system

Abstract

We provide the system. [Solution] A means for training an artificial intelligence model that collects user data and generates individually optimized design proposals based on said data, A means for providing creative project design proposals tailored to the user's preferences and skill level, utilizing the aforementioned artificial intelligence model. A means for generating information regarding the selection and procurement of necessary materials based on the aforementioned design proposal, A means for generating a process guide for implementing a project corresponding to the aforementioned design proposal, 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 persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In handmade art and handicraft production, individual users often face a lack of ideas and technical problems. Also, resources for finding suitable projects and designs are limited. Such a situation may reduce the enjoyment of creative activities. The present invention aims to solve these problems and provide an environment in which users can easily enjoy optimal projects according to their hobbies and skills.

Means for Solving the Problems

[0005] This invention provides a system that collects and analyzes user data using an artificial intelligence model and generates design proposals optimized for each user. This allows users to find projects that match their preferences and skill levels. Furthermore, based on the generated design proposals, the system provides information on selecting and procuring necessary materials, as well as process guides for effectively advancing the project. This system enables users to smoothly engage in creative activities and, by utilizing feedback after project implementation, to make even more accurate proposals.

[0006] "User data" refers to information that indicates the attributes and behaviors of individual users, such as their hobbies, skills, and past project history.

[0007] An "artificial intelligence model" is a system that has an algorithm to learn from collected user data and generate individually optimized design proposals.

[0008] A "design proposal" is a specific design or plan for a creative project that is generated according to the user's preferences and skills.

[0009] "Materials information" refers to information regarding the selection, quantity, and procurement methods of the materials and components necessary to accomplish the project.

[0010] A "process guide" is a set of instructions that explains the specific procedures and steps involved in carrying out a project selected by the user. [Brief explanation of the drawing]

[0011] [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, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0012] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0013] First, let's explain the terminology used in the following explanation.

[0014] In the following embodiments, the 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.

[0015] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0016] In the following embodiments, the 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, and the like.

[0017] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), and the like.

[0018] 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."

[0019] [First Embodiment]

[0020] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0021] 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.

[0022] 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).

[0023] 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.

[0024] 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.

[0025] 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.

[0026] 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.

[0027] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] 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".

[0032] This invention is a system for supporting the creative activities of users, and is configured as follows.

[0033] Regarding server functionality

[0034] The server has a database for collecting and storing user data. Users input information such as their hobbies, skill levels, and past project history, and the server stores this data. The server also trains an artificial intelligence model based on this data. The generated AI model has the function of producing optimized design proposals for each user and suggesting project plans based on the user's preferences and skills. Furthermore, the server creates necessary material information based on the generated design proposal and provides information on how to procure them. A process guide for carrying out the project is also generated on the server and sent to the terminal along with related information.

[0035] About the device's functions

[0036] The terminal receives information transmitted from the server and displays it in an easy-to-understand format for the user. This includes design proposals, material information, and process guides. The terminal's interface is designed for intuitive user operation, allowing for easy project selection and access to step-by-step guides. It also includes a function to send user feedback to the server after project completion.

[0037] Regarding user actions

[0038] Users review design proposals provided through their devices and select projects that match their interests and skills. Following the chosen project, they procure the provided materials and proceed with the actual production according to the process guide. The results and insights gained during the production process are sent as feedback to the server via the device, and this is then used again as training data for the AI ​​model.

[0039] Specific example

[0040] For example, suppose a user is interested in pottery and wants to make a beginner-friendly vase. In this case, the server generates a simple vase design based on collected user data, making it easy for beginners to follow. The details include the type and amount of clay needed, the shaping procedure, and what glaze should be used. This design, along with material information and a process guide, is displayed on the user's device, and the user follows the instructions to create the vase. After completion, the user sends a photo of the finished product and feedback on its usability, allowing them to receive more suitable project suggestions in the future.

[0041] Thus, the present invention provides a platform that takes into account the individual needs of users and makes creative activities easier and more fulfilling.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The server receives data entered by the user, such as hobbies, skill levels, and past project history, and stores it in a database. This data collection phase forms the basis for individual optimization.

[0045] Step 2:

[0046] The server trains an artificial intelligence model based on accumulated user data. This process learns user preferences and builds a model to generate optimized design proposals for each user.

