System

Abstract

A system is provided.SOLUTION: A generation AI loading system for improving efficiency of matching between enterprises and influencers, comprising: means for registering enterprise issue information; means for searching for and listing optimal influencers based on enterprise issue information from a database of influencers; means for presenting the listed influencers to the enterprises and selecting the influencers by the enterprises; means for notifying the selected influencers and proposing collaboration; and means for managing selection results and performing follow-up.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] With the spread of social media, the influencer market is expanding, but influencers spend a lot of time and effort building connections with companies and creating popular projects. It's also difficult for companies to estimate the effectiveness of influencers, and individual users have no way to efficiently communicate the projects they want to propose to influencers. A system that solves these problems is needed. [Means for solving the problem]

[0005] The present invention is a system equipped with a generative AI that includes a means for registering a company's project information, a means for searching for and listing optimal influencers from an influencer database based on the company's project information, a means for presenting the listed influencers to a company and allowing the company to select them, a means for notifying the selected influencers and proposing collaborations, and a means for managing and following up on the selection results. The system also includes a means for influencers to input requests and automatically generate video materials and stories using generative AI, and a means for providing the generated results to influencers to support their video production. Similarly, the system includes a means for collecting viewing data on published videos and analyzing data such as the number of views, likes, shares, and comments, and a means for generating specific improvement suggestions based on the analysis results and notifying the influencers. These issues can be adequately resolved by further including a means for displaying a voting page, allowing users to propose or vote for projects they would like influencers to undertake, and a means for tallying the voting results in real time and notifying the influencers.

[0006] "Corporate project information" refers to the details of the promotional project that a company requests from an influencer (purpose, target audience, budget, etc.).

[0007] An "influencer" is an individual or organization that has a large following and influence on social media and other online platforms.

[0008] "Database" refers to the storage within the System where information about Influencers is stored.

[0009] "Generative AI" refers to artificial intelligence that automatically generates video material and stories (scripts) using generative models.

[0010] "Viewing data" refers to data such as the number of views, likes, shares, and comments on published videos.

[0011] "Improvement suggestions" refers to analyzing viewing data and proposing specific advice and changes to improve performance.

[0012] A "voting page" refers to a webpage where users can propose projects they would like influencers to undertake or vote for projects that have already been proposed.

[0013] "User" refers to an individual who uses the platform and has the right to make suggestions and vote for influencers.

[0014] "Matching" refers to the process of searching for the most suitable influencer based on a company's project information and connecting the company with the influencer. [Brief explanation of the drawings]

[0015] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0016] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

[0018] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0019] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0020] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0021] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0023] [First embodiment]

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

[0025] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0028] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0032] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0035] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0036] The system of the present invention includes functions for registering company project information, searching a database of influencers, matching, automatically generating content, analyzing viewing data, generating improvement suggestions, and allowing users to vote.

[0037] System configuration

[0038] 1. Registering business information

[0039] Users (companies) access the system using a terminal and register project information (purpose, target audience, budget, etc.) This data is sent to the server and stored in the database.

[0040] 2. Influencer database search

[0041] The server analyzes the project information registered by the company, searches for and lists the most suitable influencers from the database, and sends this list to the company's terminal, where the company can check it.

[0042] 3. Matching

[0043] The user (company) selects the most suitable influencer from the presented influencer list and sends the selection results to the server. The selection results are saved in a database and notified to the influencer.

[0044] 4. Automated content generation

[0045] Users (influencers) input their requests into the system from their devices, and the system uses generation AI to automatically generate video materials and a story (script). The generated results are provided to the influencer via a server. The influencer then creates a video based on the provided materials and script.

[0046] 5. Analysis of viewing data and improvement proposals

[0047] The server collects viewing data for published videos and analyzes information such as the number of views, likes, shares, and comments. Based on the analysis results, it generates specific improvement suggestions to increase the number of followers, views, likes, and shares, and notifies the influencer.

[0048] 6. User voting function

[0049] The device displays a voting page to the user, allowing the user to propose projects they would like the influencer to undertake or vote on projects that have already been proposed. The server tally the voting results in real time and notify the influencer. The influencer can then consider new projects based on the voting results.

[0050] Specific examples

[0051] 1. Matching with corporate projects

[0052] Example: When a company requests promotion for a new product, the system will list suitable influencers based on the company's objectives and target audience, allowing the company to select the most suitable influencer in a short amount of time.

[0053] 2. Automated content generation

[0054] Example: When an influencer is stuck for ideas for a new video, a script on the theme of "Recommended Summer Beach Goods" is automatically generated, allowing the influencer to quickly get started on creating a video.

[0055] 3. Analysis of viewing data and improvement proposals

[0056] Example: If an influencer's recent videos aren't getting as many views as expected, the system will analyze the viewing data and provide suggestions for improvements, such as using hashtags, changing the thumbnail, or adjusting the posting time, so the influencer can improve performance in their next video production.

[0057] 4. User voting function

[0058] Example: An influencer holds a poll in the system to ask users to suggest ideas for their next video. If users vote for the idea "funny pet videos" most often, the influencer can use the results to create their next video.

[0059] The system of the present invention can facilitate communication between companies, influencers, and general users, improving both the quality and quantity of content.

[0060] The processing flow will be explained below.

[0061] Processing steps from registering company information to matching

[0062] Step 1:

[0063] The user (company) registers project information using a terminal. The company enters project details (purpose, target audience, budget, etc.) and sends them to the server.

[0064] Step 2:

[0065] The server analyzes the received job information and stores the data in the appropriate fields, allowing for smoother search processing later.

[0066] Step 3:

[0067] The server searches a database of influencers and lists the most suitable influencers based on the project information, taking into consideration factors such as past performance and areas of expertise.

[0068] Step 4:

[0069] The server generates a list of optimal influencers and sends it to the user's (company's) device.

[0070] Step 5:

[0071] The user (company) checks the influencer list displayed on the device and selects the most suitable influencer. The selection results are sent to the server.

[0072] Step 6:

[0073] The server stores the selection results in a database and notifies the selected influencers.

[0074] Process steps for auto-generating content by influencers

[0075] Step 1:

[0076] A user (influencer) accesses the system using a terminal and inputs a request for new content, detailing the theme and required elements, and sends it to the server.

[0077] Step 2:

[0078] The server runs a generation AI based on the request received and generates optimal video material and story (script).

[0079] Step 3:

[0080] The generative AI retrieves the necessary information from the database and creates materials and scripts that fit the specified theme.

[0081] Step 4:

[0082] The server sends the generated materials and scripts to the influencer's device.

[0083] Step 5:

[0084] The user (influencer) checks the materials and script generated on the device and begins video production.

[0085] Analysis of viewing data and processing steps for improvement suggestions

[0086] Step 1:

[0087] The server periodically collects viewing data for published videos, including the number of views, likes, shares, and comments.

[0088] Step 2:

[0089] The server analyzes the data collected to extract trends and patterns in viewing data and evaluate the impact of specific factors on performance.

[0090] Step 3:

[0091] The server generates specific improvement suggestions based on the analysis, such as optimizing hashtags, improving thumbnails, and adjusting posting times.

[0092] Step 4:

[0093] The server notifies the influencer's terminal of the generated improvement proposal.

[0094] Step 5:

[0095] The user (influencer) checks the suggestions on their device and reflects them in their next video production.

[0096] Steps for processing user votes

[0097] Step 1:

[0098] The device will then display a voting page where users can suggest projects they would like influencers to undertake or vote for projects that have already been proposed.

[0099] Step 2:

[0100] Users use their devices to propose or vote on projects, and the proposal and vote data are sent to the server.

[0101] Step 3:

[0102] The server compiles proposal and vote data in real time, and popular proposals are ranked.

[0103] Step 4:

[0104] The server sends the results of the survey to the influencer's device, who then uses the results to consider new projects.

[0105] Through the above processing steps, the system of the present invention can facilitate communication between companies, influencers, and users, and realize effective content creation and improved performance.

[0106] Example 1

[0107] 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."

[0108] For a company to effectively use influencers for promotion, it is necessary to select suitable influencers, create content, analyze and improve viewing data, and collect feedback from general users. However, managing these processes individually takes a great deal of time and effort, making it difficult to do so efficiently.

[0109] 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.

[0110] In this invention, the server includes: means for registering a company's project information; means for searching for and listing optimal influencers from an influencer database based on the company's project information; means for presenting the listed influencers to a company and allowing the company to select one; means for notifying the selected influencers and proposing collaboration; means for managing and following up on the selection results; means for having the selected influencers input requests for automatic content generation using a generative AI model and providing the generation results; means for collecting and analyzing viewing data of published videos and notifying improvement suggestions; and means for tallying votes from general users in real time and notifying them of material for consideration in new projects. This makes it possible to efficiently match companies and influencers and consistently manage each process of content creation and improvement.

[0111] "Means for registering company project information" is a function that allows companies to enter information such as promotion and marketing objectives, target audience, budget, etc. into the system and register it.

[0112] "A means of searching and listing the most suitable influencers from the influencer database based on a company's project information" is a function that searches for influencer information in the database based on the registered company's project information and lists the most suitable influencers.

[0113] "A means of presenting the listed influencers to companies and allowing companies to select" is a function that displays a list of searched influencers to companies, allowing them to select the most suitable influencer.

[0114] "Means of notifying selected influencers and proposing collaboration" is a function for notifying influencers selected by a company and proposing collaboration.

[0115] "Means for managing and following up on selection results" refers to a function that stores the selection results of companies and influencers in a database and allows for continuous follow-up.

[0116] "Means of having selected influencers input requests for automatic content generation using a generative AI model and providing the generated results" refers to a function that enables influencers to input requests for content generation into the system and provide them with content (video material and stories) that is automatically generated using a generative AI model.

[0117] "Means for collecting and analyzing viewing data of published videos and notifying improvement suggestions" refers to a function for collecting viewing data of published videos, analyzing this data, generating specific suggestions for performance improvement, and notifying influencers.

[0118] "A means of aggregating votes from general users in real time and notifying them of the results as material for consideration in new projects" is a function that aggregates votes from general users in real time and notifies influencers of the results as material for consideration in new projects.

[0119] The system of the present invention is a comprehensive generative AI-equipped system that includes functions for registering corporate project information, searching a database of influencers, matching, automatically generating content, analyzing viewing data, generating improvement suggestions, and voting by general users. This system efficiently performs each function using a variety of hardware and software.

[0120] First, the user (company) enters the company's project information into the device's web browser (e.g., Google Chrome). The project information includes the promotion purpose, target audience, budget, etc., and this information is sent to the server. The server then stores the received information in a database management system (e.g., MySQL).

[0121] Next, the server analyzes the project information registered by the company. This analysis uses a natural language processing algorithm (e.g., Python's NLTK library). Based on the analysis results, the server searches the database for the most suitable influencers and creates a list. This list is sent to the user's (company's) device, and the company can view the list through a web browser.

[0122] The company selects the most suitable influencer from the list of influencers provided and sends the selection results from the terminal to the server. The server stores the received selection results in a database and simultaneously sends an email notification (e.g., SendGrid) to the selected influencer to notify them of the details of the project.

[0123] Influencers use their devices to input requests into the system, which automatically generates content. The request is sent to a generative AI model (e.g., OpenAI GPT-3), which automatically generates video material and a story (script). A request for automatic generation is made by entering a prompt phrase, such as a theme like "Recommended summer beach items." The generated results are provided to the influencer via the server, allowing for quick video production.

[0124] Viewing data for published videos is collected by the server, and information such as the number of views, likes, shares, and comments is analyzed using a data analysis tool (e.g., Google Analytics). Based on the analysis results, the server generates specific improvement suggestions and notifies the influencer. The improvement suggestions include optimizing hashtags and adjusting the timing of posting.

[0125] The system also includes a voting function for general users. Users can access the voting page using their devices to propose new projects to influencers or vote for existing projects. Voting results are sent to the server in real time and tallied. The server then notifies the influencers of the voting results, which they use as information for considering new projects.

[0126] For example, if a company requests a promotion for a new product, the system will list suitable influencers based on the company's objectives and target audience. Also, if an influencer is stuck for ideas, a script on the theme of "introducing recommended summer beach items" can be automatically generated using a generative AI model. In this way, the influencer can quickly begin creating a video.

[0127] This system will facilitate collaboration between companies, influencers, and individuals, and will improve both the quality and quantity of content at the same time.

[0128] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0129] Step 1:

[0130] The user (company) accesses the system using a web browser on their device and enters project information (promotion purpose, target audience, budget, etc.). The entered information is sent from the device to the server in JSON format.

[0131] Step 2:

[0132] The server saves the received job information in a database management system (e.g. MySQL). At this time, it checks the consistency of the input information and converts it into the required data format. Once saving is complete, it returns a response to the terminal indicating that saving was successful.

[0133] Step 3:

[0134] The server retrieves project information from the database and analyzes it using a natural language processing algorithm (e.g., Python's NLTK library). Based on the analysis results, it creates search criteria for influencers. Using these analysis results, it searches the database for the most suitable influencers.

[0135] Step 4:

[0136] The server sends the list of influencers obtained as search results in JSON format to the terminal. The terminal displays the list of influencers on a web browser, allowing the user (company) to review and select from the list.

[0137] Step 5:

[0138] The user (company) selects the most suitable influencer from the influencer list displayed on the web browser. Once the selection is complete, the device sends the selection results in JSON format to the server.

[0139] Step 6:

[0140] The server stores the received selection results in a database. At this time, it validates the selection information and, once the selection is confirmed, notifies the selected influencer. Notifications are sent via an email notification service (e.g., SendGrid).

[0141] Step 7:

[0142] The user (influencer) uses a device to input a request to the system. The request (e.g., the theme or purpose of the video) is sent to the generative AI model. This request is sent from the device via the API as a prompt.

[0143] Step 8:

[0144] A generative AI model (e.g., OpenAI GPT-3) automatically generates video material and a story (script) based on the prompt. The generated information is sent to the server in JSON format. The server stores the generated results in a database and provides them to influencers.

[0145] Step 9:

[0146] The influencer will create a video using the generated results provided and upload it to a platform for publishing.

[0147] Step 10:

[0148] The server collects viewing data such as the number of views, likes, shares, and comments for published videos using the video platform's API (e.g., YouTube Data API), and stores this data in a database in real time.

[0149] Step 11:

[0150] The server analyzes the collected viewing data using a data analysis tool (e.g., the Pandas library), identifies viewing patterns and trends, and generates specific improvement proposals based on the analysis results. The generated improvement proposals are then notified to influencers.

[0151] Step 12:

[0152] The terminal displays a voting page to the user, who then uses the terminal to cast their vote, with each vote immediately transmitted to the server.

[0153] Step 13:

[0154] The server receives the voting results, stores them in a database in real time, and tallies them. The tallied results are then sent to influencers, who use them as information for considering new projects.

[0155] Step 14:

[0156] Influencers will consider new ideas based on the collected voting results, and then create new content based on those ideas using a generative AI model.

[0157] (Application example 1)

[0158] 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."

[0159] In modern content creation, matching companies with influencers is extremely important, but the process of selecting the right influencer is extremely time-consuming and inefficient. Furthermore, there is a lack of ways to quickly provide high-quality content when influencers run out of content ideas. Furthermore, there is a lack of ways to analyze the performance of published content and make specific suggestions for improvement. Finally, there are limited ways to utilize user feedback and incorporate new content ideas. A new system is needed to solve these issues.

[0160] 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.

[0161] In this invention, the server includes: means for registering a company's project information; means for searching for and listing optimal influencers from an influencer database based on the company's project information; means for presenting the listed influencers to a company and allowing the company to select them; means for notifying the selected influencers and proposing collaboration; means for managing and following up on the selection results; means for the influencer to input a request and automatically generating video materials and stories using a generative AI model; means for providing the generated results to the influencer to support video production; means for collecting viewing data on published videos and analyzing data such as the number of views, likes, shares, and comments; means for generating specific improvement suggestions based on the analysis results and notifying the influencer; means for users to propose projects they would like influencers to do or vote for projects that have already been proposed; and means for tallying the voting results in real time and notifying the influencer. This makes it possible to efficiently match companies and influencers and improve the quality and quantity of content provided.

[0162] "Company project information" refers to promotion and marketing requests that companies register in the system based on specific objectives, target audiences, budgets, etc.

[0163] An "influencer database" is a data storage device that stores information such as each influencer's profile information, past activities, and number of followers.

[0164] The "optimal influencer" is the influencer who best fits the company's project information and can maximize the expected results.

[0165] A "generative AI model" is a type of artificial intelligence that automatically generates video material and stories based on pre-trained data, and examples include GPT-4.

[0166] "Footage and Stories" refers to creative materials such as footage and scripts to be used in videos produced by influencers.

[0167] "Viewing data" refers to data that shows how users responded to published videos, including the number of views, likes, shares, and comments.

[0168] "Improvement suggestions" are specific advice and methods for improving the quality and performance of content based on the results of analysis of viewing data, etc.

[0169] "User voting" is the act of ordinary users expressing their opinions on projects they would like influencers to undertake.

[0170] "Voting results" refers to data regarding the next content plan compiled based on votes from users.

[0171] The following system is an example of an embodiment of the present invention. Each function of the system is realized by utilizing a server, a user terminal, and a generative AI model.

[0172] The first step is for a company to register its project information. The company accesses the system using a terminal and enters details of its promotion and marketing, including its objectives, target audience, budget, etc. The server receives the entered information and stores it in a database.

[0173] Next, the server analyzes the company's project information and searches for and lists the most suitable influencers from its influencer database. The list of influencers is sent from the server to the company's terminal, and the company selects influencers based on that list.

[0174] After the selection is complete, the server saves the selection results in a database and notifies the selected influencers, who can then start collaborating with the company.

[0175] If an influencer gets stuck creating content, a generative AI model is used to automatically generate video material and stories. The influencer inputs a request from their device, and the generative AI model creates a prompt based on that request, which is then used to generate content. This generated content is then provided to the influencer again via the server. For example, the prompt for a request on the theme of "introducing beach goods" would look like this:

[0176] Prompt statement:

[0177] Generate a script for the following video: "Topic: Introducing Beach Goods, Video Length: 2 minutes"

[0178] Viewing data for published videos is also an important factor. The server collects data such as the number of views, likes, shares, and comments in real time and analyzes the visual information. Based on the results of this analysis, specific suggestions for improving the quality of content are generated and notified to influencers. For example, these suggestions include "reviewing hashtags," "changing posting times," and "improving thumbnails."

[0179] The system also includes a voting function for users. The device displays a voting page to users, allowing them to propose projects they would like influencers to do or vote for projects that have already been proposed. The server tally the voting results in real time and notify the influencer. Based on these voting results, the influencer can decide on their next video project.

[0180] All of these processes are efficiently managed by a system that facilitates communication between companies, influencers, and users. The system uses hardware such as smartphones and servers, as well as software such as generative AI models (e.g., GPT-4) and cloud services (e.g., AWS Lambda and Google Cloud Functions).

[0181] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0182] Step 1:

[0183] Companies access the system through a terminal and input project information (purpose, target audience, budget, etc.). The terminal sends this information to the server, which stores the received information in a database. It receives project information as input and generates data that is stored in the database as output.

[0184] Step 2:

[0185] The server analyzes the project information registered by the company and searches for the most suitable influencers from the influencer database. It performs data analysis on the information of each influencer in the database and creates a list of influencers most suitable for the project. It receives the company's project information as input and generates a list of influencers as output.

[0186] Step 3:

[0187] The server sends the generated list of influencers to the company's terminal, where the company checks the list and selects the most suitable influencer. The server receives the list of influencers as input and generates the selected influencers as output.

[0188] Step 4:

[0189] The company sends the selection results to the server, which stores the results in a database. The server then notifies the selected influencers. It receives the selection results as input and generates notifications as output.

[0190] Step 5:

[0191] Influencers input content creation requests into the system via their devices. The server receives these requests and sends prompts to the generative AI model. The generative AI model automatically generates video material and stories based on the requests. For example, if the theme is "Introducing beach goods," the following prompts are sent to the generative AI model:

[0192] Prompt statement:

[0193] Generate a script for the following video: "Topic: Introducing Beach Goods, Video Length: 2 minutes"

[0194] It takes influencer requests as input and produces generated content as output.

[0195] Step 6:

[0196] The server sends the generated video material and story to the influencer's device, who then uses this information to create the video. The server receives the generated content as input and generates data to be sent to the influencer's device as output.

[0197] Step 7:

[0198] The server collects viewing data (number of views, likes, shares, comments, etc.) of published videos in real time. It analyzes the viewing data and generates specific improvement proposals to increase the number of followers, views, likes, and shares based on the analysis results, and notifies the influencer. It receives viewing data as input and generates improvement proposals as output.

[0199] Step 8:

[0200] The device displays a voting page to the user, allowing the user to propose projects they would like influencers to implement or vote for projects that have already been proposed. The server tally the voting results in real time and notify the influencers. It receives the user's voting information as input and generates the voting results as output.

[0201] Step 9:

[0202] The influencer will decide on the next video project based on the voting results, completing the process to enhance the interaction between users and influencers and provide content that meets users' requests. It takes the voting results as input and generates the next video project as output.

[0203] 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.

[0204] The system of the present invention includes functions for registering corporate project information, searching a database of influencers, matching, automatically generating content, analyzing viewing data, generating improvement suggestions, a voting function for users, and recognizing user emotions using an emotion engine.

[0205] System configuration

[0206] 1. Registration of business project information and emotion recognition

[0207] Users (companies) access the system using their terminals and register project information (purpose, target audience, budget, etc.). This data is sent to the server and stored in a database. The emotion engine analyzes the user's emotions regarding the company's project information and can adjust the presentation method.

