system

Abstract

We provide the system. [Solution] A means characterized by analyzing the skills and project conditions provided by the user and automatically identifying other users who can complement them using a matching algorithm, A means for generating content using an artificial intelligence model based on the user's request and modifying the content based on user feedback, A means of managing project tasks and schedules, and automatically notifying users of progress, A system that includes this.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] An object of the present invention is to provide a platform that efficiently and effectively supports creators who have problems such as lack of specialized skills, difficulty in finding collaborators, and complicated project management when conducting creative projects. Another object is to enable high-quality production even in areas outside the user's expertise by considering the user's skills and project conditions, automatically matching the optimal collaborators, and supporting content production by utilizing generative AI.

Means for Solving the Problems

[0005] This invention provides a system that analyzes a user's skills and project conditions and automatically identifies other users who can complement their skills. Furthermore, it improves the efficiency of content creation by generating content using a generative artificial intelligence model based on user requests and modifying the content in response to user feedback. It also simplifies project management by providing a function to manage project tasks and schedules and automatically notify the user of progress, thereby creating an environment where creators can concentrate on their creative activities.

[0006] "User" refers to an individual or group that uses the system to participate in creative projects.

[0007] "Skills" refer to the specialized abilities, techniques, and knowledge that a user possesses, and are elements that directly influence the project's deliverables.

[0008] "Project conditions" refer to the requirements necessary for the project to proceed, such as the specific objectives set by the user, the resources needed, and the skills required of collaborators.

[0009] A "matching algorithm" refers to a computational method for automatically finding the most suitable collaborators based on the user's skills and project requirements.

[0010] A "generative artificial intelligence model" is a collection of programs and algorithms that use AI technology to automatically generate content in response to user requests.

[0011] "Content" refers to creative works such as scenarios, illustrations, and music that are produced by users or generated using generative artificial intelligence models.

[0012] "Project management" refers to the entire set of methods and functions for planning project tasks and schedules, and for monitoring and coordinating their progress.

[0013] "Progress status" refers to an indicator that shows the current state of a project, the degree to which tasks have been completed, and measures the degree to which planned goals have been achieved. [Brief explanation of the drawing]

[0014] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14]It is a sequence diagram showing the processing flow of a data processing system in Application Example 2 when a sentiment engine is combined.

Embodiments for Carrying Out the Invention

[0015] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0016] First, the terms used in the following description will be explained.

[0017] In the following embodiments, a numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0018] In the following embodiments, a numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0019] In the following embodiments, a numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

[0023] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0024] As shown in Figure 1, the 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.

[0025] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0026] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and 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.

[0028] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0030] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0031] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 according to the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0033] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0034] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0035] This invention provides an online platform that enables creators to collaborate smoothly via the internet. This platform automatically matches users with the most suitable collaborators based on their expertise and project requirements, and supports content creation using generative AI.

[0036] To use the platform, users begin by creating an account through their device. They input their professional skills and desired project requirements and submit them to the server. The server analyzes the received data and stores it in a database. Based on this information, the server reviews other users' profiles and uses a matching algorithm to find suitable collaborators.

[0037] After matching, users can begin working on a specific project. Users input the type and details of the content they want to create into their device and send that information to the server. The server uses a generative AI model to automatically generate initial content such as illustrations, scenarios, and music. The generated content is displayed to the user via their device, and the user provides feedback as needed. Based on this feedback, the server modifies or regenerates the content.

[0038] Furthermore, in project management, the server centrally manages tasks and schedules set by users. The server tracks task priorities and progress and notifies users as needed. If progress is not on schedule, the server makes adjustments and sends reminders to ensure the project runs smoothly.

[0039] As a concrete example, let's say User A plans to produce a short animated film. User A is good at character design but not good at music production. User A provides project requirements to the server from their terminal and is matched with User B, a suitable music creator. Subsequently, User A has a scenario prototype generated by a generation AI, and User B creates music based on that scenario. The server manages the progress of the entire project, creating an environment where both Users A and B can concentrate on their creative work.

[0040] This invention resolves the challenges creators face, such as a lack of skills and difficulty finding collaborators, enabling efficient project execution.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] Users access the platform using their device and create a new account. During this process, users input their professional skills and desired project requirements, and the device transmits this information to the server.

[0044] Step 2:

[0045] The server analyzes the received user information and registers it in a dedicated database. This analysis identifies the user's skill set and project requirements, and considers mutual complementarity with other users.

[0046] Step 3:

[0047] When a user is ready to start a project, they enter the necessary project requirements into their device. The device then sends this data to the server, initiating the collaborator matching process.

[0048] Step 4:

[0049] The server runs a matching algorithm based on existing user profiles to automatically identify collaborators who meet the criteria specified by the user. It then sends notifications to the relevant users and requests their consent to become collaborators.

[0050] Step 5:

[0051] Once collaborators are confirmed, users begin creating the specific content for the project. They input the desired type and content from their device and send a request to the server.

[0052] Step 6:

[0053] The server uses a generative artificial intelligence model to generate content in the specified format. After generation, the content data is sent to the terminal and presented to the user.

[0054] Step 7:

[0055] The user reviews the generated content and provides feedback on improvements and corrections as needed, entering them into their device. The device then sends this feedback to the server.

[0056] Step 8:

[0057] The server modifies the content based on user feedback and runs the AI ​​model again if regeneration is requested. The updated content is then sent back to the device.

[0058] Step 9:

[0059] During project execution, the server centrally manages all tasks and schedules. It analyzes progress according to deadlines and priorities set by the user and provides the user with necessary information in a timely manner.

[0060] Step 10:

[0061] If the project progresses outside of the planned schedule, the server will send warnings and reminders to the user to help adjust the schedule. Based on this, the user will consider the following actions.

[0062] (Example 1)

[0063] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0064] The challenge lies in enabling creators to efficiently execute projects through online collaboration without wasting time on their own skill deficiencies or searching for collaborators. Furthermore, it requires the effective use of industrial AI in the content generation process to support creative activities by quickly incorporating feedback. Additionally, the challenge is to streamline project progress and management, providing an environment where workers can focus on their core creative work.

[0065] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0066] This invention includes a server that analyzes the technical skills and planning conditions provided by the user and automatically identifies other users who can complement them using a matching algorithm; a server that generates information using an artificial intelligence model based on the user's requests and modifies the information based on the user's feedback; and a server that manages the work and time schedule of the plan and automatically notifies the user of the progress. This makes it possible for creators to easily find collaborators with the right skills and efficiently generate content and manage projects.

[0067] "Technical capability" refers to the specialized knowledge and skills possessed by the user, and includes all the skills required to carry out the project.

[0068] "Planning conditions" refer to the goals, timeframe, budget, and other related constraints that the user sets when carrying out a project.

[0069] A "matching algorithm" refers to a computational method used to identify the most suitable collaborators based on the user's technical capabilities and project requirements.

[0070] A "generative artificial intelligence model" refers to an artificial intelligence system that can automatically generate information based on specified instructions.

[0071] "Information generation" refers to the process of creating initial information such as content, design, and sound based on user requirements and design conditions.

[0072] "Feedback" refers to evaluations and improvement requests for generated results provided by users, which the system uses to update information or processes.

[0073] "Work and time planning" refers to a comprehensive plan that includes the order in which tasks are performed and the time allocation for each task within a project.

[0074] "Progress status" refers to the state of how far a planned project has progressed within the set timeframe.

[0075] This invention provides a system that enables users to collaborate efficiently by utilizing a cloud-based online platform. The system primarily operates using servers, terminals, and generative AI models. Its specific configuration is described below.

[0076] The terminal serves as a means for users to access the system and create accounts. Through the terminal, users input and submit their technical skills and project requirements. The terminal can utilize a web browser or specialized cloud applications. This information is sent to the server and forms the basis for building user data.

[0077] The server analyzes the received technical capabilities and planning conditions and stores them in a database. Furthermore, the server uses a matching algorithm to identify the collaborator that best matches the user's requirements. This algorithm uses advanced data analysis techniques to evaluate the compatibility between users.

[0078] Once a project begins, the user inputs details of the content they want to generate via their device and sends them to the server. The server uses a generation AI model to automatically generate scenarios, designs, music, and other elements based on the specified prompts. The generated content is then delivered to the user via their device, and the server receives user feedback. Based on this feedback, the server modifies or regenerates the content.

[0079] Project progress is constantly tracked by the server, and scheduling is managed based on work and time plans. The server notifies users of progress and makes adjustments as needed if delays occur, supporting the smooth progress of the project.

[0080] As a concrete example, consider a scenario where a user creates a short animated film. The user provides character designs and a storyline outline from their device and is matched with other users skilled in music or voice acting. At each stage of the project, a generation AI is used to generate a scenario and music based on a prompt message such as, "Please generate a scenario for a short animated film. The theme is friendship, and the desired length is approximately 5 minutes." In this way, the system supports the creative activities that the user must focus on, and improves the overall efficiency of the project.

[0081] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0082] Step 1:

[0083] Users access the platform through their device and create a new account. As input, users enter their name, email address, password, technical skills, and project requirements. The device collects this data and sends it to the server. The process involves the user clicking the "Register" button, filling in the required information on the form, and then pressing the submit button, at which point the data is transferred to the server.

[0084] Step 2:

[0085] The server analyzes the received user data and stores it in the database. The input for this process is the technical capability and planning conditions obtained in step 1. The server performs data integrity checks, correctly classifying and storing the information. The output is the state where the analyzed user data is registered in the database. Specifically, the server automatically performs a validation process upon receiving data and notifies the user if any errors are found.

[0086] Step 3:

[0087] The server runs a matching algorithm based on information stored in the database to identify the most suitable collaborators. The input for this step is the technical capabilities and planning conditions of other users. Data analysis techniques are used to evaluate the likelihood of collaboration, and the output is a list of recommended collaborators. The system operates by periodically running the algorithm and notifying users of matching opportunities.

[0088] Step 4:

[0089] The user inputs details of the content they want to create and prompt text through their terminal and sends them to the server. This input includes specific content type, theme, length, and prompt text. The server processes this data using a generative AI model and generates initial content as output. Specifically, the user clicks the "Generate Content" button, enters the required conditions, submits them, and receives the generated result.

[0090] Step 5:

[0091] The server delivers the generated content to the user and collects user feedback. User feedback is required as input. The server analyzes this feedback and modifies or regenerates the content as needed. The output is the revised content. Specifically, after collecting feedback, the server uses an AI model to regenerate the content and provides the revised version to the user.

[0092] Step 6:

[0093] The server manages project progress and monitors work and time planning. Inputs are tasks, due dates, and priorities set by the user. The server uses this data to optimize the schedule and send progress notifications to the user. Outputs are the updated project schedule and progress reports. Specifically, whenever there is a progress update, the server automatically sends a notification to the user, providing guidance for the next steps.

[0094] (Application Example 1)

[0095] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0096] In content creation, there is a challenge in that creators from different specialized fields often struggle to collaborate effectively. In particular, real-time feedback and revisions within virtual reality are difficult, which can lead to an inefficient production process. Furthermore, creators are required to effectively manage the progress of their projects.

[0097] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0098] This invention includes a server that analyzes the skills and project conditions provided by the user and automatically identifies other users who can complement it using a matching algorithm; a server that generates content using an artificial intelligence model based on the user's requests and modifies the content based on the user's feedback; and a server that allows the user to view the content in real time within virtual reality and provide modification instructions. This enables creators to efficiently collaborate with members who have different expertise and provide immediate feedback using virtual reality.

[0099] A "user" is an individual or legal entity that uses this system to create content or manage projects.

[0100] "Skills" refer to the specialized abilities and knowledge that a user possesses, encompassing their level of proficiency in the areas necessary for the successful completion of the project.

[0101] "Project conditions" refer to detailed specifications and requirements regarding the content creation and tasks that the user will perform.

[0102] A "matching algorithm" is a computational method used to identify suitable collaborators based on the skills and project conditions provided by the user.

[0103] A "generative artificial intelligence model" is an AI technology that generates content in response to input requests and provides the results.

[0104] "Content" refers to produced works and digital data, and its types include illustrations, scenarios, music, and so on.

[0105] "Feedback" refers to evaluations and opinions provided by users, and may include instructions for improving or correcting the generated content.

[0106] "Virtual reality" refers to artificial environments or situations that users experience through digital devices, behaving in a way that resembles reality.

[0107] The system for realizing this application is a platform that collects user skills and project requirements, and a server analyzes this information to match suitable collaborators. This system includes terminal devices used by the user and server devices.

[0108] The user first uses a terminal to input their skills and project requirements. The terminal collects the data and sends it to a server via the network. The server uses this information and a matching algorithm to identify suitable collaborators.