[0047] Step 3:

[0048] The server uses a trained artificial intelligence model to generate personalized, optimized creative project design proposals for the user. These proposals are then filtered based on factors such as project difficulty and style.

[0049] Step 4:

[0050] Based on the generated design proposal, the server generates the types and quantities of materials needed, as well as their procurement information. This information is essential for project implementation.

[0051] Step 5:

[0052] The server creates a process guide for the user to carry out the project. The guide includes step-by-step instructions and is designed to allow the user to proceed with the work according to the procedure.

[0053] Step 6:

[0054] The terminal provides an intuitive interface that displays design proposals, material information, and process guides received from the server to the user. This interface allows the user to easily select and implement projects.

[0055] Step 7:

[0056] The user selects the project design proposal that interests them most from several options displayed on the device. The selection is based on their current interests and skill level.

[0057] Step 8:

[0058] Users follow the process guide displayed on their terminal to complete the project. Each step includes specific instructions, ensuring that users can proceed with the work without getting lost.

[0059] Step 9:

[0060] After completing a project, users input their thoughts and feedback based on the project's results into their device. This feedback is used to propose future projects and improve the artificial intelligence model.

[0061] Step 10:

[0062] The device sends the collected user feedback to the server. The server stores this information in a database and uses it to train future user data.

[0063] This process allows the system to propose the most suitable project to the user and support their creative activities.

[0064] (Example 1)

[0065] 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."

[0066] Traditionally, systems supporting creative work have struggled to present optimal design proposals tailored to individual user preferences and skill levels. Furthermore, there was a lack of mechanisms for efficiently incorporating user feedback into AI models, including providing guidelines for material selection and process execution during project execution. This has created a need for improved user satisfaction and enhanced practical support.

[0067] 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.

[0068] In this invention, the server includes means for collecting information about users and training a knowledge processing model that generates individually optimized design proposals; means for providing design proposals using the knowledge processing model; means for generating information on the selection and procurement of necessary materials; and means for transmitting information to a terminal and visualizing it. This enables the provision of design proposals for creative work optimized for each user, as well as the streamlining of material procurement and processes, and the utilization of user feedback in the AI ​​model.

[0069] A "user" is an individual or group that uses this system to perform creative work.

[0070] "Information" refers to data necessary for optimizing creative work, such as the user's hobbies, skill level, and past activity history.

[0071] A "knowledge processing model" is artificial intelligence trained to generate individually optimized design proposals based on information collected from users.

[0072] A "design proposal" refers to a detailed plan or outline of creative work that is tailored to the user's preferences and skills.

[0073] "Materials" refer to the materials and tools needed to carry out creative work.

[0074] A "terminal" is an electronic device that receives information sent from a server and is used by the user to verify it.

[0075] "Visualization" refers to displaying information in a format that is easy for users to understand.

[0076] "Evaluation information" refers to data on user feedback and user experience provided after a project has been completed.

[0077] To implement this invention, a server is first required. The server trains a knowledge processing model that generates individually optimized design proposals based on information collected from users. The server uses a high-performance database and machine learning libraries to efficiently process large amounts of data. Specifically, it stores information such as hobbies, skill levels, and past activity history entered by users through their terminals in the database. Then, it constructs a knowledge processing model using a deep learning algorithm based on this information.

[0078] The server uses the generated knowledge processing model to provide design proposals tailored to the user's individual preferences and skill level. These proposals include the types and quantities of necessary materials and the project implementation procedures. The server then transmits the generated design proposals, material information, and method / procedure data to the terminal.

[0079] The terminal displays information received from the server in an easy-to-understand manner for the user. The terminal's interface is designed for intuitive user operation, making it easy to select projects and check their progress. Using the terminal, users can select projects based on the provided design proposals, arrange for necessary materials, and proceed with creative work according to the instructed methods and procedures.

[0080] After completing a project, users provide feedback based on the results and their experience using the system. This feedback is sent to the server via the terminal and used as further training data for the knowledge processing model. This improves the accuracy of future design proposals.

[0081] For example, if a user wants to create a ceramic piece, the server will provide a vase design tailored to the user's skill level. The server will also generate and send detailed guidelines to the terminal, including information on the type of clay needed, molding procedures, and glaze selection. The user can then proceed with the project according to these guidelines and provide feedback upon completion.