[0208] 2. Influencer database search and matching

[0209] The server analyzes the project information registered by the company and searches for and lists the most suitable influencers from the database. This list is sent from the server to the company's terminal, where the company checks the list. Based on the company's decision, the influencer is notified.

[0210] 3. Automatic content generation and sentiment analysis

[0211] Users (influencers) input their requests into the system from their devices, and the generation AI automatically generates video material and a story (script). The generated results are provided to the influencer via the server. Furthermore, the emotion engine analyzes the user's feelings toward the generated content and provides feedback to the generation AI, which can be used to generate content next time.

[0212] 4. Analysis of viewing data, improvement proposals, and sentiment analysis

[0213] The server collects viewing data for published videos and analyzes information such as the number of views, likes, shares, and comments. This information is used to generate specific improvement proposals to increase the number of followers, views, likes, and shares, and these proposals are notified to influencers. Additionally, an emotion engine can be used to analyze user emotions and reflect them in improvement proposals.

[0214] 5. User voting and sentiment analysis

[0215] The device displays a voting page to users, allowing them to propose projects they would like influencers to implement or vote for projects that have already been proposed. The server tallys the voting results in real time and ranks the most popular projects. Furthermore, an emotion engine analyzes users' voting behavior and emotions toward the proposals, and notifies the influencers of this information, helping them consider new projects.

[0216] Specific examples

[0217] 1. Matching with corporate projects

[0218] Example: When a company requests a promotion for a new product, the system will generate a list of suitable influencers based on the company's objectives and target audience. As a result, the company can select the most suitable influencer in a short time. The emotion engine adjusts the offer presentation based on the user's reaction and interest level.

[0219] 2. Automatic content generation and sentiment analysis

[0220] Example: When an influencer is stuck for ideas for a new video, a script on the theme of "Introducing recommended summer beach items" is automatically generated. This allows the influencer to quickly start creating a video. Furthermore, an emotion engine analyzes user reactions and reflects them in the next content generation.

[0221] 3. Analysis of viewing data, improvement proposals, and sentiment analysis

[0222] Example: If an influencer's recent videos aren't getting as many views as expected, the system will analyze the viewing data and provide suggestions for improvement, such as using hashtags, changing thumbnails, adjusting posting times, etc. Furthermore, an emotion engine will analyze viewers' emotions and provide more specific suggestions for improvement.

[0223] 4. User voting and sentiment analysis

[0224] Example: An influencer holds a poll in the system to ask users to suggest ideas for the next video. If users vote a lot for the idea "funny pet videos," the influencer can create the next video based on the results. The emotion engine can analyze users' feelings about the votes and use the results to create new ideas.

[0225] The system of this invention facilitates communication between companies, influencers, and users, enabling effective content creation and performance improvement. The introduction of an emotion engine enables even more effective matching and improvement suggestions.

[0226] The processing flow will be explained below.

[0227] Registration of business case information and emotion recognition processing steps

[0228] Step 1:

[0229] The user (company) accesses the system using a terminal, enters project information (purpose, target audience, budget, etc.), and sends it to the server.

[0230] Step 2:

[0231] The server receives the submitted job information and stores it in a database.

[0232] Step 3:

[0233] The server uses an emotion engine to analyze the user's emotions regarding the transmitted case information.

[0234] Step 4:

[0235] The server adjusts the way the case information is presented based on the analysis results.

[0236] Influencer database search and matching process steps

[0237] Step 1:

[0238] The server analyzes the project information registered by the company and searches the database for the most suitable influencer.

[0239] Step 2:

[0240] The server lists influencers based on the search results.

[0241] Step 3:

[0242] The server generates a list of optimal influencers and sends it to the user's (company's) device.

[0243] Step 4:

[0244] The user (company) checks the influencer list presented on the device, selects the most suitable influencer, and sends the selection results to the server.

[0245] Step 5:

[0246] The server stores the selection results in a database and notifies the selected influencers.

[0247] Processing steps for automatic content generation and sentiment analysis

[0248] Step 1:

[0249] A user (influencer) accesses the system using a terminal, inputs a request for new content, and sends it to the server.

[0250] Step 2:

[0251] The server runs a generation AI based on the request received to generate optimal video material and story (script).

[0252] Step 3:

[0253] The server sends the generated materials and scripts to the influencer's device.

[0254] Step 4:

[0255] The user (influencer) checks the materials and script generated on the device and begins video production.

[0256] Step 5:

[0257] The server uses an emotion engine to analyze other users' emotions toward the content generated by the user (influencer) and feeds the results back to the generation AI.

[0258] Analysis of viewing data, improvement suggestions, and processing steps for sentiment analysis

[0259] Step 1:

[0260] The server periodically collects viewing data for published videos, including the number of views, likes, shares, and comments.

[0261] Step 2:

[0262] The server analyzes the collected data and extracts trends and patterns in the viewing data.

[0263] Step 3:

[0264] The server uses an emotion engine to analyze the user's emotions regarding the viewing data.

[0265] Step 4:

[0266] The server generates specific improvement suggestions based on the analysis, such as optimizing hashtags, improving thumbnails, and adjusting posting times.

[0267] Step 5:

[0268] The server notifies the influencer's terminal of the generated improvement proposal.

[0269] Step 6:

[0270] The user (influencer) checks the suggestions on their device and reflects them in their next video production.

[0271] User voting and sentiment analysis processing steps

[0272] Step 1:

[0273] The device will then display a voting page to the user, where the user can suggest projects they would like the influencer to undertake or vote for projects that have already been proposed.

[0274] Step 2:

[0275] Users use their devices to propose or vote on projects, and the proposal and vote data are sent to the server.

[0276] Step 3:

[0277] The server compiles proposal and vote data in real time, and popular proposals are ranked.

[0278] Step 4:

[0279] The server uses an emotion engine to analyze users' voting behavior and emotions toward the proposals.

[0280] Step 5:

[0281] The server notifies the influencer of the results of the aggregation and sentiment analysis, and the influencer uses these results to consider new projects.

[0282] Example 2

[0283] 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."

[0284] Traditional matching systems for companies and influencers face many challenges in terms of effective content creation and analysis of viewing data. Specifically, they face issues such as companies being unable to properly deliver project information to their target audiences, influencers being unable to quickly obtain appropriate video ideas, being unable to fully utilize viewing data from published content, and being unable to analyze user sentiment and reflect it in future content. They also lack functionality for collecting project ideas through user votes and analyzing and reflecting those sentiments. This makes collaboration between companies and influencers inefficient, making it difficult to improve overall performance.

[0285] 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. In this invention, the server includes: means for registering project information of a company; means for analyzing user emotions regarding the company's project information using an emotion engine and adjusting the presentation method; means for searching for and listing optimal influencers from an influencer database based on the company's project information; means for presenting the listed influencers to a company and allowing the company to select; means for notifying the selected influencer and proposing collaboration; means for managing and following up on the selection results; means for the influencer to input a request and automatically generating video materials and stories using a generation AI; means for analyzing user emotions regarding content generated using the emotion engine and providing feedback to the generation AI; means for providing the generated results to the influencer and supporting video production; means for collecting viewing data of published videos and analyzing data such as the number of views, likes, shares, and comments; means for analyzing viewer emotions using the emotion engine, generating specific improvement suggestions based on the analysis results, and notifying the influencer; and means for displaying a voting page to the user and analyzing the user's voting behavior and emotions regarding the suggestions. This will enable effective matching between companies and influencers and improve the performance of content creation.

[0286] "Company project information" refers to information about projects and transactions that a company registers in the system, including objectives, target audiences, budgets, etc.

[0287] An "emotion engine" is an engine for analyzing user emotions, extracting emotions from text and behavior and making an evaluation.

[0288] An "influencer database" is a database that accumulates information about influencers, including the number of followers, areas of expertise, and past activity history.

[0289] The "system" is an integrated platform for streamlining communication and content creation between companies, influencers, and general users.

[0290] "Generative AI" is artificial intelligence that automatically generates content based on given prompts, generating video footage and stories.

[0291] "Viewing data" refers to user reaction data to published content, including the number of views, likes, shares, comments, and the like.

[0292] "Improvement proposals" are specific methods or ideas proposed to improve content performance based on viewing data and the results of sentiment analysis.

[0293] A "voting page" is a web page where users can vote for influencers' proposals and the results are tallied in real time.

[0294] The system according to the present invention is an integrated platform for streamlining communication and content creation between companies, influencers, and general users. Specific embodiments of this system will be described below.

[0295] Registration of business project information and emotion recognition

[0296] Users (companies) access the system using a terminal and input their company's project information (purpose, target audience, budget, etc.). The terminal sends this information to the server. The server stores the received project information in a database and activates the emotion engine. The emotion engine analyzes the user's emotions based on the input information and adjusts the way the information is presented based on the results.

[0297] Examples:

[0298] For example, when a company inputs information about a project such as "promotion of a new product aimed at young people," the emotion engine evaluates emotions such as "excitement" and "attractiveness," and the server uses this information to adjust the project presentation to optimize it for the target audience.

[0299] Influencer database search and matching

[0300] The server analyzes the project information registered by the company and extracts elements such as the purpose, target audience, and budget. Next, it uses this information to search the database for the most suitable influencers and creates a list. This list is sent to the company's device. The company then checks the list on the device and selects the most suitable influencer. The server then sends a notification to the selected influencer.

[0301] Examples:

[0302] When a company requests a "fashion item promotion," the server creates a list of influencers with large numbers of followers in the fashion field and sends it to the company. The company then selects influencers based on the list and sends them notifications.

[0303] Automatic content generation and sentiment analysis

[0304] The user (influencer) inputs a theme or idea from their device. The device sends the request to the server, which passes it on to the generation AI. The generation AI automatically generates video material and a script based on the request and provides the results to the influencer. The emotion engine also analyzes the user's emotions regarding the generated content and sends that feedback to the generation AI to help with the next generation.

[0305] Examples:

[0306] When an influencer enters a theme, such as "Christmas party decoration ideas," the generative AI automatically generates a script and video material based on the theme. This generated content is sent to the influencer's device. The emotion engine also analyzes user reactions, and the results are reflected in the next content generation.

[0307] Analysis of viewing data, improvement suggestions, and sentiment analysis

[0308] The server collects viewing data (number of views, likes, shares, comments, etc.) for published videos. The server then analyzes this data and generates specific improvement suggestions to improve performance. An emotion engine analyzes viewer emotions and reflects the results in improvement suggestions. These suggestions are then sent to the influencer's device.

[0309] Examples:

[0310] If an influencer's recent videos aren't getting as many views as expected, the server analyzes the viewing data and provides specific suggestions for improvement, such as "increase hashtag usage," "change thumbnails," "adjust posting times," etc. In addition, the emotion engine analyzes viewer emotions and notifies specific suggestions for improvement.

[0311] User voting and sentiment analysis

[0312] Users (general users) can vote for influencers' proposals from their devices. The server tally the voting results in real time and ranks the most popular proposals. The emotion engine also analyzes users' voting behavior and emotions toward the proposals, and notifies the influencers of the results.

[0313] Examples:

[0314] Users vote for multiple video project ideas ("Travel Vlog," "Cooking Video," "Funny Pet Video"). If "Funny Pet Video" receives the most votes, the influencer will create the next video based on this result. The emotion engine analyzes users' emotions and uses the results to create new projects.

[0315] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0316] Step 1:

[0317] Registering business information

[0318] Users (companies) access the system from their terminals and enter project information (purpose, target audience, budget, etc.), which is then sent to the server.

[0319] Input: Company project information (purpose, target, budget)

[0320] Specific operation: A company enters and submits information about a "new product promotion aimed at young people."

[0321] Output: Sending job information to the server

[0322] Step 2:

[0323] Case information storage and sentiment analysis

[0324] The server stores the received project information in a database and activates the emotion engine, which analyzes the user's emotions regarding the company's project information and adjusts the way the information is presented based on the results.

[0325] Input: Company project information

[0326] Specific behavior: The emotion engine evaluates emotions such as "excited" or "attractive" and adjusts the presentation accordingly.

[0327] Output: Saving case information to a database, results of sentiment analysis

[0328] Step 3:

[0329] Influencer database search and matching

[0330] The server analyzes the project information, searches for the most suitable influencer from its influencer database, and then lists the search results and sends them to the company's terminal.

[0331] Input: Parsed case information

[0332] Specific operation: The server creates a list of influencers with a large number of followers in the fashion field and sends it to the company's terminal.

[0333] Output: List of influencers

[0334] Step 4:

[0335] Influencer selection by companies

[0336] The company checks the influencer list received on the device and selects the most suitable influencer. The selection results are sent to the server.

[0337] Input: Influencer list

[0338] Specific operation: A company selects the most suitable influencer from multiple influencers and sends the selection results.

[0339] Output: Information on selected influencers

[0340] Step 5:

[0341] Notification of selection and collaboration proposal

[0342] The server receives the company's selection results and sends notifications to the selected influencers, along with proposing collaborations.

[0343] Input: Information of selected influencers

[0344] What it does: Sends notifications to selected influencers and proposes collaboration.

[0345] Output: Notification and proposal to influencers

[0346] Step 6:

[0347] Influencer request and auto-generation

[0348] Users (influencers) input a theme or idea from their device and send the request to the server, which then passes the request to the AI ​​generator, which automatically generates video material and a script.

[0349] Input: Theme or idea

[0350] How it works: An influencer enters the theme "Christmas party decoration ideas" and submits it. The generative AI automatically generates content based on this.

[0351] Output: Generated footage and scripts

[0352] Step 7:

[0353] Generative content provision and feedback analysis

[0354] The server provides the generated content to the influencer's device, and the emotion engine analyzes the user's emotions toward the generated content and sends the feedback to the generation AI.

[0355] Input: Generated content

[0356] Specific operation: The emotion engine analyzes the user's reaction and uses it to generate the next content.

[0357] Output: Providing generated content to influencers, sentiment analysis results

[0358] Step 8:

[0359] Collecting viewing data and making improvement suggestions

[0360] The server collects and analyzes viewing data (number of views, likes, shares, comments, etc.) for published videos. Based on the analysis results, it generates specific improvement proposals to improve the performance of the content.

[0361] Input: Viewing data

[0362] Specific operation: The server analyzes viewing data and creates and notifies improvement suggestions such as "increasing the use of hashtags," "changing thumbnails," and "adjusting posting times."

[0363] Output: Improvement suggestions, notifications

[0364] Step 9:

[0365] User voting page and sentiment analysis

[0366] Users (general users) vote for influencers' proposals from their devices. The server tally the voting results in real time and ranks the most popular proposals. The emotion engine analyzes users' voting behavior and emotions toward the proposals, and notifies the influencers of the results.

[0367] Input: User votes

[0368] Specific operation: Users vote for "funny pet videos," etc., and the most popular projects are ranked based on the results. The emotion engine analyzes users' emotions and notifies influencers of the results.

[0369] Output: Voting results tallied, sentiment analysis results notified

[0370] (Application example 2)

[0371] 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."

[0372] Currently, companies spend a great deal of time and effort establishing marketing strategies that utilize influencers, but it is difficult to quickly and accurately find the most suitable influencers. In addition, it is difficult to select the appropriate robots and generate optimal work plans for factory manufacturing operations, and there is a need to improve work efficiency and reduce error rates. Therefore, a new system is needed that can simultaneously improve the efficiency of corporate marketing activities and factory manufacturing operations.

[0373] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0374] In this invention, the server includes means for registering project information of a company, means for searching for and listing optimal influencers from an influencer database based on the project information of the company, means for presenting the listed influencers to the company and allowing the company to select them, means for notifying the selected influencers and proposing collaboration, means for managing and following up on the selection results, means for registering project information of manufacturing work, means for searching for and listing appropriate factory robots from a database based on the project information of the manufacturing work, means for presenting the listed factory robots to a manufacturing manager and allowing the manufacturing manager to select them, means for notifying the selected factory robot and generating a work plan, and means for managing the selection results and following up on the work, thereby enabling the efficiency of corporate marketing activities and factory manufacturing work to be improved.

[0375] "Company project information" refers to information such as details of the project or campaign that the company wants to implement, its objectives, target audience, budget, etc.

[0376] An "influencer" is an individual or organization that has a large number of followers or viewers on the Internet and can influence a company's products or services.

[0377] A "database" refers to a collection of data used by the system to manage and search, such as information on corporate projects, influencers, and factory robots.

[0378] "Generative AI" refers to artificial intelligence that automatically generates video materials and work plans based on specified conditions and requirements.

[0379] An "emotion engine" refers to analytical software that analyzes a user's emotional state and provides appropriate feedback and suggestions for improvement based on that information.

[0380] "Factory robot" refers to an automated machine used to perform production tasks in a manufacturing plant.

[0381] A "work plan" refers to a plan that specifically defines a series of work procedures and processes to be carried out by factory robots.

[0382] "Viewing data" refers to data based on user viewing behavior, such as the number of views, likes, shares, and comments on published videos.

[0383] "Improvement proposals" refer to specific plans and instructions for improving efficiency and results based on the analysis of viewing data and work data.

[0384] "Follow-up" refers to the ongoing management, supervision, and support of influencers and factory robots.

[0385] The system of the present invention includes functions for registering company project information, searching and matching a database of influencers and factory robots, automatically generating work plans, analyzing viewing and work data, generating improvement proposals, providing user and employee feedback, and analyzing using an emotion engine. The entire system process is implemented using Python and the Flask framework. Each function is described in detail below.

[0386] Registering company project information

[0387] Corporate administrators, who are users, access the system using a smartphone or head-mounted display and register project information (purpose, target audience, budget, etc.). This data is sent to the server and stored in a database. An emotion engine is used to analyze employees' emotions regarding the registered project information and adjust the display method.

[0388] Database search and matching of influencers and factory robots

[0389] The server analyzes the registered project information, searches for and lists the most suitable candidates from the influencer or factory robot database. This list is sent from the server to the user's device, which is the company administrator or manufacturing manager, who then checks the list and selects the most suitable candidate. The selected influencer or robot is notified, and collaboration and work plans are carried out.

[0390] Automatic generation of work plans and sentiment analysis

[0391] When influencers or factory robots run out of ideas for new content or work plans, the generative AI automatically generates video footage and work plans. The generated results are provided via a server. In addition, an emotion engine analyzes the emotions of employees and users regarding the generated content and plans and provides feedback to the generative AI.

[0392] Analysis of viewing and work data and suggestions for improvement

[0393] The server collects viewing data for published videos and analyzes information such as the number of views, likes, shares, and comments. Similarly, it collects robot manufacturing work data and analyzes work efficiency and error rates. From this, specific improvement proposals are generated and notified to influencers and manufacturing managers. An emotion engine is used to analyze the emotions of users and employees and reflect them in improvement proposals.

[0394] User and employee feedback features and sentiment analysis

[0395] A voting page is displayed where users and employees can provide feedback to influencers and factory robots. The server compiles the feedback results in real time and ranks the most popular plans and work procedures. An emotion engine analyzes the sentiment of the feedback and notifies influencers and production managers, helping them consider new plans and work procedures.

[0396] Specific examples

[0397] When a company registers project information to "promote a new product," the system lists the most suitable influencers. The company administrator selects the influencers, and the generation AI automatically generates a script with the theme "Introducing recommended summer beach items." Similarly, when a manufacturing factory registers a project to "improve the assembly process for a new product," the system selects the most suitable factory robot and automatically generates new assembly procedures. Employees use head-mounted displays to check the new procedures and provide feedback. The emotion engine analyzes the feedback and reflects it in the next improvement proposal.

[0398] Prompt Sentence Examples

[0399] "Please suggest the optimal robot and work plan to improve the assembly process for a new product."

[0400] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0401] Step 1: Registering the project information

[0402] Subject: User

[0403] The user, a company manager, uses a smartphone or head-mounted display to input project information (purpose, target audience, budget, etc.). This information is sent from the device to a server and stored in a database. The emotion engine analyzes the employee's emotional state regarding the registered project information and adjusts the display method accordingly.

[0404] Input: Company administrator inputs case information

[0405] Data processing / calculation: The emotion engine analyzes employee emotions based on input case information.

[0406] Output: Adjusted display method

[0407] Step 2: Finding influencers and factory robots

[0408] Subject: Server

[0409] The server analyzes the project information entered by the company administrator and searches for the most suitable influencer from the influencer database or the most suitable robot from the factory robot database. This list is then sent from the server to the terminal of the company administrator or manufacturing manager.

[0410] Input: Project information

[0411] Data processing / calculation: Retrieving data from the database and filtering

[0412] Output: A list of the best influencers or factory robots

[0413] Step 3: Candidate selection and notification

[0414] Subject: User

[0415] The company manager or manufacturing manager reviews the submitted list and selects the most suitable influencer or factory robot. The selection results are sent to the server and notified to the selected influencer or robot.

[0416] Input: A list of the best influencers and robots

[0417] Data processing / calculation: List display and selection

[0418] Output: Notification of selection results

[0419] Step 4: Automatically generate a work plan

[0420] Subject: Server

[0421] When influencers or factory robots run out of ideas for new content or work plans, generative AI automatically generates video footage and work plans, and the generated results are provided to the influencers or robots from the server.

[0422] Input: Prompt sentence for generative AI model

[0423] Data processing / calculation: Generative AI generates content and work plans

[0424] Output: Auto-generated content and work plans

[0425] Step 5: Analyze the feedback

[0426] Subject: Server

[0427] The server aggregates feedback from users and employees in real time through the feedback page. The emotion engine analyzes the emotions in response to the feedback and notifies the results to influencers and production managers.