[0109] Next, the server generates content using an artificial intelligence model in response to the user's request. This generated content is delivered to the user in real time via the terminal. The user can view the content in virtual reality and provide feedback via voice or text. The feedback is sent back to the server, which uses it to modify or regenerate the content.

[0110] This system can be used, for example, when a creator is producing animation. If user A, who specializes in character design, needs a collaborator for music production, the server will identify a suitable music creator and match them with user B. By allowing user A to review the scenario using a virtual reality device and provide immediate feedback on the music, production efficiency is improved.

[0111] An example of a prompt could be: "Generate a prototype character design for a new anime series. The background is a city at night, and the character is a friendly dog."

[0112] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0113] Step 1:

[0114] The terminal receives information about skills and project requirements from the user as input. This data is formatted into a standard format and sent to the server via the network.

[0115] Step 2:

[0116] The server analyzes the user's skills and project requirements received from the terminal. The analysis uses text processing algorithms to convert the input data into numerical values ​​and categories, which are then stored in a database. The stored data is then used as input for a matching algorithm.

[0117] Step 3:

[0118] The server runs a matching algorithm to identify the collaborators best suited to the project requirements. It then cross-references the database based on skill similarity and project objectives to list the most relevant users. This list is output as a suggestion to the users.

[0119] Step 4:

[0120] After a user is matched with a collaborator, the device sends a content generation request to the server as input. The request includes prompts and image concepts.

[0121] Step 5:

[0122] The server uses a generative artificial intelligence model to generate content based on user requests. The generated content is temporarily stored on the server and then output to the terminal.

[0123] Step 6:

[0124] Users view the content provided on their device using a virtual reality device and provide feedback on that content. This feedback is sent to the server in either voice or text format.

[0125] Step 7:

[0126] The server analyzes the feedback, performs necessary data processing, and then reruns the generating artificial intelligence model to modify or regenerate the content. The analyzed feedback is used to adjust parameters for the next regeneration, and new content is generated. This content is then output to the terminal again.

[0127] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0128] This invention combines an online platform for creators to collaborate efficiently on projects with an emotion engine that recognizes user emotions. This system automatically matches users with collaborators based on their required skills and project requirements, generates necessary content, and analyzes the user's emotional state to support project progress.

[0129] Users access the system using a terminal and enter the information necessary to start a project. This includes the user's professional skills, project goals, and desired collaborator requirements. The terminal sends this information to the server, which uses a matching algorithm to identify other suitable users.

[0130] After the project begins, users can request content generation via their devices. The server uses a generative artificial intelligence model to generate initial content and present it to the user. During this process, an emotion engine analyzes the user's feedback and evaluates their emotional state. Based on this, the server adjusts the content according to the user's emotional changes and makes more appropriate suggestions.

[0131] For example, if a user provides dissatisfied feedback on generated content, the sentiment engine recognizes this, and the server takes the user's feelings into consideration when suggesting modifications or alternatives. Furthermore, if stress or decreased motivation is detected during project progress, the server adjusts task management and scheduling, and provides notifications and advice to reduce the user's psychological burden.

[0132] As a concrete example, suppose user C is working on a music project and the emotion engine detects a saturation point based on user feedback. In this case, the server can suggest new music samples to help revise the production method or provide inspiration.

[0133] This invention enables not only matching collaborators and content generation, but also project management that takes user emotions into account, thereby providing an efficient and comfortable creative environment.

[0134] The following describes the processing flow.

[0135] Step 1:

[0136] Users log in to the system using their device and enter or update their profile. This includes information about their professional skills and project requirements. The device then sends this information to the server.

[0137] Step 2:

[0138] The server analyzes the information received from the user and stores it in a database. Next, it runs a matching algorithm to identify other users who could be the best collaborators based on the user's skill set and project requirements.

[0139] Step 3:

[0140] Once a match is complete, the server sends a notification to the matched user and potential collaborators. Users can check the notification via their device and accept or decline the collaboration as needed.

[0141] Step 4:

[0142] Once a project starts, the user enters details about the project and desired content into their device. Based on this information, the device sends a request to the server to generate the content.

[0143] Step 5:

[0144] The server utilizes a generative artificial intelligence model to generate content in a specified format. This generated content is then presented to the user via the terminal.

[0145] Step 6:

[0146] Users review the presented content and use their devices to provide feedback. This feedback includes comments and suggestions for improvement regarding the content.

[0147] Step 7:

[0148] The device sends user feedback to the server, and the emotion engine analyzes that feedback to evaluate the user's emotions.

[0149] Step 8:

[0150] The server suggests content modifications or additions based on the user's emotional state detected by the emotion engine. If necessary, it restarts the AI ​​model to generate the modified content.

[0151] Step 9:

[0152] During project execution, the server manages all project tasks and schedules, and uses an emotion engine to monitor user motivation and psychological state.

[0153] Step 10:

[0154] If the server detects an abnormality in the user's emotional state, for example, if stress levels are high, it will send a notification to adjust tasks and schedules and suggest improvements to alleviate psychological burden.

[0155] (Example 2)

[0156] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0157] Modern project management requires users with diverse skill sets to collaborate efficiently. However, identifying the right collaborators, adjusting content to consider users' emotional states, and streamlining task management are often insufficient. This can lead to decreased project efficiency and user satisfaction.

[0158] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0159] In this invention, the server includes means for analyzing data and project conditions provided by the user and identifying other users using a corresponding algorithm; means for generating data using a generative machine learning model based on the user's requests and modifying the data based on the user's feedback; and means for adjusting content in response to emotional changes using an emotion analysis engine that analyzes the user's emotional state. This enables optimal matching of collaborators in a project, customization of content that takes user emotions into consideration, and efficient task management.

[0160] A "user" refers to a person who provides information about a specific project and works with collaborators through the system to advance the project.

[0161] "Data" refers to information provided by users as project requirements, including elements necessary for project execution such as expertise, objectives, and conditions.

[0162] "Project requirements" are a set of technical and functional requirements necessary to accomplish a particular project.

[0163] A "correspondence algorithm" refers to a computational method used to identify the most suitable collaborator based on user-provided data and project conditions.

[0164] A "generative machine learning model" is a model that generates data or content based on specified prompt sentences, and it is a mechanism that enables the automatic generation of output in response to user requests.

[0165] "Feedback" refers to the opinions and evaluations that users provide regarding generated content, and is the input information that the system uses to correct its output based on that feedback.

[0166] A "sentiment analysis engine" refers to technology that monitors and analyzes user feedback and behavior to evaluate the user's emotional state.

[0167] "Content" refers to data and documents generated by generative machine learning models as information and ideas that support the achievement of project goals.

[0168] This system consists of a user terminal, a server that processes data, an emotion engine that analyzes the user's emotional state, and a generative machine learning model.

[0169] The user uses a terminal to input the information necessary to start the project. This information includes their professional skills, project objectives, and desired collaborators. The terminal then sends this information to the server.

[0170] The server uses a correspondence algorithm based on the information it receives to identify appropriate collaborators. This allows users with different skill sets to collaborate effectively on the project. Furthermore, when a user requests content generation, the server uses a generative machine learning model to generate initial content. In this process, prompts are provided to the generative machine learning model to generate content. For example, a prompt such as "Generate initial ideas that fit the concept of this project" can be used.

[0171] When a user provides feedback on the generated content, the emotion engine analyzes that feedback. Based on this analysis, the server adjusts the content, taking the user's emotional state into consideration, and suggests changes.

[0172] For example, if a user provides feedback that "a more creative approach is needed," the server uses this feedback to give new prompts to the generative machine learning model, generating revised or additional content. In this way, the entire system works together to support project progress.

[0173] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0174] Step 1:

[0175] The user uses a terminal to input information about the project. This input includes professional skills, project objectives, and collaborator requirements. The terminal sends this input data to the server. Specifically, the user fills in the required information in a form and clicks the "Submit" button.

[0176] Step 2:

[0177] The server applies a matching algorithm based on the received information. Using user information and project conditions as input, it processes the data to obtain output that identifies suitable collaborators. This process includes computationally matching skill sets to identify the most suitable collaborators.

[0178] Step 3:

[0179] After the project starts, the user requests content generation via their device. This request is sent to the server, which activates the generation AI model. The input is the user's prompt text, and the output is the generated initial content. Specifically, the AI ​​model interprets the prompt based on the "summary of desired content" provided by the user and generates the content.

[0180] Step 4:

[0181] The server provides the generated content to the user and awaits user feedback. The user uses their device to input their thoughts and suggestions for improvement regarding the content. This feedback is sent to the server, where an emotion analysis engine analyzes its content. The server receives the user's emotional expression as input and obtains an emotional result as output.

[0182] Step 5:

[0183] The server performs necessary data corrections and generates alternatives based on the results of the sentiment analysis engine. The input is the analysis results, and the output is adjusted content. Specifically, this includes actions such as changing the tone and structure of the content according to the sentiment data.

[0184] Step 6:

[0185] As the user's project progresses, the server manages the project's progress and sends notifications to the user as needed. This process involves schedule changes and task adjustments based on changes in emotional state and stress detection. The server receives these inputs and outputs messages to the user recommending rest or suggesting new tasks.

[0186] (Application Example 2)

[0187] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0188] This invention aims to solve problems such as delays in project progress and declines in quality due to changes in user emotions when matching collaborators and generating content on an online platform that allows creators to efficiently advance projects. Furthermore, it is necessary to effectively manage stress and decreased motivation during project progress.

[0189] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0190] This invention includes a server that analyzes the skills and project conditions provided by the user and automatically identifies other users who can complement it using a matching algorithm; a server that generates content using a generative artificial intelligence model based on the user's requests and modifies the content based on the user's feedback; and a server that analyzes the user's emotional state and uses an emotion engine to support the progress of the project. This enables flexible management in response to changes in the user's emotions during the project and efficient project execution.

[0191] A "user" is an individual or organization that uses this system to collaborate on projects or create content.

[0192] "Skills" refer to the specific professional abilities and knowledge that a user possesses, and are essential elements for project progress and matching with other users.

[0193] "Project conditions" refer to the requirements and limitations necessary to carry out the project, and include information that users enter into the system.

[0194] A "matching algorithm" is a set of computational methods or formulas used to automatically identify the most suitable collaborators based on the user's skills and project requirements.

[0195] A "generative artificial intelligence model" is a machine learning-based system that automatically generates content such as text and images based on prompts.

[0196] An "emotion engine" is a system that analyzes the user's emotional state from their input and feedback, and uses that analysis to support project progress.

[0197] Task management is the process of organizing the various tasks necessary for the progress of a project and tracking its progress.

[0198] A "schedule" refers to the deadlines and timetables set for project tasks, and is an element that contributes to the efficient execution of a project.

[0199] This invention is a system for efficiently facilitating collaboration among creators on a content distribution platform. Users access the platform using a dedicated terminal or smartphone and input the information necessary to start a project. This information includes the user's skills and project requirements. The information transmitted from the terminal is sent to a server, which uses a matching algorithm to automatically identify other suitable users. The server also uses a generative artificial intelligence model to generate content based on the user's requests and an emotion engine to analyze the user's emotional state. Based on the results of this analysis, the content is modified or improved.

[0200] The server uses the Affectiva SDK and other tools to recognize user emotions and support project progress. The emotion engine analyzes emotions from user input and feedback, and based on this, suggests new content and adjusts the schedule. If user emotions affect project progress, the server uses generative AI models (e.g., OpenAI®'s GPT-3® or DALL-E) to generate appropriate alternatives. Furthermore, project task management uses Trello API and Asana API to manage and optimize progress in real time.

[0201] As a concrete example, if a visual artist is working on a project using an online platform and the emotion engine detects a creative block, the server sends a prompt message to the AI ​​model: "User feedback indicates a creative block. Please suggest new ideas to inspire this user." This prompt then generates new design suggestions. In this way, the system enables flexible project management that responds to changes in the user's emotions, resulting in efficient content generation.

[0202] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0203] Step 1:

[0204] The user accesses the online platform using their device and enters their skills and project requirements. This information becomes input data, and the device prepares to send it to the server.

[0205] Step 2:

[0206] The server analyzes the user's skills and project requirements received. A matching algorithm is then used to automatically identify suitable collaborators. The input is the user's skill information, and the output is a list of collaborators.

[0207] Step 3:

[0208] When a user requests content generation, the server uses a generational artificial intelligence model to generate content based on the request. During this process, data is sent to the model using prompts, and the generated content is returned to the user as output.

[0209] Step 4:

[0210] When a user provides feedback on generated content, the server uses an emotion engine to analyze the user's emotional state from that feedback. The input is the user's feedback, and the output is the analyzed emotional state.

