

[1108] "Performance data" is data that indicates the results of a virtual character's activities.

[1109] An "interaction" is an interaction that takes place between a user and a virtual character.

[1110] An "engagement report" is a report that summarizes the reactions and engagement of users and audience members based on the activities of virtual characters.

[1111] An "emotion recognition engine" is an engine for analyzing emotions from a user's text, voice, facial expressions, etc.

[1112] "Emotion data" refers to emotional information collected and analyzed by an emotion recognition engine.

[1113] "Automatic action generation" is the process of automatically generating the actions and responses of a virtual character based on analyzed emotional data.

[1114] The system of the present invention uses a generation algorithm to generate a virtual character (hereinafter referred to as an AI influencer) based on specified characteristics, and provides a set of means for users to customize, manage, and recognize emotions of the virtual character. Specific embodiments of the system are described below.

[1115] AI influencer generation

[1116] Upon receiving a request from a user, the server launches an algorithm to generate an AI influencer. This algorithm uses deep learning technologies such as "StyleGAN" and "DALL-E." The user inputs characteristic data such as gender, age, appearance, and personality, and the server uses that data to generate a realistic virtual character that matches the specified characteristics. For example, by specifying the user's desired hairstyle, eye color, mouth shape, etc., the generated AI influencer will have an appearance that reflects these characteristics.

[1117] Customization

[1118] The server stores the generated AI influencer data on a dedicated web portal, which users can access. Through the web portal, users can fine-tune the AI ​​influencer's facial features, tone of voice, personality attributes, and more, allowing them to create a virtual character that best suits their brand or campaign.

[1119] Setting a schedule

[1120] Users can access the schedule management screen on the web portal and set the AI ​​influencer's activity dates and times. For example, they can set a schedule to post on social media on a specific day of the week or a monthly live broadcast. The server stores this setting information and reflects it in the AI ​​influencer's activities.

[1121] Managing Performance

[1122] The server collects and analyzes the AI ​​influencer's activity data in real time, such as engagement rates after posting on social media, increases or decreases in the number of followers, and the content of comments, and visualizes this data and displays it on a dashboard, allowing users to check the AI ​​influencer's performance at a glance.

[1123] Interaction

[1124] Users can use a dedicated device (PC or smartphone) to use the chat function to interact with the AI ​​influencer. For example, they can hold a Q&A session during live streaming or instruct the AI ​​influencer to introduce a product in real time. By sending specific prompts, they can instruct the AI ​​influencer to take specific actions. For example, they can send a prompt such as, "Please explain the appeal of the new product to viewers."

[1125] Generate engagement reports

[1126] Based on the collected activity data, the server automatically generates engagement reports, which provide details such as campaign effectiveness measurements, engagement trends, and follower responses, and are delivered to users via email or dashboard.

[1127] Emotion recognition implementation

[1128] The server uses an emotion recognition engine to collect and analyze the user's emotional data. Using techniques such as text analysis, voice recognition, and facial expression analysis, the server can grasp the user's emotional state. For example, it can analyze the user's emotional state, such as "excited" or "sad," in real time.

[1129] Emotion-Based Interaction

[1130] The server automatically generates the AI ​​influencer's actions based on the analyzed emotional data. This means that when a user gives instructions to the AI ​​influencer through the chat function, the AI ​​influencer can generate optimal responses and actions taking into account the emotional data. For example, if a viewer is excited, the AI ​​influencer will show a "good-looking surprised reaction."

[1131] As a concrete example, a user can create an AI influencer named "Alex" to promote a new game character and customize its appearance, voice, and personality. The user sets Alex's activity date and time to introduce new game content every Wednesday. The server manages this activity, collects and analyzes engagement data, and displays it on a dashboard. During live broadcasts, users can also instruct the influencer to answer questions in real time through the chat function. Furthermore, the server uses an emotion recognition engine to analyze viewer reactions and automatically generate appropriate reactions and follow-ups based on the results.

[1132] In this way, the system of the present invention solves many of the problems in the current influencer market and enables natural, sophisticated interaction and highly efficient engagement.

[1133] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1134] Step 1:

[1135] The user logs in to a dedicated web portal and enters the AI ​​influencer's characteristic data (gender, age, appearance, personality, etc.). The input data is sent to the server and saved as characteristic data. Specifically, the user enters the characteristic data into the input form and presses the "Submit" button.

[1136] Step 2:

[1137] The server launches a generation algorithm based on the received characteristic data. A generation algorithm (e.g., StyleGAN or DALL-E) is used to generate a virtual character. The input characteristic data is fed into a deep learning model to generate realistic image data of the virtual character. The output image data is stored on the server.

[1138] Step 3:

[1139] The server stores the generated AI influencer data in a dedicated database and reflects it on a user-accessible web portal. Specifically, the server writes the generated character data to the database and calls an API to update the information on the web portal.

[1140] Step 4:

[1141] A user accesses the web portal and displays the customization screen, where the user fine-tunes the virtual character's characteristics (e.g., facial contours, hairstyle, tone of voice, etc.). When the user saves the changes, the input data is sent to the server and updated as the virtual character's characteristic data.

[1142] Step 5:

[1143] The server reflects the updated characteristic data and uses the deep learning model again to generate new image data. The new image data is saved in the database, and the character is regenerated with the user's customizations reflected.

[1144] Step 6:

[1145] The user accesses the schedule management screen on the web portal and sets the AI ​​influencer's activity dates and times. For example, they enter a posting schedule to introduce new gaming content every Wednesday. The input data is sent to the server and saved as a schedule.

[1146] Step 7:

[1147] The server receives the schedule information and automatically executes the AI ​​influencer's activities based on the specified date and time. For example, it posts to social media according to the schedule. It also stores data related to the activities (e.g., post content, posting date and time) in a database.

[1148] Step 8:

[1149] The server collects and analyzes the AI ​​influencer's activity data in real time. For example, it collects and stores data such as engagement rates, follower count changes, and comment content after social media posts in a database. The collected data is analyzed using analysis software (e.g., Python libraries such as Pandas and Matplotlib), and the results are displayed on a dashboard.

[1150] Step 9:

[1151] Users can use a dedicated device to use the chat function to interact with the AI ​​influencer. For example, during live streaming, the user can send a prompt such as, "Please explain the appeal of the new product to the viewers." The chat contents are sent to the server and saved.

[1152] Step 10:

[1153] The server analyzes the chat content and generates the AI ​​influencer's actions based on the specified instructions. For example, it analyzes the prompt text and uses a generative AI model to generate appropriate videos and text. The generated content is then instantly sent to the user's device.

[1154] Step 11:

[1155] The server generates an engagement report based on the collected activity data. The report includes campaign effectiveness measurement, engagement trends, follower reactions, etc. The generated report is provided to the user via email or dashboard.

[1156] Step 12:

[1157] The server uses an emotion recognition engine to collect and analyze user emotion data. For example, it performs text analysis, voice recognition, and facial expression analysis of viewers during live streaming. The collected emotion data is stored in a database.

[1158] Step 13:

[1159] The server automatically generates the AI ​​influencer's actions based on the analyzed emotional data. For example, if the viewer is excited, the AI ​​influencer will generate an action that shows a "good-looking surprised reaction." The generated content is instantly sent to the user's device.

[1160] As described above, by performing specific actions, inputs, data processing, and outputs at each step, users can use highly customized virtual characters to achieve natural, advanced interactions and highly efficient engagement.

[1161] (Application example 2)

[1162] 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."

[1163] The problem that this invention aims to solve is to provide a system for content distribution using virtual characters (AI influencers) that allows users to easily create and customize virtual characters and smoothly realize two-way communication with users. In particular, the aim is to enable appropriate reactions based on viewer emotions in real time in a format that can be used on devices such as smartphones. In addition, the aim is to maximize the effect of engagement by efficiently collecting and analyzing character performance data and visually presenting it.