[0428] Input: Feedback data and emotion data

[0429] Data processing / calculation: Analysis and aggregation using emotion engine

[0430] Output: Notification of analysis results

[0431] Step 6: Analyze viewing and task data

[0432] Subject: Server

[0433] The server collects viewing data for published videos and work data from factory robots, and analyzes the number of views, likes, shares, comments, work efficiency, error rates, etc. Based on this, it generates specific improvement proposals and notifies them to influencers and production managers.

[0434] Input: Viewing and working data

[0435] Data processing / calculation: Data analysis using analytical algorithms

[0436] Output: Specific improvement suggestions

[0437] 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.

[0438] 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.

[0439] 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.

[0440] [Second embodiment]

[0441] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0442] 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.

[0443] 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).

[0444] 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.

[0445] 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.

[0446] 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).

[0447] 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.

[0448] 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.

[0449] 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.

[0450] 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.

[0451] 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.

[0452] 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."

[0453] The system of the present invention includes functions for registering company project information, searching a database of influencers, matching, automatically generating content, analyzing viewing data, generating improvement suggestions, and allowing users to vote.

[0454] System configuration

[0455] 1. Registering business information

[0456] Users (companies) access the system using a terminal and register project information (purpose, target audience, budget, etc.) This data is sent to the server and stored in the database.

[0457] 2. Influencer database search

[0458] The server analyzes the project information registered by the company, searches for and lists the most suitable influencers from the database, and sends this list to the company's terminal, where the company can check it.

[0459] 3. Matching

[0460] The user (company) selects the most suitable influencer from the presented influencer list and sends the selection results to the server. The selection results are saved in a database and notified to the influencer.

[0461] 4. Automated content generation

[0462] Users (influencers) input their requests into the system from their devices, and the system uses generation AI to automatically generate video materials and a story (script). The generated results are provided to the influencer via a server. The influencer then creates a video based on the provided materials and script.

[0463] 5. Analysis of viewing data and improvement proposals

[0464] The server collects viewing data for published videos and analyzes information such as the number of views, likes, shares, and comments. Based on the analysis results, it generates specific improvement suggestions to increase the number of followers, views, likes, and shares, and notifies the influencer.

[0465] 6. User voting function

[0466] The device displays a voting page to the user, allowing the user to propose projects they would like the influencer to undertake or vote on projects that have already been proposed. The server tally the voting results in real time and notify the influencer. The influencer can then consider new projects based on the voting results.

[0467] Specific examples

[0468] 1. Matching with corporate projects

[0469] Example: When a company requests promotion for a new product, the system will list suitable influencers based on the company's objectives and target audience, allowing the company to select the most suitable influencer in a short amount of time.

[0470] 2. Automated content generation

[0471] Example: When an influencer is stuck for ideas for a new video, a script on the theme of "Recommended Summer Beach Goods" is automatically generated, allowing the influencer to quickly get started on creating a video.

[0472] 3. Analysis of viewing data and improvement proposals

[0473] Example: If an influencer's recent videos aren't getting as many views as expected, the system will analyze the viewing data and provide suggestions for improvements, such as using hashtags, changing the thumbnail, or adjusting the posting time, so the influencer can improve performance in their next video production.

[0474] 4. User voting function

[0475] Example: An influencer holds a poll in the system to ask users to suggest ideas for their next video. If users vote for the idea "funny pet videos" most often, the influencer can use the results to create their next video.

[0476] The system of the present invention can facilitate communication between companies, influencers, and general users, improving both the quality and quantity of content.

[0477] The processing flow will be explained below.

[0478] Processing steps from registering company information to matching

[0479] Step 1:

[0480] The user (company) registers project information using a terminal. The company enters project details (purpose, target audience, budget, etc.) and sends them to the server.

[0481] Step 2:

[0482] The server analyzes the received job information and stores the data in the appropriate fields, allowing for smoother search processing later.

[0483] Step 3:

[0484] The server searches a database of influencers and lists the most suitable influencers based on the project information, taking into consideration factors such as past performance and areas of expertise.

[0485] Step 4:

[0486] The server generates a list of optimal influencers and sends it to the user's (company's) device.

[0487] Step 5:

[0488] The user (company) checks the influencer list displayed on the device and selects the most suitable influencer. The selection results are sent to the server.

[0489] Step 6:

[0490] The server stores the selection results in a database and notifies the selected influencers.

[0491] Process steps for auto-generating content by influencers

[0492] Step 1:

[0493] A user (influencer) accesses the system using a terminal and inputs a request for new content, detailing the theme and required elements, and sends it to the server.

[0494] Step 2:

[0495] The server runs a generation AI based on the request received and generates optimal video material and story (script).

[0496] Step 3:

[0497] The generative AI retrieves the necessary information from the database and creates materials and scripts that fit the specified theme.

[0498] Step 4:

[0499] The server sends the generated materials and scripts to the influencer's device.

[0500] Step 5:

[0501] The user (influencer) checks the materials and script generated on the device and begins video production.

[0502] Analysis of viewing data and processing steps for improvement suggestions

[0503] Step 1:

[0504] The server periodically collects viewing data for published videos, including the number of views, likes, shares, and comments.

[0505] Step 2:

[0506] The server analyzes the data collected to extract trends and patterns in viewing data and evaluate the impact of specific factors on performance.

[0507] Step 3:

[0508] The server generates specific improvement suggestions based on the analysis, such as optimizing hashtags, improving thumbnails, and adjusting posting times.

[0509] Step 4:

[0510] The server notifies the influencer's terminal of the generated improvement proposal.

[0511] Step 5:

[0512] The user (influencer) checks the suggestions on their device and reflects them in their next video production.

[0513] Steps for processing user votes

[0514] Step 1:

[0515] The device will then display a voting page where users can suggest projects they would like influencers to undertake or vote for projects that have already been proposed.

[0516] Step 2:

[0517] Users use their devices to propose or vote on projects, and the proposal and vote data are sent to the server.

[0518] Step 3:

[0519] The server compiles proposal and vote data in real time, and popular proposals are ranked.

[0520] Step 4:

[0521] The server sends the results of the survey to the influencer's device, who then uses the results to consider new projects.

[0522] Through the above processing steps, the system of the present invention can facilitate communication between companies, influencers, and users, and realize effective content creation and improved performance.

[0523] Example 1

[0524] 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."

[0525] For a company to effectively use influencers for promotion, it is necessary to select suitable influencers, create content, analyze and improve viewing data, and collect feedback from general users. However, managing these processes individually takes a great deal of time and effort, making it difficult to do so efficiently.

[0526] 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.

[0527] In this invention, the server includes: means for registering a company's project information; means for searching for and listing optimal influencers from an influencer database based on the company's project information; means for presenting the listed influencers to a company and allowing the company to select one; means for notifying the selected influencers and proposing collaboration; means for managing and following up on the selection results; means for having the selected influencers input requests for automatic content generation using a generative AI model and providing the generation results; means for collecting and analyzing viewing data of published videos and notifying improvement suggestions; and means for tallying votes from general users in real time and notifying them of material for consideration in new projects. This makes it possible to efficiently match companies and influencers and consistently manage each process of content creation and improvement.

[0528] "Means for registering company project information" is a function that allows companies to enter information such as promotion and marketing objectives, target audience, budget, etc. into the system and register it.

[0529] "A means of searching and listing the most suitable influencers from the influencer database based on a company's project information" is a function that searches for influencer information in the database based on the registered company's project information and lists the most suitable influencers.

[0530] "A means of presenting the listed influencers to companies and allowing companies to select" is a function that displays a list of searched influencers to companies, allowing them to select the most suitable influencer.

[0531] "Means of notifying selected influencers and proposing collaboration" is a function for notifying influencers selected by a company and proposing collaboration.

[0532] "Means for managing and following up on selection results" refers to a function that stores the selection results of companies and influencers in a database and allows for continuous follow-up.

[0533] "Means of having selected influencers input requests for automatic content generation using a generative AI model and providing the generated results" refers to a function that enables influencers to input requests for content generation into the system and provide them with content (video material and stories) that is automatically generated using a generative AI model.

[0534] "Means for collecting and analyzing viewing data of published videos and notifying improvement suggestions" refers to a function for collecting viewing data of published videos, analyzing this data, generating specific suggestions for performance improvement, and notifying influencers.

[0535] "A means of aggregating votes from general users in real time and notifying them of the results as material for consideration in new projects" is a function that aggregates votes from general users in real time and notifies influencers of the results as material for consideration in new projects.

[0536] The system of the present invention is a comprehensive generative AI-equipped system that includes functions for registering corporate project information, searching a database of influencers, matching, automatically generating content, analyzing viewing data, generating improvement suggestions, and voting by general users. This system efficiently performs each function using a variety of hardware and software.

[0537] First, the user (company) enters the company's project information into the device's web browser (e.g., Google Chrome). The project information includes the promotion purpose, target audience, budget, etc., and this information is sent to the server. The server then stores the received information in a database management system (e.g., MySQL).

[0538] Next, the server analyzes the project information registered by the company. This analysis uses a natural language processing algorithm (e.g., Python's NLTK library). Based on the analysis results, the server searches the database for the most suitable influencers and creates a list. This list is sent to the user's (company's) device, and the company can view the list through a web browser.

[0539] The company selects the most suitable influencer from the list of influencers provided and sends the selection results from the terminal to the server. The server stores the received selection results in a database and simultaneously sends an email notification (e.g., SendGrid) to the selected influencer to notify them of the details of the project.

[0540] Influencers use their devices to input requests into the system, which automatically generates content. The request is sent to a generative AI model (e.g., OpenAI GPT-3), which automatically generates video material and a story (script). A request for automatic generation is made by entering a prompt phrase, such as a theme like "Recommended summer beach items." The generated results are provided to the influencer via the server, allowing for quick video production.

[0541] Viewing data for published videos is collected by the server, and information such as the number of views, likes, shares, and comments is analyzed using a data analysis tool (e.g., Google Analytics). Based on the analysis results, the server generates specific improvement suggestions and notifies the influencer. The improvement suggestions include optimizing hashtags and adjusting the timing of posting.

[0542] The system also includes a voting function for general users. Users can access the voting page using their devices to propose new projects to influencers or vote for existing projects. Voting results are sent to the server in real time and tallied. The server then notifies the influencers of the voting results, which they use as information for considering new projects.

[0543] For example, if a company requests a promotion for a new product, the system will list suitable influencers based on the company's objectives and target audience. Also, if an influencer is stuck for ideas, a script on the theme of "introducing recommended summer beach items" can be automatically generated using a generative AI model. In this way, the influencer can quickly begin creating a video.

[0544] This system will facilitate collaboration between companies, influencers, and individuals, and will improve both the quality and quantity of content at the same time.

[0545] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0546] Step 1:

[0547] The user (company) accesses the system using a web browser on their device and enters project information (promotion purpose, target audience, budget, etc.). The entered information is sent from the device to the server in JSON format.

[0548] Step 2:

[0549] The server saves the received job information in a database management system (e.g. MySQL). At this time, it checks the consistency of the input information and converts it into the required data format. Once saving is complete, it returns a response to the terminal indicating that saving was successful.

[0550] Step 3:

[0551] The server retrieves project information from the database and analyzes it using a natural language processing algorithm (e.g., Python's NLTK library). Based on the analysis results, it creates search criteria for influencers. Using these analysis results, it searches the database for the most suitable influencers.

[0552] Step 4:

[0553] The server sends the list of influencers obtained as search results in JSON format to the terminal. The terminal displays the list of influencers on a web browser, allowing the user (company) to review and select from the list.

[0554] Step 5:

[0555] The user (company) selects the most suitable influencer from the influencer list displayed on the web browser. Once the selection is complete, the device sends the selection results in JSON format to the server.

[0556] Step 6:

[0557] The server stores the received selection results in a database. At this time, it validates the selection information and, once the selection is confirmed, notifies the selected influencer. Notifications are sent via an email notification service (e.g., SendGrid).

[0558] Step 7:

[0559] The user (influencer) uses a device to input a request to the system. The request (e.g., the theme or purpose of the video) is sent to the generative AI model. This request is sent from the device via the API as a prompt.

[0560] Step 8:

[0561] A generative AI model (e.g., OpenAI GPT-3) automatically generates video material and a story (script) based on the prompt. The generated information is sent to the server in JSON format. The server stores the generated results in a database and provides them to influencers.

[0562] Step 9:

[0563] The influencer will create a video using the generated results provided and upload it to a platform for publishing.

[0564] Step 10:

[0565] The server collects viewing data such as the number of views, likes, shares, and comments for published videos using the video platform's API (e.g., YouTube Data API), and stores this data in a database in real time.

[0566] Step 11:

[0567] The server analyzes the collected viewing data using a data analysis tool (e.g., the Pandas library), identifies viewing patterns and trends, and generates specific improvement proposals based on the analysis results. The generated improvement proposals are then notified to influencers.

[0568] Step 12:

[0569] The terminal displays a voting page to the user, who then uses the terminal to cast their vote, with each vote immediately transmitted to the server.

[0570] Step 13:

[0571] The server receives the voting results, stores them in a database in real time, and tallies them. The tallied results are then sent to influencers, who use them as information for considering new projects.

[0572] Step 14:

[0573] Influencers will consider new ideas based on the collected voting results, and then create new content based on those ideas using a generative AI model.

[0574] (Application example 1)

[0575] 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."

[0576] In modern content creation, matching companies with influencers is extremely important, but the process of selecting the right influencer is extremely time-consuming and inefficient. Furthermore, there is a lack of ways to quickly provide high-quality content when influencers run out of content ideas. Furthermore, there is a lack of ways to analyze the performance of published content and make specific suggestions for improvement. Finally, there are limited ways to utilize user feedback and incorporate new content ideas. A new system is needed to solve these issues.

[0577] 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.

[0578] In this invention, the server includes: means for registering a company's project information; means for searching for and listing optimal influencers from an influencer database based on the company's project information; means for presenting the listed influencers to a company and allowing the company to select them; means for notifying the selected influencers and proposing collaboration; means for managing and following up on the selection results; means for the influencer to input a request and automatically generating video materials and stories using a generative AI model; means for providing the generated results to the influencer to support video production; means for collecting viewing data on published videos and analyzing data such as the number of views, likes, shares, and comments; means for generating specific improvement suggestions based on the analysis results and notifying the influencer; means for users to propose projects they would like influencers to do or vote for projects that have already been proposed; and means for tallying the voting results in real time and notifying the influencer. This makes it possible to efficiently match companies and influencers and improve the quality and quantity of content provided.

[0579] "Company project information" refers to promotion and marketing requests that companies register in the system based on specific objectives, target audiences, budgets, etc.

[0580] An "influencer database" is a data storage device that stores information such as each influencer's profile information, past activities, and number of followers.

[0581] The "optimal influencer" is the influencer who best fits the company's project information and can maximize the expected results.

[0582] A "generative AI model" is a type of artificial intelligence that automatically generates video material and stories based on pre-trained data, and examples include GPT-4.

[0583] "Footage and Stories" refers to creative materials such as footage and scripts to be used in videos produced by influencers.

[0584] "Viewing data" refers to data that shows how users responded to published videos, including the number of views, likes, shares, and comments.

[0585] "Improvement suggestions" are specific advice and methods for improving the quality and performance of content based on the results of analysis of viewing data, etc.

[0586] "User voting" is the act of ordinary users expressing their opinions on projects they would like influencers to undertake.

[0587] "Voting results" refers to data regarding the next content plan compiled based on votes from users.

[0588] The following system is an example of an embodiment of the present invention. Each function of the system is realized by utilizing a server, a user terminal, and a generative AI model.

[0589] The first step is for a company to register its project information. The company accesses the system using a terminal and enters details of its promotion and marketing, including its objectives, target audience, budget, etc. The server receives the entered information and stores it in a database.

[0590] Next, the server analyzes the company's project information and searches for and lists the most suitable influencers from its influencer database. The list of influencers is sent from the server to the company's terminal, and the company selects influencers based on that list.

[0591] After the selection is complete, the server saves the selection results in a database and notifies the selected influencers, who can then start collaborating with the company.

[0592] If an influencer gets stuck creating content, a generative AI model is used to automatically generate video material and stories. The influencer inputs a request from their device, and the generative AI model creates a prompt based on that request, which is then used to generate content. This generated content is then provided to the influencer again via the server. For example, the prompt for a request on the theme of "introducing beach goods" would look like this:

[0593] Prompt statement:

[0594] Generate a script for the following video: "Topic: Introducing Beach Goods, Video Length: 2 minutes"

[0595] Viewing data for published videos is also an important factor. The server collects data such as the number of views, likes, shares, and comments in real time and analyzes the visual information. Based on the results of this analysis, specific suggestions for improving the quality of content are generated and notified to influencers. For example, these suggestions include "reviewing hashtags," "changing posting times," and "improving thumbnails."

[0596] The system also includes a voting function for users. The device displays a voting page to users, allowing them to propose projects they would like influencers to do or vote for projects that have already been proposed. The server tally the voting results in real time and notify the influencer. Based on these voting results, the influencer can decide on their next video project.

[0597] All of these processes are efficiently managed by a system that facilitates communication between companies, influencers, and users. The system uses hardware such as smartphones and servers, as well as software such as generative AI models (e.g., GPT-4) and cloud services (e.g., AWS Lambda and Google Cloud Functions).

[0598] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0599] Step 1:

[0600] Companies access the system through a terminal and input project information (purpose, target audience, budget, etc.). The terminal sends this information to the server, which stores the received information in a database. It receives project information as input and generates data that is stored in the database as output.

[0601] Step 2:

[0602] The server analyzes the project information registered by the company and searches for the most suitable influencers from the influencer database. It performs data analysis on the information of each influencer in the database and creates a list of influencers most suitable for the project. It receives the company's project information as input and generates a list of influencers as output.

[0603] Step 3:

[0604] The server sends the generated list of influencers to the company's terminal, where the company checks the list and selects the most suitable influencer. The server receives the list of influencers as input and generates the selected influencers as output.

[0605] Step 4:

[0606] The company sends the selection results to the server, which stores the results in a database. The server then notifies the selected influencers. It receives the selection results as input and generates notifications as output.

[0607] Step 5:

[0608] Influencers input content creation requests into the system via their devices. The server receives these requests and sends prompts to the generative AI model. The generative AI model automatically generates video material and stories based on the requests. For example, if the theme is "Introducing beach goods," the following prompts are sent to the generative AI model:

[0609] Prompt statement:

[0610] Generate a script for the following video: "Topic: Introducing Beach Goods, Video Length: 2 minutes"

[0611] It takes influencer requests as input and produces generated content as output.

[0612] Step 6:

[0613] The server sends the generated video material and story to the influencer's device, who then uses this information to create the video. The server receives the generated content as input and generates data to be sent to the influencer's device as output.

[0614] Step 7:

[0615] The server collects viewing data (number of views, likes, shares, comments, etc.) of published videos in real time. It analyzes the viewing data and generates specific improvement proposals to increase the number of followers, views, likes, and shares based on the analysis results, and notifies the influencer. It receives viewing data as input and generates improvement proposals as output.

[0616] Step 8:

[0617] The device displays a voting page to the user, allowing the user to propose projects they would like influencers to implement or vote for projects that have already been proposed. The server tally the voting results in real time and notify the influencers. It receives the user's voting information as input and generates the voting results as output.

[0618] Step 9:

[0619] The influencer will decide on the next video project based on the voting results, completing the process to enhance the interaction between users and influencers and provide content that meets users' requests. It takes the voting results as input and generates the next video project as output.

[0620] 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.

[0621] The system of the present invention includes functions for registering corporate project information, searching a database of influencers, matching, automatically generating content, analyzing viewing data, generating improvement suggestions, a voting function for users, and recognizing user emotions using an emotion engine.

[0622] System configuration

[0623] 1. Registration of business project information and emotion recognition

[0624] Users (companies) access the system using their terminals and register project information (purpose, target audience, budget, etc.). This data is sent to the server and stored in a database. The emotion engine analyzes the user's emotions regarding the company's project information and can adjust the presentation method.

[0625] 2. Influencer database search and matching

[0626] The server analyzes the project information registered by the company and searches for and lists the most suitable influencers from the database. This list is sent from the server to the company's terminal, where the company checks the list. Based on the company's decision, the influencer is notified.

[0627] 3. Automatic content generation and sentiment analysis

[0628] Users (influencers) input their requests into the system from their devices, and the generation AI automatically generates video material and a story (script). The generated results are provided to the influencer via the server. Furthermore, the emotion engine analyzes the user's feelings toward the generated content and provides feedback to the generation AI, which can be used to generate content next time.

[0629] 4. Analysis of viewing data, improvement proposals, and sentiment analysis

[0630] The server collects viewing data for published videos and analyzes information such as the number of views, likes, shares, and comments. This information is used to generate specific improvement proposals to increase the number of followers, views, likes, and shares, and these proposals are notified to influencers. Additionally, an emotion engine can be used to analyze user emotions and reflect them in improvement proposals.

[0631] 5. User voting and sentiment analysis

[0632] The device displays a voting page to users, allowing them to propose projects they would like influencers to implement or vote for projects that have already been proposed. The server tallys the voting results in real time and ranks the most popular projects. Furthermore, an emotion engine analyzes users' voting behavior and emotions toward the proposals, and notifies the influencers of this information, helping them consider new projects.

[0633] Specific examples

[0634] 1. Matching with corporate projects

[0635] Example: When a company requests a promotion for a new product, the system will generate a list of suitable influencers based on the company's objectives and target audience. As a result, the company can select the most suitable influencer in a short time. The emotion engine adjusts the offer presentation based on the user's reaction and interest level.