[0082] An example of a prompt message would be: "Please propose a beginner-friendly pottery project. The project should involve making a vase, and include specific materials and instructions."

[0083] Thus, the present invention provides a system for supporting creative work tailored to the individual needs of users.

[0084] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0085] Step 1:

[0086] The server receives user input data through the terminal and stores it in a database. This input data includes the user's hobbies, skill level, and past activity history. The server uses this data to prepare the foundational data for providing personalized content to individual users. Specifically, it standardizes data formats, corrects missing data, and saves the prepared data.

[0087] Step 2:

[0088] The server trains a knowledge processing model based on the collected data. This process applies machine learning algorithms to generate design proposals tailored to the user's interests and skills. The input is the user's database information, and the output is a individually optimized AI model. The server repeatedly trains the model through this process to improve its accuracy.

[0089] Step 3:

[0090] The server uses a trained AI model to generate a design proposal optimized for the user. The input is the AI ​​model and user data, and the output is a specific project design proposal. This includes a list of necessary materials, work procedures, and implementation methods. Specifically, the server generates a design proposal based on prompts for each user and sends the results to the terminal.

[0091] Step 4:

[0092] The terminal receives design proposals and related information sent from the server and visualizes them for the user. The input is data from the server, and the output is the project design proposal displayed on the user's screen. The terminal displays information in a format easily understood by the user and supports selection and operation. Specifically, it provides step-by-step guides on the interface, allowing the user to easily understand the next action.

[0093] Step 5:

[0094] Users review design proposals displayed on their devices and select and proceed with projects based on their interests and skills. Inputs are the design proposals received from the device and the user's selections, while output is the actual result of completing the project. Specifically, users procure the necessary materials for the project and create the artwork according to the instructions.

[0095] Step 6:

[0096] After completing a project, users input feedback on the results and their experience into a terminal. This feedback is then sent back to the server, becoming further training data for the knowledge processing model. The input is the user's feedback itself, and the output is the improved AI model. The server uses this to improve the accuracy of future project proposals and increase user satisfaction.

[0097] (Application Example 1)

[0098] 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."

[0099] In recent years, there has been a growing demand in the field of creative activities for the efficient generation of plans and designs tailored to individual needs. However, conventional systems struggle to provide optimal plans that meet the diverse preferences and skills of users. Furthermore, particularly in the industrial sector, there is a lack of guidance on the design of precision machinery and equipment, as well as on the procurement of parts and manufacturing processes.

[0100] 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.

[0101] In this invention, the server includes means for training an information processing model that collects information about the user and generates individually optimized plans based on said information; means for providing creative work plans tailored to the user's preferences and knowledge level using said information processing model; and means for generating information regarding the selection and procurement of necessary materials based on said plans. This enables users to efficiently engage in creative activities and design and manufacture machinery and equipment in industrial fields that appropriately address their individual needs.

[0102] An "information processing model" is an algorithm trained to generate individually optimized plans and design proposals based on information about the user.

[0103] A "plan" is a specific project proposal that shows creative work or the design of mechanical devices tailored to the user's preferences and knowledge level.

[0104] "Materials" refers to the materials and components necessary to realize the plan, and it is the subject from which information regarding their selection and procurement is generated.

[0105] A "process guide" is a document that provides detailed instructions on the procedures and methods for carrying out work based on a plan.

[0106] This invention relates to a system that generates and provides project plans tailored to the diverse needs of users. The system mainly consists of a server and terminals. The server is equipped with an information processing model that collects and analyzes information about the user. This information includes the user's preferences, knowledge level, and past project history. Using this data, the server trains a generative AI model to create a project plan optimized for the user.

[0107] Furthermore, the server generates information on selecting and procuring materials according to the plan. This process includes information on machine parts and other necessary materials. A detailed process guide for implementing the project is also created on the server and sent to the terminal.

[0108] The terminal provides a user interface, clearly displaying the draft plan, material information, and process guide sent from the server. Based on this information, users can perform creative work and design machinery and equipment in accordance with the draft plan. After completing a project, users can send feedback to the server via the terminal, which is used to further train the information processing model.

[0109] As a concrete example, consider a scenario where a factory engineer is designing a new packaging robot. By inputting the engineer's past project history, skills, and target product information, the server generates an optimal robot design proposal. A materials list and detailed manufacturing process guide are also generated, allowing the user to efficiently proceed with the design work. An example of a prompt message being used would be, "Generate an optimal product packaging robot design proposal based on this engineer's past project history and target product."