[0211] Step 5:

[0212] Based on the emotion engine's analysis results, the server modifies the content as needed. Furthermore, it proposes new content and adjusts the schedule to ensure the project progresses smoothly. This process utilizes the project's task management tool, and the adjusted task schedule is provided to the user as output.

[0213] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0214] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0215] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0216] [Second Embodiment]

[0217] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0218] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0219] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0220] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0221] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0222] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0223] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0224] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0225] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0226] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0227] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0228] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0229] This invention provides an online platform that enables creators to collaborate smoothly via the internet. This platform automatically matches users with the most suitable collaborators based on their expertise and project requirements, and supports content creation using generative AI.

[0230] To use the platform, users begin by creating an account through their device. They input their professional skills and desired project requirements and submit them to the server. The server analyzes the received data and stores it in a database. Based on this information, the server reviews other users' profiles and uses a matching algorithm to find suitable collaborators.

[0231] After matching, users can begin working on a specific project. Users input the type and details of the content they want to create into their device and send that information to the server. The server uses a generative AI model to automatically generate initial content such as illustrations, scenarios, and music. The generated content is displayed to the user via their device, and the user provides feedback as needed. Based on this feedback, the server modifies or regenerates the content.

[0232] Furthermore, in project management, the server centrally manages tasks and schedules set by users. The server tracks task priorities and progress and notifies users as needed. If progress is not on schedule, the server makes adjustments and sends reminders to ensure the project runs smoothly.

[0233] As a concrete example, let's say User A plans to produce a short animated film. User A is good at character design but not good at music production. User A provides project requirements to the server from their terminal and is matched with User B, a suitable music creator. Subsequently, User A has a scenario prototype generated by a generation AI, and User B creates music based on that scenario. The server manages the progress of the entire project, creating an environment where both Users A and B can concentrate on their creative work.

[0234] This invention resolves the challenges creators face, such as a lack of skills and difficulty finding collaborators, enabling efficient project execution.

[0235] The following describes the processing flow.

[0236] Step 1:

[0237] Users access the platform using their device and create a new account. During this process, users input their professional skills and desired project requirements, and the device transmits this information to the server.

[0238] Step 2:

[0239] The server analyzes the received user information and registers it in a dedicated database. This analysis identifies the user's skill set and project requirements, and considers mutual complementarity with other users.

[0240] Step 3:

[0241] When a user is ready to start a project, they enter the necessary project requirements into their device. The device then sends this data to the server, initiating the collaborator matching process.

[0242] Step 4:

[0243] The server runs a matching algorithm based on existing user profiles to automatically identify collaborators who meet the criteria specified by the user. It then sends notifications to the relevant users and requests their consent to become collaborators.

[0244] Step 5:

[0245] Once collaborators are confirmed, users begin creating the specific content for the project. They input the desired type and content from their device and send a request to the server.

[0246] Step 6:

[0247] The server uses a generative artificial intelligence model to generate content in the specified format. After generation, the content data is sent to the terminal and presented to the user.

[0248] Step 7:

[0249] The user reviews the generated content and provides feedback on improvements and corrections as needed, entering them into their device. The device then sends this feedback to the server.

[0250] Step 8:

[0251] The server modifies the content based on user feedback and runs the AI ​​model again if regeneration is requested. The updated content is then sent back to the device.

[0252] Step 9:

[0253] During project execution, the server centrally manages all tasks and schedules. It analyzes progress according to deadlines and priorities set by the user and provides the user with necessary information in a timely manner.

[0254] Step 10:

[0255] If the project progresses outside of the planned schedule, the server will send warnings and reminders to the user to help adjust the schedule. Based on this, the user will consider the following actions.

[0256] (Example 1)

[0257] Next, we will describe Example 1. 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."

[0258] The challenge lies in enabling creators to efficiently execute projects through online collaboration without wasting time on their own skill deficiencies or searching for collaborators. Furthermore, it requires the effective use of industrial AI in the content generation process to support creative activities by quickly incorporating feedback. Additionally, the challenge is to streamline project progress and management, providing an environment where workers can focus on their core creative work.

[0259] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0260] This invention includes a server that analyzes the technical skills and planning conditions provided by the user and automatically identifies other users who can complement them using a matching algorithm; a server that generates information using an artificial intelligence model based on the user's requests and modifies the information based on the user's feedback; and a server that manages the work and time schedule of the plan and automatically notifies the user of the progress. This makes it possible for creators to easily find collaborators with the right skills and efficiently generate content and manage projects.

[0261] "Technical capability" refers to the specialized knowledge and skills possessed by the user, and includes all the skills required to carry out the project.

[0262] "Planning conditions" refer to the goals, timeframe, budget, and other related constraints that the user sets when carrying out a project.

[0263] A "matching algorithm" refers to a computational method used to identify the most suitable collaborators based on the user's technical capabilities and project requirements.

[0264] A "generative artificial intelligence model" refers to an artificial intelligence system that can automatically generate information based on specified instructions.

[0265] "Information generation" refers to the process of creating initial information such as content, design, and sound based on user requirements and design conditions.

[0266] "Feedback" refers to evaluations and improvement requests for generated results provided by users, which the system uses to update information or processes.

[0267] "Work and time planning" refers to a comprehensive plan that includes the order in which tasks are performed and the time allocation for each task within a project.

[0268] "Progress status" refers to the state of how far a planned project has progressed within the set timeframe.

[0269] This invention provides a system that enables users to collaborate efficiently by utilizing a cloud-based online platform. The system primarily operates using servers, terminals, and generative AI models. Its specific configuration is described below.

[0270] The terminal serves as a means for users to access the system and create accounts. Through the terminal, users input and submit their technical skills and project requirements. The terminal can utilize a web browser or specialized cloud applications. This information is sent to the server and forms the basis for building user data.

[0271] The server analyzes the received technical capabilities and planning conditions and stores them in a database. Furthermore, the server uses a matching algorithm to identify the collaborator that best matches the user's requirements. This algorithm uses advanced data analysis techniques to evaluate the compatibility between users.

[0272] Once a project begins, the user inputs details of the content they want to generate via their device and sends them to the server. The server uses a generation AI model to automatically generate scenarios, designs, music, and other elements based on the specified prompts. The generated content is then delivered to the user via their device, and the server receives user feedback. Based on this feedback, the server modifies or regenerates the content.

[0273] Project progress is constantly tracked by the server, and scheduling is managed based on work and time plans. The server notifies users of progress and makes adjustments as needed if delays occur, supporting the smooth progress of the project.

[0274] As a concrete example, consider a scenario where a user creates a short animated film. The user provides character designs and a storyline outline from their device and is matched with other users skilled in music or voice acting. At each stage of the project, a generation AI is used to generate a scenario and music based on a prompt message such as, "Please generate a scenario for a short animated film. The theme is friendship, and the desired length is approximately 5 minutes." In this way, the system supports the creative activities that the user must focus on, and improves the overall efficiency of the project.

[0275] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0276] Step 1:

[0277] Users access the platform through their device and create a new account. As input, users enter their name, email address, password, technical skills, and project requirements. The device collects this data and sends it to the server. The process involves the user clicking the "Register" button, filling in the required information on the form, and then pressing the submit button, at which point the data is transferred to the server.

[0278] Step 2:

[0279] The server analyzes the received user data and stores it in the database. The inputs in this process are the technical capabilities and planned conditions obtained in Step 1. The server performs a data integrity check and correctly classifies and stores the information. The output is the state where the analyzed user data is registered in the database. As a specific operation, when receiving data, the server automatically executes a validation process and notifies the user if there are any deficiencies.

[0280] Step 3:

[0281] The server executes a matching algorithm based on the information accumulated in the database to identify the optimal collaborators. The inputs for this step are the technical capabilities and planned conditions of other users. Using data analysis techniques, the possibility of cooperation is evaluated, and as output, a list of recommended collaborators is generated. As an operation, the algorithm is executed periodically and the users are notified of the matching.

[0282] Step 4:

[0283] The user inputs the details of the content to be created and the prompt text through the terminal and sends it to the server. The inputs include the specific type, theme, length of the content, and the prompt text, etc. The server uses the generative AI model to process these data and generates initial content as output. As a specific operation, the user clicks the "Content Generation" button, enters the necessary conditions and then sends them, and receives the generation result.

[0284] Step 5:

[0285] The server conveys the generated content to the user and collects the user's feedback. As input, feedback from the user is required. The server analyzes this feedback and, if necessary, modifies or regenerates the content. The output is the revised content. As a specific operation, after collecting the feedback, the server uses an AI model to perform regeneration and provides the revised version to the user.

[0286] Step 6:

[0287] The server manages the progress of the project and monitors the work and time plan. The input is the tasks, deadlines, and priorities set by the user. The server optimizes the schedule using this data and sends progress notifications to the user. The output is the updated schedule and progress report of the project. As a specific operation, every time there is an update in progress, the server automatically sends a notification to the user and provides guidance for the next step.

[0288] (Application Example 1)

[0289] Next, Application Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0290] In content creation, there is an issue that it is difficult for creators in different specialized fields to effectively collaborate. In particular, real-time feedback and modification within virtual reality are difficult, and the production process may become inefficient. Furthermore, creators are required to effectively manage the progress of the project.

[0291] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0292] This invention includes a server that analyzes the skills and project conditions provided by the user and automatically identifies other users who can complement it using a matching algorithm; a server that generates content using an artificial intelligence model based on the user's requests and modifies the content based on the user's feedback; and a server that allows the user to view the content in real time within virtual reality and provide modification instructions. This enables creators to efficiently collaborate with members who have different expertise and provide immediate feedback using virtual reality.

[0293] A "user" is an individual or legal entity that uses this system to create content or manage projects.

[0294] "Skills" refer to the specialized abilities and knowledge that a user possesses, encompassing their level of proficiency in the areas necessary for the successful completion of the project.

[0295] "Project conditions" refer to detailed specifications and requirements regarding the content creation and tasks that the user will perform.

[0296] A "matching algorithm" is a computational method used to identify suitable collaborators based on the skills and project conditions provided by the user.

[0297] A "generative artificial intelligence model" is an AI technology that generates content in response to input requests and provides the results.

[0298] "Content" refers to produced works and digital data, and its types include illustrations, scenarios, music, and so on.

[0299] "Feedback" refers to evaluations and opinions provided by users, and may include instructions for improving or correcting the generated content.

[0300] "Virtual reality" refers to artificial environments or situations that users experience through digital devices, behaving in a way that resembles reality.

[0301] The system for realizing this application is a platform that collects user skills and project requirements, and a server analyzes this information to match suitable collaborators. This system includes terminal devices used by the user and server devices.

[0302] The user first uses a terminal to input their skills and project requirements. The terminal collects the data and sends it to a server via the network. The server uses this information and a matching algorithm to identify suitable collaborators.

[0303] Next, the server generates content using an artificial intelligence model in response to the user's request. This generated content is delivered to the user in real time via the terminal. The user can view the content in virtual reality and provide feedback via voice or text. The feedback is sent back to the server, which uses it to modify or regenerate the content.

[0304] This system can be used, for example, when a creator is producing animation. If user A, who specializes in character design, needs a collaborator for music production, the server will identify a suitable music creator and match them with user B. By allowing user A to review the scenario using a virtual reality device and provide immediate feedback on the music, production efficiency is improved.

[0305] An example of a prompt could be: "Generate a prototype character design for a new anime series. The background is a city at night, and the character is a friendly dog."

[0306] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0307] Step 1:

[0308] The terminal receives information on skills and project conditions from the user as input. This data is formatted into a standard format and sent to the server via the network.

[0309] Step 2:

[0310] The server analyzes the user's skills and project conditions received from the terminal. For the analysis, a text processing algorithm is used to convert the input data into numerical values or categories and save it in the database. The saved data is used as input for the matching algorithm.

[0311] Step 3:

[0312] The server executes the matching algorithm to identify the most suitable collaborators for the project conditions. Here, the database is queried based on skill similarity and project objectives, and the most relevant users are listed. This list is output as a proposal to the user.

[0313] Step 4:

[0314] After the user is matched with a collaborator, the terminal sends a request for content generation to the server as input. The request includes a prompt sentence or image concept.

[0315] Step 5:

[0316] The server uses a generative artificial intelligence model to generate content based on the user's request. The generated content is temporarily saved on the server and then output to the terminal.

[0317] Step 6:

[0318] Users view the content provided on their device using a virtual reality device and provide feedback on that content. This feedback is sent to the server in either voice or text format.