[1164] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for generating a virtual character based on specified characteristics using a generation algorithm; means for customizing the characteristics of the virtual character in response to a user request; means for setting a schedule for the virtual character based on a user request; means for collecting and analyzing activity data of the virtual character in real time; means for presenting performance data of the virtual character to the user; means for executing an interaction between the virtual character and the user; means for generating an engagement report based on the activity of the virtual character and providing it to the user; means for live streaming the virtual character on a smartphone terminal; means for responding in real time to comments from users and viewers via a chat function and analyzing their emotions; and means for automatically generating a reaction of the virtual character based on the emotion data. This allows users to easily and effectively deliver content using virtual characters. Furthermore, by reacting in response to the viewer's emotions, interaction with the viewer can be deepened and engagement can be improved.

[1165] A "generation algorithm" is an algorithm for generating a virtual character based on characteristic data input by a user.

[1166] A "virtual character" is a character that has user-specified characteristics and is generated by a generation algorithm.

[1167] "Means for customization" refers to means by which a user can fine-tune the appearance, personality, voice, etc. of a generated virtual character.

[1168] The "means for setting a schedule" is a means for a user to set the date, time, and frequency of a virtual character's activities.

[1169] "Means for collecting and analyzing activity data in real time" refers to means for collecting and analyzing data on the behavior and performance of a virtual character in real time.

[1170] "Means for presenting performance data" means means for presenting collected and analyzed information about the performance of a virtual character to a user.

[1171] "Means for performing interaction" refers to means by which a user can have two-way communication with a virtual character in real time.

[1172] The "means for generating and providing an engagement report" refers to a means for generating an engagement report based on the activities of a virtual character and providing it to a user.

[1173] "Means for live streaming on a smartphone terminal" refers to means for live streaming of a virtual character using a smartphone terminal.

[1174] The "chat function" is a function that allows users and viewers to exchange messages in real time.

[1175] "Means for analyzing emotions" refers to the means of analyzing viewers' comments and reactions and understanding their emotions.

[1176] The "means for automatically generating a reaction" is a means for automatically generating an appropriate reaction of a virtual character based on the analyzed emotion data.

[1177] The system of the present invention generates a virtual character (hereinafter referred to as an AI influencer) based on specified characteristics and provides a set of means for users to customize, manage, and recognize emotions of the virtual character. Furthermore, the present invention uses a smartphone as a platform, allowing users to easily perform live streaming using the virtual character.

[1178] System configuration

[1179] The system uses the following hardware and software:

[1180] Hardware: Smartphone (iOS, Android)

[1181] Software: Python, TensorFlow, Flutter, RestAPI, Firebase

[1182] Implementation of the generation algorithm

[1183] The server receives requests from users and generates virtual characters using a generation algorithm based on deep learning technology (TensorFlow) to generate realistic virtual characters based on input characteristic data.

[1184] Customization features

[1185] The generated virtual character data is stored in Firebase, and users can access it through a dedicated web portal or app to freely customize the character's appearance, voice, personality, etc. This allows users to create a character that best suits their brand or campaign.

[1186] Schedule management

[1187] Users can set the dates and times for their virtual characters to be active within the app, and the schedule is managed by Firebase, making it easy to schedule posts and live streams at specific times.

[1188] Performance Analysis

[1189] The activity data of the virtual characters is collected in real time and analyzed by the server. The analysis results are provided to users via a Rest API and displayed visually on a dashboard, including engagement rates, follower counts, and comment content.

[1190] Interaction Features

[1191] Users can use the chat feature to have two-way communication with the virtual character, which is particularly useful during live broadcasts, allowing users to direct answers to viewer questions in real time.

[1192] Emotion Recognition and Response Generation

[1193] The server uses emotion analysis technology (TextBlob) to analyze viewers' comments and reactions and understand their emotions. Based on the analysis results, the virtual character's reaction is automatically generated. For example, if a viewer is excited, the character will respond appropriately, such as showing a happy expression.

[1194] Specific examples

[1195] Consider a case where a user creates an AI influencer named "Yuki" to introduce a game.

[1196] The user inputs characteristic data to generate a character named "Yuki," which includes gender: female, age: 20, appearance: black hair, blue eyes, and personality: cheerful.

[1197] Customize your character's appearance, voice, and personality, and set a schedule that introduces new game content every Wednesday.

[1198] Viewer comment: "This game is so much fun!"

[1199] TextBlob analyzes the "positive" emotion, and the AI ​​influencer "Yuki" responds with a "happy!" expression, saying, "I'm glad everyone is having fun!"

[1200] Prompt Sentence Examples

[1201] Users can create a new AI influencer named "Yuki." The user's characteristics are: gender: female, age: 20, physical features: black hair, blue eyes, personality: cheerful. Yuki will introduce new game content and hold live streams every Wednesday. Users can customize Yuki's appearance, voice, and personality, and instruct her to answer questions in real time during the live stream. At the same time, the system analyzes emotions from viewer comments and generates appropriate reactions.

[1202] In this way, by using the system according to the present invention, users can achieve high engagement while also achieving natural interaction with viewers.

[1203] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1204] Step 1: User input of characteristic data

[1205] The user enters the virtual character's name, gender, age, appearance, personality, and other characteristic data into the smartphone app's input form, which is then sent to the server.

[1206] Input: Characteristic data (name, gender, age, appearance, personality, etc.)

[1207] Output: Attribute data sent to the server

[1208] Step 2: Generate a virtual character

[1209] The server then launches a generation algorithm based on the received characteristic data, using deep learning technology (TensorFlow) to generate a realistic virtual character.

[1210] Input: characteristic data

[1211] Output: Generated virtual character

[1212] Step 3: Customizing your virtual character

[1213] Users can customize the generated character through a dedicated web portal or app, fine-tuning appearance, voice, personality, etc., and then finalize the character. The customized character data is stored in Firebase.

[1214] Input: User customization data

[1215] Output: Customized character data

[1216] Step 4: Set a schedule

[1217] Users can set the dates and times for their virtual characters to be active within the app, and posts and live streams are scheduled based on the specified dates and times. Schedule data is also stored in Firebase.

[1218] Input: Schedule information

[1219] Output: Saved schedule data

[1220] Step 5: Go Live

[1221] At the specified date and time, a live broadcast using a virtual character will begin on a smartphone device, and users can monitor the broadcast in real time and manage interactions with viewers.

[1222] Input: Scheduled date and time

[1223] Output:Start live streaming

[1224] Step 6: Interact with your audience

[1225] During the live broadcast, users and viewers can exchange messages using the chat function, and questions can be sent in real time, which the virtual characters will respond to.

[1226] Input: Viewer comments and questions

[1227] Output: Response message from virtual character

[1228] Step 7: Emotion Recognition and Response Generation

[1229] The server uses TextBlob to perform sentiment analysis based on the viewers' comments, and automatically generates appropriate responses for the virtual character based on the sentiment data.

[1230] Input: Viewer comments

[1231] Output: Auto-generated responses of virtual characters

[1232] Step 8: Collect and analyze performance data

[1233] The server collects and analyzes real-time activity data of the virtual characters during live broadcasts, including engagement rates, increases or decreases in the number of followers, and the content of comments.

[1234] Input: Activity data during live streaming

[1235] Output: Parsed performance data

[1236] Step 9: Presenting the results

[1237] The server analyzes performance data and visually presents it to the user through a dashboard, allowing the user to decide on the next action to take.

[1238] Input: Parsed performance data

[1239] Output: Data presented to the user

[1240] Step 10: Generate and deliver engagement reports

[1241] The server generates and provides to the user an engagement report based on the collected and analyzed activity data, which details campaign effectiveness measurements, engagement trends, and follower responses.