[0636] 2. Automatic content generation and sentiment analysis

[0637] Example: When an influencer is stuck for ideas for a new video, a script on the theme of "Introducing recommended summer beach items" is automatically generated. This allows the influencer to quickly start creating a video. Furthermore, an emotion engine analyzes user reactions and reflects them in the next content generation.

[0638] 3. Analysis of viewing data, improvement proposals, and sentiment analysis

[0639] Example: If an influencer's recent videos aren't getting as many views as expected, the system will analyze the viewing data and provide suggestions for improvement, such as using hashtags, changing thumbnails, adjusting posting times, etc. Furthermore, an emotion engine will analyze viewers' emotions and provide more specific suggestions for improvement.

[0640] 4. User voting and sentiment analysis

[0641] Example: An influencer holds a poll in the system to ask users to suggest ideas for the next video. If users vote a lot for the idea "funny pet videos," the influencer can create the next video based on the results. The emotion engine can analyze users' feelings about the votes and use the results to create new ideas.

[0642] The system of this invention facilitates communication between companies, influencers, and users, enabling effective content creation and performance improvement. The introduction of an emotion engine enables even more effective matching and improvement suggestions.

[0643] The processing flow will be explained below.

[0644] Registration of business case information and emotion recognition processing steps

[0645] Step 1:

[0646] The user (company) accesses the system using a terminal, enters project information (purpose, target audience, budget, etc.), and sends it to the server.

[0647] Step 2:

[0648] The server receives the submitted job information and stores it in a database.

[0649] Step 3:

[0650] The server uses an emotion engine to analyze the user's emotions regarding the transmitted case information.

[0651] Step 4:

[0652] The server adjusts the way the case information is presented based on the analysis results.

[0653] Influencer database search and matching process steps

[0654] Step 1:

[0655] The server analyzes the project information registered by the company and searches the database for the most suitable influencer.

[0656] Step 2:

[0657] The server lists influencers based on the search results.

[0658] Step 3:

[0659] The server generates a list of optimal influencers and sends it to the user's (company's) device.

[0660] Step 4:

[0661] The user (company) checks the influencer list presented on the device, selects the most suitable influencer, and sends the selection results to the server.

[0662] Step 5:

[0663] The server stores the selection results in a database and notifies the selected influencers.

[0664] Processing steps for automatic content generation and sentiment analysis

[0665] Step 1:

[0666] A user (influencer) accesses the system using a terminal, inputs a request for new content, and sends it to the server.

[0667] Step 2:

[0668] The server runs a generation AI based on the request received to generate optimal video material and story (script).

[0669] Step 3:

[0670] The server sends the generated materials and scripts to the influencer's device.

[0671] Step 4:

[0672] The user (influencer) checks the materials and script generated on the device and begins video production.

[0673] Step 5:

[0674] The server uses an emotion engine to analyze other users' emotions toward the content generated by the user (influencer) and feeds the results back to the generation AI.

[0675] Analysis of viewing data, improvement suggestions, and processing steps for sentiment analysis

[0676] Step 1:

[0677] The server periodically collects viewing data for published videos, including the number of views, likes, shares, and comments.

[0678] Step 2:

[0679] The server analyzes the collected data and extracts trends and patterns in the viewing data.

[0680] Step 3:

[0681] The server uses an emotion engine to analyze the user's emotions regarding the viewing data.

[0682] Step 4:

[0683] The server generates specific improvement suggestions based on the analysis, such as optimizing hashtags, improving thumbnails, and adjusting posting times.

[0684] Step 5:

[0685] The server notifies the influencer's terminal of the generated improvement proposal.

[0686] Step 6:

[0687] The user (influencer) checks the suggestions on their device and reflects them in their next video production.

[0688] User voting and sentiment analysis processing steps

[0689] Step 1:

[0690] The device will then display a voting page to the user, where the user can suggest projects they would like the influencer to undertake or vote for projects that have already been proposed.

[0691] Step 2:

[0692] Users use their devices to propose or vote on projects, and the proposal and vote data are sent to the server.

[0693] Step 3:

[0694] The server compiles proposal and vote data in real time, and popular proposals are ranked.

[0695] Step 4:

[0696] The server uses an emotion engine to analyze users' voting behavior and emotions toward the proposals.

[0697] Step 5:

[0698] The server notifies the influencer of the results of the aggregation and sentiment analysis, and the influencer uses these results to consider new projects.

[0699] Example 2

[0700] 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."

[0701] Traditional matching systems for companies and influencers face many challenges in terms of effective content creation and analysis of viewing data. Specifically, they face issues such as companies being unable to properly deliver project information to their target audiences, influencers being unable to quickly obtain appropriate video ideas, being unable to fully utilize viewing data from published content, and being unable to analyze user sentiment and reflect it in future content. They also lack functionality for collecting project ideas through user votes and analyzing and reflecting those sentiments. This makes collaboration between companies and influencers inefficient, making it difficult to improve overall performance.

[0702] 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. In this invention, the server includes: means for registering project information of a company; means for analyzing user emotions regarding the company's project information using an emotion engine and adjusting the presentation method; means for searching for and listing optimal influencers from an influencer database based on the company's project information; means for presenting the listed influencers to a company and allowing the company to select; means for notifying the selected influencer and proposing collaboration; means for managing and following up on the selection results; means for the influencer to input a request and automatically generating video materials and stories using a generation AI; means for analyzing user emotions regarding content generated using the emotion engine and providing feedback to the generation AI; means for providing the generated results to the influencer and supporting video production; means for collecting viewing data of published videos and analyzing data such as the number of views, likes, shares, and comments; means for analyzing viewer emotions using the emotion engine, generating specific improvement suggestions based on the analysis results, and notifying the influencer; and means for displaying a voting page to the user and analyzing the user's voting behavior and emotions regarding the suggestions. This will enable effective matching between companies and influencers and improve the performance of content creation.

[0703] "Company project information" refers to information about projects and transactions that a company registers in the system, including objectives, target audiences, budgets, etc.

[0704] An "emotion engine" is an engine for analyzing user emotions, extracting emotions from text and behavior and making an evaluation.

[0705] An "influencer database" is a database that accumulates information about influencers, including the number of followers, areas of expertise, and past activity history.

[0706] The "system" is an integrated platform for streamlining communication and content creation between companies, influencers, and general users.

[0707] "Generative AI" is artificial intelligence that automatically generates content based on given prompts, generating video footage and stories.

[0708] "Viewing data" refers to user reaction data to published content, including the number of views, likes, shares, comments, and the like.

[0709] "Improvement proposals" are specific methods or ideas proposed to improve content performance based on viewing data and the results of sentiment analysis.

[0710] A "voting page" is a web page where users can vote for influencers' proposals and the results are tallied in real time.

[0711] The system according to the present invention is an integrated platform for streamlining communication and content creation between companies, influencers, and general users. Specific embodiments of this system will be described below.

[0712] Registration of business project information and emotion recognition

[0713] Users (companies) access the system using a terminal and input their company's project information (purpose, target audience, budget, etc.). The terminal sends this information to the server. The server stores the received project information in a database and activates the emotion engine. The emotion engine analyzes the user's emotions based on the input information and adjusts the way the information is presented based on the results.

[0714] Examples:

[0715] For example, when a company inputs information about a project such as "promotion of a new product aimed at young people," the emotion engine evaluates emotions such as "excitement" and "attractiveness," and the server uses this information to adjust the project presentation to optimize it for the target audience.

[0716] Influencer database search and matching

[0717] The server analyzes the project information registered by the company and extracts elements such as the purpose, target audience, and budget. Next, it uses this information to search the database for the most suitable influencers and creates a list. This list is sent to the company's device. The company then checks the list on the device and selects the most suitable influencer. The server then sends a notification to the selected influencer.

[0718] Examples:

[0719] When a company requests a "fashion item promotion," the server creates a list of influencers with large numbers of followers in the fashion field and sends it to the company. The company then selects influencers based on the list and sends them notifications.

[0720] Automatic content generation and sentiment analysis

[0721] The user (influencer) inputs a theme or idea from their device. The device sends the request to the server, which passes it on to the generation AI. The generation AI automatically generates video material and a script based on the request and provides the results to the influencer. The emotion engine also analyzes the user's emotions regarding the generated content and sends that feedback to the generation AI to help with the next generation.

[0722] Examples:

[0723] When an influencer enters a theme, such as "Christmas party decoration ideas," the generative AI automatically generates a script and video material based on the theme. This generated content is sent to the influencer's device. The emotion engine also analyzes user reactions, and the results are reflected in the next content generation.

[0724] Analysis of viewing data, improvement suggestions, and sentiment analysis

[0725] The server collects viewing data (number of views, likes, shares, comments, etc.) for published videos. The server then analyzes this data and generates specific improvement suggestions to improve performance. An emotion engine analyzes viewer emotions and reflects the results in improvement suggestions. These suggestions are then sent to the influencer's device.

[0726] Examples:

[0727] If an influencer's recent videos aren't getting as many views as expected, the server analyzes the viewing data and provides specific suggestions for improvement, such as "increase hashtag usage," "change thumbnails," "adjust posting times," etc. In addition, the emotion engine analyzes viewer emotions and notifies specific suggestions for improvement.

[0728] User voting and sentiment analysis

[0729] Users (general users) can vote for influencers' proposals from their devices. The server tally the voting results in real time and ranks the most popular proposals. The emotion engine also analyzes users' voting behavior and emotions toward the proposals, and notifies the influencers of the results.

[0730] Examples:

[0731] Users vote for multiple video project ideas ("Travel Vlog," "Cooking Video," "Funny Pet Video"). If "Funny Pet Video" receives the most votes, the influencer will create the next video based on this result. The emotion engine analyzes users' emotions and uses the results to create new projects.

[0732] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0733] Step 1:

[0734] Registering business information

[0735] Users (companies) access the system from their terminals and enter project information (purpose, target audience, budget, etc.), which is then sent to the server.

[0736] Input: Company project information (purpose, target, budget)

[0737] Specific operation: A company enters and submits information about a "new product promotion aimed at young people."

[0738] Output: Sending job information to the server

[0739] Step 2:

[0740] Case information storage and sentiment analysis

[0741] The server stores the received project information in a database and activates the emotion engine, which analyzes the user's emotions regarding the company's project information and adjusts the way the information is presented based on the results.

[0742] Input: Company project information

[0743] Specific behavior: The emotion engine evaluates emotions such as "excited" or "attractive" and adjusts the presentation accordingly.

[0744] Output: Saving case information to a database, results of sentiment analysis

[0745] Step 3:

[0746] Influencer database search and matching

[0747] The server analyzes the project information, searches for the most suitable influencer from its influencer database, and then lists the search results and sends them to the company's terminal.

[0748] Input: Parsed case information

[0749] Specific operation: The server creates a list of influencers with a large number of followers in the fashion field and sends it to the company's terminal.

[0750] Output: List of influencers

[0751] Step 4:

[0752] Influencer selection by companies

[0753] The company checks the influencer list received on the device and selects the most suitable influencer. The selection results are sent to the server.

[0754] Input: Influencer list

[0755] Specific operation: A company selects the most suitable influencer from multiple influencers and sends the selection results.

[0756] Output: Information on selected influencers

[0757] Step 5:

[0758] Notification of selection and collaboration proposal

[0759] The server receives the company's selection results and sends notifications to the selected influencers, along with proposing collaborations.

[0760] Input: Information of selected influencers

[0761] What it does: Sends notifications to selected influencers and proposes collaboration.

[0762] Output: Notification and proposal to influencers

[0763] Step 6:

[0764] Influencer request and auto-generation

[0765] Users (influencers) input a theme or idea from their device and send the request to the server, which then passes the request to the AI ​​generator, which automatically generates video material and a script.

[0766] Input: Theme or idea

[0767] How it works: An influencer enters the theme "Christmas party decoration ideas" and submits it. The generative AI automatically generates content based on this.

[0768] Output: Generated footage and scripts

[0769] Step 7:

[0770] Generative content provision and feedback analysis

[0771] The server provides the generated content to the influencer's device, and the emotion engine analyzes the user's emotions toward the generated content and sends the feedback to the generation AI.

[0772] Input: Generated content

[0773] Specific operation: The emotion engine analyzes the user's reaction and uses it to generate the next content.

[0774] Output: Providing generated content to influencers, sentiment analysis results

[0775] Step 8:

[0776] Collecting viewing data and making improvement suggestions

[0777] The server collects and analyzes viewing data (number of views, likes, shares, comments, etc.) for published videos. Based on the analysis results, it generates specific improvement proposals to improve the performance of the content.

[0778] Input: Viewing data

[0779] Specific operation: The server analyzes viewing data and creates and notifies improvement suggestions such as "increasing the use of hashtags," "changing thumbnails," and "adjusting posting times."

[0780] Output: Improvement suggestions, notifications

[0781] Step 9:

[0782] User voting page and sentiment analysis

[0783] Users (general users) vote for influencers' proposals from their devices. The server tally the voting results in real time and ranks the most popular proposals. The emotion engine analyzes users' voting behavior and emotions toward the proposals, and notifies the influencers of the results.

[0784] Input: User votes

[0785] Specific operation: Users vote for "funny pet videos," etc., and the most popular projects are ranked based on the results. The emotion engine analyzes users' emotions and notifies influencers of the results.

[0786] Output: Voting results tallied, sentiment analysis results notified

[0787] (Application example 2)

[0788] 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."

[0789] Currently, companies spend a great deal of time and effort establishing marketing strategies that utilize influencers, but it is difficult to quickly and accurately find the most suitable influencers. In addition, it is difficult to select the appropriate robots and generate optimal work plans for factory manufacturing operations, and there is a need to improve work efficiency and reduce error rates. Therefore, a new system is needed that can simultaneously improve the efficiency of corporate marketing activities and factory manufacturing operations.

[0790] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0791] In this invention, the server includes means for registering project information of a company, means for searching for and listing optimal influencers from an influencer database based on the project information of the company, means for presenting the listed influencers to the company and allowing the company to select them, means for notifying the selected influencers and proposing collaboration, means for managing and following up on the selection results, means for registering project information of manufacturing work, means for searching for and listing appropriate factory robots from a database based on the project information of the manufacturing work, means for presenting the listed factory robots to a manufacturing manager and allowing the manufacturing manager to select them, means for notifying the selected factory robot and generating a work plan, and means for managing the selection results and following up on the work, thereby enabling the efficiency of corporate marketing activities and factory manufacturing work to be improved.

[0792] "Company project information" refers to information such as details of the project or campaign that the company wants to implement, its objectives, target audience, budget, etc.

[0793] An "influencer" is an individual or organization that has a large number of followers or viewers on the Internet and can influence a company's products or services.

[0794] A "database" refers to a collection of data used by the system to manage and search, such as information on corporate projects, influencers, and factory robots.

[0795] "Generative AI" refers to artificial intelligence that automatically generates video materials and work plans based on specified conditions and requirements.

[0796] An "emotion engine" refers to analytical software that analyzes a user's emotional state and provides appropriate feedback and suggestions for improvement based on that information.

[0797] "Factory robot" refers to an automated machine used to perform production tasks in a manufacturing plant.

[0798] A "work plan" refers to a plan that specifically defines a series of work procedures and processes to be carried out by factory robots.

[0799] "Viewing data" refers to data based on user viewing behavior, such as the number of views, likes, shares, and comments on published videos.

[0800] "Improvement proposals" refer to specific plans and instructions for improving efficiency and results based on the analysis of viewing data and work data.

[0801] "Follow-up" refers to the ongoing management, supervision, and support of influencers and factory robots.

[0802] The system of the present invention includes functions for registering company project information, searching and matching a database of influencers and factory robots, automatically generating work plans, analyzing viewing and work data, generating improvement proposals, providing user and employee feedback, and analyzing using an emotion engine. The entire system process is implemented using Python and the Flask framework. Each function is described in detail below.

[0803] Registering company project information

[0804] Corporate administrators, who are users, access the system using a smartphone or head-mounted display and register project information (purpose, target audience, budget, etc.). This data is sent to the server and stored in a database. An emotion engine is used to analyze employees' emotions regarding the registered project information and adjust the display method.

[0805] Database search and matching of influencers and factory robots

[0806] The server analyzes the registered project information, searches for and lists the most suitable candidates from the influencer or factory robot database. This list is sent from the server to the user's device, which is the company administrator or manufacturing manager, who then checks the list and selects the most suitable candidate. The selected influencer or robot is notified, and collaboration and work plans are carried out.

[0807] Automatic generation of work plans and sentiment analysis

[0808] When influencers or factory robots run out of ideas for new content or work plans, the generative AI automatically generates video footage and work plans. The generated results are provided via a server. In addition, an emotion engine analyzes the emotions of employees and users regarding the generated content and plans and provides feedback to the generative AI.

[0809] Analysis of viewing and work data and suggestions for improvement

[0810] The server collects viewing data for published videos and analyzes information such as the number of views, likes, shares, and comments. Similarly, it collects robot manufacturing work data and analyzes work efficiency and error rates. From this, specific improvement proposals are generated and notified to influencers and manufacturing managers. An emotion engine is used to analyze the emotions of users and employees and reflect them in improvement proposals.

[0811] User and employee feedback features and sentiment analysis

[0812] A voting page is displayed where users and employees can provide feedback to influencers and factory robots. The server compiles the feedback results in real time and ranks the most popular plans and work procedures. An emotion engine analyzes the sentiment of the feedback and notifies influencers and production managers, helping them consider new plans and work procedures.

[0813] Specific examples

[0814] When a company registers project information to "promote a new product," the system lists the most suitable influencers. The company administrator selects the influencers, and the generation AI automatically generates a script with the theme "Introducing recommended summer beach items." Similarly, when a manufacturing factory registers a project to "improve the assembly process for a new product," the system selects the most suitable factory robot and automatically generates new assembly procedures. Employees use head-mounted displays to check the new procedures and provide feedback. The emotion engine analyzes the feedback and reflects it in the next improvement proposal.

[0815] Prompt Sentence Examples

[0816] "Please suggest the optimal robot and work plan to improve the assembly process for a new product."

[0817] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0818] Step 1: Registering the project information

[0819] Subject: User

[0820] The user, a company manager, uses a smartphone or head-mounted display to input project information (purpose, target audience, budget, etc.). This information is sent from the device to a server and stored in a database. The emotion engine analyzes the employee's emotional state regarding the registered project information and adjusts the display method accordingly.

[0821] Input: Company administrator inputs case information

[0822] Data processing / calculation: The emotion engine analyzes employee emotions based on input case information.

[0823] Output: Adjusted display method

[0824] Step 2: Finding influencers and factory robots

[0825] Subject: Server

[0826] The server analyzes the project information entered by the company administrator and searches for the most suitable influencer from the influencer database or the most suitable robot from the factory robot database. This list is then sent from the server to the terminal of the company administrator or manufacturing manager.

[0827] Input: Project information

[0828] Data processing / calculation: Retrieving data from the database and filtering

[0829] Output: A list of the best influencers or factory robots

[0830] Step 3: Candidate selection and notification

[0831] Subject: User

[0832] The company manager or manufacturing manager reviews the submitted list and selects the most suitable influencer or factory robot. The selection results are sent to the server and notified to the selected influencer or robot.

[0833] Input: A list of the best influencers and robots

[0834] Data processing / calculation: List display and selection

[0835] Output: Notification of selection results

[0836] Step 4: Automatically generate a work plan

[0837] Subject: Server

[0838] When influencers or factory robots run out of ideas for new content or work plans, generative AI automatically generates video footage and work plans, and the generated results are provided to the influencers or robots from the server.

[0839] Input: Prompt sentence for generative AI model

[0840] Data processing / calculation: Generative AI generates content and work plans

[0841] Output: Auto-generated content and work plans

[0842] Step 5: Analyze the feedback

[0843] Subject: Server

[0844] The server aggregates feedback from users and employees in real time through the feedback page. The emotion engine analyzes the emotions in response to the feedback and notifies the results to influencers and production managers.

[0845] Input: Feedback data and emotion data

[0846] Data processing / calculation: Analysis and aggregation using emotion engine

[0847] Output: Notification of analysis results

[0848] Step 6: Analyze viewing and task data

[0849] Subject: Server

[0850] The server collects viewing data for published videos and work data from factory robots, and analyzes the number of views, likes, shares, comments, work efficiency, error rates, etc. Based on this, it generates specific improvement proposals and notifies them to influencers and production managers.

[0851] Input: Viewing and working data

[0852] Data processing / calculation: Data analysis using analytical algorithms

[0853] Output: Specific improvement suggestions

[0854] 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.

[0855] 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.

[0856] 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.

[0857] [Third embodiment]

[0858] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0859] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0860] 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).

[0861] 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.

[0862] 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.

[0863] 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).

[0864] 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.

[0865] 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.

[0866] 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.

[0867] 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.

[0868] 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.

[0869] 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."

[0870] The system of the present invention includes functions for registering company project information, searching a database of influencers, matching, automatically generating content, analyzing viewing data, generating improvement suggestions, and allowing users to vote.

[0871] System configuration

[0872] 1. Registering business information

[0873] Users (companies) access the system using a terminal and register project information (purpose, target audience, budget, etc.) This data is sent to the server and stored in the database.

[0874] 2. Influencer database search

[0875] The server analyzes the project information registered by the company, searches for and lists the most suitable influencers from the database, and sends this list to the company's terminal, where the company can check it.

[0876] 3. Matching

[0877] The user (company) selects the most suitable influencer from the presented influencer list and sends the selection results to the server. The selection results are saved in a database and notified to the influencer.