[0319] Step 7:

[0320] The server analyzes the feedback, performs necessary data processing, and then reruns the generating artificial intelligence model to modify or regenerate the content. The analyzed feedback is used to adjust parameters for the next regeneration, and new content is generated. This content is then output to the terminal again.

[0321] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0322] This invention combines an online platform for creators to collaborate efficiently on projects with an emotion engine that recognizes user emotions. This system automatically matches users with collaborators based on their required skills and project requirements, generates necessary content, and analyzes the user's emotional state to support project progress.

[0323] Users access the system using a terminal and enter the information necessary to start a project. This includes the user's professional skills, project goals, and desired collaborator requirements. The terminal sends this information to the server, which uses a matching algorithm to identify other suitable users.

[0324] After the project begins, users can request content generation via their devices. The server uses a generative artificial intelligence model to generate initial content and present it to the user. During this process, an emotion engine analyzes the user's feedback and evaluates their emotional state. Based on this, the server adjusts the content according to the user's emotional changes and makes more appropriate suggestions.

[0325] For example, if a user provides dissatisfied feedback on generated content, the sentiment engine recognizes this, and the server takes the user's feelings into consideration when suggesting modifications or alternatives. Furthermore, if stress or decreased motivation is detected during project progress, the server adjusts task management and scheduling, and provides notifications and advice to reduce the user's psychological burden.

[0326] As a concrete example, suppose user C is working on a music project and the emotion engine detects a saturation point based on user feedback. In this case, the server can suggest new music samples to help revise the production method or provide inspiration.

[0327] This invention enables not only matching collaborators and content generation, but also project management that takes user emotions into account, thereby providing an efficient and comfortable creative environment.

[0328] The following describes the processing flow.

[0329] Step 1:

[0330] Users log in to the system using their device and enter or update their profile. This includes information about their professional skills and project requirements. The device then sends this information to the server.

[0331] Step 2:

[0332] The server analyzes the information received from the user and stores it in a database. Next, it runs a matching algorithm to identify other users who could be the best collaborators based on the user's skill set and project requirements.

[0333] Step 3:

[0334] Once a match is complete, the server sends a notification to the matched user and potential collaborators. Users can check the notification via their device and accept or decline the collaboration as needed.

[0335] Step 4:

[0336] Once a project starts, the user enters details about the project and desired content into their device. Based on this information, the device sends a request to the server to generate the content.

[0337] Step 5:

[0338] The server utilizes a generative artificial intelligence model to generate content in a specified format. This generated content is then presented to the user via the terminal.

[0339] Step 6:

[0340] Users review the presented content and use their devices to provide feedback. This feedback includes comments and suggestions for improvement regarding the content.

[0341] Step 7:

[0342] The device sends user feedback to the server, and the emotion engine analyzes that feedback to evaluate the user's emotions.

[0343] Step 8:

[0344] The server suggests content modifications or additions based on the user's emotional state detected by the emotion engine. If necessary, it restarts the AI ​​model to generate the modified content.

[0345] Step 9:

[0346] During project execution, the server manages all project tasks and schedules, and uses an emotion engine to monitor user motivation and psychological state.

[0347] Step 10:

[0348] If the server detects an abnormality in the user's emotional state, for example, if stress levels are high, it will send a notification to adjust tasks and schedules and suggest improvements to alleviate psychological burden.

[0349] (Example 2)

[0350] Next, we will describe Example 2. 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".

[0351] Modern project management requires users with diverse skill sets to collaborate efficiently. However, identifying the right collaborators, adjusting content to consider users' emotional states, and streamlining task management are often insufficient. This can lead to decreased project efficiency and user satisfaction.

[0352] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0353] In this invention, the server includes means for analyzing data and project conditions provided by the user and identifying other users using a corresponding algorithm; means for generating data using a generative machine learning model based on the user's requests and modifying the data based on the user's feedback; and means for adjusting content in response to emotional changes using an emotion analysis engine that analyzes the user's emotional state. This enables optimal matching of collaborators in a project, customization of content that takes user emotions into consideration, and efficient task management.

[0354] A "user" refers to a person who provides information about a specific project and works with collaborators through the system to advance the project.

[0355] "Data" refers to information provided by users as project requirements, including elements necessary for project execution such as expertise, objectives, and conditions.

[0356] "Project requirements" are a set of technical and functional requirements necessary to accomplish a particular project.

[0357] A "correspondence algorithm" refers to a computational method used to identify the most suitable collaborator based on user-provided data and project conditions.

[0358] A "generative machine learning model" is a model that generates data or content based on specified prompt sentences, and it is a mechanism that enables the automatic generation of output in response to user requests.

[0359] "Feedback" refers to the opinions and evaluations that users provide regarding generated content, and is the input information that the system uses to correct its output based on that feedback.

[0360] A "sentiment analysis engine" refers to technology that monitors and analyzes user feedback and behavior to evaluate the user's emotional state.

[0361] "Content" refers to data and documents generated by generative machine learning models as information and ideas that support the achievement of project goals.

[0362] This system consists of a user terminal, a server that processes data, an emotion engine that analyzes the user's emotional state, and a generative machine learning model.

[0363] The user uses a terminal to input the information necessary to start the project. This information includes their professional skills, project objectives, and desired collaborators. The terminal then sends this information to the server.

[0364] The server uses a correspondence algorithm based on the information it receives to identify appropriate collaborators. This allows users with different skill sets to collaborate effectively on the project. Furthermore, when a user requests content generation, the server uses a generative machine learning model to generate initial content. In this process, prompts are provided to the generative machine learning model to generate content. For example, a prompt such as "Generate initial ideas that fit the concept of this project" can be used.

[0365] When a user provides feedback on the generated content, the emotion engine analyzes that feedback. Based on this analysis, the server adjusts the content, taking the user's emotional state into consideration, and suggests changes.

[0366] For example, if a user provides feedback that "a more creative approach is needed," the server uses this feedback to give new prompts to the generative machine learning model, generating revised or additional content. In this way, the entire system works together to support project progress.

[0367] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0368] Step 1:

[0369] The user uses a terminal to input information about the project. This input includes professional skills, project objectives, and collaborator requirements. The terminal sends this input data to the server. Specifically, the user fills in the required information in a form and clicks the "Submit" button.

[0370] Step 2:

[0371] The server applies a matching algorithm based on the received information. Using user information and project conditions as input, it processes the data to obtain output that identifies suitable collaborators. This process includes computationally matching skill sets to identify the most suitable collaborators.

[0372] Step 3:

[0373] After the project starts, the user requests content generation via their device. This request is sent to the server, which activates the generation AI model. The input is the user's prompt text, and the output is the generated initial content. Specifically, the AI ​​model interprets the prompt based on the "summary of desired content" provided by the user and generates the content.

[0374] Step 4:

[0375] The server provides the generated content to the user and awaits user feedback. The user uses their device to input their thoughts and suggestions for improvement regarding the content. This feedback is sent to the server, where an emotion analysis engine analyzes its content. The server receives the user's emotional expression as input and obtains an emotional result as output.

[0376] Step 5:

[0377] The server performs necessary data corrections and generates alternatives based on the results of the sentiment analysis engine. The input is the analysis results, and the output is adjusted content. Specifically, this includes actions such as changing the tone and structure of the content according to the sentiment data.

[0378] Step 6:

[0379] As the user's project progresses, the server manages the project's progress and sends notifications to the user as needed. This process involves schedule changes and task adjustments based on changes in emotional state and stress detection. The server receives these inputs and outputs messages to the user recommending rest or suggesting new tasks.

[0380] (Application Example 2)

[0381] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0382] This invention aims to solve problems such as delays in project progress and declines in quality due to changes in user emotions when matching collaborators and generating content on an online platform that allows creators to efficiently advance projects. Furthermore, it is necessary to effectively manage stress and decreased motivation during project progress.

[0383] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0384] This invention includes a server that analyzes the skills and project conditions provided by the user and automatically identifies other users who can complement it using a matching algorithm; a server that generates content using a generative artificial intelligence model based on the user's requests and modifies the content based on the user's feedback; and a server that analyzes the user's emotional state and uses an emotion engine to support the progress of the project. This enables flexible management in response to changes in the user's emotions during the project and efficient project execution.

[0385] A "user" is an individual or organization that uses this system to collaborate on projects or create content.

[0386] "Skills" refer to the specific professional abilities and knowledge that a user possesses, and are essential elements for project progress and matching with other users.

[0387] "Project conditions" refer to the requirements and limitations necessary to carry out the project, and include information that users enter into the system.

[0388] A "matching algorithm" is a set of computational methods or formulas used to automatically identify the most suitable collaborators based on the user's skills and project requirements.

[0389] A "generative artificial intelligence model" is a machine learning-based system that automatically generates content such as text and images based on prompts.

[0390] An "emotion engine" is a system that analyzes the user's emotional state from their input and feedback, and uses that analysis to support project progress.

[0391] Task management is the process of organizing the various tasks necessary for the progress of a project and tracking its progress.

[0392] A "schedule" refers to the deadlines and timetables set for project tasks, and is an element that contributes to the efficient execution of a project.

[0393] This invention is a system for efficiently facilitating collaboration among creators on a content distribution platform. Users access the platform using a dedicated terminal or smartphone and input the information necessary to start a project. This information includes the user's skills and project requirements. The information transmitted from the terminal is sent to a server, which uses a matching algorithm to automatically identify other suitable users. The server also uses a generative artificial intelligence model to generate content based on the user's requests and an emotion engine to analyze the user's emotional state. Based on the results of this analysis, the content is modified or improved.

[0394] The server uses the Affectiva SDK and other tools to recognize user emotions and support project progress. The emotion engine analyzes emotions from user input and feedback, and based on this, suggests new content and adjusts the schedule. If user emotions affect project progress, the server uses generative AI models (e.g., OpenAI's GPT-3 or DALL-E) to generate appropriate alternatives. Furthermore, project task management utilizes Trello and Asana APIs to manage and optimize progress in real time.

[0395] As a concrete example, if a visual artist is working on a project using an online platform and the emotion engine detects a creative block, the server sends a prompt message to the AI ​​model: "User feedback indicates a creative block. Please suggest new ideas to inspire this user." This prompt then generates new design suggestions. In this way, the system enables flexible project management that responds to changes in the user's emotions, resulting in efficient content generation.

[0396] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0397] Step 1:

[0398] The user accesses the online platform using their device and enters their skills and project requirements. This information becomes input data, and the device prepares to send it to the server.

[0399] Step 2:

[0400] The server analyzes the user's skills and project requirements received. A matching algorithm is then used to automatically identify suitable collaborators. The input is the user's skill information, and the output is a list of collaborators.

[0401] Step 3:

[0402] When a user requests content generation, the server uses a generational artificial intelligence model to generate content based on the request. During this process, data is sent to the model using prompts, and the generated content is returned to the user as output.

[0403] Step 4:

[0404] When a user provides feedback on generated content, the server uses an emotion engine to analyze the user's emotional state from that feedback. The input is the user's feedback, and the output is the analyzed emotional state.

[0405] Step 5:

[0406] Based on the emotion engine's analysis results, the server modifies the content as needed. Furthermore, it proposes new content and adjusts the schedule to ensure the project progresses smoothly. This process utilizes the project's task management tool, and the adjusted task schedule is provided to the user as output.

[0407] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0408] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0409] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0410] [Third Embodiment]

[0411] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0412] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0413] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0414] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0415] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0416] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0417] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0418] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0419] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0420] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0421] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0422] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0423] This invention provides an online platform that enables creators to collaborate smoothly via the internet. This platform automatically matches users with the most suitable collaborators based on their expertise and project requirements, and supports content creation using generative AI.

[0424] To use the platform, users begin by creating an account through their device. They input their professional skills and desired project requirements and submit them to the server. The server analyzes the received data and stores it in a database. Based on this information, the server reviews other users' profiles and uses a matching algorithm to find suitable collaborators.

[0425] After matching, users can begin working on a specific project. Users input the type and details of the content they want to create into their device and send that information to the server. The server uses a generative AI model to automatically generate initial content such as illustrations, scenarios, and music. The generated content is displayed to the user via their device, and the user provides feedback as needed. Based on this feedback, the server modifies or regenerates the content.

[0426] Furthermore, in project management, the server centrally manages tasks and schedules set by users. The server tracks task priorities and progress and notifies users as needed. If progress is not on schedule, the server makes adjustments and sends reminders to ensure the project runs smoothly.

[0427] As a concrete example, let's say User A plans to produce a short animated film. User A is good at character design but not good at music production. User A provides project requirements to the server from their terminal and is matched with User B, a suitable music creator. Subsequently, User A has a scenario prototype generated by a generation AI, and User B creates music based on that scenario. The server manages the progress of the entire project, creating an environment where both Users A and B can concentrate on their creative work.