[1242] Input: Collected and analyzed activity data

[1243] Output: Generated engagement report

[1244] Through these steps, users can create and customize virtual characters to achieve effective live streaming and high engagement.

[1245] 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.

[1246] 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.

[1247] 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.

[1248] [Fourth embodiment]

[1249] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1250] 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.

[1251] 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).

[1252] 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.

[1253] 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.

[1254] 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).

[1255] 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.

[1256] 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.

[1257] 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.

[1258] 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.

[1259] 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.

[1260] 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.

[1261] 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."

[1262] The system of the present invention uses a generation algorithm to generate a virtual character (hereinafter referred to as an AI influencer) based on specified characteristics, and provides a set of means for users to customize and manage the character. Specific embodiments of the system are described below.

[1263] AI influencer generation

[1264] The server receives a user request and launches an AI influencer generation algorithm. This algorithm uses generative AI technology to create a realistic virtual character based on input characteristic data (e.g., gender, age, appearance, personality, etc.). For example, the algorithm determines the user's desired hairstyle, eye color, mouth shape, etc. based on the specified characteristics, and a deep learning model then creates a more realistic depiction.

[1265] Customization

[1266] The server stores information about the generated AI influencer on a dedicated web portal, which users can access. After logging in, users can fine-tune their virtual character through a customization screen. For example, they can fine-tune facial contours, select a tone of voice, and set personality attributes. This allows users to create a virtual character that best suits their brand or campaign.

[1267] Setting a schedule

[1268] Users will be provided with a schedule management screen where they can set the AI ​​influencer's activity dates and times, for example, they can schedule social media posts on a specific day of the week or a monthly live stream.

[1269] Managing Performance

[1270] The server collects and analyzes the AI ​​influencer's activity data in real time. For example, after posting on social media, it analyzes and accumulates data on engagement rates, increases or decreases in the number of followers, and the content of comments. The server analyzes this data and displays it visually to the user through a dashboard.

[1271] Interaction

[1272] A chat function is provided that allows users to directly interact with the AI ​​influencer using a dedicated device. For example, users can hold Q&A sessions during live streaming or give instructions on product introductions in real time. This enables two-way communication between the virtual character and the audience.

[1273] Generate engagement reports

[1274] The server generates engagement reports based on the collected activity data. These reports provide detailed information on campaign effectiveness, engagement trends, follower reactions, etc. Reports are generated automatically and provided to users via email or dashboard.

[1275] Specific examples

[1276] For example, a cosmetics brand user might create an AI influencer named "Luna" to promote a new product. The user can customize Luna's hairstyle, skin color, and voice tone to match the brand's image. Then, the user schedules her to post on social media every Friday and to promote the new product via live streaming once a month. The server manages this, collecting and analyzing engagement data in real time and displaying it on a dashboard. Through the dashboard, the user can monitor performance and receive engagement reports to support effective marketing activities.

[1277] In this way, the system according to the present invention solves the problems in the current influencer market and realizes stable and highly efficient engagement.

[1278] The processing flow will be explained below.

[1279] Step 1:

[1280] The user logs in to a dedicated web portal, then inputs a request to generate an AI influencer and specifies characteristic data (e.g., gender, age, appearance, personality, etc.).

[1281] Step 2:

[1282] The server receives the user's request and triggers a generation algorithm based on the specified characteristic data, which uses deep learning techniques to generate a base model of the AI ​​influencer.

[1283] Step 3:

[1284] The server then trains the generated base model to make it look and behave realistically, resulting in an AI influencer with realistic portrayals.

[1285] Step 4:

[1286] The server saves the generated AI influencer prototype on a dedicated web portal and notifies the user when the generation is complete. The user can then log in to the portal to view the generated character.

[1287] Step 5:

[1288] Users can access a customization screen to fine-tune appearance (e.g., hairstyle, eye color, mouth shape, etc.), voice tone, personality settings, etc. By adjusting these settings, users can create a virtual character that matches their brand image or individual needs.

[1289] Step 6:

[1290] The server reflects the customization data entered by the user and generates the final AI influencer, which is then stored on a dedicated web portal and presented to the user for review.

[1291] Step 7:

[1292] Users can access the schedule management screen and specify the days and times when the AI ​​influencer will be active. For example, users can set up social media posts on a specific day of the week and live stream once a month.

[1293] Step 8:

[1294] The server automatically saves the user-specified schedule in a database and creates an activity plan for the AI ​​influencer, ensuring that all actions are carried out as planned.

[1295] Step 9:

[1296] The server collects real-time activity data of AI influencers, including engagement rates, follower count increases and decreases, and comment content.

[1297] Step 10:

[1298] The server analyzes the collected data and presents it to the user visually through a dashboard, including engagement status and performance trends.

[1299] Step 11:

[1300] Users can access the chat function using a dedicated device and give instructions to the AI ​​influencer in real time. For example, users can instruct the AI ​​influencer to make comments or introduce products during live broadcasts.

[1301] Step 12:

[1302] The server analyzes the user's instructions and has the AI ​​influencer take appropriate action, enabling automatic and dynamic interactions.

[1303] Step 13:

[1304] The server generates an engagement report based on the collected activity data, which includes campaign effectiveness metrics and recommendations for improvement.

[1305] Step 14:

[1306] The server will provide the generated engagement report to the user via email or dashboard, allowing the user to review it and use it to improve their next marketing strategy.

[1307] Example 1

[1308] 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."

[1309] Conventional virtual character systems have had problems such as difficulty in achieving realistic depictions based on complex user-specified characteristics, customizing the generated characters, managing schedules, collecting and analyzing real-time performance data, interacting with users, and generating and providing detailed engagement reports. As a result, companies and individuals have had difficulty efficiently managing characters in marketing activities and brand promotions, preventing them from maximizing engagement effects.

[1310] 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.

[1311] In this invention, the server includes: means for generating a virtual character based on specified characteristics using a generation algorithm; means for saving the virtual character's characteristic data in a database and displaying it on a dedicated web portal; means for customizing the virtual character's characteristics according to a user's request; means for providing a dedicated schedule management screen and setting the virtual character's schedule according to a user's request; means for collecting and analyzing activity data in real time via the virtual character's social media API; means for visualizing the analyzed data on a dashboard and presenting it to the user; means for providing a chat function for the user to give instructions to the user in real time through interactions with the virtual character; and means for automatically generating an engagement report based on the virtual character's activity data and providing it to the user. This allows users to efficiently perform processes from virtual character generation and customization to activity management, data analysis, and report generation through a consistent system.

[1312] A "generation algorithm" is a computational method for generating a virtual character using specified characteristic data, and primarily utilizes deep learning technology.

[1313] "Characteristic data" is information including the gender, age, appearance, personality, etc. of a virtual character, and is an input value provided by a user.

[1314] "Database" means an information storage system for managing and storing information about generated and customized virtual characters.

[1315] "Web Portal" means an online platform for accessing, managing, and adjusting information about user-generated and customized virtual characters.

[1316] "Customization" refers to the act of a user adjusting the characteristics (e.g., appearance, tone of voice, personality attributes, etc.) of a generated virtual character to fine-tune it to their own needs.

[1317] The "schedule management screen" is an interface that allows the user to set the activity dates and times of the virtual character, and is provided in a calendar or list format.

[1318] "Social Media API" refers to the application programming interface provided by a social media platform and is the technology used to collect activity data (e.g., engagement data) of virtual characters.

[1319] A "dashboard" is an interface for visually displaying analyzed activity data, allowing users to check the performance of their virtual characters in real time.

[1320] "Chat function" refers to a means of communication that allows users to exchange messages with virtual characters in real time, and is primarily used for live streaming and question and answer sessions.