[0878] 4. Automated content generation

[0879] Users (influencers) input their requests into the system from their devices, and the system uses generation AI to automatically generate video materials and a story (script). The generated results are provided to the influencer via a server. The influencer then creates a video based on the provided materials and script.

[0880] 5. Analysis of viewing data and improvement proposals

[0881] The server collects viewing data for published videos and analyzes information such as the number of views, likes, shares, and comments. Based on the analysis results, it generates specific improvement suggestions to increase the number of followers, views, likes, and shares, and notifies the influencer.

[0882] 6. User voting function

[0883] The device displays a voting page to the user, allowing the user to propose projects they would like the influencer to undertake or vote on projects that have already been proposed. The server tally the voting results in real time and notify the influencer. The influencer can then consider new projects based on the voting results.

[0884] Specific examples

[0885] 1. Matching with corporate projects

[0886] Example: When a company requests promotion for a new product, the system will list suitable influencers based on the company's objectives and target audience, allowing the company to select the most suitable influencer in a short amount of time.

[0887] 2. Automated content generation

[0888] Example: When an influencer is stuck for ideas for a new video, a script on the theme of "Recommended Summer Beach Goods" is automatically generated, allowing the influencer to quickly get started on creating a video.

[0889] 3. Analysis of viewing data and improvement proposals

[0890] Example: If an influencer's recent videos aren't getting as many views as expected, the system will analyze the viewing data and provide suggestions for improvements, such as using hashtags, changing the thumbnail, or adjusting the posting time, so the influencer can improve performance in their next video production.

[0891] 4. User voting function

[0892] Example: An influencer holds a poll in the system to ask users to suggest ideas for their next video. If users vote for the idea "funny pet videos" most often, the influencer can use the results to create their next video.

[0893] The system of the present invention can facilitate communication between companies, influencers, and general users, improving both the quality and quantity of content.

[0894] The processing flow will be explained below.

[0895] Processing steps from registering company information to matching

[0896] Step 1:

[0897] The user (company) registers project information using a terminal. The company enters project details (purpose, target audience, budget, etc.) and sends them to the server.

[0898] Step 2:

[0899] The server analyzes the received job information and stores the data in the appropriate fields, allowing for smoother search processing later.

[0900] Step 3:

[0901] The server searches a database of influencers and lists the most suitable influencers based on the project information, taking into consideration factors such as past performance and areas of expertise.

[0902] Step 4:

[0903] The server generates a list of optimal influencers and sends it to the user's (company's) device.

[0904] Step 5:

[0905] The user (company) checks the influencer list displayed on the device and selects the most suitable influencer. The selection results are sent to the server.

[0906] Step 6:

[0907] The server stores the selection results in a database and notifies the selected influencers.

[0908] Process steps for auto-generating content by influencers

[0909] Step 1:

[0910] A user (influencer) accesses the system using a terminal and inputs a request for new content, detailing the theme and required elements, and sends it to the server.

[0911] Step 2:

[0912] The server runs a generation AI based on the request received and generates optimal video material and story (script).

[0913] Step 3:

[0914] The generative AI retrieves the necessary information from the database and creates materials and scripts that fit the specified theme.

[0915] Step 4:

[0916] The server sends the generated materials and scripts to the influencer's device.

[0917] Step 5:

[0918] The user (influencer) checks the materials and script generated on the device and begins video production.

[0919] Analysis of viewing data and processing steps for improvement suggestions

[0920] Step 1:

[0921] The server periodically collects viewing data for published videos, including the number of views, likes, shares, and comments.

[0922] Step 2:

[0923] The server analyzes the data collected to extract trends and patterns in viewing data and evaluate the impact of specific factors on performance.

[0924] Step 3:

[0925] The server generates specific improvement suggestions based on the analysis, such as optimizing hashtags, improving thumbnails, and adjusting posting times.

[0926] Step 4:

[0927] The server notifies the influencer's terminal of the generated improvement proposal.

[0928] Step 5:

[0929] The user (influencer) checks the suggestions on their device and reflects them in their next video production.

[0930] Steps for processing user votes

[0931] Step 1:

[0932] The device will then display a voting page where users can suggest projects they would like influencers to undertake or vote for projects that have already been proposed.

[0933] Step 2:

[0934] Users use their devices to propose or vote on projects, and the proposal and vote data are sent to the server.

[0935] Step 3:

[0936] The server compiles proposal and vote data in real time, and popular proposals are ranked.

[0937] Step 4:

[0938] The server sends the results of the survey to the influencer's device, who then uses the results to consider new projects.

[0939] Through the above processing steps, the system of the present invention can facilitate communication between companies, influencers, and users, and realize effective content creation and improved performance.

[0940] Example 1

[0941] 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."

[0942] For a company to effectively use influencers for promotion, it is necessary to select suitable influencers, create content, analyze and improve viewing data, and collect feedback from general users. However, managing these processes individually takes a great deal of time and effort, making it difficult to do so efficiently.

[0943] 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.

[0944] In this invention, the server includes: means for registering a company's project information; means for searching for and listing optimal influencers from an influencer database based on the company's project information; means for presenting the listed influencers to a company and allowing the company to select one; means for notifying the selected influencers and proposing collaboration; means for managing and following up on the selection results; means for having the selected influencers input requests for automatic content generation using a generative AI model and providing the generation results; means for collecting and analyzing viewing data of published videos and notifying improvement suggestions; and means for tallying votes from general users in real time and notifying them of material for consideration in new projects. This makes it possible to efficiently match companies and influencers and consistently manage each process of content creation and improvement.

[0945] "Means for registering company project information" is a function that allows companies to enter information such as promotion and marketing objectives, target audience, budget, etc. into the system and register it.

[0946] "A means of searching and listing the most suitable influencers from the influencer database based on a company's project information" is a function that searches for influencer information in the database based on the registered company's project information and lists the most suitable influencers.

[0947] "A means of presenting the listed influencers to companies and allowing companies to select" is a function that displays a list of searched influencers to companies, allowing them to select the most suitable influencer.

[0948] "Means of notifying selected influencers and proposing collaboration" is a function for notifying influencers selected by a company and proposing collaboration.

[0949] "Means for managing and following up on selection results" refers to a function that stores the selection results of companies and influencers in a database and allows for continuous follow-up.

[0950] "Means of having selected influencers input requests for automatic content generation using a generative AI model and providing the generated results" refers to a function that enables influencers to input requests for content generation into the system and provide them with content (video material and stories) that is automatically generated using a generative AI model.

[0951] "Means for collecting and analyzing viewing data of published videos and notifying improvement suggestions" refers to a function for collecting viewing data of published videos, analyzing this data, generating specific suggestions for performance improvement, and notifying influencers.

[0952] "A means of aggregating votes from general users in real time and notifying them of the results as material for consideration in new projects" is a function that aggregates votes from general users in real time and notifies influencers of the results as material for consideration in new projects.

[0953] The system of the present invention is a comprehensive generative AI-equipped system that includes functions for registering corporate project information, searching a database of influencers, matching, automatically generating content, analyzing viewing data, generating improvement suggestions, and voting by general users. This system efficiently performs each function using a variety of hardware and software.

[0954] First, the user (company) enters the company's project information into the device's web browser (e.g., Google Chrome). The project information includes the promotion purpose, target audience, budget, etc., and this information is sent to the server. The server then stores the received information in a database management system (e.g., MySQL).

[0955] Next, the server analyzes the project information registered by the company. This analysis uses a natural language processing algorithm (e.g., Python's NLTK library). Based on the analysis results, the server searches the database for the most suitable influencers and creates a list. This list is sent to the user's (company's) device, and the company can view the list through a web browser.

[0956] The company selects the most suitable influencer from the list of influencers provided and sends the selection results from the terminal to the server. The server stores the received selection results in a database and simultaneously sends an email notification (e.g., SendGrid) to the selected influencer to notify them of the details of the project.

[0957] Influencers use their devices to input requests into the system, which automatically generates content. The request is sent to a generative AI model (e.g., OpenAI GPT-3), which automatically generates video material and a story (script). A request for automatic generation is made by entering a prompt phrase, such as a theme like "Recommended summer beach items." The generated results are provided to the influencer via the server, allowing for quick video production.

[0958] Viewing data for published videos is collected by the server, and information such as the number of views, likes, shares, and comments is analyzed using a data analysis tool (e.g., Google Analytics). Based on the analysis results, the server generates specific improvement suggestions and notifies the influencer. The improvement suggestions include optimizing hashtags and adjusting the timing of posting.

[0959] The system also includes a voting function for general users. Users can access the voting page using their devices to propose new projects to influencers or vote for existing projects. Voting results are sent to the server in real time and tallied. The server then notifies the influencers of the voting results, which they use as information for considering new projects.

[0960] For example, if a company requests a promotion for a new product, the system will list suitable influencers based on the company's objectives and target audience. Also, if an influencer is stuck for ideas, a script on the theme of "introducing recommended summer beach items" can be automatically generated using a generative AI model. In this way, the influencer can quickly begin creating a video.

[0961] This system will facilitate collaboration between companies, influencers, and individuals, and will improve both the quality and quantity of content at the same time.

[0962] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0963] Step 1:

[0964] The user (company) accesses the system using a web browser on their device and enters project information (promotion purpose, target audience, budget, etc.). The entered information is sent from the device to the server in JSON format.

[0965] Step 2:

[0966] The server saves the received job information in a database management system (e.g. MySQL). At this time, it checks the consistency of the input information and converts it into the required data format. Once saving is complete, it returns a response to the terminal indicating that saving was successful.

[0967] Step 3:

[0968] The server retrieves project information from the database and analyzes it using a natural language processing algorithm (e.g., Python's NLTK library). Based on the analysis results, it creates search criteria for influencers. Using these analysis results, it searches the database for the most suitable influencers.

[0969] Step 4:

[0970] The server sends the list of influencers obtained as search results in JSON format to the terminal. The terminal displays the list of influencers on a web browser, allowing the user (company) to review and select from the list.

[0971] Step 5:

[0972] The user (company) selects the most suitable influencer from the influencer list displayed on the web browser. Once the selection is complete, the device sends the selection results in JSON format to the server.

[0973] Step 6:

[0974] The server stores the received selection results in a database. At this time, it validates the selection information and, once the selection is confirmed, notifies the selected influencer. Notifications are sent via an email notification service (e.g., SendGrid).

[0975] Step 7:

[0976] The user (influencer) uses a device to input a request to the system. The request (e.g., the theme or purpose of the video) is sent to the generative AI model. This request is sent from the device via the API as a prompt.

[0977] Step 8:

[0978] A generative AI model (e.g., OpenAI GPT-3) automatically generates video material and a story (script) based on the prompt. The generated information is sent to the server in JSON format. The server stores the generated results in a database and provides them to influencers.

[0979] Step 9:

[0980] The influencer will create a video using the generated results provided and upload it to a platform for publishing.

[0981] Step 10:

[0982] The server collects viewing data such as the number of views, likes, shares, and comments for published videos using the video platform's API (e.g., YouTube Data API), and stores this data in a database in real time.

[0983] Step 11:

[0984] The server analyzes the collected viewing data using a data analysis tool (e.g., the Pandas library), identifies viewing patterns and trends, and generates specific improvement proposals based on the analysis results. The generated improvement proposals are then notified to influencers.

[0985] Step 12:

[0986] The terminal displays a voting page to the user, who then uses the terminal to cast their vote, with each vote immediately transmitted to the server.

[0987] Step 13:

[0988] The server receives the voting results, stores them in a database in real time, and tallies them. The tallied results are then sent to influencers, who use them as information for considering new projects.

[0989] Step 14:

[0990] Influencers will consider new ideas based on the collected voting results, and then create new content based on those ideas using a generative AI model.

[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 modern content creation, matching companies with influencers is extremely important, but the process of selecting the right influencer is extremely time-consuming and inefficient. Furthermore, there is a lack of ways to quickly provide high-quality content when influencers run out of content ideas. Furthermore, there is a lack of ways to analyze the performance of published content and make specific suggestions for improvement. Finally, there are limited ways to utilize user feedback and incorporate new content ideas. A new system is needed to solve these issues.

[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 registering a company's project information; means for searching for and listing optimal influencers from an influencer database based on the company's project information; means for presenting the listed influencers to a company and allowing the company to select them; means for notifying the selected influencers and proposing collaboration; means for managing and following up on the selection results; means for the influencer to input a request and automatically generating video materials and stories using a generative AI model; means for providing the generated results to the influencer to support video production; means for collecting viewing data on published videos and analyzing data such as the number of views, likes, shares, and comments; means for generating specific improvement suggestions based on the analysis results and notifying the influencer; means for users to propose projects they would like influencers to do or vote for projects that have already been proposed; and means for tallying the voting results in real time and notifying the influencer. This makes it possible to efficiently match companies and influencers and improve the quality and quantity of content provided.

[0996] "Company project information" refers to promotion and marketing requests that companies register in the system based on specific objectives, target audiences, budgets, etc.

[0997] An "influencer database" is a data storage device that stores information such as each influencer's profile information, past activities, and number of followers.

[0998] The "optimal influencer" is the influencer who best fits the company's project information and can maximize the expected results.

[0999] A "generative AI model" is a type of artificial intelligence that automatically generates video material and stories based on pre-trained data, and examples include GPT-4.

[1000] "Footage and Stories" refers to creative materials such as footage and scripts to be used in videos produced by influencers.

[1001] "Viewing data" refers to data that shows how users responded to published videos, including the number of views, likes, shares, and comments.

[1002] "Improvement suggestions" are specific advice and methods for improving the quality and performance of content based on the results of analysis of viewing data, etc.

[1003] "User voting" is the act of ordinary users expressing their opinions on projects they would like influencers to undertake.

[1004] "Voting results" refers to data regarding the next content plan compiled based on votes from users.

[1005] The following system is an example of an embodiment of the present invention. Each function of the system is realized by utilizing a server, a user terminal, and a generative AI model.

[1006] The first step is for a company to register its project information. The company accesses the system using a terminal and enters details of its promotion and marketing, including its objectives, target audience, budget, etc. The server receives the entered information and stores it in a database.

[1007] Next, the server analyzes the company's project information and searches for and lists the most suitable influencers from its influencer database. The list of influencers is sent from the server to the company's terminal, and the company selects influencers based on that list.

[1008] After the selection is complete, the server saves the selection results in a database and notifies the selected influencers, who can then start collaborating with the company.

[1009] If an influencer gets stuck creating content, a generative AI model is used to automatically generate video material and stories. The influencer inputs a request from their device, and the generative AI model creates a prompt based on that request, which is then used to generate content. This generated content is then provided to the influencer again via the server. For example, the prompt for a request on the theme of "introducing beach goods" would look like this:

[1010] Prompt statement:

[1011] Generate a script for the following video: "Topic: Introducing Beach Goods, Video Length: 2 minutes"

[1012] Viewing data for published videos is also an important factor. The server collects data such as the number of views, likes, shares, and comments in real time and analyzes the visual information. Based on the results of this analysis, specific suggestions for improving the quality of content are generated and notified to influencers. For example, these suggestions include "reviewing hashtags," "changing posting times," and "improving thumbnails."

[1013] The system also includes a voting function for users. The device displays a voting page to users, allowing them to propose projects they would like influencers to do or vote for projects that have already been proposed. The server tally the voting results in real time and notify the influencer. Based on these voting results, the influencer can decide on their next video project.

[1014] All of these processes are efficiently managed by a system that facilitates communication between companies, influencers, and users. The system uses hardware such as smartphones and servers, as well as software such as generative AI models (e.g., GPT-4) and cloud services (e.g., AWS Lambda and Google Cloud Functions).

[1015] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1016] Step 1:

[1017] Companies access the system through a terminal and input project information (purpose, target audience, budget, etc.). The terminal sends this information to the server, which stores the received information in a database. It receives project information as input and generates data that is stored in the database as output.

[1018] Step 2:

[1019] The server analyzes the project information registered by the company and searches for the most suitable influencers from the influencer database. It performs data analysis on the information of each influencer in the database and creates a list of influencers most suitable for the project. It receives the company's project information as input and generates a list of influencers as output.

[1020] Step 3:

[1021] The server sends the generated list of influencers to the company's terminal, where the company checks the list and selects the most suitable influencer. The server receives the list of influencers as input and generates the selected influencers as output.

[1022] Step 4:

[1023] The company sends the selection results to the server, which stores the results in a database. The server then notifies the selected influencers. It receives the selection results as input and generates notifications as output.

[1024] Step 5:

[1025] Influencers input content creation requests into the system via their devices. The server receives these requests and sends prompts to the generative AI model. The generative AI model automatically generates video material and stories based on the requests. For example, if the theme is "Introducing beach goods," the following prompts are sent to the generative AI model:

[1026] Prompt statement:

[1027] Generate a script for the following video: "Topic: Introducing Beach Goods, Video Length: 2 minutes"

[1028] It takes influencer requests as input and produces generated content as output.

[1029] Step 6:

[1030] The server sends the generated video material and story to the influencer's device, who then uses this information to create the video. The server receives the generated content as input and generates data to be sent to the influencer's device as output.

[1031] Step 7:

[1032] The server collects viewing data (number of views, likes, shares, comments, etc.) of published videos in real time. It analyzes the viewing data and generates specific improvement proposals to increase the number of followers, views, likes, and shares based on the analysis results, and notifies the influencer. It receives viewing data as input and generates improvement proposals as output.

[1033] Step 8:

[1034] The device displays a voting page to the user, allowing the user to propose projects they would like influencers to implement or vote for projects that have already been proposed. The server tally the voting results in real time and notify the influencers. It receives the user's voting information as input and generates the voting results as output.

[1035] Step 9:

[1036] The influencer will decide on the next video project based on the voting results, completing the process to enhance the interaction between users and influencers and provide content that meets users' requests. It takes the voting results as input and generates the next video project as output.

[1037] 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.

[1038] The system of the present invention includes functions for registering corporate project information, searching a database of influencers, matching, automatically generating content, analyzing viewing data, generating improvement suggestions, a voting function for users, and recognizing user emotions using an emotion engine.

[1039] System configuration

[1040] 1. Registration of business project information and emotion recognition

[1041] Users (companies) access the system using their terminals and register project information (purpose, target audience, budget, etc.). This data is sent to the server and stored in a database. The emotion engine analyzes the user's emotions regarding the company's project information and can adjust the presentation method.

[1042] 2. Influencer database search and matching

[1043] The server analyzes the project information registered by the company and searches for and lists the most suitable influencers from the database. This list is sent from the server to the company's terminal, where the company checks the list. Based on the company's decision, the influencer is notified.

[1044] 3. Automatic content generation and sentiment analysis

[1045] Users (influencers) input their requests into the system from their devices, and the generation AI automatically generates video material and a story (script). The generated results are provided to the influencer via the server. Furthermore, the emotion engine analyzes the user's feelings toward the generated content and provides feedback to the generation AI, which can be used to generate content next time.

[1046] 4. Analysis of viewing data, improvement proposals, and sentiment analysis

[1047] The server collects viewing data for published videos and analyzes information such as the number of views, likes, shares, and comments. This information is used to generate specific improvement proposals to increase the number of followers, views, likes, and shares, and these proposals are notified to influencers. Additionally, an emotion engine can be used to analyze user emotions and reflect them in improvement proposals.

[1048] 5. User voting and sentiment analysis

[1049] The device displays a voting page to users, allowing them to propose projects they would like influencers to implement or vote for projects that have already been proposed. The server tallys the voting results in real time and ranks the most popular projects. Furthermore, an emotion engine analyzes users' voting behavior and emotions toward the proposals, and notifies the influencers of this information, helping them consider new projects.

[1050] Specific examples

[1051] 1. Matching with corporate projects

[1052] Example: When a company requests a promotion for a new product, the system will generate a list of suitable influencers based on the company's objectives and target audience. As a result, the company can select the most suitable influencer in a short time. The emotion engine adjusts the offer presentation based on the user's reaction and interest level.

[1053] 2. Automatic content generation and sentiment analysis

[1054] Example: When an influencer is stuck for ideas for a new video, a script on the theme of "Introducing recommended summer beach items" is automatically generated. This allows the influencer to quickly start creating a video. Furthermore, an emotion engine analyzes user reactions and reflects them in the next content generation.

[1055] 3. Analysis of viewing data, improvement proposals, and sentiment analysis

[1056] Example: If an influencer's recent videos aren't getting as many views as expected, the system will analyze the viewing data and provide suggestions for improvement, such as using hashtags, changing thumbnails, adjusting posting times, etc. Furthermore, an emotion engine will analyze viewers' emotions and provide more specific suggestions for improvement.

[1057] 4. User voting and sentiment analysis

[1058] Example: An influencer holds a poll in the system to ask users to suggest ideas for the next video. If users vote a lot for the idea "funny pet videos," the influencer can create the next video based on the results. The emotion engine can analyze users' feelings about the votes and use the results to create new ideas.

[1059] The system of this invention facilitates communication between companies, influencers, and users, enabling effective content creation and performance improvement. The introduction of an emotion engine enables even more effective matching and improvement suggestions.

[1060] The processing flow will be explained below.

[1061] Registration of business case information and emotion recognition processing steps

[1062] Step 1:

[1063] The user (company) accesses the system using a terminal, enters project information (purpose, target audience, budget, etc.), and sends it to the server.

[1064] Step 2:

[1065] The server receives the submitted job information and stores it in a database.

[1066] Step 3:

[1067] The server uses an emotion engine to analyze the user's emotions regarding the transmitted case information.

[1068] Step 4:

[1069] The server adjusts the way the case information is presented based on the analysis results.