[0428] This invention resolves the challenges creators face, such as a lack of skills and difficulty finding collaborators, enabling efficient project execution.

[0429] The following describes the processing flow.

[0430] Step 1:

[0431] Users access the platform using their device and create a new account. During this process, users input their professional skills and desired project requirements, and the device transmits this information to the server.

[0432] Step 2:

[0433] The server analyzes the received user information and registers it in a dedicated database. This analysis identifies the user's skill set and project requirements, and considers mutual complementarity with other users.

[0434] Step 3:

[0435] When a user is ready to start a project, they enter the necessary project requirements into their device. The device then sends this data to the server, initiating the collaborator matching process.

[0436] Step 4:

[0437] The server runs a matching algorithm based on existing user profiles to automatically identify collaborators who meet the criteria specified by the user. It then sends notifications to the relevant users and requests their consent to become collaborators.

[0438] Step 5:

[0439] Once collaborators are confirmed, users begin creating the specific content for the project. They input the desired type and content from their device and send a request to the server.

[0440] Step 6:

[0441] The server uses a generative artificial intelligence model to generate content in the specified format. After generation, the content data is sent to the terminal and presented to the user.

[0442] Step 7:

[0443] The user reviews the generated content and provides feedback on improvements and corrections as needed, entering them into their device. The device then sends this feedback to the server.

[0444] Step 8:

[0445] The server modifies the content based on user feedback and runs the AI ​​model again if regeneration is requested. The updated content is then sent back to the device.

[0446] Step 9:

[0447] During project execution, the server centrally manages all tasks and schedules. It analyzes progress according to deadlines and priorities set by the user and provides the user with necessary information in a timely manner.

[0448] Step 10:

[0449] If the project progresses outside of the planned schedule, the server will send warnings and reminders to the user to help adjust the schedule. Based on this, the user will consider the following actions.

[0450] (Example 1)

[0451] Next, we will describe Example 1. 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."

[0452] The challenge lies in enabling creators to efficiently execute projects through online collaboration without wasting time on their own skill deficiencies or searching for collaborators. Furthermore, it requires the effective use of industrial AI in the content generation process to support creative activities by quickly incorporating feedback. Additionally, the challenge is to streamline project progress and management, providing an environment where workers can focus on their core creative work.

[0453] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0454] This invention includes a server that analyzes the technical skills and planning conditions provided by the user and automatically identifies other users who can complement them using a matching algorithm; a server that generates information using an artificial intelligence model based on the user's requests and modifies the information based on the user's feedback; and a server that manages the work and time schedule of the plan and automatically notifies the user of the progress. This makes it possible for creators to easily find collaborators with the right skills and efficiently generate content and manage projects.

[0455] "Technical capability" refers to the specialized knowledge and skills possessed by the user, and includes all the skills required to carry out the project.

[0456] "Planning conditions" refer to the goals, timeframe, budget, and other related constraints that the user sets when carrying out a project.

[0457] A "matching algorithm" refers to a computational method used to identify the most suitable collaborators based on the user's technical capabilities and project requirements.

[0458] A "generative artificial intelligence model" refers to an artificial intelligence system that can automatically generate information based on specified instructions.

[0459] "Information generation" refers to the process of creating initial information such as content, design, and sound based on user requirements and design conditions.

[0460] "Feedback" refers to evaluations and improvement requests for generated results provided by users, which the system uses to update information or processes.

[0461] "Work and time planning" refers to a comprehensive plan that includes the order in which tasks are performed and the time allocation for each task within a project.

[0462] "Progress status" refers to the state of how far a planned project has progressed within the set timeframe.

[0463] This invention provides a system that enables users to collaborate efficiently by utilizing a cloud-based online platform. The system primarily operates using servers, terminals, and generative AI models. Its specific configuration is described below.

[0464] The terminal serves as a means for users to access the system and create accounts. Through the terminal, users input and submit their technical skills and project requirements. The terminal can utilize a web browser or specialized cloud applications. This information is sent to the server and forms the basis for building user data.

[0465] The server analyzes the received technical capabilities and planning conditions and stores them in a database. Furthermore, the server uses a matching algorithm to identify the collaborator that best matches the user's requirements. This algorithm uses advanced data analysis techniques to evaluate the compatibility between users.

[0466] Once a project begins, the user inputs details of the content they want to generate via their device and sends them to the server. The server uses a generation AI model to automatically generate scenarios, designs, music, and other elements based on the specified prompts. The generated content is then delivered to the user via their device, and the server receives user feedback. Based on this feedback, the server modifies or regenerates the content.

[0467] Project progress is constantly tracked by the server, and scheduling is managed based on work and time plans. The server notifies users of progress and makes adjustments as needed if delays occur, supporting the smooth progress of the project.

[0468] As a concrete example, consider a scenario where a user creates a short animated film. The user provides character designs and a storyline outline from their device and is matched with other users skilled in music or voice acting. At each stage of the project, a generation AI is used to generate a scenario and music based on a prompt message such as, "Please generate a scenario for a short animated film. The theme is friendship, and the desired length is approximately 5 minutes." In this way, the system supports the creative activities that the user must focus on, and improves the overall efficiency of the project.

[0469] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0470] Step 1:

[0471] Users access the platform through their device and create a new account. As input, users enter their name, email address, password, technical skills, and project requirements. The device collects this data and sends it to the server. The process involves the user clicking the "Register" button, filling in the required information on the form, and then pressing the submit button, at which point the data is transferred to the server.

[0472] Step 2:

[0473] The server analyzes the received user data and stores it in the database. The input for this process is the technical capability and planning conditions obtained in step 1. The server performs data integrity checks, correctly classifying and storing the information. The output is the state where the analyzed user data is registered in the database. Specifically, the server automatically performs a validation process upon receiving data and notifies the user if any errors are found.

[0474] Step 3:

[0475] The server runs a matching algorithm based on information stored in the database to identify the most suitable collaborators. The input for this step is the technical capabilities and planning conditions of other users. Data analysis techniques are used to evaluate the likelihood of collaboration, and the output is a list of recommended collaborators. The system operates by periodically running the algorithm and notifying users of matching opportunities.

[0476] Step 4:

[0477] The user inputs details of the content they want to create and prompt text through their terminal and sends them to the server. This input includes specific content type, theme, length, and prompt text. The server processes this data using a generative AI model and generates initial content as output. Specifically, the user clicks the "Generate Content" button, enters the required conditions, submits them, and receives the generated result.

[0478] Step 5:

[0479] The server delivers the generated content to the user and collects user feedback. User feedback is required as input. The server analyzes this feedback and modifies or regenerates the content as needed. The output is the revised content. Specifically, after collecting feedback, the server uses an AI model to regenerate the content and provides the revised version to the user.

[0480] Step 6:

[0481] The server manages project progress and monitors work and time planning. Inputs are tasks, due dates, and priorities set by the user. The server uses this data to optimize the schedule and send progress notifications to the user. Outputs are the updated project schedule and progress reports. Specifically, whenever there is a progress update, the server automatically sends a notification to the user, providing guidance for the next steps.

[0482] (Application Example 1)

[0483] Next, we will explain Application Example 1. In the following explanation, 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."

[0484] In content creation, there is a challenge in that creators from different specialized fields often struggle to collaborate effectively. In particular, real-time feedback and revisions within virtual reality are difficult, which can lead to an inefficient production process. Furthermore, creators are required to effectively manage the progress of their projects.

[0485] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0486] This invention includes a server that analyzes the skills and project conditions provided by the user and automatically identifies other users who can complement it using a matching algorithm; a server that generates content using an artificial intelligence model based on the user's requests and modifies the content based on the user's feedback; and a server that allows the user to view the content in real time within virtual reality and provide modification instructions. This enables creators to efficiently collaborate with members who have different expertise and provide immediate feedback using virtual reality.

[0487] A "user" is an individual or legal entity that uses this system to create content or manage projects.

[0488] "Skills" refer to the specialized abilities and knowledge that a user possesses, encompassing their level of proficiency in the areas necessary for the successful completion of the project.

[0489] "Project conditions" refer to detailed specifications and requirements regarding the content creation and tasks that the user will perform.

[0490] A "matching algorithm" is a computational method used to identify suitable collaborators based on the skills and project conditions provided by the user.

[0491] A "generative artificial intelligence model" is an AI technology that generates content in response to input requests and provides the results.

[0492] "Content" refers to produced works and digital data, and its types include illustrations, scenarios, music, and so on.

[0493] "Feedback" refers to evaluations and opinions provided by users, and may include instructions for improving or correcting the generated content.

[0494] "Virtual reality" refers to artificial environments or situations that users experience through digital devices, behaving in a way that resembles reality.

[0495] The system for realizing this application is a platform that collects user skills and project requirements, and a server analyzes this information to match suitable collaborators. This system includes terminal devices used by the user and server devices.

[0496] The user first uses a terminal to input their skills and project requirements. The terminal collects the data and sends it to a server via the network. The server uses this information and a matching algorithm to identify suitable collaborators.

[0497] Next, the server generates content using an artificial intelligence model in response to the user's request. This generated content is delivered to the user in real time via the terminal. The user can view the content in virtual reality and provide feedback via voice or text. The feedback is sent back to the server, which uses it to modify or regenerate the content.

[0498] This system can be used, for example, when a creator is producing animation. If user A, who specializes in character design, needs a collaborator for music production, the server will identify a suitable music creator and match them with user B. By allowing user A to review the scenario using a virtual reality device and provide immediate feedback on the music, production efficiency is improved.

[0499] An example of a prompt could be: "Generate a prototype character design for a new anime series. The background is a city at night, and the character is a friendly dog."

[0500] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0501] Step 1:

[0502] The terminal receives information about skills and project requirements from the user as input. This data is formatted into a standard format and sent to the server via the network.

[0503] Step 2:

[0504] The server analyzes the user's skills and project requirements received from the terminal. The analysis uses text processing algorithms to convert the input data into numerical values ​​and categories, which are then stored in a database. The stored data is then used as input for a matching algorithm.

[0505] Step 3:

[0506] The server runs a matching algorithm to identify the collaborators best suited to the project requirements. It then cross-references the database based on skill similarity and project objectives to list the most relevant users. This list is output as a suggestion to the users.

[0507] Step 4:

[0508] After a user is matched with a collaborator, the device sends a content generation request to the server as input. The request includes prompts and image concepts.

[0509] Step 5:

[0510] The server uses a generative artificial intelligence model to generate content based on user requests. The generated content is temporarily stored on the server and then output to the terminal.

[0511] Step 6:

[0512] Users view the content provided on their device using a virtual reality device and provide feedback on that content. This feedback is sent to the server in either voice or text format.

[0513] Step 7:

[0514] The server analyzes the feedback, performs necessary data processing, and then reruns the generating artificial intelligence model to modify or regenerate the content. The analyzed feedback is used to adjust parameters for the next regeneration, and new content is generated. This content is then output to the terminal again.

[0515] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0516] This invention combines an online platform for creators to collaborate efficiently on projects with an emotion engine that recognizes user emotions. This system automatically matches users with collaborators based on their required skills and project requirements, generates necessary content, and analyzes the user's emotional state to support project progress.

[0517] Users access the system using a terminal and enter the information necessary to start a project. This includes the user's professional skills, project goals, and desired collaborator requirements. The terminal sends this information to the server, which uses a matching algorithm to identify other suitable users.

[0518] After the project begins, users can request content generation via their devices. The server uses a generative artificial intelligence model to generate initial content and present it to the user. During this process, an emotion engine analyzes the user's feedback and evaluates their emotional state. Based on this, the server adjusts the content according to the user's emotional changes and makes more appropriate suggestions.

[0519] For example, if a user provides dissatisfied feedback on generated content, the sentiment engine recognizes this, and the server takes the user's feelings into consideration when suggesting modifications or alternatives. Furthermore, if stress or decreased motivation is detected during project progress, the server adjusts task management and scheduling, and provides notifications and advice to reduce the user's psychological burden.

[0520] As a concrete example, suppose user C is working on a music project and the emotion engine detects a saturation point based on user feedback. In this case, the server can suggest new music samples to help revise the production method or provide inspiration.

[0521] This invention enables not only matching collaborators and content generation, but also project management that takes user emotions into account, thereby providing an efficient and comfortable creative environment.

[0522] The following describes the processing flow.

[0523] Step 1:

[0524] Users log in to the system using their device and enter or update their profile. This includes information about their professional skills and project requirements. The device then sends this information to the server.