[1321] "Engagement Report" means a report detailing engagement results and trends based on virtual character activity data, provided to Users to assist them in effective campaign management.

[1322] The system of the present invention generates, customizes, schedules, manages and analyzes activity data, and generates and provides interaction and engagement reports for virtual characters (hereinafter referred to as AI influencers) through a dedicated server and user terminals. Detailed embodiments of the system are described below.

[1323] AI influencer generation

[1324] The server receives a request from the user. This request includes characteristic data of the virtual character (e.g., gender, age, appearance, personality, etc.). The server generates a virtual character based on these characteristics using, for example, a generative AI model running on Google Cloud Platform (GCP). The generated character is then detailed based on the characteristic data to achieve a realistic depiction using deep learning technology.

[1325] Customization

[1326] The server stores the generated AI influencer data in a database and displays it on a dedicated web portal. Users can access the customization screen by logging in to this web portal. On the customization screen, users can fine-tune their virtual character (e.g., adjust facial contours, select voice tone, and set personality attributes). The user interface (UI) is designed for intuitive operation, using sliders and drop-down menus.

[1327] Setting a schedule

[1328] Users can set the AI ​​influencer's activity dates and times by accessing a dedicated schedule management screen. Using the management screen, which is provided in calendar and list format, users can enter schedules for social media posts and live broadcasts on specific dates and times. For example, they can set specific schedules such as "posting new product introductions every Friday at 10:00 AM."

[1329] Managing Performance

[1330] The server uses social media APIs (e.g., Facebook Graph API, Twitter API) to collect real-time activity data on AI influencers. The acquired data includes engagement rates, increases or decreases in the number of followers, and comment content. This data is analyzed by the server and evaluated as engagement performance. Tools such as Google Analytics and IBM Watson Analytics are used for the analysis.

[1331] Dashboard visualization

[1332] The server displays the analyzed data on a dashboard, which is designed to allow users to intuitively view the data using visualization tools (e.g., D3.js, Chart.js). Through this dashboard, users can monitor the performance of their virtual characters in real time and make adjustments as needed.

[1333] Interaction

[1334] Using a dedicated device, users can access a chat function to interact with the virtual character, allowing them to answer questions and give instructions in real time during the live broadcast, enabling two-way communication between the virtual character and the viewer.

[1335] Generate engagement reports

[1336] The server automatically generates engagement reports based on the collected activity data. These reports include detailed records of campaign effectiveness, engagement trends, follower responses, etc. The generated reports are sent to users via email or a dashboard.

[1337] Specific examples

[1338] For example, a cosmetics brand user might create an AI influencer named "Luna" to promote a new product. The user can customize Luna's hairstyle, skin color, and voice tone to match the brand's image. Next, the user can schedule Luna to post on social media every Friday and to promote the new product via live streaming once a month. The server manages these schedules, collects and analyzes engagement data in real time, and displays it on a dashboard. Through the dashboard, the user can monitor performance and receive engagement reports to support effective marketing activities.

[1339] Example prompt sentence:

[1340] We want to generate an AI influencer called "Luna" to promote a new product. Luna's characteristics are as follows:

[1341] Gender: Female

[1342] Age: 25

[1343] Hairstyle: Long Straight

[1344] Eye color: Blue

[1345] Personality: Cheerful and energetic

[1346] Based on this, please generate a virtual character that is as realistic as possible, provide a customization screen for Luna, and once the setup is complete, schedule a social media post every Friday and a monthly live stream.

[1347] As described above, the system according to the present invention realizes efficient and sophisticated virtual character management and promotion in the marketing activities of companies and individuals.

[1348] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1349] Step 1:

[1350] The server receives the user request.

[1351] Input: The user enters the virtual character's characteristic data (e.g., gender, age, appearance, personality, etc.) in a form on the web portal.

[1352] Data processing and data calculation: The server receives the input characteristic data and converts it into an appropriate format, for example, converting text data into numerical data, to create an input dataset for the generative AI model.

[1353] Output: The characteristic data is the input dataset, properly formatted.

[1354] Step 2:

[1355] The server launches the generative AI model.

[1356] Input: The formatted input dataset.

[1357] Data processing and data calculation: The server uses a deep learning model built with TensorFlow or PyTorch, for example, to perform calculations to generate a virtual character based on the characteristic data.

[1358] Output: A 3D model or 2D image of the generated virtual character is output.

[1359] Step 3:

[1360] The server stores the generated AI influencer data in a database.

[1361] Input: 3D model and 2D image data of the generated virtual character.

[1362] Data processing and data calculation: The server processes the data to properly index and store it in the database.

[1363] Output: Virtual character information stored in a database.

[1364] Step 4:

[1365] The server displays the generated AI influencer data on a web portal.

[1366] Input: Generated virtual character information stored in a database.

[1367] Data processing and data calculation: The server processes the data to display it in the appropriate format on the web portal.

[1368] Output: The generated character is displayed in the customization screen of the web portal which the user can log in and view.

[1369] Step 5:

[1370] The user customizes the virtual character.

[1371] Input: Fine-tuning information for the virtual character entered by the user through the customization screen (e.g., adjusting facial contours, selecting voice tone, setting personality attributes).

[1372] Data Processing and Data Calculation: The server performs the data processing necessary to update the attributes of the virtual character based on the user's customization information.

[1373] Output: Final virtual character data based on user customized attributes.

[1374] Step 6:

[1375] The user accesses the schedule management screen and sets the activity date and time of the virtual character.

[1376] Input: User-generated scheduling information (e.g., scheduling a social media post or live stream on a specific date and time).

[1377] Data processing and data calculation: The server stores schedule information in a database and performs processing to properly manage it.

[1378] Output: Schedule information stored in the database.

[1379] Step 7:

[1380] The server collects and analyzes the activity data of the virtual characters in real time.

[1381] Input: Activity data obtained from social media APIs (e.g., engagement rate, follower count increase / decrease, comment content, etc.).

[1382] Data processing and data calculation: The server uses data analysis tools to analyze the acquired data and calculate the engagement performance.

[1383] Output: Parsed engagement data.

[1384] Step 8:

[1385] The server visualizes and displays the analytical data on a dashboard.

[1386] Input: Parsed engagement data.

[1387] Data processing and data calculations: The server generates graphs and charts of the data using visualization tools (e.g., D3.js, Chart.js).

[1388] Output: Visualized data that users can view in a dashboard.

[1389] Step 9:

[1390] Users interact with virtual characters in real time through a chat function.

[1391] Input: Real-time messages and instructions from the user.

[1392] Data processing and data calculation: The server processes chat messages and causes the virtual characters to react appropriately.

[1393] Output: The responses and actions of the virtual character.

[1394] Step 10:

[1395] The server generates an engagement report based on the collected activity data and provides it to the user.

[1396] Input: Parsed activity data and engagement information.

[1397] Data processing and data calculation: The server uses a report generation tool (e.g., Tableau, Microsoft Power BI) to automatically generate engagement reports.

[1398] Output: Engagement reports delivered to users (via emails and dashboards).

[1399] Through the above steps, the system according to the present invention enables users to efficiently create and manage virtual characters, as well as collect and analyze engagement data.

[1400] (Application example 1)

[1401] 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."

[1402] In recent years, advertising and marketing activities using virtual characters have become more common, but their operation requires high costs and specialized skills. Furthermore, collecting and analyzing engagement data in real time is difficult, making it time-consuming to measure effectiveness. Furthermore, there are limited ways for users to interact with virtual characters in real time, which creates the challenge of insufficient two-way communication.

[1403] 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.