[1070] Influencer database search and matching process steps

[1071] Step 1:

[1072] The server analyzes the project information registered by the company and searches the database for the most suitable influencer.

[1073] Step 2:

[1074] The server lists influencers based on the search results.

[1075] Step 3:

[1076] The server generates a list of optimal influencers and sends it to the user's (company's) device.

[1077] Step 4:

[1078] The user (company) checks the influencer list presented on the device, selects the most suitable influencer, and sends the selection results to the server.

[1079] Step 5:

[1080] The server stores the selection results in a database and notifies the selected influencers.

[1081] Processing steps for automatic content generation and sentiment analysis

[1082] Step 1:

[1083] A user (influencer) accesses the system using a terminal, inputs a request for new content, and sends it to the server.

[1084] Step 2:

[1085] The server runs a generation AI based on the request received to generate optimal video material and story (script).

[1086] Step 3:

[1087] The server sends the generated materials and scripts to the influencer's device.

[1088] Step 4:

[1089] The user (influencer) checks the materials and script generated on the device and begins video production.

[1090] Step 5:

[1091] The server uses an emotion engine to analyze other users' emotions toward the content generated by the user (influencer) and feeds the results back to the generation AI.

[1092] Analysis of viewing data, improvement suggestions, and processing steps for sentiment analysis

[1093] Step 1:

[1094] The server periodically collects viewing data for published videos, including the number of views, likes, shares, and comments.

[1095] Step 2:

[1096] The server analyzes the collected data and extracts trends and patterns in the viewing data.

[1097] Step 3:

[1098] The server uses an emotion engine to analyze the user's emotions regarding the viewing data.

[1099] Step 4:

[1100] The server generates specific improvement suggestions based on the analysis, such as optimizing hashtags, improving thumbnails, and adjusting posting times.

[1101] Step 5:

[1102] The server notifies the influencer's terminal of the generated improvement proposal.

[1103] Step 6:

[1104] The user (influencer) checks the suggestions on their device and reflects them in their next video production.

[1105] User voting and sentiment analysis processing steps

[1106] Step 1:

[1107] The device will then display a voting page to the user, where the user can suggest projects they would like the influencer to undertake or vote for projects that have already been proposed.

[1108] Step 2:

[1109] Users use their devices to propose or vote on projects, and the proposal and vote data are sent to the server.

[1110] Step 3:

[1111] The server compiles proposal and vote data in real time, and popular proposals are ranked.

[1112] Step 4:

[1113] The server uses an emotion engine to analyze users' voting behavior and emotions toward the proposals.

[1114] Step 5:

[1115] The server notifies the influencer of the results of the aggregation and sentiment analysis, and the influencer uses these results to consider new projects.

[1116] Example 2

[1117] 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."

[1118] Traditional matching systems for companies and influencers face many challenges in terms of effective content creation and analysis of viewing data. Specifically, they face issues such as companies being unable to properly deliver project information to their target audiences, influencers being unable to quickly obtain appropriate video ideas, being unable to fully utilize viewing data from published content, and being unable to analyze user sentiment and reflect it in future content. They also lack functionality for collecting project ideas through user votes and analyzing and reflecting those sentiments. This makes collaboration between companies and influencers inefficient, making it difficult to improve overall performance.

[1119] 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. In this invention, the server includes: means for registering project information of a company; means for analyzing user emotions regarding the company's project information using an emotion engine and adjusting the presentation method; means for searching for and listing optimal influencers from an influencer database based on the company's project information; means for presenting the listed influencers to a company and allowing the company to select; means for notifying the selected influencer and proposing collaboration; means for managing and following up on the selection results; means for the influencer to input a request and automatically generating video materials and stories using a generation AI; means for analyzing user emotions regarding content generated using the emotion engine and providing feedback to the generation AI; means for providing the generated results to the influencer and supporting video production; means for collecting viewing data of published videos and analyzing data such as the number of views, likes, shares, and comments; means for analyzing viewer emotions using the emotion engine, generating specific improvement suggestions based on the analysis results, and notifying the influencer; and means for displaying a voting page to the user and analyzing the user's voting behavior and emotions regarding the suggestions. This will enable effective matching between companies and influencers and improve the performance of content creation.

[1120] "Company project information" refers to information about projects and transactions that a company registers in the system, including objectives, target audiences, budgets, etc.

[1121] An "emotion engine" is an engine for analyzing user emotions, extracting emotions from text and behavior and making an evaluation.

[1122] An "influencer database" is a database that accumulates information about influencers, including the number of followers, areas of expertise, and past activity history.

[1123] The "system" is an integrated platform for streamlining communication and content creation between companies, influencers, and general users.

[1124] "Generative AI" is artificial intelligence that automatically generates content based on given prompts, generating video footage and stories.

[1125] "Viewing data" refers to user reaction data to published content, including the number of views, likes, shares, comments, and the like.

[1126] "Improvement proposals" are specific methods or ideas proposed to improve content performance based on viewing data and the results of sentiment analysis.

[1127] A "voting page" is a web page where users can vote for influencers' proposals and the results are tallied in real time.

[1128] The system according to the present invention is an integrated platform for streamlining communication and content creation between companies, influencers, and general users. Specific embodiments of this system will be described below.

[1129] Registration of business project information and emotion recognition

[1130] Users (companies) access the system using a terminal and input their company's project information (purpose, target audience, budget, etc.). The terminal sends this information to the server. The server stores the received project information in a database and activates the emotion engine. The emotion engine analyzes the user's emotions based on the input information and adjusts the way the information is presented based on the results.

[1131] Examples:

[1132] For example, when a company inputs information about a project such as "promotion of a new product aimed at young people," the emotion engine evaluates emotions such as "excitement" and "attractiveness," and the server uses this information to adjust the project presentation to optimize it for the target audience.

[1133] Influencer database search and matching

[1134] The server analyzes the project information registered by the company and extracts elements such as the purpose, target audience, and budget. Next, it uses this information to search the database for the most suitable influencers and creates a list. This list is sent to the company's device. The company then checks the list on the device and selects the most suitable influencer. The server then sends a notification to the selected influencer.

[1135] Examples:

[1136] When a company requests a "fashion item promotion," the server creates a list of influencers with large numbers of followers in the fashion field and sends it to the company. The company then selects influencers based on the list and sends them notifications.

[1137] Automatic content generation and sentiment analysis

[1138] The user (influencer) inputs a theme or idea from their device. The device sends the request to the server, which passes it on to the generation AI. The generation AI automatically generates video material and a script based on the request and provides the results to the influencer. The emotion engine also analyzes the user's emotions regarding the generated content and sends that feedback to the generation AI to help with the next generation.

[1139] Examples:

[1140] When an influencer enters a theme, such as "Christmas party decoration ideas," the generative AI automatically generates a script and video material based on the theme. This generated content is sent to the influencer's device. The emotion engine also analyzes user reactions, and the results are reflected in the next content generation.

[1141] Analysis of viewing data, improvement suggestions, and sentiment analysis

[1142] The server collects viewing data (number of views, likes, shares, comments, etc.) for published videos. The server then analyzes this data and generates specific improvement suggestions to improve performance. An emotion engine analyzes viewer emotions and reflects the results in improvement suggestions. These suggestions are then sent to the influencer's device.

[1143] Examples:

[1144] If an influencer's recent videos aren't getting as many views as expected, the server analyzes the viewing data and provides specific suggestions for improvement, such as "increase hashtag usage," "change thumbnails," "adjust posting times," etc. In addition, the emotion engine analyzes viewer emotions and notifies specific suggestions for improvement.

[1145] User voting and sentiment analysis

[1146] Users (general users) can vote for influencers' proposals from their devices. The server tally the voting results in real time and ranks the most popular proposals. The emotion engine also analyzes users' voting behavior and emotions toward the proposals, and notifies the influencers of the results.

[1147] Examples:

[1148] Users vote for multiple video project ideas ("Travel Vlog," "Cooking Video," "Funny Pet Video"). If "Funny Pet Video" receives the most votes, the influencer will create the next video based on this result. The emotion engine analyzes users' emotions and uses the results to create new projects.

[1149] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1150] Step 1:

[1151] Registering business information

[1152] Users (companies) access the system from their terminals and enter project information (purpose, target audience, budget, etc.), which is then sent to the server.

[1153] Input: Company project information (purpose, target, budget)

[1154] Specific operation: A company enters and submits information about a "new product promotion aimed at young people."

[1155] Output: Sending job information to the server

[1156] Step 2:

[1157] Case information storage and sentiment analysis

[1158] The server stores the received project information in a database and activates the emotion engine, which analyzes the user's emotions regarding the company's project information and adjusts the way the information is presented based on the results.

[1159] Input: Company project information

[1160] Specific behavior: The emotion engine evaluates emotions such as "excited" or "attractive" and adjusts the presentation accordingly.

[1161] Output: Saving case information to a database, results of sentiment analysis

[1162] Step 3:

[1163] Influencer database search and matching

[1164] The server analyzes the project information, searches for the most suitable influencer from its influencer database, and then lists the search results and sends them to the company's terminal.

[1165] Input: Parsed case information

[1166] Specific operation: The server creates a list of influencers with a large number of followers in the fashion field and sends it to the company's terminal.

[1167] Output: List of influencers

[1168] Step 4:

[1169] Influencer selection by companies

[1170] The company checks the influencer list received on the device and selects the most suitable influencer. The selection results are sent to the server.

[1171] Input: Influencer list

[1172] Specific operation: A company selects the most suitable influencer from multiple influencers and sends the selection results.

[1173] Output: Information on selected influencers

[1174] Step 5:

[1175] Notification of selection and collaboration proposal

[1176] The server receives the company's selection results and sends notifications to the selected influencers, along with proposing collaborations.

[1177] Input: Information of selected influencers

[1178] What it does: Sends notifications to selected influencers and proposes collaboration.

[1179] Output: Notification and proposal to influencers

[1180] Step 6:

[1181] Influencer request and auto-generation

[1182] Users (influencers) input a theme or idea from their device and send the request to the server, which then passes the request to the AI ​​generator, which automatically generates video material and a script.

[1183] Input: Theme or idea

[1184] How it works: An influencer enters the theme "Christmas party decoration ideas" and submits it. The generative AI automatically generates content based on this.

[1185] Output: Generated footage and scripts

[1186] Step 7:

[1187] Generative content provision and feedback analysis

[1188] The server provides the generated content to the influencer's device, and the emotion engine analyzes the user's emotions toward the generated content and sends the feedback to the generation AI.

[1189] Input: Generated content

[1190] Specific operation: The emotion engine analyzes the user's reaction and uses it to generate the next content.

[1191] Output: Providing generated content to influencers, sentiment analysis results

[1192] Step 8:

[1193] Collecting viewing data and making improvement suggestions

[1194] The server collects and analyzes viewing data (number of views, likes, shares, comments, etc.) for published videos. Based on the analysis results, it generates specific improvement proposals to improve the performance of the content.

[1195] Input: Viewing data

[1196] Specific operation: The server analyzes viewing data and creates and notifies improvement suggestions such as "increasing the use of hashtags," "changing thumbnails," and "adjusting posting times."

[1197] Output: Improvement suggestions, notifications

[1198] Step 9:

[1199] User voting page and sentiment analysis

[1200] Users (general users) vote for influencers' proposals from their devices. The server tally the voting results in real time and ranks the most popular proposals. The emotion engine analyzes users' voting behavior and emotions toward the proposals, and notifies the influencers of the results.

[1201] Input: User votes

[1202] Specific operation: Users vote for "funny pet videos," etc., and the most popular projects are ranked based on the results. The emotion engine analyzes users' emotions and notifies influencers of the results.

[1203] Output: Voting results tallied, sentiment analysis results notified

[1204] (Application example 2)

[1205] 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."

[1206] Currently, companies spend a great deal of time and effort establishing marketing strategies that utilize influencers, but it is difficult to quickly and accurately find the most suitable influencers. In addition, it is difficult to select the appropriate robots and generate optimal work plans for factory manufacturing operations, and there is a need to improve work efficiency and reduce error rates. Therefore, a new system is needed that can simultaneously improve the efficiency of corporate marketing activities and factory manufacturing operations.

[1207] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1208] In this invention, the server includes means for registering project information of a company, means for searching for and listing optimal influencers from an influencer database based on the project information of the company, means for presenting the listed influencers to the company and allowing the company to select them, means for notifying the selected influencers and proposing collaboration, means for managing and following up on the selection results, means for registering project information of manufacturing work, means for searching for and listing appropriate factory robots from a database based on the project information of the manufacturing work, means for presenting the listed factory robots to a manufacturing manager and allowing the manufacturing manager to select them, means for notifying the selected factory robot and generating a work plan, and means for managing the selection results and following up on the work, thereby enabling the efficiency of corporate marketing activities and factory manufacturing work to be improved.

[1209] "Company project information" refers to information such as details of the project or campaign that the company wants to implement, its objectives, target audience, budget, etc.

[1210] An "influencer" is an individual or organization that has a large number of followers or viewers on the Internet and can influence a company's products or services.

[1211] A "database" refers to a collection of data used by the system to manage and search, such as information on corporate projects, influencers, and factory robots.

[1212] "Generative AI" refers to artificial intelligence that automatically generates video materials and work plans based on specified conditions and requirements.

[1213] An "emotion engine" refers to analytical software that analyzes a user's emotional state and provides appropriate feedback and suggestions for improvement based on that information.

[1214] "Factory robot" refers to an automated machine used to perform production tasks in a manufacturing plant.

[1215] A "work plan" refers to a plan that specifically defines a series of work procedures and processes to be carried out by factory robots.

[1216] "Viewing data" refers to data based on user viewing behavior, such as the number of views, likes, shares, and comments on published videos.

[1217] "Improvement proposals" refer to specific plans and instructions for improving efficiency and results based on the analysis of viewing data and work data.

[1218] "Follow-up" refers to the ongoing management, supervision, and support of influencers and factory robots.

[1219] The system of the present invention includes functions for registering company project information, searching and matching a database of influencers and factory robots, automatically generating work plans, analyzing viewing and work data, generating improvement proposals, providing user and employee feedback, and analyzing using an emotion engine. The entire system process is implemented using Python and the Flask framework. Each function is described in detail below.

[1220] Registering company project information

[1221] Corporate administrators, who are users, access the system using a smartphone or head-mounted display and register project information (purpose, target audience, budget, etc.). This data is sent to the server and stored in a database. An emotion engine is used to analyze employees' emotions regarding the registered project information and adjust the display method.

[1222] Database search and matching of influencers and factory robots

[1223] The server analyzes the registered project information, searches for and lists the most suitable candidates from the influencer or factory robot database. This list is sent from the server to the user's device, which is the company administrator or manufacturing manager, who then checks the list and selects the most suitable candidate. The selected influencer or robot is notified, and collaboration and work plans are carried out.

[1224] Automatic generation of work plans and sentiment analysis

[1225] When influencers or factory robots run out of ideas for new content or work plans, the generative AI automatically generates video footage and work plans. The generated results are provided via a server. In addition, an emotion engine analyzes the emotions of employees and users regarding the generated content and plans and provides feedback to the generative AI.

[1226] Analysis of viewing and work data and suggestions for improvement

[1227] The server collects viewing data for published videos and analyzes information such as the number of views, likes, shares, and comments. Similarly, it collects robot manufacturing work data and analyzes work efficiency and error rates. From this, specific improvement proposals are generated and notified to influencers and manufacturing managers. An emotion engine is used to analyze the emotions of users and employees and reflect them in improvement proposals.

[1228] User and employee feedback features and sentiment analysis

[1229] A voting page is displayed where users and employees can provide feedback to influencers and factory robots. The server compiles the feedback results in real time and ranks the most popular plans and work procedures. An emotion engine analyzes the sentiment of the feedback and notifies influencers and production managers, helping them consider new plans and work procedures.

[1230] Specific examples

[1231] When a company registers project information to "promote a new product," the system lists the most suitable influencers. The company administrator selects the influencers, and the generation AI automatically generates a script with the theme "Introducing recommended summer beach items." Similarly, when a manufacturing factory registers a project to "improve the assembly process for a new product," the system selects the most suitable factory robot and automatically generates new assembly procedures. Employees use head-mounted displays to check the new procedures and provide feedback. The emotion engine analyzes the feedback and reflects it in the next improvement proposal.

[1232] Prompt Sentence Examples

[1233] "Please suggest the optimal robot and work plan to improve the assembly process for a new product."

[1234] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1235] Step 1: Registering the project information

[1236] Subject: User

[1237] The user, a company manager, uses a smartphone or head-mounted display to input project information (purpose, target audience, budget, etc.). This information is sent from the device to a server and stored in a database. The emotion engine analyzes the employee's emotional state regarding the registered project information and adjusts the display method accordingly.

[1238] Input: Company administrator inputs case information

[1239] Data processing / calculation: The emotion engine analyzes employee emotions based on input case information.

[1240] Output: Adjusted display method

[1241] Step 2: Finding influencers and factory robots

[1242] Subject: Server

[1243] The server analyzes the project information entered by the company administrator and searches for the most suitable influencer from the influencer database or the most suitable robot from the factory robot database. This list is then sent from the server to the terminal of the company administrator or manufacturing manager.

[1244] Input: Project information

[1245] Data processing / calculation: Retrieving data from the database and filtering

[1246] Output: A list of the best influencers or factory robots

[1247] Step 3: Candidate selection and notification

[1248] Subject: User

[1249] The company manager or manufacturing manager reviews the submitted list and selects the most suitable influencer or factory robot. The selection results are sent to the server and notified to the selected influencer or robot.

[1250] Input: A list of the best influencers and robots

[1251] Data processing / calculation: List display and selection

[1252] Output: Notification of selection results

[1253] Step 4: Automatically generate a work plan

[1254] Subject: Server

[1255] When influencers or factory robots run out of ideas for new content or work plans, generative AI automatically generates video footage and work plans, and the generated results are provided to the influencers or robots from the server.

[1256] Input: Prompt sentence for generative AI model

[1257] Data processing / calculation: Generative AI generates content and work plans

[1258] Output: Auto-generated content and work plans

[1259] Step 5: Analyze the feedback

[1260] Subject: Server

[1261] The server aggregates feedback from users and employees in real time through the feedback page. The emotion engine analyzes the emotions in response to the feedback and notifies the results to influencers and production managers.

[1262] Input: Feedback data and emotion data

[1263] Data processing / calculation: Analysis and aggregation using emotion engine

[1264] Output: Notification of analysis results

[1265] Step 6: Analyze viewing and task data

[1266] Subject: Server

[1267] The server collects viewing data for published videos and work data from factory robots, and analyzes the number of views, likes, shares, comments, work efficiency, error rates, etc. Based on this, it generates specific improvement proposals and notifies them to influencers and production managers.

[1268] Input: Viewing and working data

[1269] Data processing / calculation: Data analysis using analytical algorithms

[1270] Output: Specific improvement suggestions

[1271] 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.

[1272] 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.

[1273] 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.

[1274] [Fourth embodiment]

[1275] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1276] 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.

[1277] 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).

[1278] 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.

[1279] 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.

[1280] 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).

[1281] 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.

[1282] 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.

[1283] 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.

[1284] 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.

[1285] 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.

[1286] 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.

[1287] 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."

[1288] The system of the present invention includes functions for registering company project information, searching a database of influencers, matching, automatically generating content, analyzing viewing data, generating improvement suggestions, and allowing users to vote.

[1289] System configuration

[1290] 1. Registering business information

[1291] Users (companies) access the system using a terminal and register project information (purpose, target audience, budget, etc.) This data is sent to the server and stored in the database.

[1292] 2. Influencer database search

[1293] The server analyzes the project information registered by the company, searches for and lists the most suitable influencers from the database, and sends this list to the company's terminal, where the company can check it.

[1294] 3. Matching

[1295] The user (company) selects the most suitable influencer from the presented influencer list and sends the selection results to the server. The selection results are saved in a database and notified to the influencer.

[1296] 4. Automated content generation

[1297] Users (influencers) input their requests into the system from their devices, and the system uses generation AI to automatically generate video materials and a story (script). The generated results are provided to the influencer via a server. The influencer then creates a video based on the provided materials and script.

[1298] 5. Analysis of viewing data and improvement proposals

[1299] The server collects viewing data for published videos and analyzes information such as the number of views, likes, shares, and comments. Based on the analysis results, it generates specific improvement suggestions to increase the number of followers, views, likes, and shares, and notifies the influencer.

[1300] 6. User voting function

[1301] The device displays a voting page to the user, allowing the user to propose projects they would like the influencer to undertake or vote on projects that have already been proposed. The server tally the voting results in real time and notify the influencer. The influencer can then consider new projects based on the voting results.

[1302] Specific examples

[1303] 1. Matching with corporate projects

[1304] Example: When a company requests promotion for a new product, the system will list suitable influencers based on the company's objectives and target audience, allowing the company to select the most suitable influencer in a short amount of time.

[1305] 2. Automated content generation

[1306] Example: When an influencer is stuck for ideas for a new video, a script on the theme of "Recommended Summer Beach Goods" is automatically generated, allowing the influencer to quickly get started on creating a video.

[1307] 3. Analysis of viewing data and improvement proposals

[1308] Example: If an influencer's recent videos aren't getting as many views as expected, the system will analyze the viewing data and provide suggestions for improvements, such as using hashtags, changing the thumbnail, or adjusting the posting time, so the influencer can improve performance in their next video production.

[1309] 4. User voting function

[1310] Example: An influencer holds a poll in the system to ask users to suggest ideas for their next video. If users vote for the idea "funny pet videos" most often, the influencer can use the results to create their next video.