[0525] Step 2:

[0526] The server analyzes the information received from the user and stores it in a database. Next, it runs a matching algorithm to identify other users who could be the best collaborators based on the user's skill set and project requirements.

[0527] Step 3:

[0528] Once a match is complete, the server sends a notification to the matched user and potential collaborators. Users can check the notification via their device and accept or decline the collaboration as needed.

[0529] Step 4:

[0530] Once a project starts, the user enters details about the project and desired content into their device. Based on this information, the device sends a request to the server to generate the content.

[0531] Step 5:

[0532] The server utilizes a generative artificial intelligence model to generate content in a specified format. This generated content is then presented to the user via the terminal.

[0533] Step 6:

[0534] Users review the presented content and use their devices to provide feedback. This feedback includes comments and suggestions for improvement regarding the content.

[0535] Step 7:

[0536] The device sends user feedback to the server, and the emotion engine analyzes that feedback to evaluate the user's emotions.

[0537] Step 8:

[0538] The server suggests content modifications or additions based on the user's emotional state detected by the emotion engine. If necessary, it restarts the AI ​​model to generate the modified content.

[0539] Step 9:

[0540] During project execution, the server manages all project tasks and schedules, and uses an emotion engine to monitor user motivation and psychological state.

[0541] Step 10:

[0542] If the server detects an abnormality in the user's emotional state, for example, if stress levels are high, it will send a notification to adjust tasks and schedules and suggest improvements to alleviate psychological burden.

[0543] (Example 2)

[0544] Next, we will describe Example 2. 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."

[0545] Modern project management requires users with diverse skill sets to collaborate efficiently. However, identifying the right collaborators, adjusting content to consider users' emotional states, and streamlining task management are often insufficient. This can lead to decreased project efficiency and user satisfaction.

[0546] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0547] In this invention, the server includes means for analyzing data and project conditions provided by the user and identifying other users using a corresponding algorithm; means for generating data using a generative machine learning model based on the user's requests and modifying the data based on the user's feedback; and means for adjusting content in response to emotional changes using an emotion analysis engine that analyzes the user's emotional state. This enables optimal matching of collaborators in a project, customization of content that takes user emotions into consideration, and efficient task management.

[0548] A "user" refers to a person who provides information about a specific project and works with collaborators through the system to advance the project.

[0549] "Data" refers to information provided by users as project requirements, including elements necessary for project execution such as expertise, objectives, and conditions.

[0550] "Project requirements" are a set of technical and functional requirements necessary to accomplish a particular project.

[0551] A "correspondence algorithm" refers to a computational method used to identify the most suitable collaborator based on user-provided data and project conditions.

[0552] A "generative machine learning model" is a model that generates data or content based on specified prompt sentences, and it is a mechanism that enables the automatic generation of output in response to user requests.

[0553] "Feedback" refers to the opinions and evaluations that users provide regarding generated content, and is the input information that the system uses to correct its output based on that feedback.

[0554] A "sentiment analysis engine" refers to technology that monitors and analyzes user feedback and behavior to evaluate the user's emotional state.

[0555] "Content" refers to data and documents generated by generative machine learning models as information and ideas that support the achievement of project goals.

[0556] This system consists of a user terminal, a server that processes data, an emotion engine that analyzes the user's emotional state, and a generative machine learning model.

[0557] The user uses a terminal to input the information necessary to start the project. This information includes their professional skills, project objectives, and desired collaborators. The terminal then sends this information to the server.

[0558] The server uses a correspondence algorithm based on the information it receives to identify appropriate collaborators. This allows users with different skill sets to collaborate effectively on the project. Furthermore, when a user requests content generation, the server uses a generative machine learning model to generate initial content. In this process, prompts are provided to the generative machine learning model to generate content. For example, a prompt such as "Generate initial ideas that fit the concept of this project" can be used.

[0559] When a user provides feedback on the generated content, the emotion engine analyzes that feedback. Based on this analysis, the server adjusts the content, taking the user's emotional state into consideration, and suggests changes.

[0560] For example, if a user provides feedback that "a more creative approach is needed," the server uses this feedback to give new prompts to the generative machine learning model, generating revised or additional content. In this way, the entire system works together to support project progress.

[0561] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0562] Step 1:

[0563] The user uses a terminal to input information about the project. This input includes professional skills, project objectives, and collaborator requirements. The terminal sends this input data to the server. Specifically, the user fills in the required information in a form and clicks the "Submit" button.

[0564] Step 2:

[0565] The server applies a matching algorithm based on the received information. Using user information and project conditions as input, it processes the data to obtain output that identifies suitable collaborators. This process includes computationally matching skill sets to identify the most suitable collaborators.

[0566] Step 3:

[0567] After the project starts, the user requests content generation via their device. This request is sent to the server, which activates the generation AI model. The input is the user's prompt text, and the output is the generated initial content. Specifically, the AI ​​model interprets the prompt based on the "summary of desired content" provided by the user and generates the content.

[0568] Step 4:

[0569] The server provides the generated content to the user and awaits user feedback. The user uses their device to input their thoughts and suggestions for improvement regarding the content. This feedback is sent to the server, where an emotion analysis engine analyzes its content. The server receives the user's emotional expression as input and obtains an emotional result as output.

[0570] Step 5:

[0571] The server performs necessary data corrections and generates alternatives based on the results of the sentiment analysis engine. The input is the analysis results, and the output is adjusted content. Specifically, this includes actions such as changing the tone and structure of the content according to the sentiment data.

[0572] Step 6:

[0573] As the user's project progresses, the server manages the project's progress and sends notifications to the user as needed. This process involves schedule changes and task adjustments based on changes in emotional state and stress detection. The server receives these inputs and outputs messages to the user recommending rest or suggesting new tasks.

[0574] (Application Example 2)

[0575] Next, we will explain application example 2. In the following explanation, 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."

[0576] This invention aims to solve problems such as delays in project progress and declines in quality due to changes in user emotions when matching collaborators and generating content on an online platform that allows creators to efficiently advance projects. Furthermore, it is necessary to effectively manage stress and decreased motivation during project progress.

[0577] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0578] This invention includes a server that analyzes the skills and project conditions provided by the user and automatically identifies other users who can complement it using a matching algorithm; a server that generates content using a generative artificial intelligence model based on the user's requests and modifies the content based on the user's feedback; and a server that analyzes the user's emotional state and uses an emotion engine to support the progress of the project. This enables flexible management in response to changes in the user's emotions during the project and efficient project execution.

[0579] A "user" is an individual or organization that uses this system to collaborate on projects or create content.

[0580] "Skills" refer to the specific professional abilities and knowledge that a user possesses, and are essential elements for project progress and matching with other users.

[0581] "Project conditions" refer to the requirements and limitations necessary to carry out the project, and include information that users enter into the system.

[0582] A "matching algorithm" is a set of computational methods or formulas used to automatically identify the most suitable collaborators based on the user's skills and project requirements.

[0583] A "generative artificial intelligence model" is a machine learning-based system that automatically generates content such as text and images based on prompts.

[0584] An "emotion engine" is a system that analyzes the user's emotional state from their input and feedback, and uses that analysis to support project progress.

[0585] Task management is the process of organizing the various tasks necessary for the progress of a project and tracking its progress.

[0586] A "schedule" refers to the deadlines and timetables set for project tasks, and is an element that contributes to the efficient execution of a project.

[0587] This invention is a system for efficiently facilitating collaboration among creators on a content distribution platform. Users access the platform using a dedicated terminal or smartphone and input the information necessary to start a project. This information includes the user's skills and project requirements. The information transmitted from the terminal is sent to a server, which uses a matching algorithm to automatically identify other suitable users. The server also uses a generative artificial intelligence model to generate content based on the user's requests and an emotion engine to analyze the user's emotional state. Based on the results of this analysis, the content is modified or improved.

[0588] The server uses the Affectiva SDK and other tools to recognize user emotions and support project progress. The emotion engine analyzes emotions from user input and feedback, and based on this, suggests new content and adjusts the schedule. If user emotions affect project progress, the server uses generative AI models (e.g., OpenAI's GPT-3 or DALL-E) to generate appropriate alternatives. Furthermore, project task management utilizes Trello and Asana APIs to manage and optimize progress in real time.

[0589] As a concrete example, if a visual artist is working on a project using an online platform and the emotion engine detects a creative block, the server sends a prompt message to the AI ​​model: "User feedback indicates a creative block. Please suggest new ideas to inspire this user." This prompt then generates new design suggestions. In this way, the system enables flexible project management that responds to changes in the user's emotions, resulting in efficient content generation.

[0590] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0591] Step 1:

[0592] The user accesses the online platform using their device and enters their skills and project requirements. This information becomes input data, and the device prepares to send it to the server.

[0593] Step 2:

[0594] The server analyzes the user's skills and project requirements received. A matching algorithm is then used to automatically identify suitable collaborators. The input is the user's skill information, and the output is a list of collaborators.

[0595] Step 3:

[0596] When a user requests content generation, the server uses a generational artificial intelligence model to generate content based on the request. During this process, data is sent to the model using prompts, and the generated content is returned to the user as output.

[0597] Step 4:

[0598] When a user provides feedback on generated content, the server uses an emotion engine to analyze the user's emotional state from that feedback. The input is the user's feedback, and the output is the analyzed emotional state.

[0599] Step 5:

[0600] Based on the emotion engine's analysis results, the server modifies the content as needed. Furthermore, it proposes new content and adjusts the schedule to ensure the project progresses smoothly. This process utilizes the project's task management tool, and the adjusted task schedule is provided to the user as output.

[0601] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0602] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0603] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0604] [Fourth Embodiment]

[0605] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0606] As shown in Figure 7, the 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.

[0607] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0608] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0609] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0610] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0611] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0612] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive 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 robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0613] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0614] The specific processing program 56 is an example of a "program" relating to the technology of this 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.

[0615] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0616] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0617] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0618] This invention provides an online platform that enables creators to collaborate smoothly via the internet. This platform automatically matches users with the most suitable collaborators based on their expertise and project requirements, and supports content creation using generative AI.

[0619] To use the platform, users begin by creating an account through their device. They input their professional skills and desired project requirements and submit them to the server. The server analyzes the received data and stores it in a database. Based on this information, the server reviews other users' profiles and uses a matching algorithm to find suitable collaborators.

[0620] After matching, users can begin working on a specific project. Users input the type and details of the content they want to create into their device and send that information to the server. The server uses a generative AI model to automatically generate initial content such as illustrations, scenarios, and music. The generated content is displayed to the user via their device, and the user provides feedback as needed. Based on this feedback, the server modifies or regenerates the content.

[0621] Furthermore, in project management, the server centrally manages tasks and schedules set by users. The server tracks task priorities and progress and notifies users as needed. If progress is not on schedule, the server makes adjustments and sends reminders to ensure the project runs smoothly.

[0622] As a concrete example, let's say User A plans to produce a short animated film. User A is good at character design but not good at music production. User A provides project requirements to the server from their terminal and is matched with User B, a suitable music creator. Subsequently, User A has a scenario prototype generated by a generation AI, and User B creates music based on that scenario. The server manages the progress of the entire project, creating an environment where both Users A and B can concentrate on their creative work.

[0623] This invention resolves the challenges creators face, such as a lack of skills and difficulty finding collaborators, enabling efficient project execution.

[0624] The following describes the processing flow.

[0625] Step 1:

[0626] Users access the platform using their device and create a new account. During this process, users input their professional skills and desired project requirements, and the device transmits this information to the server.

[0627] Step 2:

[0628] The server analyzes the received user information and registers it in a dedicated database. This analysis identifies the user's skill set and project requirements, and considers mutual complementarity with other users.

[0629] Step 3:

[0630] When a user is ready to start a project, they enter the necessary project requirements into their device. The device then sends this data to the server, initiating the collaborator matching process.

[0631] Step 4:

[0632] The server runs a matching algorithm based on existing user profiles to automatically identify collaborators who meet the criteria specified by the user. It then sends notifications to the relevant users and requests their consent to become collaborators.

[0633] Step 5:

[0634] Once collaborators are confirmed, users begin creating the specific content for the project. They input the desired type and content from their device and send a request to the server.

[0635] Step 6:

[0636] The server uses a generative artificial intelligence model to generate content in the specified format. After generation, the content data is sent to the terminal and presented to the user.

[0637] Step 7:

[0638] The user reviews the generated content and provides feedback on improvements and corrections as needed, entering them into their device. The device then sends this feedback to the server.

[0639] Step 8:

[0640] The server modifies the content based on user feedback and runs the AI ​​model again if regeneration is requested. The updated content is then sent back to the device.