[1404] In this invention, the server includes means for generating a virtual character based on specified characteristics using a generation algorithm, means for customizing the characteristics of the virtual character in response to a user request, means for setting a schedule for the virtual character in response to a user request, means for collecting and analyzing activity data of the virtual character in real time, means for presenting performance data of the virtual character to the user, means for executing an interaction between the virtual character and the user, means for generating an engagement report based on the activity of the virtual character and providing it to the user, means for providing a smartphone application, means for executing and managing an advertising campaign using the virtual character, and means for collecting engagement data using a social media API. This enables users to efficiently and effectively conduct advertising and marketing activities using virtual characters without requiring specialized skills.

[1405] A "generation algorithm" is an algorithm for automatically generating a virtual character based on specified characteristic data.

[1406] A "virtual character" is a digital character with specific characteristics created by a generative algorithm.

[1407] "Characteristics" refers to characteristic data used to create a virtual character, such as gender, age, appearance, and personality.

[1408] "Customization" refers to changing or adjusting the characteristics of a generated virtual character based on the user's requests.

[1409] "Schedule setting" is a function for setting the date, time and frequency of a virtual character's activities.

[1410] "Activity data" is data about all activities performed by a virtual character.

[1411] "Collecting and analyzing in real time" refers to a process in which activity data of a virtual character is collected at that moment and analyzed immediately.

[1412] "Performance Data" means data related to the results of a virtual character's activities, such as engagement rate and number of followers.

[1413] "Interaction" refers to real-time interaction between a virtual character and a user.

[1414] An "engagement report" is a report created based on data based on the activities of virtual characters.

[1415] A "smartphone application" is a software application that runs on a smartphone.

[1416] An "advertising campaign" is a planned advertising effort to promote a particular product or service.

[1417] A "social media API" is an externally accessible programmatic interface provided by a social media platform.

[1418] The system of the present invention uses a generation algorithm to generate a virtual character (hereinafter referred to as an AI influencer) based on specified characteristics, and provides a set of means for users to customize and manage the character. Specific embodiments of the system are described below.

[1419] 1. AI influencer generation

[1420] The server receives a user request and launches a generative algorithm, which uses generative AI technology to create a realistic virtual character based on the user's specified characteristics (e.g., gender, age, appearance, personality, etc.). For example, the algorithm determines the user's desired hairstyle, eye color, mouth shape, etc., based on the specified characteristics, and a deep learning model (e.g., TensorFlow or PyTorch) then creates a more realistic depiction.

[1421] 2. Customization

[1422] The server stores information about the generated AI influencer in a database (e.g., Amazon RDS) and makes it accessible to users. Users can fine-tune the virtual character through the customization screen of the smartphone application. For example, they can fine-tune the facial contours, select the tone of voice, and set personality attributes. This allows users to create an AI influencer that is best suited to their brand or campaign.

[1423] 3. Set a schedule

[1424] Users can access the smartphone application's schedule management screen and set the AI ​​influencer's activity dates and times. For example, they can set it to post on social media on a specific day of the week and to broadcast live once a month. The server automatically schedules the activities based on this information and carries them out at the specified dates and times.

[1425] 4. Performance Management

[1426] The server collects and analyzes activity data in real time using social media APIs (e.g., Twitter API, Instagram Graph API), collecting information such as engagement rates, increases or decreases in the number of followers, and comment content, and generates analytical results using Python's Pandas and Matplotlib.

[1427] 5. Interaction

[1428] Users can use chat features (e.g., Socket.IO) to interact with AI influencers in real time, for example, by answering questions during live streaming or giving instructions for product introductions in real time.

[1429] 6. Generate engagement reports

[1430] The server generates engagement reports based on the collected activity data, detailing campaign effectiveness, engagement trends, follower reactions, etc. Reports are generated automatically and provided to users via email (e.g., SendGrid) or an in-application dashboard.

[1431] Specific examples

[1432] Suppose a cosmetics brand user wants to generate an AI influencer to promote a new product. They can customize the generated character by entering a prompt such as "Generate a character that is female, 25 years old, with a short bob, blue eyes, and a lively personality." They then schedule a social media post every Friday and a monthly live stream to introduce the new product. The server manages this, collecting and analyzing engagement data in real time and displaying it on a dashboard. Users can monitor performance through the dashboard and receive engagement reports to support effective marketing activities.

[1433] In this way, the system according to the present invention enables users to easily and efficiently carry out advertising and marketing activities that utilize virtual characters.

[1434] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1435] Step 1:

[1436] The server receives a prompt from the user: "Generate a character that is female, 25 years old, with a short bob haircut, blue eyes, and a lively personality." Based on the input characteristic data, it generates a virtual character using a generative AI model (TensorFlow or PyTorch). This generative AI model uses deep learning technology to create a realistic depiction based on the input characteristic data. The resulting virtual character data is stored in a database (Amazon RDS).

[1437] Step 2:

[1438] The user accesses the customization screen via a smartphone application. The server retrieves information about the generated AI influencer from the database and presents it to the user. The user then fine-tunes characteristics such as hairstyle, eye color, facial contours, voice tone, and personality attributes. The adjusted data is sent to the server, which then uses the AI ​​model again to generate an updated virtual character and update the database.

[1439] Step 3:

[1440] The user accesses the schedule management screen and sets the activity schedule for the virtual character, for example, posting to social media every Friday, live streaming once a month, etc. The server saves this schedule data in a database and schedules tasks to be executed at the specified date and time.

[1441] Step 4:

[1442] The server executes the virtual character's posts at the specified date and time using social media APIs (Twitter API, Instagram Graph API), and if live streaming is set, starts streaming using the appropriate streaming API, so that the virtual character's activities are executed according to the schedule set by the user.

[1443] Step 5:

[1444] After a social media post or live stream is completed, the server collects engagement data (likes, comments, number of followers, etc.) in real time. The data obtained through the social media API is used to analyze the data using Python's Pandas and Matplotlib. The analysis results are displayed on a dashboard using visualization tools (D3.js, Chart.js).

[1445] Step 6:

[1446] Users use the chat function (Socket.IO) of a smartphone application to interact with the AI ​​influencer in real time. The server receives chat messages from users, and the AI ​​influencer reacts based on them.

[1447] Step 7:

[1448] The server generates engagement reports based on the collected engagement data, detailing campaign effectiveness, engagement trends, follower reactions, etc. Reports are generated automatically and provided to users via email (SendGrid) or an in-application dashboard.

[1449] Through the above steps, the system according to the present invention enables users to efficiently carry out advertising and marketing activities that utilize virtual characters.

[1450] 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.

[1451] The system of the present invention uses a generation algorithm to generate a virtual character (hereinafter referred to as an AI influencer) based on specified characteristics, and provides a set of means for users to customize, manage, and recognize emotions of the virtual character. Specific embodiments of the system are described below.

[1452] AI influencer generation

[1453] The server receives a user request and launches an AI influencer generation algorithm. This algorithm uses generative AI technology to create a realistic virtual character based on input characteristic data (e.g., gender, age, appearance, personality, etc.). For example, the algorithm determines the user's desired hairstyle, eye color, mouth shape, etc. based on the specified characteristics, and a deep learning model then creates a more realistic depiction.

[1454] Customization

[1455] The server stores information about the generated AI influencer on a dedicated web portal, which users can access. After logging in, users can fine-tune their virtual character through a customization screen. For example, they can fine-tune facial contours, select a tone of voice, and set personality attributes. This allows users to create a virtual character that best suits their brand or campaign.

[1456] Setting a schedule

[1457] Users will be provided with a schedule management screen where they can set the AI ​​influencer's activity dates and times, for example, they can schedule social media posts on a specific day of the week or a monthly live stream.

[1458] Managing Performance

[1459] The server collects and analyzes the AI ​​influencer's activity data in real time. For example, after posting on social media, it analyzes and accumulates data on engagement rates, increases or decreases in the number of followers, and the content of comments. The server analyzes this data and displays it visually to the user through a dashboard.