[0110] This invention will improve the efficiency and precision of design and manufacturing processes in creative activities and industrial fields.

[0111] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0112] Step 1:

[0113] The server receives information sent from the user. User preferences, knowledge level, and past project history are provided as input. This data is stored in a database, and an information processing model uses it to build a user profile.

[0114] Step 2:

[0115] The server trains a generation AI model based on the stored user information. This model is tuned to generate individually optimized plan proposals, taking into account skill levels and past history. The input is user information, and the output is the optimized plan proposal generated by the AI ​​model.

[0116] Step 3:

[0117] The server uses a generative AI model to build a plan optimized for the user. This process takes into account the user's preferences and skill level. During plan construction, prompts are used, and the AI ​​generates design proposals and project details. The output is the plan.

[0118] Step 4:

[0119] The server generates a list of necessary materials and procurement information based on the proposed plan. The server analyzes the plan, selects the materials needed for its implementation, and extracts relevant procurement methods from the industrial database. The input is the proposed plan, and the output is a list of materials.

[0120] Step 5:

[0121] The terminal receives the project plan, material list, and process guide sent from the server and displays them through the user interface. The user then uses this information to carry out the project according to the plan. Input is data from the server, and output is the presentation of information to the user.

[0122] Step 6:

[0123] Users complete projects and send feedback from their terminals to the server, including results and opinions from the implementation process. This generates new data, which is then used as training data for subsequent information processing models. The input is user feedback, and the output is an updated user profile.

[0124] 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.

[0125] This invention is a system that supports users' creative activities and provides a more personalized experience, and has the following configuration.

[0126] Regarding server functionality

[0127] The server collects and manages user data, including the user's hobbies, skill level, past project history, and emotional data acquired by the emotion engine. The collected data is used to train an artificial intelligence model, forming the basis for generating user-optimized design proposals. The emotion engine recognizes emotions in real time during user interactions and transmits this data to the server. The server utilizes this emotional data to propose project plans that are tailored not only to past behavioral history but also to the user's emotional state.

[0128] About the device's functions

[0129] The terminal plays a crucial role in displaying design proposals, material information, and process guides sent from the server. The terminal also incorporates or connects an emotion engine to monitor the user's emotions as they interact with the system. It can dynamically change the interface on the terminal in response to emotional changes. For example, if the user is stressed, it can offer simpler projects or simplify the interface, demonstrating flexibility in this regard.

[0130] Regarding user actions

[0131] Users select projects based on information provided on their devices. The options reflect their preferences, skill levels, and emotional states provided by the emotion engine. As users work on projects, the emotion engine continuously monitors their state and sends feedback to the server as the project progresses. This creates a feedback loop for receiving optimized suggestions.

[0132] Specific example

[0133] For example, if a user expresses interest in creating a new pottery piece, the server, in addition to the user's past project data, uses an emotion engine to determine whether the user is experiencing "enjoyment." If the user expresses positive emotions, the server suggests a challenging design that slightly pushes the user's skills. On the other hand, if the user expresses neutral or negative emotions, it suggests a more basic and fulfilling design. After completion, the user provides feedback on their emotional changes and the project, which is then reflected in future suggestions.

[0134] This technology allows users to engage in creative activities not only by improving their skills, but also by increasing their emotional satisfaction. Through this, the system aims to provide a more engaging and sustainable user experience.

[0135] The following describes the processing flow.

[0136] Step 1:

[0137] The server receives data about users' hobbies and skill levels and stores it in a database. This creates a foundation for providing a customized experience for each user.

[0138] Step 2:

[0139] As soon as the user begins interacting with the system, the device activates a built-in or connected emotion engine to monitor the user's emotional state in real time. The emotion engine acquires data through methods such as facial recognition and voice analysis.

[0140] Step 3:

[0141] The emotion engine analyzes the acquired emotion data and sends the results to the server. This allows the server to understand the user's current emotional state.

[0142] Step 4:

[0143] The server updates and trains the artificial intelligence model using collected sentiment data and historical user data. Adjustments are made here to enable the model to suggest projects based on emotions.

[0144] Step 5:

[0145] The server applies a trained artificial intelligence model to generate creative project designs tailored to the user's preferences, skill level, and emotional state.