[0641] Step 9:

[0642] During project execution, the server centrally manages all tasks and schedules. It analyzes progress according to deadlines and priorities set by the user and provides the user with necessary information in a timely manner.

[0643] Step 10:

[0644] If the project progresses outside of the planned schedule, the server will send warnings and reminders to the user to help adjust the schedule. Based on this, the user will consider the following actions.

[0645] (Example 1)

[0646] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0647] The challenge lies in enabling creators to efficiently execute projects through online collaboration without wasting time on their own skill deficiencies or searching for collaborators. Furthermore, it requires the effective use of industrial AI in the content generation process to support creative activities by quickly incorporating feedback. Additionally, the challenge is to streamline project progress and management, providing an environment where workers can focus on their core creative work.

[0648] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0649] This invention includes a server that analyzes the technical skills and planning conditions provided by the user and automatically identifies other users who can complement them using a matching algorithm; a server that generates information using an artificial intelligence model based on the user's requests and modifies the information based on the user's feedback; and a server that manages the work and time schedule of the plan and automatically notifies the user of the progress. This makes it possible for creators to easily find collaborators with the right skills and efficiently generate content and manage projects.

[0650] "Technical capability" refers to the specialized knowledge and skills possessed by the user, and includes all the skills required to carry out the project.

[0651] "Planning conditions" refer to the goals, timeframe, budget, and other related constraints that the user sets when carrying out a project.

[0652] A "matching algorithm" refers to a computational method used to identify the most suitable collaborators based on the user's technical capabilities and project requirements.

[0653] A "generative artificial intelligence model" refers to an artificial intelligence system that can automatically generate information based on specified instructions.

[0654] "Information generation" refers to the process of creating initial information such as content, design, and sound based on user requirements and design conditions.

[0655] "Feedback" refers to evaluations and improvement requests for generated results provided by users, which the system uses to update information or processes.

[0656] "Work and time planning" refers to a comprehensive plan that includes the order in which tasks are performed and the time allocation for each task within a project.

[0657] "Progress status" refers to the state of how far a planned project has progressed within the set timeframe.

[0658] This invention provides a system that enables users to collaborate efficiently by utilizing a cloud-based online platform. The system primarily operates using servers, terminals, and generative AI models. Its specific configuration is described below.

[0659] The terminal serves as a means for users to access the system and create accounts. Through the terminal, users input and submit their technical skills and project requirements. The terminal can utilize a web browser or specialized cloud applications. This information is sent to the server and forms the basis for building user data.

[0660] The server analyzes the received technical capabilities and planning conditions and stores them in a database. Furthermore, the server uses a matching algorithm to identify the collaborator that best matches the user's requirements. This algorithm uses advanced data analysis techniques to evaluate the compatibility between users.

[0661] Once a project begins, the user inputs details of the content they want to generate via their device and sends them to the server. The server uses a generation AI model to automatically generate scenarios, designs, music, and other elements based on the specified prompts. The generated content is then delivered to the user via their device, and the server receives user feedback. Based on this feedback, the server modifies or regenerates the content.

[0662] Project progress is constantly tracked by the server, and scheduling is managed based on work and time plans. The server notifies users of progress and makes adjustments as needed if delays occur, supporting the smooth progress of the project.

[0663] As a concrete example, consider a scenario where a user creates a short animated film. The user provides character designs and a storyline outline from their device and is matched with other users skilled in music or voice acting. At each stage of the project, a generation AI is used to generate a scenario and music based on a prompt message such as, "Please generate a scenario for a short animated film. The theme is friendship, and the desired length is approximately 5 minutes." In this way, the system supports the creative activities that the user must focus on, and improves the overall efficiency of the project.

[0664] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0665] Step 1:

[0666] Users access the platform through their device and create a new account. As input, users enter their name, email address, password, technical skills, and project requirements. The device collects this data and sends it to the server. The process involves the user clicking the "Register" button, filling in the required information on the form, and then pressing the submit button, at which point the data is transferred to the server.

[0667] Step 2:

[0668] The server analyzes the received user data and stores it in the database. The input for this process is the technical capability and planning conditions obtained in step 1. The server performs data integrity checks, correctly classifying and storing the information. The output is the state where the analyzed user data is registered in the database. Specifically, the server automatically performs a validation process upon receiving data and notifies the user if any errors are found.

[0669] Step 3:

[0670] The server runs a matching algorithm based on information stored in the database to identify the most suitable collaborators. The input for this step is the technical capabilities and planning conditions of other users. Data analysis techniques are used to evaluate the likelihood of collaboration, and the output is a list of recommended collaborators. The system operates by periodically running the algorithm and notifying users of matching opportunities.

[0671] Step 4:

[0672] The user inputs details of the content they want to create and prompt text through their terminal and sends them to the server. This input includes specific content type, theme, length, and prompt text. The server processes this data using a generative AI model and generates initial content as output. Specifically, the user clicks the "Generate Content" button, enters the required conditions, submits them, and receives the generated result.

[0673] Step 5:

[0674] The server delivers the generated content to the user and collects user feedback. User feedback is required as input. The server analyzes this feedback and modifies or regenerates the content as needed. The output is the revised content. Specifically, after collecting feedback, the server uses an AI model to regenerate the content and provides the revised version to the user.

[0675] Step 6:

[0676] The server manages project progress and monitors work and time planning. Inputs are tasks, due dates, and priorities set by the user. The server uses this data to optimize the schedule and send progress notifications to the user. Outputs are the updated project schedule and progress reports. Specifically, whenever there is a progress update, the server automatically sends a notification to the user, providing guidance for the next steps.

[0677] (Application Example 1)

[0678] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0679] In content creation, there is a challenge in that creators from different specialized fields often struggle to collaborate effectively. In particular, real-time feedback and revisions within virtual reality are difficult, which can lead to an inefficient production process. Furthermore, creators are required to effectively manage the progress of their projects.

[0680] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0681] This invention includes a server that analyzes the skills and project conditions provided by the user and automatically identifies other users who can complement it using a matching algorithm; a server that generates content using an artificial intelligence model based on the user's requests and modifies the content based on the user's feedback; and a server that allows the user to view the content in real time within virtual reality and provide modification instructions. This enables creators to efficiently collaborate with members who have different expertise and provide immediate feedback using virtual reality.

[0682] A "user" is an individual or legal entity that uses this system to create content or manage projects.

[0683] "Skills" refer to the specialized abilities and knowledge that a user possesses, encompassing their level of proficiency in the areas necessary for the successful completion of the project.

[0684] "Project conditions" refer to detailed specifications and requirements regarding the content creation and tasks that the user will perform.

[0685] A "matching algorithm" is a computational method used to identify suitable collaborators based on the skills and project conditions provided by the user.

[0686] A "generative artificial intelligence model" is an AI technology that generates content in response to input requests and provides the results.

[0687] "Content" refers to produced works and digital data, and its types include illustrations, scenarios, music, and so on.

[0688] "Feedback" refers to evaluations and opinions provided by users, and may include instructions for improving or correcting the generated content.

[0689] "Virtual reality" refers to artificial environments or situations that users experience through digital devices, behaving in a way that resembles reality.

[0690] The system for realizing this application is a platform that collects user skills and project requirements, and a server analyzes this information to match suitable collaborators. This system includes terminal devices used by the user and server devices.

[0691] The user first uses a terminal to input their skills and project requirements. The terminal collects the data and sends it to a server via the network. The server uses this information and a matching algorithm to identify suitable collaborators.

[0692] Next, the server generates content using an artificial intelligence model in response to the user's request. This generated content is delivered to the user in real time via the terminal. The user can view the content in virtual reality and provide feedback via voice or text. The feedback is sent back to the server, which uses it to modify or regenerate the content.

[0693] This system can be used, for example, when a creator is producing animation. If user A, who specializes in character design, needs a collaborator for music production, the server will identify a suitable music creator and match them with user B. By allowing user A to review the scenario using a virtual reality device and provide immediate feedback on the music, production efficiency is improved.

[0694] An example of a prompt could be: "Generate a prototype character design for a new anime series. The background is a city at night, and the character is a friendly dog."

[0695] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0696] Step 1:

[0697] The terminal receives information about skills and project requirements from the user as input. This data is formatted into a standard format and sent to the server via the network.

[0698] Step 2:

[0699] The server analyzes the user's skills and project requirements received from the terminal. The analysis uses text processing algorithms to convert the input data into numerical values ​​and categories, which are then stored in a database. The stored data is then used as input for a matching algorithm.

[0700] Step 3:

[0701] The server runs a matching algorithm to identify the collaborators best suited to the project requirements. It then cross-references the database based on skill similarity and project objectives to list the most relevant users. This list is output as a suggestion to the users.

[0702] Step 4:

[0703] After a user is matched with a collaborator, the device sends a content generation request to the server as input. The request includes prompts and image concepts.

[0704] Step 5:

[0705] The server uses a generative artificial intelligence model to generate content based on user requests. The generated content is temporarily stored on the server and then output to the terminal.

[0706] Step 6:

[0707] Users view the content provided on their device using a virtual reality device and provide feedback on that content. This feedback is sent to the server in either voice or text format.

[0708] Step 7:

[0709] The server analyzes the feedback, performs necessary data processing, and then reruns the generating artificial intelligence model to modify or regenerate the content. The analyzed feedback is used to adjust parameters for the next regeneration, and new content is generated. This content is then output to the terminal again.

[0710] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0711] This invention combines an online platform for creators to collaborate efficiently on projects with an emotion engine that recognizes user emotions. This system automatically matches users with collaborators based on their required skills and project requirements, generates necessary content, and analyzes the user's emotional state to support project progress.

[0712] Users access the system using a terminal and enter the information necessary to start a project. This includes the user's professional skills, project goals, and desired collaborator requirements. The terminal sends this information to the server, which uses a matching algorithm to identify other suitable users.

[0713] After the project begins, users can request content generation via their devices. The server uses a generative artificial intelligence model to generate initial content and present it to the user. During this process, an emotion engine analyzes the user's feedback and evaluates their emotional state. Based on this, the server adjusts the content according to the user's emotional changes and makes more appropriate suggestions.

[0714] For example, if a user provides dissatisfied feedback on generated content, the sentiment engine recognizes this, and the server takes the user's feelings into consideration when suggesting modifications or alternatives. Furthermore, if stress or decreased motivation is detected during project progress, the server adjusts task management and scheduling, and provides notifications and advice to reduce the user's psychological burden.

[0715] As a concrete example, suppose user C is working on a music project and the emotion engine detects a saturation point based on user feedback. In this case, the server can suggest new music samples to help revise the production method or provide inspiration.

[0716] This invention enables not only matching collaborators and content generation, but also project management that takes user emotions into account, thereby providing an efficient and comfortable creative environment.

[0717] The following describes the processing flow.

[0718] Step 1:

[0719] Users log in to the system using their device and enter or update their profile. This includes information about their professional skills and project requirements. The device then sends this information to the server.

[0720] Step 2:

[0721] The server analyzes the information received from the user and stores it in a database. Next, it runs a matching algorithm to identify other users who could be the best collaborators based on the user's skill set and project requirements.

[0722] Step 3:

[0723] Once a match is complete, the server sends a notification to the matched user and potential collaborators. Users can check the notification via their device and accept or decline the collaboration as needed.

[0724] Step 4:

[0725] Once a project starts, the user enters details about the project and desired content into their device. Based on this information, the device sends a request to the server to generate the content.

[0726] Step 5:

[0727] The server utilizes a generative artificial intelligence model to generate content in a specified format. This generated content is then presented to the user via the terminal.

[0728] Step 6:

[0729] Users review the presented content and use their devices to provide feedback. This feedback includes comments and suggestions for improvement regarding the content.

[0730] Step 7:

[0731] The device sends user feedback to the server, and the emotion engine analyzes that feedback to evaluate the user's emotions.

[0732] Step 8:

[0733] The server suggests content modifications or additions based on the user's emotional state detected by the emotion engine. If necessary, it restarts the AI ​​model to generate the modified content.

[0734] Step 9:

[0735] During project execution, the server manages all project tasks and schedules, and uses an emotion engine to monitor user motivation and psychological state.

[0736] Step 10:

[0737] If the server detects an abnormality in the user's emotional state, for example, if stress levels are high, it will send a notification to adjust tasks and schedules and suggest improvements to alleviate psychological burden.

[0738] (Example 2)

[0739] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0740] Modern project management requires users with diverse skill sets to collaborate efficiently. However, identifying the right collaborators, adjusting content to consider users' emotional states, and streamlining task management are often insufficient. This can lead to decreased project efficiency and user satisfaction.