[1460] Interaction

[1461] A chat function is provided that allows users to directly interact with the AI ​​influencer using a dedicated device. For example, users can hold Q&A sessions during live streaming or give instructions on product introductions in real time. This enables two-way communication between the virtual character and the audience.

[1462] Generate engagement reports

[1463] The server generates engagement reports based on the collected activity data. These reports provide detailed information on campaign effectiveness, engagement trends, follower reactions, etc. Reports are generated automatically and provided to users via email or dashboard.

[1464] Emotion recognition implementation

[1465] The server uses an emotion engine to collect and analyze the user's emotional data. For example, it can grasp the user's emotional state in real time using techniques such as emotion analysis of text, emotion recognition from voice, and facial expression analysis.

[1466] Emotion-Based Interaction

[1467] The server automatically generates actions for the AI ​​influencer based on the analyzed emotional data. When a user gives instructions to the AI ​​influencer through the chat function, the emotional data is taken into consideration to generate optimal responses and actions. This function enables natural interactions that are in tune with the user's emotions.

[1468] Specific examples

[1469] For example, suppose a user creates an AI influencer named "Alex" to promote a new game character. The user customizes Alex's appearance, voice, and personality, and sets a schedule for introducing new game content every Wednesday. The server manages this activity, collects and analyzes engagement data, and displays it on a dashboard. During live broadcasts, users can also instruct the AI ​​influencer to answer questions in real time through the chat function. Furthermore, the server uses an emotion engine to analyze viewer reactions and automatically generate appropriate reactions and follow-ups based on the results. For example, if viewers are excited, the AI ​​influencer may show a "good-looking surprised reaction."

[1470] In this way, the system of the present invention solves the problems in the current influencer market and achieves stable, highly efficient engagement and natural interaction.

[1471] The processing flow will be explained below.

[1472] Step 1:

[1473] Users log in to a dedicated web portal and input a request to generate an AI influencer, specifying characteristic data (e.g., gender, age, appearance, personality, etc.).

[1474] Step 2:

[1475] The server receives the user's request and triggers a generation algorithm based on the specified characteristic data, which uses deep learning techniques to generate a base model of the AI ​​influencer.

[1476] Step 3:

[1477] The server then trains the generated base model to make it look and behave realistically, resulting in an AI influencer with realistic portrayals.

[1478] Step 4:

[1479] The server saves the generated AI influencer prototype on a dedicated web portal and notifies the user when the generation is complete. The user can then log in to the portal to view the generated character.

[1480] Step 5:

[1481] Users can access a customization screen to fine-tune appearance (e.g., hairstyle, eye color, mouth shape, etc.), voice tone, personality settings, etc. By adjusting these settings, users can create a virtual character that matches their brand image or individual needs.

[1482] Step 6:

[1483] The server reflects the customization data entered by the user and generates the final AI influencer, which is then stored on a dedicated web portal and presented to the user for review.

[1484] Step 7:

[1485] Users can access the schedule management screen and specify the days and times when the AI ​​influencer will be active. For example, users can set up social media posts on a specific day of the week and live stream once a month.

[1486] Step 8:

[1487] The server automatically saves the user-specified schedule in a database and creates an activity plan for the AI ​​influencer, ensuring that all actions are carried out as planned.

[1488] Step 9:

[1489] The server collects real-time activity data of AI influencers, including engagement rates, follower count increases and decreases, and comment content.

[1490] Step 10:

[1491] The server analyzes the collected data and presents it to the user visually through a dashboard, including engagement status and performance trends.

[1492] Step 11:

[1493] Users can access the chat function using a dedicated device and give instructions to the AI ​​influencer in real time. For example, users can instruct the AI ​​influencer to make comments or introduce products during live broadcasts.

[1494] Step 12:

[1495] The server analyzes the user's instructions and has the AI ​​influencer take appropriate action, enabling automatic and dynamic interactions.

[1496] Step 13:

[1497] The server uses an emotion engine to collect and analyze the user's emotional data. For example, it can grasp the user's emotional state in real time using techniques such as emotion analysis of text, emotion recognition from voice, and facial expression analysis.

[1498] Step 14:

[1499] The server automatically generates actions for the AI ​​influencer based on the analyzed emotional data. For example, if the user is excited, the AI ​​influencer will respond accordingly and show a facial expression.

[1500] Step 15:

[1501] The server generates an engagement report based on the collected activity data, which includes campaign effectiveness metrics and recommendations for improvement.

[1502] Step 16:

[1503] The server will provide the generated engagement report to the user via email or dashboard, allowing the user to review it and use it to improve their next marketing strategy.

[1504] Example 2

[1505] 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."

[1506] In conventional virtual character systems, the extent to which users can customize their characters is limited, and there is a lack of means to properly collect and analyze character activity data, making it difficult to achieve high levels of engagement and real-time interaction. Another problem is the lack of technology for natural interactions that take user emotions into account.

[1507] 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.

[1508] In this invention, the server includes: means for generating a virtual character based on specified characteristics using a generation algorithm; means for customizing the characteristics of the virtual character in response to a user request; means for setting a schedule for the virtual character in response to a user request; means for collecting and analyzing activity data of the virtual character in real time; means for presenting performance data of the virtual character to the user; means for executing an interaction between the virtual character and the user; means for generating an engagement report based on the activity of the virtual character and providing it to the user; means for having an emotion recognition engine for collecting and analyzing emotion data of the user; and means for automatically generating actions for the virtual character based on the analyzed emotion data. This enables a user to highly customize a character, enabling real-time interaction and natural responses according to the user's emotions, and realizing a virtual character system.

[1509] A "generation algorithm" is an algorithm for generating a virtual character based on specified characteristic data.

[1510] "Characteristic data" refers to data including the gender, age, appearance, personality, etc. of a virtual character.

[1511] A "virtual character" is a character that is generated by a generation algorithm and drawn based on characteristic data.

[1512] "Customization" is the process of adjusting the characteristics of a virtual character according to the user's requirements.

[1513] "Schedule setting" is the act of scheduling the activities of a virtual character on dates and times specified by the user.

[1514] "Activity data" is data relating to activities performed by a virtual character.

[1515] "Real-time collection" refers to the process of collecting activity data of virtual characters in real time.

[1516] "Analysis" is the process of analyzing collected data and converting it into meaningful information.

[1517] "Performance data" is data that indicates the results of a virtual character's activities.

[1518] An "interaction" is an interaction that takes place between a user and a virtual character.

[1519] An "engagement report" is a report that summarizes the reactions and engagement of users and audience members based on the activities of virtual characters.

[1520] An "emotion recognition engine" is an engine for analyzing emotions from a user's text, voice, facial expressions, etc.

[1521] "Emotion data" refers to emotional information collected and analyzed by an emotion recognition engine.

[1522] "Automatic action generation" is the process of automatically generating the actions and responses of a virtual character based on analyzed emotional data.

[1523] The system of the present invention uses a generation algorithm to generate a virtual character (hereinafter referred to as an AI influencer) based on specified characteristics, and provides a set of means for users to customize, manage, and recognize emotions of the virtual character. Specific embodiments of the system are described below.

[1524] AI influencer generation

[1525] Upon receiving a request from a user, the server launches an algorithm to generate an AI influencer. This algorithm uses deep learning technologies such as "StyleGAN" and "DALL-E." The user inputs characteristic data such as gender, age, appearance, and personality, and the server uses that data to generate a realistic virtual character that matches the specified characteristics. For example, by specifying the user's desired hairstyle, eye color, mouth shape, etc., the generated AI influencer will have an appearance that reflects these characteristics.