[0146] Step 6:

[0147] The server sends the generated design proposal, necessary material information, and a step-by-step process guide for the project to the terminal.

[0148] Step 7:

[0149] The device displays the received information in a user interface, allowing the user to select the project they find most appealing.

[0150] Step 8:

[0151] The user selects a project based on the information displayed on their device and begins creative activity following the process guide. Throughout this process, the emotion engine tracks the user's emotions and collects data to improve suggestions based on the situation.

[0152] Step 9:

[0153] After the project is completed, users provide feedback via their devices, including their impressions and changes in feelings. This feedback is stored in a database on the server and used to improve future proposals.

[0154] Step 10:

[0155] The device sends the collected feedback data to the server. The server receives this data and uses it to further train the human model. This successful experience allows the system to continue providing a user-friendly and more personalized user experience.

[0156] (Example 2)

[0157] 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".

[0158] In modern creative activities, there is a demand for providing individually optimized design proposals that take into account each user's hobbies, skill level, and emotional state. However, conventional systems have faced the challenge of not being able to adequately analyze changes in users' emotions and reflect them in project proposals. As a result, it is difficult for users to maintain their interest and engage in creative activities in a sustained manner.

[0159] 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.

[0160] In this invention, the server includes means for collecting information about the user and training a machine learning system for generating individually optimized creative plan proposals based on said information; means for providing plan proposals tailored to the user's preferences and skill level using the machine learning system; and means for analyzing the user's emotional state in real time and reflecting that data in the proposed plan proposals and procedure guides. This makes it possible to provide individually optimized project proposals that accurately reflect the user's emotional state.

[0161] "Information" refers to data about the user, including their hobbies, skill level, emotional state, and past activity history.

[0162] A "machine learning system" refers to a system that has algorithms for analyzing data based on collected user information and generating individually optimized plan proposals.

[0163] A "plan" refers to the design and execution plan of creative activities tailored to the user's preferences and technical level, and includes the projects and procedures provided.

[0164] "Resources" refer to items such as materials and tools that are necessary to carry out creative activities.

[0165] A "procedure guide" refers to a document or display containing explanations and instructions that guide users through each step of a creative activity.

[0166] "Emotional state" refers to data that indicates the user's psychological state, including changes in the user's emotions acquired in real time.

[0167] This invention is a system for supporting users' creative activities. This system mainly consists of servers, terminals, and users who utilize them.

[0168] Server operation:

[0169] The server manages information collected from users and builds a database based on the user's hobbies, skill level, and emotional state. This information is analyzed by machine learning algorithms using generative AI models and forms the basis for generating personalized project plans for each user. The server also analyzes diverse data, including the user's real-time emotional data, and forms instructions for the AI ​​model through prompts. For example, it might generate a prompt such as, "Create a new pottery project proposal based on the user's recent project history and current emotional data. Recommend a challenging design if the emotional state is positive, and a basic design if the emotional state is neutral or negative."

[0170] Device operation:

[0171] The terminal provides the user with a plan, necessary resource information, and procedural guides sent from the server. Furthermore, the terminal has a built-in or connected emotion engine that monitors the user's emotional changes in real time. By dynamically changing the terminal's user interface in response to the emotional data, a more personalized experience can be provided.

[0172] User actions:

[0173] Users select a project proposal that interests them from those displayed on their device and begin their creative activity. The user's feelings towards the selected project are continuously monitored, and their feedback is sent to the server. Based on this feedback, the server further optimizes its next proposal.

[0174] Thus, the system of the present invention, by combining machine learning and real-time sentiment analysis, provides users with an experience that not only promotes creative activity but also enhances emotional satisfaction.

[0175] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0176] Step 1:

[0177] The server collects user hobbies, skill levels, past project history, and real-time sentiment data. Input is diverse information obtained from the user, and output is a database containing this information. Database updates reflect the latest sentiment data, ensuring accurate user profiles.

[0178] Step 2:

[0179] The server uses the collected data to train a generative AI model. The input is user information stored in a database, and the output is the parameters of the AI ​​model used to generate a plan optimized for the user. Specifically, the server executes machine learning algorithms to extract patterns and trends and improve the performance of the AI ​​model.