[0741] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0742] In this invention, the server includes means for analyzing data and project conditions provided by the user and identifying other users using a corresponding algorithm; means for generating data using a generative machine learning model based on the user's requests and modifying the data based on the user's feedback; and means for adjusting content in response to emotional changes using an emotion analysis engine that analyzes the user's emotional state. This enables optimal matching of collaborators in a project, customization of content that takes user emotions into consideration, and efficient task management.

[0743] A "user" refers to a person who provides information about a specific project and works with collaborators through the system to advance the project.

[0744] "Data" refers to information provided by users as project requirements, including elements necessary for project execution such as expertise, objectives, and conditions.

[0745] "Project requirements" are a set of technical and functional requirements necessary to accomplish a particular project.

[0746] A "correspondence algorithm" refers to a computational method used to identify the most suitable collaborator based on user-provided data and project conditions.

[0747] A "generative machine learning model" is a model that generates data or content based on specified prompt sentences, and it is a mechanism that enables the automatic generation of output in response to user requests.

[0748] "Feedback" refers to the opinions and evaluations that users provide regarding generated content, and is the input information that the system uses to correct its output based on that feedback.

[0749] A "sentiment analysis engine" refers to technology that monitors and analyzes user feedback and behavior to evaluate the user's emotional state.

[0750] "Content" refers to data and documents generated by generative machine learning models as information and ideas that support the achievement of project goals.

[0751] This system consists of a user terminal, a server that processes data, an emotion engine that analyzes the user's emotional state, and a generative machine learning model.

[0752] The user uses a terminal to input the information necessary to start the project. This information includes their professional skills, project objectives, and desired collaborators. The terminal then sends this information to the server.

[0753] The server uses a correspondence algorithm based on the information it receives to identify appropriate collaborators. This allows users with different skill sets to collaborate effectively on the project. Furthermore, when a user requests content generation, the server uses a generative machine learning model to generate initial content. In this process, prompts are provided to the generative machine learning model to generate content. For example, a prompt such as "Generate initial ideas that fit the concept of this project" can be used.

[0754] When a user provides feedback on the generated content, the emotion engine analyzes that feedback. Based on this analysis, the server adjusts the content, taking the user's emotional state into consideration, and suggests changes.

[0755] For example, if a user provides feedback that "a more creative approach is needed," the server uses this feedback to give new prompts to the generative machine learning model, generating revised or additional content. In this way, the entire system works together to support project progress.

[0756] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0757] Step 1:

[0758] The user uses a terminal to input information about the project. This input includes professional skills, project objectives, and collaborator requirements. The terminal sends this input data to the server. Specifically, the user fills in the required information in a form and clicks the "Submit" button.

[0759] Step 2:

[0760] The server applies a matching algorithm based on the received information. Using user information and project conditions as input, it processes the data to obtain output that identifies suitable collaborators. This process includes computationally matching skill sets to identify the most suitable collaborators.

[0761] Step 3:

[0762] After the project starts, the user requests content generation via their device. This request is sent to the server, which activates the generation AI model. The input is the user's prompt text, and the output is the generated initial content. Specifically, the AI ​​model interprets the prompt based on the "summary of desired content" provided by the user and generates the content.

[0763] Step 4:

[0764] The server provides the generated content to the user and awaits user feedback. The user uses their device to input their thoughts and suggestions for improvement regarding the content. This feedback is sent to the server, where an emotion analysis engine analyzes its content. The server receives the user's emotional expression as input and obtains an emotional result as output.

[0765] Step 5:

[0766] The server performs necessary data corrections and generates alternatives based on the results of the sentiment analysis engine. The input is the analysis results, and the output is adjusted content. Specifically, this includes actions such as changing the tone and structure of the content according to the sentiment data.

[0767] Step 6:

[0768] As the user's project progresses, the server manages the project's progress and sends notifications to the user as needed. This process involves schedule changes and task adjustments based on changes in emotional state and stress detection. The server receives these inputs and outputs messages to the user recommending rest or suggesting new tasks.

[0769] (Application Example 2)

[0770] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0771] This invention aims to solve problems such as delays in project progress and declines in quality due to changes in user emotions when matching collaborators and generating content on an online platform that allows creators to efficiently advance projects. Furthermore, it is necessary to effectively manage stress and decreased motivation during project progress.

[0772] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0773] This invention includes a server that analyzes the skills and project conditions provided by the user and automatically identifies other users who can complement it using a matching algorithm; a server that generates content using a generative artificial intelligence model based on the user's requests and modifies the content based on the user's feedback; and a server that analyzes the user's emotional state and uses an emotion engine to support the progress of the project. This enables flexible management in response to changes in the user's emotions during the project and efficient project execution.

[0774] A "user" is an individual or organization that uses this system to collaborate on projects or create content.

[0775] "Skills" refer to the specific professional abilities and knowledge that a user possesses, and are essential elements for project progress and matching with other users.

[0776] "Project conditions" refer to the requirements and limitations necessary to carry out the project, and include information that users enter into the system.

[0777] A "matching algorithm" is a set of computational methods or formulas used to automatically identify the most suitable collaborators based on the user's skills and project requirements.

[0778] A "generative artificial intelligence model" is a machine learning-based system that automatically generates content such as text and images based on prompts.

[0779] An "emotion engine" is a system that analyzes the user's emotional state from their input and feedback, and uses that analysis to support project progress.

[0780] Task management is the process of organizing the various tasks necessary for the progress of a project and tracking its progress.

[0781] A "schedule" refers to the deadlines and timetables set for project tasks, and is an element that contributes to the efficient execution of a project.

[0782] This invention is a system for efficiently facilitating collaboration among creators on a content distribution platform. Users access the platform using a dedicated terminal or smartphone and input the information necessary to start a project. This information includes the user's skills and project requirements. The information transmitted from the terminal is sent to a server, which uses a matching algorithm to automatically identify other suitable users. The server also uses a generative artificial intelligence model to generate content based on the user's requests and an emotion engine to analyze the user's emotional state. Based on the results of this analysis, the content is modified or improved.

[0783] The server uses the Affectiva SDK and other tools to recognize user emotions and support project progress. The emotion engine analyzes emotions from user input and feedback, and based on this, suggests new content and adjusts the schedule. If user emotions affect project progress, the server uses generative AI models (e.g., OpenAI's GPT-3 or DALL-E) to generate appropriate alternatives. Furthermore, project task management utilizes Trello and Asana APIs to manage and optimize progress in real time.

[0784] As a concrete example, if a visual artist is working on a project using an online platform and the emotion engine detects a creative block, the server sends a prompt message to the AI ​​model: "User feedback indicates a creative block. Please suggest new ideas to inspire this user." This prompt then generates new design suggestions. In this way, the system enables flexible project management that responds to changes in the user's emotions, resulting in efficient content generation.

[0785] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0786] Step 1:

[0787] The user accesses the online platform using their device and enters their skills and project requirements. This information becomes input data, and the device prepares to send it to the server.

[0788] Step 2:

[0789] The server analyzes the user's skills and project requirements received. A matching algorithm is then used to automatically identify suitable collaborators. The input is the user's skill information, and the output is a list of collaborators.

[0790] Step 3:

[0791] When a user requests content generation, the server uses a generational artificial intelligence model to generate content based on the request. During this process, data is sent to the model using prompts, and the generated content is returned to the user as output.

[0792] Step 4:

[0793] When a user provides feedback on generated content, the server uses an emotion engine to analyze the user's emotional state from that feedback. The input is the user's feedback, and the output is the analyzed emotional state.

[0794] Step 5:

[0795] Based on the emotion engine's analysis results, the server modifies the content as needed. Furthermore, it proposes new content and adjusts the schedule to ensure the project progresses smoothly. This process utilizes the project's task management tool, and the adjusted task schedule is provided to the user as output.

[0796] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0797] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0798] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0799] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0800] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0801] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0802] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0803] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0804] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0805] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0806] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0807] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0808] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0809] 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.

[0810] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0811] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0812] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0813] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0814] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0815] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0816] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0817] The following is further disclosed regarding the embodiments described above.

[0818] (Claim 1)

[0819] A means characterized by analyzing the skills and project conditions provided by the user and automatically identifying other users who can complement them using a matching algorithm,

[0820] A means for generating content using an artificial intelligence model based on the user's request and modifying the content based on user feedback,

[0821] A means of managing project tasks and schedules, and automatically notifying users of progress,

[0822] A system that includes this.

[0823] (Claim 2)

[0824] The system according to claim 1, wherein the matching algorithm identifies the most suitable collaborator, taking into account the user's professional skill set and the project's objectives.

[0825] (Claim 3)

[0826] The system according to claim 1, wherein the progress management of the project optimizes the schedule based on deadlines and priorities set by the user.

[0827] "Example 1"

[0828] (Claim 1)

[0829] A means for analyzing the technical capabilities and planning conditions provided by the user and automatically identifying other users who can complement them using a matching algorithm,

[0830] A means for generating information using an artificial intelligence model based on the user's request and modifying the information based on the user's feedback,

[0831] A means of managing the planned tasks and time schedules, and automatically notifying users of the progress,

[0832] A means of checking the integrity of data provided by users through their devices and notifying them of any deficiencies,

[0833] A means for receiving feedback on the generated information and making corrections or regenerations as necessary,

[0834] A means of providing guidance for the next step according to the user's progress,

[0835] A system that includes this.

[0836] (Claim 2)

[0837] The system according to claim 1, wherein the matching algorithm identifies the most suitable collaborator, taking into account the user's technical expertise and the objectives of the project.

[0838] (Claim 3)

[0839] The system according to claim 1, wherein the progress management of the aforementioned plan optimizes the time plan based on deadlines and priorities set by the user.

[0840] "Application Example 1"

[0841] (Claim 1)

[0842] A means characterized by analyzing the skills and project conditions provided by the user and automatically identifying other users who can complement them using a matching algorithm,

[0843] A means for generating content using an artificial intelligence model based on the user's request and modifying the content based on user feedback,

[0844] A means of managing project tasks and schedules, and automatically notifying users of progress,

[0845] A means by which users can view content in real time within a virtual reality environment and provide correction instructions,

[0846] A system that includes this.

[0847] (Claim 2)

[0848] The system according to claim 1, wherein the matching algorithm identifies the most suitable collaborator, taking into account the user's professional skill set and the project's objectives.

[0849] (Claim 3)

[0850] The system according to claim 1, wherein the progress management of the project optimizes the schedule based on deadlines and priorities set by the user.

[0851] "Example 2 of combining an emotion engine"

[0852] (Claim 1)

[0853] A means of analyzing data and project conditions provided by users and identifying other users using a corresponding algorithm,

[0854] A means for generating data using a generative machine learning model based on the user's request and modifying the data based on user feedback,

[0855] A means of adjusting content in response to emotional changes using an emotion analysis engine that analyzes the user's emotional state,

[0856] A means for managing the tasks and progress of the aforementioned project and notifying users,

[0857] A system that includes this.

[0858] (Claim 2)

[0859] The system according to claim 1, wherein the corresponding algorithm identifies the most suitable collaborator, taking into account the user's expertise and the project's objectives.

[0860] (Claim 3)

[0861] The system according to claim 1, wherein the project progress management optimizes the plan based on deadlines and priorities set by the user.

[0862] "Application example 2 when combining with an emotional engine"

[0863] (Claim 1)

[0864] A means for analyzing the skills and project requirements provided by the user and automatically identifying other users who can complement them using a matching algorithm,

[0865] A means for generating content using an artificial intelligence model based on the user's request and modifying the content based on user feedback,

[0866] A means for using an emotion engine to analyze the emotional state of the user and support the progress of the project,

[0867] A means of managing project tasks and schedules, and automatically notifying users of progress,

[0868] A system that includes this.

[0869] (Claim 2)

[0870] The system according to claim 1, wherein the matching algorithm identifies the most suitable collaborator by taking into account the user's professional skill set and project objectives, and further refines the collaborator suggestions by analyzing the user's emotional state.

[0871] (Claim 3)

[0872] The system according to claim 1, wherein the project progress management optimizes the schedule based on deadlines and priorities set by the user, and adjusts the schedule while taking into account the user's emotional state. [Explanation of Symbols]

[0873] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means characterized by analyzing the skills and project conditions provided by the user and automatically identifying other users who can complement them using a matching algorithm, A means for generating content using an artificial intelligence model based on the user's request and modifying the content based on user feedback, A means of managing project tasks and schedules, and automatically notifying users of progress, A system that includes this.

2. The system according to claim 1, wherein the matching algorithm identifies the most suitable collaborator, taking into account the user's professional skill set and the project's objectives.

3. The system according to claim 1, wherein the progress management of the project optimizes the schedule based on deadlines and priorities set by the user.

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