[1526] Customization

[1527] The server stores the generated AI influencer data on a dedicated web portal, which users can access. Through the web portal, users can fine-tune the AI ​​influencer's facial features, tone of voice, personality attributes, and more, allowing them to create a virtual character that best suits their brand or campaign.

[1528] Setting a schedule

[1529] Users can access the schedule management screen on the web portal and set the AI ​​influencer's activity dates and times. For example, they can set a schedule to post on social media on a specific day of the week or a monthly live broadcast. The server stores this setting information and reflects it in the AI ​​influencer's activities.

[1530] Managing Performance

[1531] The server collects and analyzes the AI ​​influencer's activity data in real time, such as engagement rates after posting on social media, increases or decreases in the number of followers, and the content of comments, and visualizes this data and displays it on a dashboard, allowing users to check the AI ​​influencer's performance at a glance.

[1532] Interaction

[1533] Users can use a dedicated device (PC or smartphone) to use the chat function to interact with the AI ​​influencer. For example, they can hold a Q&A session during live streaming or instruct the AI ​​influencer to introduce a product in real time. By sending specific prompts, they can instruct the AI ​​influencer to take specific actions. For example, they can send a prompt such as, "Please explain the appeal of the new product to viewers."

[1534] Generate engagement reports

[1535] Based on the collected activity data, the server automatically generates engagement reports, which provide details such as campaign effectiveness measurements, engagement trends, and follower responses, and are delivered to users via email or dashboard.

[1536] Emotion recognition implementation

[1537] The server uses an emotion recognition engine to collect and analyze the user's emotional data. Using techniques such as text analysis, voice recognition, and facial expression analysis, the server can grasp the user's emotional state. For example, it can analyze the user's emotional state, such as "excited" or "sad," in real time.

[1538] Emotion-Based Interaction

[1539] The server automatically generates the AI ​​influencer's actions based on the analyzed emotional data. This means that when a user gives instructions to the AI ​​influencer through the chat function, the AI ​​influencer can generate optimal responses and actions taking into account the emotional data. For example, if a viewer is excited, the AI ​​influencer will show a "good-looking surprised reaction."

[1540] As a concrete example, a user can create an AI influencer named "Alex" to promote a new game character and customize its appearance, voice, and personality. The user sets Alex's activity date and time to introduce new game content every Wednesday. The server manages this activity, collects and analyzes engagement data, and displays it on a dashboard. During live broadcasts, users can also instruct the influencer to answer questions in real time through the chat function. Furthermore, the server uses an emotion recognition engine to analyze viewer reactions and automatically generate appropriate reactions and follow-ups based on the results.

[1541] In this way, the system of the present invention solves many of the problems in the current influencer market and enables natural, sophisticated interaction and highly efficient engagement.

[1542] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1543] Step 1:

[1544] The user logs in to a dedicated web portal and enters the AI ​​influencer's characteristic data (gender, age, appearance, personality, etc.). The input data is sent to the server and saved as characteristic data. Specifically, the user enters the characteristic data into the input form and presses the "Submit" button.

[1545] Step 2:

[1546] The server launches a generation algorithm based on the received characteristic data. A generation algorithm (e.g., StyleGAN or DALL-E) is used to generate a virtual character. The input characteristic data is fed into a deep learning model to generate realistic image data of the virtual character. The output image data is stored on the server.

[1547] Step 3:

[1548] The server stores the generated AI influencer data in a dedicated database and reflects it on a user-accessible web portal. Specifically, the server writes the generated character data to the database and calls an API to update the information on the web portal.

[1549] Step 4:

[1550] A user accesses the web portal and displays the customization screen, where the user fine-tunes the virtual character's characteristics (e.g., facial contours, hairstyle, tone of voice, etc.). When the user saves the changes, the input data is sent to the server and updated as the virtual character's characteristic data.

[1551] Step 5:

[1552] The server reflects the updated characteristic data and uses the deep learning model again to generate new image data. The new image data is saved in the database, and the character is regenerated with the user's customizations reflected.

[1553] Step 6:

[1554] The user accesses the schedule management screen on the web portal and sets the AI ​​influencer's activity dates and times. For example, they enter a posting schedule to introduce new gaming content every Wednesday. The input data is sent to the server and saved as a schedule.

[1555] Step 7:

[1556] The server receives the schedule information and automatically executes the AI ​​influencer's activities based on the specified date and time. For example, it posts to social media according to the schedule. It also stores data related to the activities (e.g., post content, posting date and time) in a database.

[1557] Step 8:

[1558] The server collects and analyzes the AI ​​influencer's activity data in real time. For example, it collects and stores data such as engagement rates, follower count changes, and comment content after social media posts in a database. The collected data is analyzed using analysis software (e.g., Python libraries such as Pandas and Matplotlib), and the results are displayed on a dashboard.

[1559] Step 9:

[1560] Users can use a dedicated device to use the chat function to interact with the AI ​​influencer. For example, during live streaming, the user can send a prompt such as, "Please explain the appeal of the new product to the viewers." The chat contents are sent to the server and saved.

[1561] Step 10:

[1562] The server analyzes the chat content and generates the AI ​​influencer's actions based on the specified instructions. For example, it analyzes the prompt text and uses a generative AI model to generate appropriate videos and text. The generated content is then instantly sent to the user's device.

[1563] Step 11:

[1564] The server generates an engagement report based on the collected activity data. The report includes campaign effectiveness measurement, engagement trends, follower reactions, etc. The generated report is provided to the user via email or dashboard.

[1565] Step 12:

[1566] The server uses an emotion recognition engine to collect and analyze user emotion data. For example, it performs text analysis, voice recognition, and facial expression analysis of viewers during live streaming. The collected emotion data is stored in a database.

[1567] Step 13:

[1568] The server automatically generates the AI ​​influencer's actions based on the analyzed emotional data. For example, if the viewer is excited, the AI ​​influencer will generate an action that shows a "good-looking surprised reaction." The generated content is instantly sent to the user's device.

[1569] As described above, by performing specific actions, inputs, data processing, and outputs at each step, users can use highly customized virtual characters to achieve natural, advanced interactions and highly efficient engagement.

[1570] (Application example 2)

[1571] 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."

[1572] The problem that this invention aims to solve is to provide a system for content distribution using virtual characters (AI influencers) that allows users to easily create and customize virtual characters and smoothly realize two-way communication with users. In particular, the aim is to enable appropriate reactions based on viewer emotions in real time in a format that can be used on devices such as smartphones. In addition, the aim is to maximize the effect of engagement by efficiently collecting and analyzing character performance data and visually presenting it.

[1573] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for generating a virtual character based on specified characteristics using a generation algorithm; means for customizing the characteristics of the virtual character in response to a user request; means for setting a schedule for the virtual character based on a user request; means for collecting and analyzing activity data of the virtual character in real time; means for presenting performance data of the virtual character to the user; means for executing an interaction between the virtual character and the user; means for generating an engagement report based on the activity of the virtual character and providing it to the user; means for live streaming the virtual character on a smartphone terminal; means for responding in real time to comments from users and viewers via a chat function and analyzing their emotions; and means for automatically generating a reaction of the virtual character based on the emotion data. This allows users to easily and effectively deliver content using virtual characters. Furthermore, by reacting in response to the viewer's emotions, interaction with the viewer can be deepened and engagement can be improved.

[1574] A "generation algorithm" is an algorithm for generating a virtual character based on characteristic data input by a user.

[1575] A "virtual character" is a character that has user-specified characteristics and is generated by a generation algorithm.

[1576] "Means for customization" refers to means by which a user can fine-tune the appearance, personality, voice, etc. of a generated virtual character.

[1577] The "means for setting a schedule" is a means for a user to set the date, time, and frequency of a virtual character's activities.