[0180] Step 3:

[0181] The server uses a trained AI model to generate project plans tailored to the user's preferences and skill level. The input consists of the AI ​​model and user information, while the output is a individually optimized project plan. During this process, the server generates prompts and issues instructions to the AI ​​model.

[0182] Step 4:

[0183] The terminal displays the user with the plan, resource information, and procedure guide received from the server. The input is data from the server, and the output is information presented visually to the user. Specifically, the terminal displays data on its screen and provides an interface that allows the user to easily operate it.

[0184] Step 5:

[0185] The user selects a project of interest based on the proposed plan displayed on the terminal. The input is the proposed plan presented on the terminal, and the output is the user's decision regarding the selected plan.

[0186] Step 6:

[0187] The device initiates creative activities based on a selected plan while collecting real-time emotional data through an emotion engine. The input is the user's emotional changes, and the output is the emotional data sent to the server. During this process, the device adjusts its interface according to the situation to optimize the user experience.

[0188] Step 7:

[0189] The server receives feedback data from users after project completion and incorporates it into proposals for future projects. The input is user feedback and sentiment data, and the output is algorithmic improvements to optimize future proposals. Specifically, it analyzes the feedback and uses the data as training material for an AI model.

[0190] (Application Example 2)

[0191] 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".

[0192] Conventional design proposal generation systems rely solely on user preferences and skills, failing to consider emotional satisfaction. This is a major drawback, as they cannot provide a truly optimal experience for each individual. Furthermore, the inability to flexibly modify the interface to accommodate user emotions can diminish immersion in the project.

[0193] 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.

[0194] In this invention, the server includes means for collecting user data and training an artificial intelligence model that generates individually optimized design proposals; means for providing creative project design proposals tailored to the user's preferences and skill level; and means for acquiring user emotional data in real time and adjusting suggestions or actions based on that data according to the emotional state. This makes it possible to provide users with an optimal project experience that is emotionally satisfying.

[0195] A "personally optimized design proposal" is a design suggestion that has been adjusted by an artificial intelligence model based on the individual user's preferences and skill level.

[0196] An "artificial intelligence model" is a mathematical algorithm that is trained using user data and generates design proposals based on specific requirements.

[0197] "Emotional data" refers to information that indicates a user's emotional state, and is data collected in real time based on facial expressions, tone of voice, and other factors.

[0198] "Adjusting suggestions or actions" refers to the process of dynamically changing the information and content provided in response to the user's emotional state.

[0199] "Dynamically changing" means that the display and content of the user interface, etc., change in real time, so that it responds immediately to the user's situation.

[0200] A "project experience" is a series of activities in which a user engages in creative work, following proposed design plans and guides.

[0201] This invention is implemented by combining various technologies to further optimize the individual user experience.

[0202] The server plays a central role in aggregating user preferences, skill levels, and emotional data. The server uses a database to record users' past behavioral history and current emotional states. Based on this information, an artificial intelligence model is used to train and generate individually optimized design proposals. The AI ​​modeling used here includes deep learning frameworks such as Tensorflow®, a representative example.

[0203] The device serves as the primary interface between the user and the system. The device incorporates an emotion recognition engine that collects user emotion data in real time through its camera and microphone. This data is sent to a server and used to generate optimal suggestions for the user. On the device, React Native is used as the UI framework to dynamically change the user interface and display flexible suggestions tailored to the user's emotions.

[0204] As a concrete example, when a user of a smartphone app is browsing a specific virtual product, the emotion recognition engine provides insights to increase the user's interest. For instance, it might display a prompt such as, "You seem to like this product," and then suggest related products. An example of such a prompt would be, "What is the best next action if the user shows positive emotions while browsing a specific product?"

[0205] Users can receive personalized project suggestions on their devices, select projects based on those suggestions, and proceed with them. The server and device work together to create an emotionally satisfying experience for the user.

[0206] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0207] Step 1:

[0208] The server aggregates user preferences, skill levels, past behavioral history, and sentiment data into a database. This provides the foundational data for training an artificial intelligence model. The input is user profile and sentiment measurement data, and the output is a training dataset.

[0209] Step 2:

[0210] The server uses TensorFlow to train an artificial intelligence model and build a model that generates individually optimized design proposals. In this process, it takes a training dataset from a database as input and outputs an optimized AI model. Specifically, the AI ​​model learns an algorithm for generating design proposals based on user profiles.