[1311] The system of the present invention can facilitate communication between companies, influencers, and general users, improving both the quality and quantity of content.

[1312] The processing flow will be explained below.

[1313] Processing steps from registering company information to matching

[1314] Step 1:

[1315] The user (company) registers project information using a terminal. The company enters project details (purpose, target audience, budget, etc.) and sends them to the server.

[1316] Step 2:

[1317] The server analyzes the received job information and stores the data in the appropriate fields, allowing for smoother search processing later.

[1318] Step 3:

[1319] The server searches a database of influencers and lists the most suitable influencers based on the project information, taking into consideration factors such as past performance and areas of expertise.

[1320] Step 4:

[1321] The server generates a list of optimal influencers and sends it to the user's (company's) device.

[1322] Step 5:

[1323] The user (company) checks the influencer list displayed on the device and selects the most suitable influencer. The selection results are sent to the server.

[1324] Step 6:

[1325] The server stores the selection results in a database and notifies the selected influencers.

[1326] Process steps for auto-generating content by influencers

[1327] Step 1:

[1328] A user (influencer) accesses the system using a terminal and inputs a request for new content, detailing the theme and required elements, and sends it to the server.

[1329] Step 2:

[1330] The server runs a generation AI based on the request received and generates optimal video material and story (script).

[1331] Step 3:

[1332] The generative AI retrieves the necessary information from the database and creates materials and scripts that fit the specified theme.

[1333] Step 4:

[1334] The server sends the generated materials and scripts to the influencer's device.

[1335] Step 5:

[1336] The user (influencer) checks the materials and script generated on the device and begins video production.

[1337] Analysis of viewing data and processing steps for improvement suggestions

[1338] Step 1:

[1339] The server periodically collects viewing data for published videos, including the number of views, likes, shares, and comments.

[1340] Step 2:

[1341] The server analyzes the data collected to extract trends and patterns in viewing data and evaluate the impact of specific factors on performance.

[1342] Step 3:

[1343] The server generates specific improvement suggestions based on the analysis, such as optimizing hashtags, improving thumbnails, and adjusting posting times.

[1344] Step 4:

[1345] The server notifies the influencer's terminal of the generated improvement proposal.

[1346] Step 5:

[1347] The user (influencer) checks the suggestions on their device and reflects them in their next video production.

[1348] Steps for processing user votes

[1349] Step 1:

[1350] The device will then display a voting page where users can suggest projects they would like influencers to undertake or vote for projects that have already been proposed.

[1351] Step 2:

[1352] Users use their devices to propose or vote on projects, and the proposal and vote data are sent to the server.

[1353] Step 3:

[1354] The server compiles proposal and vote data in real time, and popular proposals are ranked.

[1355] Step 4:

[1356] The server sends the results of the survey to the influencer's device, who then uses the results to consider new projects.

[1357] Through the above processing steps, the system of the present invention can facilitate communication between companies, influencers, and users, and realize effective content creation and improved performance.

[1358] Example 1

[1359] 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."

[1360] For a company to effectively use influencers for promotion, it is necessary to select suitable influencers, create content, analyze and improve viewing data, and collect feedback from general users. However, managing these processes individually takes a great deal of time and effort, making it difficult to do so efficiently.

[1361] 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.

[1362] In this invention, the server includes: means for registering a company's project information; means for searching for and listing optimal influencers from an influencer database based on the company's project information; means for presenting the listed influencers to a company and allowing the company to select one; means for notifying the selected influencers and proposing collaboration; means for managing and following up on the selection results; means for having the selected influencers input requests for automatic content generation using a generative AI model and providing the generation results; means for collecting and analyzing viewing data of published videos and notifying improvement suggestions; and means for tallying votes from general users in real time and notifying them of material for consideration in new projects. This makes it possible to efficiently match companies and influencers and consistently manage each process of content creation and improvement.

[1363] "Means for registering company project information" is a function that allows companies to enter information such as promotion and marketing objectives, target audience, budget, etc. into the system and register it.

[1364] "A means of searching and listing the most suitable influencers from the influencer database based on a company's project information" is a function that searches for influencer information in the database based on the registered company's project information and lists the most suitable influencers.

[1365] "A means of presenting the listed influencers to companies and allowing companies to select" is a function that displays a list of searched influencers to companies, allowing them to select the most suitable influencer.

[1366] "Means of notifying selected influencers and proposing collaboration" is a function for notifying influencers selected by a company and proposing collaboration.

[1367] "Means for managing and following up on selection results" refers to a function that stores the selection results of companies and influencers in a database and allows for continuous follow-up.

[1368] "Means of having selected influencers input requests for automatic content generation using a generative AI model and providing the generated results" refers to a function that enables influencers to input requests for content generation into the system and provide them with content (video material and stories) that is automatically generated using a generative AI model.

[1369] "Means for collecting and analyzing viewing data of published videos and notifying improvement suggestions" refers to a function for collecting viewing data of published videos, analyzing this data, generating specific suggestions for performance improvement, and notifying influencers.

[1370] "A means of aggregating votes from general users in real time and notifying them of the results as material for consideration in new projects" is a function that aggregates votes from general users in real time and notifies influencers of the results as material for consideration in new projects.

[1371] The system of the present invention is a comprehensive generative AI-equipped system that includes functions for registering corporate project information, searching a database of influencers, matching, automatically generating content, analyzing viewing data, generating improvement suggestions, and voting by general users. This system efficiently performs each function using a variety of hardware and software.

[1372] First, the user (company) enters the company's project information into the device's web browser (e.g., Google Chrome). The project information includes the promotion purpose, target audience, budget, etc., and this information is sent to the server. The server then stores the received information in a database management system (e.g., MySQL).

[1373] Next, the server analyzes the project information registered by the company. This analysis uses a natural language processing algorithm (e.g., Python's NLTK library). Based on the analysis results, the server searches the database for the most suitable influencers and creates a list. This list is sent to the user's (company's) device, and the company can view the list through a web browser.

[1374] The company selects the most suitable influencer from the list of influencers provided and sends the selection results from the terminal to the server. The server stores the received selection results in a database and simultaneously sends an email notification (e.g., SendGrid) to the selected influencer to notify them of the details of the project.

[1375] Influencers use their devices to input requests into the system, which automatically generates content. The request is sent to a generative AI model (e.g., OpenAI GPT-3), which automatically generates video material and a story (script). A request for automatic generation is made by entering a prompt phrase, such as a theme like "Recommended summer beach items." The generated results are provided to the influencer via the server, allowing for quick video production.

[1376] Viewing data for published videos is collected by the server, and information such as the number of views, likes, shares, and comments is analyzed using a data analysis tool (e.g., Google Analytics). Based on the analysis results, the server generates specific improvement suggestions and notifies the influencer. The improvement suggestions include optimizing hashtags and adjusting the timing of posting.

[1377] The system also includes a voting function for general users. Users can access the voting page using their devices to propose new projects to influencers or vote for existing projects. Voting results are sent to the server in real time and tallied. The server then notifies the influencers of the voting results, which they use as information for considering new projects.

[1378] For example, if a company requests a promotion for a new product, the system will list suitable influencers based on the company's objectives and target audience. Also, if an influencer is stuck for ideas, a script on the theme of "introducing recommended summer beach items" can be automatically generated using a generative AI model. In this way, the influencer can quickly begin creating a video.

[1379] This system will facilitate collaboration between companies, influencers, and individuals, and will improve both the quality and quantity of content at the same time.

[1380] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1381] Step 1:

[1382] The user (company) accesses the system using a web browser on their device and enters project information (promotion purpose, target audience, budget, etc.). The entered information is sent from the device to the server in JSON format.

[1383] Step 2:

[1384] The server saves the received job information in a database management system (e.g. MySQL). At this time, it checks the consistency of the input information and converts it into the required data format. Once saving is complete, it returns a response to the terminal indicating that saving was successful.

[1385] Step 3:

[1386] The server retrieves project information from the database and analyzes it using a natural language processing algorithm (e.g., Python's NLTK library). Based on the analysis results, it creates search criteria for influencers. Using these analysis results, it searches the database for the most suitable influencers.

[1387] Step 4:

[1388] The server sends the list of influencers obtained as search results in JSON format to the terminal. The terminal displays the list of influencers on a web browser, allowing the user (company) to review and select from the list.

[1389] Step 5:

[1390] The user (company) selects the most suitable influencer from the influencer list displayed on the web browser. Once the selection is complete, the device sends the selection results in JSON format to the server.

[1391] Step 6:

[1392] The server stores the received selection results in a database. At this time, it validates the selection information and, once the selection is confirmed, notifies the selected influencer. Notifications are sent via an email notification service (e.g., SendGrid).

[1393] Step 7:

[1394] The user (influencer) uses a device to input a request to the system. The request (e.g., the theme or purpose of the video) is sent to the generative AI model. This request is sent from the device via the API as a prompt.

[1395] Step 8:

[1396] A generative AI model (e.g., OpenAI GPT-3) automatically generates video material and a story (script) based on the prompt. The generated information is sent to the server in JSON format. The server stores the generated results in a database and provides them to influencers.

[1397] Step 9:

[1398] The influencer will create a video using the generated results provided and upload it to a platform for publishing.

[1399] Step 10:

[1400] The server collects viewing data such as the number of views, likes, shares, and comments for published videos using the video platform's API (e.g., YouTube Data API), and stores this data in a database in real time.

[1401] Step 11:

[1402] The server analyzes the collected viewing data using a data analysis tool (e.g., the Pandas library), identifies viewing patterns and trends, and generates specific improvement proposals based on the analysis results. The generated improvement proposals are then notified to influencers.

[1403] Step 12:

[1404] The terminal displays a voting page to the user, who then uses the terminal to cast their vote, with each vote immediately transmitted to the server.

[1405] Step 13:

[1406] The server receives the voting results, stores them in a database in real time, and tallies them. The tallied results are then sent to influencers, who use them as information for considering new projects.

[1407] Step 14:

[1408] Influencers will consider new ideas based on the collected voting results, and then create new content based on those ideas using a generative AI model.

[1409] (Application example 1)

[1410] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1411] In modern content creation, matching companies with influencers is extremely important, but the process of selecting the right influencer is extremely time-consuming and inefficient. Furthermore, there is a lack of ways to quickly provide high-quality content when influencers run out of content ideas. Furthermore, there is a lack of ways to analyze the performance of published content and make specific suggestions for improvement. Finally, there are limited ways to utilize user feedback and incorporate new content ideas. A new system is needed to solve these issues.

[1412] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1413] In this invention, the server includes: means for registering a company's project information; means for searching for and listing optimal influencers from an influencer database based on the company's project information; means for presenting the listed influencers to a company and allowing the company to select them; means for notifying the selected influencers and proposing collaboration; means for managing and following up on the selection results; means for the influencer to input a request and automatically generating video materials and stories using a generative AI model; means for providing the generated results to the influencer to support video production; means for collecting viewing data on published videos and analyzing data such as the number of views, likes, shares, and comments; means for generating specific improvement suggestions based on the analysis results and notifying the influencer; means for users to propose projects they would like influencers to do or vote for projects that have already been proposed; and means for tallying the voting results in real time and notifying the influencer. This makes it possible to efficiently match companies and influencers and improve the quality and quantity of content provided.

[1414] "Company project information" refers to promotion and marketing requests that companies register in the system based on specific objectives, target audiences, budgets, etc.

[1415] An "influencer database" is a data storage device that stores information such as each influencer's profile information, past activities, and number of followers.

[1416] The "optimal influencer" is the influencer who best fits the company's project information and can maximize the expected results.

[1417] A "generative AI model" is a type of artificial intelligence that automatically generates video material and stories based on pre-trained data, and examples include GPT-4.

[1418] "Footage and Stories" refers to creative materials such as footage and scripts to be used in videos produced by influencers.

[1419] "Viewing data" refers to data that shows how users responded to published videos, including the number of views, likes, shares, and comments.

[1420] "Improvement suggestions" are specific advice and methods for improving the quality and performance of content based on the results of analysis of viewing data, etc.

[1421] "User voting" is the act of ordinary users expressing their opinions on projects they would like influencers to undertake.

[1422] "Voting results" refers to data regarding the next content plan compiled based on votes from users.

[1423] The following system is an example of an embodiment of the present invention. Each function of the system is realized by utilizing a server, a user terminal, and a generative AI model.

[1424] The first step is for a company to register its project information. The company accesses the system using a terminal and enters details of its promotion and marketing, including its objectives, target audience, budget, etc. The server receives the entered information and stores it in a database.

[1425] Next, the server analyzes the company's project information and searches for and lists the most suitable influencers from its influencer database. The list of influencers is sent from the server to the company's terminal, and the company selects influencers based on that list.

[1426] After the selection is complete, the server saves the selection results in a database and notifies the selected influencers, who can then start collaborating with the company.

[1427] If an influencer gets stuck creating content, a generative AI model is used to automatically generate video material and stories. The influencer inputs a request from their device, and the generative AI model creates a prompt based on that request, which is then used to generate content. This generated content is then provided to the influencer again via the server. For example, the prompt for a request on the theme of "introducing beach goods" would look like this:

[1428] Prompt statement:

[1429] Generate a script for the following video: "Topic: Introducing Beach Goods, Video Length: 2 minutes"

[1430] Viewing data for published videos is also an important factor. The server collects data such as the number of views, likes, shares, and comments in real time and analyzes the visual information. Based on the results of this analysis, specific suggestions for improving the quality of content are generated and notified to influencers. For example, these suggestions include "reviewing hashtags," "changing posting times," and "improving thumbnails."

[1431] The system also includes a voting function for users. The device displays a voting page to users, allowing them to propose projects they would like influencers to do or vote for projects that have already been proposed. The server tally the voting results in real time and notify the influencer. Based on these voting results, the influencer can decide on their next video project.

[1432] All of these processes are efficiently managed by a system that facilitates communication between companies, influencers, and users. The system uses hardware such as smartphones and servers, as well as software such as generative AI models (e.g., GPT-4) and cloud services (e.g., AWS Lambda and Google Cloud Functions).

[1433] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1434] Step 1:

[1435] Companies access the system through a terminal and input project information (purpose, target audience, budget, etc.). The terminal sends this information to the server, which stores the received information in a database. It receives project information as input and generates data that is stored in the database as output.

[1436] Step 2:

[1437] The server analyzes the project information registered by the company and searches for the most suitable influencers from the influencer database. It performs data analysis on the information of each influencer in the database and creates a list of influencers most suitable for the project. It receives the company's project information as input and generates a list of influencers as output.

[1438] Step 3:

[1439] The server sends the generated list of influencers to the company's terminal, where the company checks the list and selects the most suitable influencer. The server receives the list of influencers as input and generates the selected influencers as output.

[1440] Step 4:

[1441] The company sends the selection results to the server, which stores the results in a database. The server then notifies the selected influencers. It receives the selection results as input and generates notifications as output.

[1442] Step 5:

[1443] Influencers input content creation requests into the system via their devices. The server receives these requests and sends prompts to the generative AI model. The generative AI model automatically generates video material and stories based on the requests. For example, if the theme is "Introducing beach goods," the following prompts are sent to the generative AI model:

[1444] Prompt statement:

[1445] Generate a script for the following video: "Topic: Introducing Beach Goods, Video Length: 2 minutes"

[1446] It takes influencer requests as input and produces generated content as output.

[1447] Step 6:

[1448] The server sends the generated video material and story to the influencer's device, who then uses this information to create the video. The server receives the generated content as input and generates data to be sent to the influencer's device as output.

[1449] Step 7:

[1450] The server collects viewing data (number of views, likes, shares, comments, etc.) of published videos in real time. It analyzes the viewing data and generates specific improvement proposals to increase the number of followers, views, likes, and shares based on the analysis results, and notifies the influencer. It receives viewing data as input and generates improvement proposals as output.

[1451] Step 8:

[1452] The device displays a voting page to the user, allowing the user to propose projects they would like influencers to implement or vote for projects that have already been proposed. The server tally the voting results in real time and notify the influencers. It receives the user's voting information as input and generates the voting results as output.

[1453] Step 9:

[1454] The influencer will decide on the next video project based on the voting results, completing the process to enhance the interaction between users and influencers and provide content that meets users' requests. It takes the voting results as input and generates the next video project as output.

[1455] 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.

[1456] The system of the present invention includes functions for registering corporate project information, searching a database of influencers, matching, automatically generating content, analyzing viewing data, generating improvement suggestions, a voting function for users, and recognizing user emotions using an emotion engine.

[1457] System configuration

[1458] 1. Registration of business project information and emotion recognition

[1459] Users (companies) access the system using their terminals and register project information (purpose, target audience, budget, etc.). This data is sent to the server and stored in a database. The emotion engine analyzes the user's emotions regarding the company's project information and can adjust the presentation method.

[1460] 2. Influencer database search and matching

[1461] The server analyzes the project information registered by the company and searches for and lists the most suitable influencers from the database. This list is sent from the server to the company's terminal, where the company checks the list. Based on the company's decision, the influencer is notified.

[1462] 3. Automatic content generation and sentiment analysis

[1463] Users (influencers) input their requests into the system from their devices, and the generation AI automatically generates video material and a story (script). The generated results are provided to the influencer via the server. Furthermore, the emotion engine analyzes the user's feelings toward the generated content and provides feedback to the generation AI, which can be used to generate content next time.

[1464] 4. Analysis of viewing data, improvement proposals, and sentiment analysis

[1465] The server collects viewing data for published videos and analyzes information such as the number of views, likes, shares, and comments. This information is used to generate specific improvement proposals to increase the number of followers, views, likes, and shares, and these proposals are notified to influencers. Additionally, an emotion engine can be used to analyze user emotions and reflect them in improvement proposals.

[1466] 5. User voting and sentiment analysis

[1467] The device displays a voting page to users, allowing them to propose projects they would like influencers to implement or vote for projects that have already been proposed. The server tallys the voting results in real time and ranks the most popular projects. Furthermore, an emotion engine analyzes users' voting behavior and emotions toward the proposals, and notifies the influencers of this information, helping them consider new projects.

[1468] Specific examples

[1469] 1. Matching with corporate projects

[1470] Example: When a company requests a promotion for a new product, the system will generate a list of suitable influencers based on the company's objectives and target audience. As a result, the company can select the most suitable influencer in a short time. The emotion engine adjusts the offer presentation based on the user's reaction and interest level.

[1471] 2. Automatic content generation and sentiment analysis

[1472] Example: When an influencer is stuck for ideas for a new video, a script on the theme of "Introducing recommended summer beach items" is automatically generated. This allows the influencer to quickly start creating a video. Furthermore, an emotion engine analyzes user reactions and reflects them in the next content generation.

[1473] 3. Analysis of viewing data, improvement proposals, and sentiment analysis

[1474] Example: If an influencer's recent videos aren't getting as many views as expected, the system will analyze the viewing data and provide suggestions for improvement, such as using hashtags, changing thumbnails, adjusting posting times, etc. Furthermore, an emotion engine will analyze viewers' emotions and provide more specific suggestions for improvement.

[1475] 4. User voting and sentiment analysis

[1476] Example: An influencer holds a poll in the system to ask users to suggest ideas for the next video. If users vote a lot for the idea "funny pet videos," the influencer can create the next video based on the results. The emotion engine can analyze users' feelings about the votes and use the results to create new ideas.

[1477] The system of this invention facilitates communication between companies, influencers, and users, enabling effective content creation and performance improvement. The introduction of an emotion engine enables even more effective matching and improvement suggestions.

[1478] The processing flow will be explained below.

[1479] Registration of business case information and emotion recognition processing steps

[1480] Step 1:

[1481] The user (company) accesses the system using a terminal, enters project information (purpose, target audience, budget, etc.), and sends it to the server.

[1482] Step 2:

[1483] The server receives the submitted job information and stores it in a database.

[1484] Step 3:

[1485] The server uses an emotion engine to analyze the user's emotions regarding the transmitted case information.

[1486] Step 4:

[1487] The server adjusts the way the case information is presented based on the analysis results.

[1488] Influencer database search and matching process steps

[1489] Step 1:

[1490] The server analyzes the project information registered by the company and searches the database for the most suitable influencer.

[1491] Step 2:

[1492] The server lists influencers based on the search results.

[1493] Step 3:

[1494] The server generates a list of optimal influencers and sends it to the user's (company's) device.

[1495] Step 4:

[1496] The user (company) checks the influencer list presented on the device, selects the most suitable influencer, and sends the selection results to the server.

[1497] Step 5:

[1498] The server stores the selection results in a database and notifies the selected influencers.

[1499] Processing steps for automatic content generation and sentiment analysis

[1500] Step 1:

[1501] A user (influencer) accesses the system using a terminal, inputs a request for new content, and sends it to the server.

[1502] Step 2:

[1503] The server runs a generation AI based on the request received to generate optimal video material and story (script).

[1504] Step 3:

[1505] The server sends the generated materials and scripts to the influencer's device.

[1506] Step 4:

[1507] The user (influencer) checks the materials and script generated on the device and begins video production.

[1508] Step 5:

[1509] The server uses an emotion engine to analyze other users' emotions toward the content generated by the user (influencer) and feeds the results back to the generation AI.

[1510] Analysis of viewing data, improvement suggestions, and processing steps for sentiment analysis

[1511] Step 1:

[1512] The server periodically collects viewing data for published videos, including the number of views, likes, shares, and comments.

[1513] Step 2:

[1514] The server analyzes the collected data and extracts trends and patterns in the viewing data.

[1515] Step 3:

[1516] The server uses an emotion engine to analyze the user's emotions regarding the viewing data.