[1578] "Means for collecting and analyzing activity data in real time" refers to means for collecting and analyzing data on the behavior and performance of a virtual character in real time.

[1579] "Means for presenting performance data" means means for presenting collected and analyzed information about the performance of a virtual character to a user.

[1580] "Means for performing interaction" refers to means by which a user can have two-way communication with a virtual character in real time.

[1581] The "means for generating and providing an engagement report" refers to a means for generating an engagement report based on the activities of a virtual character and providing it to a user.

[1582] "Means for live streaming on a smartphone terminal" refers to means for live streaming of a virtual character using a smartphone terminal.

[1583] The "chat function" is a function that allows users and viewers to exchange messages in real time.

[1584] "Means for analyzing emotions" refers to the means of analyzing viewers' comments and reactions and understanding their emotions.

[1585] The "means for automatically generating a reaction" is a means for automatically generating an appropriate reaction of a virtual character based on the analyzed emotion data.

[1586] The system of the present invention generates a virtual character (hereinafter referred to as an AI influencer) based on specified characteristics and provides a set of means for users to customize, manage, and recognize emotions of the virtual character. Furthermore, the present invention uses a smartphone as a platform, allowing users to easily perform live streaming using the virtual character.

[1587] System configuration

[1588] The system uses the following hardware and software:

[1589] Hardware: Smartphone (iOS, Android)

[1590] Software: Python, TensorFlow, Flutter, RestAPI, Firebase

[1591] Implementation of the generation algorithm

[1592] The server receives requests from users and generates virtual characters using a generation algorithm based on deep learning technology (TensorFlow) to generate realistic virtual characters based on input characteristic data.

[1593] Customization features

[1594] The generated virtual character data is stored in Firebase, and users can access it through a dedicated web portal or app to freely customize the character's appearance, voice, personality, etc. This allows users to create a character that best suits their brand or campaign.

[1595] Schedule management

[1596] Users can set the dates and times for their virtual characters to be active within the app, and the schedule is managed by Firebase, making it easy to schedule posts and live streams at specific times.

[1597] Performance Analysis

[1598] The activity data of the virtual characters is collected in real time and analyzed by the server. The analysis results are provided to users via a Rest API and displayed visually on a dashboard, including engagement rates, follower counts, and comment content.

[1599] Interaction Features

[1600] Users can use the chat feature to have two-way communication with the virtual character, which is particularly useful during live broadcasts, allowing users to direct answers to viewer questions in real time.

[1601] Emotion Recognition and Response Generation

[1602] The server uses emotion analysis technology (TextBlob) to analyze viewers' comments and reactions and understand their emotions. Based on the analysis results, the virtual character's reaction is automatically generated. For example, if a viewer is excited, the character will respond appropriately, such as showing a happy expression.

[1603] Specific examples

[1604] Consider a case where a user creates an AI influencer named "Yuki" to introduce a game.

[1605] The user inputs characteristic data to generate a character named "Yuki," which includes gender: female, age: 20, appearance: black hair, blue eyes, and personality: cheerful.

[1606] Customize your character's appearance, voice, and personality, and set a schedule that introduces new game content every Wednesday.

[1607] Viewer comment: "This game is so much fun!"

[1608] TextBlob analyzes the "positive" emotion, and the AI ​​influencer "Yuki" responds with a "happy!" expression, saying, "I'm glad everyone is having fun!"

[1609] Prompt Sentence Examples

[1610] Users can create a new AI influencer named "Yuki." The user's characteristics are: gender: female, age: 20, physical features: black hair, blue eyes, personality: cheerful. Yuki will introduce new game content and hold live streams every Wednesday. Users can customize Yuki's appearance, voice, and personality, and instruct her to answer questions in real time during the live stream. At the same time, the system analyzes emotions from viewer comments and generates appropriate reactions.

[1611] In this way, by using the system according to the present invention, users can achieve high engagement while also achieving natural interaction with viewers.

[1612] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1613] Step 1: User input of characteristic data

[1614] The user enters the virtual character's name, gender, age, appearance, personality, and other characteristic data into the smartphone app's input form, which is then sent to the server.

[1615] Input: Characteristic data (name, gender, age, appearance, personality, etc.)

[1616] Output: Attribute data sent to the server

[1617] Step 2: Generate a virtual character

[1618] The server then launches a generation algorithm based on the received characteristic data, using deep learning technology (TensorFlow) to generate a realistic virtual character.

[1619] Input: characteristic data

[1620] Output: Generated virtual character

[1621] Step 3: Customizing your virtual character

[1622] Users can customize the generated character through a dedicated web portal or app, fine-tuning appearance, voice, personality, etc., and then finalize the character. The customized character data is stored in Firebase.

[1623] Input: User customization data

[1624] Output: Customized character data

[1625] Step 4: Set a schedule

[1626] Users can set the dates and times for their virtual characters to be active within the app, and posts and live streams are scheduled based on the specified dates and times. Schedule data is also stored in Firebase.

[1627] Input: Schedule information

[1628] Output: Saved schedule data

[1629] Step 5: Go Live

[1630] At the specified date and time, a live broadcast using a virtual character will begin on a smartphone device, and users can monitor the broadcast in real time and manage interactions with viewers.

[1631] Input: Scheduled date and time

[1632] Output:Start live streaming

[1633] Step 6: Interact with your audience

[1634] During the live broadcast, users and viewers can exchange messages using the chat function, and questions can be sent in real time, which the virtual characters will respond to.

[1635] Input: Viewer comments and questions

[1636] Output: Response message from virtual character

[1637] Step 7: Emotion Recognition and Response Generation

[1638] The server uses TextBlob to perform sentiment analysis based on the viewers' comments, and automatically generates appropriate responses for the virtual character based on the sentiment data.

[1639] Input: Viewer comments

[1640] Output: Auto-generated responses of virtual characters

[1641] Step 8: Collect and analyze performance data

[1642] The server collects and analyzes real-time activity data of the virtual characters during live broadcasts, including engagement rates, increases or decreases in the number of followers, and the content of comments.

[1643] Input: Activity data during live streaming

[1644] Output: Parsed performance data

[1645] Step 9: Presenting the results

[1646] The server analyzes performance data and visually presents it to the user through a dashboard, allowing the user to decide on the next action to take.

[1647] Input: Parsed performance data

[1648] Output: Data presented to the user

[1649] Step 10: Generate and deliver engagement reports

[1650] The server generates and provides to the user an engagement report based on the collected and analyzed activity data, which details campaign effectiveness measurements, engagement trends, and follower responses.

[1651] Input: Collected and analyzed activity data

[1652] Output: Generated engagement report

[1653] Through these steps, users can create and customize virtual characters to achieve effective live streaming and high engagement.

[1654] 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.

[1655] 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.

[1656] 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.

[1657] 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.

[1658] 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.

[1659] 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.

[1660] 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).

[1661] 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.

[1662] 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."

[1663] 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.

[1664] 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).

[1665] 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.

[1666] 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.

[1667] 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.

[1668] 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.

[1669] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1670] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1671] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1672] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1673] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1674] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated ...

Claims

1. means for generating a virtual character based on specified characteristics using a generation algorithm; means for customizing the characteristics of the virtual character according to user requirements; means for setting a schedule for a virtual character based on a user request; A means for collecting and analyzing virtual character activity data in real time; means for presenting performance data of the virtual character to a user; means for effecting interaction between a virtual character and a user; means for generating and providing to a user an engagement report based on the activities of the virtual character; A system including:

2. The system of claim 1 , wherein the generation algorithm includes deep learning techniques to achieve realistic depictions based on specified characteristics.

3. 10. The system of claim 1, further comprising a chat function for allowing a user to give instructions in real time through interaction with the virtual character.

Images