[1517] Step 4:

[1518] The server generates specific improvement suggestions based on the analysis, such as optimizing hashtags, improving thumbnails, and adjusting posting times.

[1519] Step 5:

[1520] The server notifies the influencer's terminal of the generated improvement proposal.

[1521] Step 6:

[1522] The user (influencer) checks the suggestions on their device and reflects them in their next video production.

[1523] User voting and sentiment analysis processing steps

[1524] Step 1:

[1525] The device will then display a voting page to the user, where the user can suggest projects they would like the influencer to undertake or vote for projects that have already been proposed.

[1526] Step 2:

[1527] Users use their devices to propose or vote on projects, and the proposal and vote data are sent to the server.

[1528] Step 3:

[1529] The server compiles proposal and vote data in real time, and popular proposals are ranked.

[1530] Step 4:

[1531] The server uses an emotion engine to analyze users' voting behavior and emotions toward the proposals.

[1532] Step 5:

[1533] The server notifies the influencer of the results of the aggregation and sentiment analysis, and the influencer uses these results to consider new projects.

[1534] Example 2

[1535] 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."

[1536] Traditional matching systems for companies and influencers face many challenges in terms of effective content creation and analysis of viewing data. Specifically, they face issues such as companies being unable to properly deliver project information to their target audiences, influencers being unable to quickly obtain appropriate video ideas, being unable to fully utilize viewing data from published content, and being unable to analyze user sentiment and reflect it in future content. They also lack functionality for collecting project ideas through user votes and analyzing and reflecting those sentiments. This makes collaboration between companies and influencers inefficient, making it difficult to improve overall performance.

[1537] 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. In this invention, the server includes: means for registering project information of a company; means for analyzing user emotions regarding the company's project information using an emotion engine and adjusting the presentation method; means for searching for and listing optimal influencers from an influencer database based on the company's project information; means for presenting the listed influencers to a company and allowing the company to select; means for notifying the selected influencer and proposing collaboration; means for managing and following up on the selection results; means for the influencer to input a request and automatically generating video materials and stories using a generation AI; means for analyzing user emotions regarding content generated using the emotion engine and providing feedback to the generation AI; means for providing the generated results to the influencer and supporting video production; means for collecting viewing data of published videos and analyzing data such as the number of views, likes, shares, and comments; means for analyzing viewer emotions using the emotion engine, generating specific improvement suggestions based on the analysis results, and notifying the influencer; and means for displaying a voting page to the user and analyzing the user's voting behavior and emotions regarding the suggestions. This will enable effective matching between companies and influencers and improve the performance of content creation.

[1538] "Company project information" refers to information about projects and transactions that a company registers in the system, including objectives, target audiences, budgets, etc.

[1539] An "emotion engine" is an engine for analyzing user emotions, extracting emotions from text and behavior and making an evaluation.

[1540] An "influencer database" is a database that accumulates information about influencers, including the number of followers, areas of expertise, and past activity history.

[1541] The "system" is an integrated platform for streamlining communication and content creation between companies, influencers, and general users.

[1542] "Generative AI" is artificial intelligence that automatically generates content based on given prompts, generating video footage and stories.

[1543] "Viewing data" refers to user reaction data to published content, including the number of views, likes, shares, comments, and the like.

[1544] "Improvement proposals" are specific methods or ideas proposed to improve content performance based on viewing data and the results of sentiment analysis.

[1545] A "voting page" is a web page where users can vote for influencers' proposals and the results are tallied in real time.

[1546] The system according to the present invention is an integrated platform for streamlining communication and content creation between companies, influencers, and general users. Specific embodiments of this system will be described below.

[1547] Registration of business project information and emotion recognition

[1548] Users (companies) access the system using a terminal and input their company's project information (purpose, target audience, budget, etc.). The terminal sends this information to the server. The server stores the received project information in a database and activates the emotion engine. The emotion engine analyzes the user's emotions based on the input information and adjusts the way the information is presented based on the results.

[1549] Examples:

[1550] For example, when a company inputs information about a project such as "promotion of a new product aimed at young people," the emotion engine evaluates emotions such as "excitement" and "attractiveness," and the server uses this information to adjust the project presentation to optimize it for the target audience.

[1551] Influencer database search and matching

[1552] The server analyzes the project information registered by the company and extracts elements such as the purpose, target audience, and budget. Next, it uses this information to search the database for the most suitable influencers and creates a list. This list is sent to the company's device. The company then checks the list on the device and selects the most suitable influencer. The server then sends a notification to the selected influencer.

[1553] Examples:

[1554] When a company requests a "fashion item promotion," the server creates a list of influencers with large numbers of followers in the fashion field and sends it to the company. The company then selects influencers based on the list and sends them notifications.

[1555] Automatic content generation and sentiment analysis

[1556] The user (influencer) inputs a theme or idea from their device. The device sends the request to the server, which passes it on to the generation AI. The generation AI automatically generates video material and a script based on the request and provides the results to the influencer. The emotion engine also analyzes the user's emotions regarding the generated content and sends that feedback to the generation AI to help with the next generation.

[1557] Examples:

[1558] When an influencer enters a theme, such as "Christmas party decoration ideas," the generative AI automatically generates a script and video material based on the theme. This generated content is sent to the influencer's device. The emotion engine also analyzes user reactions, and the results are reflected in the next content generation.

[1559] Analysis of viewing data, improvement suggestions, and sentiment analysis

[1560] The server collects viewing data (number of views, likes, shares, comments, etc.) for published videos. The server then analyzes this data and generates specific improvement suggestions to improve performance. An emotion engine analyzes viewer emotions and reflects the results in improvement suggestions. These suggestions are then sent to the influencer's device.

[1561] Examples:

[1562] If an influencer's recent videos aren't getting as many views as expected, the server analyzes the viewing data and provides specific suggestions for improvement, such as "increase hashtag usage," "change thumbnails," "adjust posting times," etc. In addition, the emotion engine analyzes viewer emotions and notifies specific suggestions for improvement.

[1563] User voting and sentiment analysis

[1564] Users (general users) can vote for influencers' proposals from their devices. The server tally the voting results in real time and ranks the most popular proposals. The emotion engine also analyzes users' voting behavior and emotions toward the proposals, and notifies the influencers of the results.

[1565] Examples:

[1566] Users vote for multiple video project ideas ("Travel Vlog," "Cooking Video," "Funny Pet Video"). If "Funny Pet Video" receives the most votes, the influencer will create the next video based on this result. The emotion engine analyzes users' emotions and uses the results to create new projects.

[1567] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1568] Step 1:

[1569] Registering business information

[1570] Users (companies) access the system from their terminals and enter project information (purpose, target audience, budget, etc.), which is then sent to the server.

[1571] Input: Company project information (purpose, target, budget)

[1572] Specific operation: A company enters and submits information about a "new product promotion aimed at young people."

[1573] Output: Sending job information to the server

[1574] Step 2:

[1575] Case information storage and sentiment analysis

[1576] The server stores the received project information in a database and activates the emotion engine, which analyzes the user's emotions regarding the company's project information and adjusts the way the information is presented based on the results.

[1577] Input: Company project information

[1578] Specific behavior: The emotion engine evaluates emotions such as "excited" or "attractive" and adjusts the presentation accordingly.

[1579] Output: Saving case information to a database, results of sentiment analysis

[1580] Step 3:

[1581] Influencer database search and matching

[1582] The server analyzes the project information, searches for the most suitable influencer from its influencer database, and then lists the search results and sends them to the company's terminal.

[1583] Input: Parsed case information

[1584] Specific operation: The server creates a list of influencers with a large number of followers in the fashion field and sends it to the company's terminal.

[1585] Output: List of influencers

[1586] Step 4:

[1587] Influencer selection by companies

[1588] The company checks the influencer list received on the device and selects the most suitable influencer. The selection results are sent to the server.

[1589] Input: Influencer list

[1590] Specific operation: A company selects the most suitable influencer from multiple influencers and sends the selection results.

[1591] Output: Information on selected influencers

[1592] Step 5:

[1593] Notification of selection and collaboration proposal

[1594] The server receives the company's selection results and sends notifications to the selected influencers, along with proposing collaborations.

[1595] Input: Information of selected influencers

[1596] What it does: Sends notifications to selected influencers and proposes collaboration.

[1597] Output: Notification and proposal to influencers

[1598] Step 6:

[1599] Influencer request and auto-generation

[1600] Users (influencers) input a theme or idea from their device and send the request to the server, which then passes the request to the AI ​​generator, which automatically generates video material and a script.

[1601] Input: Theme or idea

[1602] How it works: An influencer enters the theme "Christmas party decoration ideas" and submits it. The generative AI automatically generates content based on this.

[1603] Output: Generated footage and scripts

[1604] Step 7:

[1605] Generative content provision and feedback analysis

[1606] The server provides the generated content to the influencer's device, and the emotion engine analyzes the user's emotions toward the generated content and sends the feedback to the generation AI.

[1607] Input: Generated content

[1608] Specific operation: The emotion engine analyzes the user's reaction and uses it to generate the next content.

[1609] Output: Providing generated content to influencers, sentiment analysis results

[1610] Step 8:

[1611] Collecting viewing data and making improvement suggestions

[1612] The server collects and analyzes viewing data (number of views, likes, shares, comments, etc.) for published videos. Based on the analysis results, it generates specific improvement proposals to improve the performance of the content.

[1613] Input: Viewing data

[1614] Specific operation: The server analyzes viewing data and creates and notifies improvement suggestions such as "increasing the use of hashtags," "changing thumbnails," and "adjusting posting times."

[1615] Output: Improvement suggestions, notifications

[1616] Step 9:

[1617] User voting page and sentiment analysis

[1618] Users (general users) vote for influencers' proposals from their devices. The server tally the voting results in real time and ranks the most popular proposals. The emotion engine analyzes users' voting behavior and emotions toward the proposals, and notifies the influencers of the results.

[1619] Input: User votes

[1620] Specific operation: Users vote for "funny pet videos," etc., and the most popular projects are ranked based on the results. The emotion engine analyzes users' emotions and notifies influencers of the results.

[1621] Output: Voting results tallied, sentiment analysis results notified

[1622] (Application example 2)

[1623] 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."

[1624] Currently, companies spend a great deal of time and effort establishing marketing strategies that utilize influencers, but it is difficult to quickly and accurately find the most suitable influencers. In addition, it is difficult to select the appropriate robots and generate optimal work plans for factory manufacturing operations, and there is a need to improve work efficiency and reduce error rates. Therefore, a new system is needed that can simultaneously improve the efficiency of corporate marketing activities and factory manufacturing operations.

[1625] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1626] In this invention, the server includes means for registering project information of a company, means for searching for and listing optimal influencers from an influencer database based on the project information of the company, means for presenting the listed influencers to the company and allowing the company to select them, means for notifying the selected influencers and proposing collaboration, means for managing and following up on the selection results, means for registering project information of manufacturing work, means for searching for and listing appropriate factory robots from a database based on the project information of the manufacturing work, means for presenting the listed factory robots to a manufacturing manager and allowing the manufacturing manager to select them, means for notifying the selected factory robot and generating a work plan, and means for managing the selection results and following up on the work, thereby enabling the efficiency of corporate marketing activities and factory manufacturing work to be improved.

[1627] "Company project information" refers to information such as details of the project or campaign that the company wants to implement, its objectives, target audience, budget, etc.

[1628] An "influencer" is an individual or organization that has a large number of followers or viewers on the Internet and can influence a company's products or services.

[1629] A "database" refers to a collection of data used by the system to manage and search, such as information on corporate projects, influencers, and factory robots.

[1630] "Generative AI" refers to artificial intelligence that automatically generates video materials and work plans based on specified conditions and requirements.

[1631] An "emotion engine" refers to analytical software that analyzes a user's emotional state and provides appropriate feedback and suggestions for improvement based on that information.

[1632] "Factory robot" refers to an automated machine used to perform production tasks in a manufacturing plant.

[1633] A "work plan" refers to a plan that specifically defines a series of work procedures and processes to be carried out by factory robots.

[1634] "Viewing data" refers to data based on user viewing behavior, such as the number of views, likes, shares, and comments on published videos.

[1635] "Improvement proposals" refer to specific plans and instructions for improving efficiency and results based on the analysis of viewing data and work data.

[1636] "Follow-up" refers to the ongoing management, supervision, and support of influencers and factory robots.

[1637] The system of the present invention includes functions for registering company project information, searching and matching a database of influencers and factory robots, automatically generating work plans, analyzing viewing and work data, generating improvement proposals, providing user and employee feedback, and analyzing using an emotion engine. The entire system process is implemented using Python and the Flask framework. Each function is described in detail below.

[1638] Registering company project information

[1639] Corporate administrators, who are users, access the system using a smartphone or head-mounted display and register project information (purpose, target audience, budget, etc.). This data is sent to the server and stored in a database. An emotion engine is used to analyze employees' emotions regarding the registered project information and adjust the display method.

[1640] Database search and matching of influencers and factory robots

[1641] The server analyzes the registered project information, searches for and lists the most suitable candidates from the influencer or factory robot database. This list is sent from the server to the user's device, which is the company administrator or manufacturing manager, who then checks the list and selects the most suitable candidate. The selected influencer or robot is notified, and collaboration and work plans are carried out.

[1642] Automatic generation of work plans and sentiment analysis

[1643] When influencers or factory robots run out of ideas for new content or work plans, the generative AI automatically generates video footage and work plans. The generated results are provided via a server. In addition, an emotion engine analyzes the emotions of employees and users regarding the generated content and plans and provides feedback to the generative AI.

[1644] Analysis of viewing and work data and suggestions for improvement

[1645] The server collects viewing data for published videos and analyzes information such as the number of views, likes, shares, and comments. Similarly, it collects robot manufacturing work data and analyzes work efficiency and error rates. From this, specific improvement proposals are generated and notified to influencers and manufacturing managers. An emotion engine is used to analyze the emotions of users and employees and reflect them in improvement proposals.

[1646] User and employee feedback features and sentiment analysis

[1647] A voting page is displayed where users and employees can provide feedback to influencers and factory robots. The server compiles the feedback results in real time and ranks the most popular plans and work procedures. An emotion engine analyzes the sentiment of the feedback and notifies influencers and production managers, helping them consider new plans and work procedures.

[1648] Specific examples

[1649] When a company registers project information to "promote a new product," the system lists the most suitable influencers. The company administrator selects the influencers, and the generation AI automatically generates a script with the theme "Introducing recommended summer beach items." Similarly, when a manufacturing factory registers a project to "improve the assembly process for a new product," the system selects the most suitable factory robot and automatically generates new assembly procedures. Employees use head-mounted displays to check the new procedures and provide feedback. The emotion engine analyzes the feedback and reflects it in the next improvement proposal.

[1650] Prompt Sentence Examples

[1651] "Please suggest the optimal robot and work plan to improve the assembly process for a new product."

[1652] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1653] Step 1: Registering the project information

[1654] Subject: User

[1655] The user, a company manager, uses a smartphone or head-mounted display to input project information (purpose, target audience, budget, etc.). This information is sent from the device to a server and stored in a database. The emotion engine analyzes the employee's emotional state regarding the registered project information and adjusts the display method accordingly.

[1656] Input: Company administrator inputs case information

[1657] Data processing / calculation: The emotion engine analyzes employee emotions based on input case information.

[1658] Output: Adjusted display method

[1659] Step 2: Finding influencers and factory robots

[1660] Subject: Server

[1661] The server analyzes the project information entered by the company administrator and searches for the most suitable influencer from the influencer database or the most suitable robot from the factory robot database. This list is then sent from the server to the terminal of the company administrator or manufacturing manager.

[1662] Input: Project information

[1663] Data processing / calculation: Retrieving data from the database and filtering

[1664] Output: A list of the best influencers or factory robots

[1665] Step 3: Candidate selection and notification

[1666] Subject: User

[1667] The company manager or manufacturing manager reviews the submitted list and selects the most suitable influencer or factory robot. The selection results are sent to the server and notified to the selected influencer or robot.

[1668] Input: A list of the best influencers and robots

[1669] Data processing / calculation: List display and selection

[1670] Output: Notification of selection results

[1671] Step 4: Automatically generate a work plan

[1672] Subject: Server

[1673] When influencers or factory robots run out of ideas for new content or work plans, generative AI automatically generates video footage and work plans, and the generated results are provided to the influencers or robots from the server.

[1674] Input: Prompt sentence for generative AI model

[1675] Data processing / calculation: Generative AI generates content and work plans

[1676] Output: Auto-generated content and work plans

[1677] Step 5: Analyze the feedback

[1678] Subject: Server

[1679] The server aggregates feedback from users and employees in real time through the feedback page. The emotion engine analyzes the emotions in response to the feedback and notifies the results to influencers and production managers.

[1680] Input: Feedback data and emotion data

[1681] Data processing / calculation: Analysis and aggregation using emotion engine

[1682] Output: Notification of analysis results

[1683] Step 6: Analyze viewing and task data

[1684] Subject: Server

[1685] The server collects viewing data for published videos and work data from factory robots, and analyzes the number of views, likes, shares, comments, work efficiency, error rates, etc. Based on this, it generates specific improvement proposals and notifies them to influencers and production managers.

[1686] Input: Viewing and working data

[1687] Data processing / calculation: Data analysis using analytical algorithms

[1688] Output: Specific improvement suggestions

[1689] 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.

[1690] 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.

[1691] 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.

[1692] 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.

[1693] 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.

[1694] 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.

[1695] 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).

[1696] 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.

[1697] 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."

[1698] 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.

[1699] 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).

[1700] 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.

[1701] 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.

[1702] 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.

[1703] 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.

[1704] 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.

[1705] 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.

[1706] 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.

[1707] 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.

[1708] 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.

[1709] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1710] The following is further disclosed regarding the above embodiment.

[1711] (Claim 1)

[1712] A means of registering company project information,

[1713] A means to search and list the most suitable influencers from the influencer database based on the company's project information,

[1714] The list of influencers will be presented to companies, and the companies will be able to select them.

[1715] A means to notify selected influencers and propose collaborations;

[1716] A means of managing and following up on the selection results;

[1717] A generative AI-powered system that streamlines matching between companies and influencers.

[1718] (Claim 2)

[1719] A means for the influencer to input a request and automatically generate video materials and stories using a generation AI;

[1720] A means of providing the generated results to influencers to assist them in video production;

[1721] 10. The system of claim 1, comprising:

[1722] (Claim 3)

[1723] A means to collect viewing data for published videos and analyze data such as the number of views, likes, shares, and comments.

[1724] A means for generating specific improvement proposals based on the analysis results and notifying the influencers;

[1725] 10. The system of claim 1, comprising:

[1726] (Claim 4)

[1727] A voting page will be displayed, allowing users to propose or vote for projects they would like influencers to do.

[1728] A way to tally voting results in real time and notify influencers,

[1729] 10. The system of claim 1, comprising:

[1730] "Example 1"

[1731] (Claim 1)

[1732] A means of registering company project information,

[1733] A means to search and list the most suitable influencers from the influencer database based on the company's project information,

[1734] The list of influencers will be presented to companies, and the companies will be able to select them.

[1735] A means to notify selected influencers and propose collaborations;

[1736] A means of managing and following up on the selection results;

[1737] A means for having the selected influencers input requests for automatically generating content using the generative AI model and providing the generated results;

[1738] A means to collect and analyze viewing data of published videos and notify improvement suggestions;

[1739] A means of tallying votes from general users in real time and providing information for consideration in new projects,

[1740] A generative AI-powered system that streamlines matching between companies and influencers.

[1741] (Claim 2)

[1742] Influencers can input their requests and a generative AI model will automatically generate video footage and stories.

[1743] The system of claim 1, further comprising means for providing the generated results to influencers to assist them in creating videos.

[1744] (Claim 3)

[1745] A means to collect viewing data for published videos and analyze data such as the number of views, likes, shares, and comments.

[1746] The system according to claim 1, further comprising means for generating specific improvement proposals based on the analysis results and notifying the influencers of the proposals.

[1747] "Application Example 1"

[1748] (Claim 1)

[1749] A means of registering company project information,

[1750] A means to search and list the most suitable influencers from the influencer database based on the company's project information,

[1751] The list of influencers will be presented to companies, and the companies will be able to select them.

[1752] A means to notify selected influencers and propose collaborations;

[1753] A means of managing and following up on the selection results;

[1754] Influencers can input their requests and a generative AI model will automatically generate video footage and stories.

[1755] A means of providing the generated results to influencers to assist them in video production;

[1756] A means to collect viewing data for published videos and analyze data such as the number of views, likes, shares, and comments.

[1757] A means for generating specific improvement proposals based on the analysis results and notifying the influencers;

[1758] A way for users to propose projects they would like influencers to do or vote for projects that have already been proposed,

[1759] A way to tally voting r...

Claims

1. A means of registering company project information, A means to search and list the most suitable influencers from the influencer database based on the company's project information, The list of influencers will be presented to companies, and the companies will be able to select them. A means to notify selected influencers and propose collaborations; A means of managing and following up on the selection results; A generative AI-powered system that streamlines matching between companies and influencers.

2. A means for the influencer to input a request and automatically generate video materials and stories using a generation AI; A means of providing the generated results to influencers to assist them in video production; The system of claim 1 , comprising:

3. A means to collect viewing data for published videos and analyze data such as the number of views, likes, shares, and comments. A means for generating specific improvement proposals based on the analysis results and notifying the influencers; The system of claim 1 , comprising:

4. A voting page will be displayed, allowing users to propose or vote for projects they would like influencers to do. A way to tally voting results in real time and notify influencers, The system of claim 1 , comprising:

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