[0211] Step 3:

[0212] The device uses its camera and microphone to collect real-time emotional data from the user. An emotion recognition engine analyzes this data and quantifies the emotional state. The input is audio and video data, and the output is a score of the user's emotional state.

[0213] Step 4:

[0214] The device sends collected sentiment data to the server. This data is used to adjust project suggestions generated by the AI. The input consists of sentiment scores and contextual information about the user's current interaction, while the output is data used to refine the suggestions.

[0215] Step 5:

[0216] The server uses an AI model to generate optimal project suggestions or actions based on the user's emotional state and sends them to the terminal. The input is a trained AI model and emotional state data, and the output is a customized project suggestion.

[0217] Step 6:

[0218] The terminal displays received project proposals in a user interface, offering the user choices. Here, the interface dynamically changes using the React Native framework. The input is project proposals from the server, and the output is the display of proposals to the user.

[0219] Step 7:

[0220] The user selects a project from the presented options and proceeds to implementation. The user's actions here are crucial for forming a future feedback loop, and the results are returned to the server. This process is part of a continuous cycle of optimizing the user experience. The input is the user's selection action, and the output is the project data being executed.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] [Second Embodiment]

[0225] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0226] 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.

[0227] 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).

[0228] 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.

[0229] 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.

[0230] 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).

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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.

[0235] 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.

[0236] 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".

[0237] This invention is a system for supporting the creative activities of users, and is configured as follows.

[0238] Regarding server functionality

[0239] The server has a database for collecting and storing user data. Users input information such as their hobbies, skill levels, and past project history, and the server stores this data. The server also trains an artificial intelligence model based on this data. The generated AI model has the function of producing optimized design proposals for each user and suggesting project plans based on the user's preferences and skills. Furthermore, the server creates necessary material information based on the generated design proposal and provides information on how to procure them. A process guide for carrying out the project is also generated on the server and sent to the terminal along with related information.

[0240] About the device's functions

[0241] The terminal receives information transmitted from the server and displays it in an easy-to-understand format for the user. This includes design proposals, material information, and process guides. The terminal's interface is designed for intuitive user operation, allowing for easy project selection and access to step-by-step guides. It also includes a function to send user feedback to the server after project completion.

[0242] Regarding user actions

[0243] Users review design proposals provided through their devices and select projects that match their interests and skills. Following the chosen project, they procure the provided materials and proceed with the actual production according to the process guide. The results and insights gained during the production process are sent as feedback to the server via the device, and this is then used again as training data for the AI ​​model.

[0244] Specific example

[0245] For example, suppose a user is interested in pottery and wants to make a beginner-friendly vase. In this case, the server generates a simple vase design based on collected user data, making it easy for beginners to follow. The details include the type and amount of clay needed, the shaping procedure, and what glaze should be used. This design, along with material information and a process guide, is displayed on the user's device, and the user follows the instructions to create the vase. After completion, the user sends a photo of the finished product and feedback on its usability, allowing them to receive more suitable project suggestions in the future.

[0246] Thus, the present invention provides a platform that takes into account the individual needs of users and makes creative activities easier and more fulfilling.

[0247] The following describes the processing flow.

[0248] Step 1:

[0249] The server receives data entered by the user, such as hobbies, skill levels, and past project history, and stores it in a database. This data collection phase forms the basis for individual optimization.

[0250] Step 2:

[0251] The server trains an artificial intelligence model based on accumulated user data. This process learns user preferences and builds a model to generate optimized design proposals for each user.

[0252] Step 3:

[0253] The server uses a trained artificial intelligence model to generate personalized, optimized creative project design proposals for the user. These proposals are then filtered based on factors such as project difficulty and style.

[0254] Step 4:

[0255] Based on the generated design proposal, the server generates the types and quantities of materials needed, as well as their procurement information. This information is essential for project implementation.

[0256] Step 5:

[0257] The server creates a process guide for the user to carry out the project. The guide includes step-by-step instructions and is designed to allow the user to proceed with the work according to the procedure.

[0258] Step 6:

[0259] The terminal provides an intuitive interface that displays design proposals, material information, and process guides received from the server to the user. This interface allows the user to easily select and implement projects.

[0260] Step 7:

[0261] The user selects the project design proposal that interests them most from several options displayed on the device. The selection is based on their current interests and skill level.

[0262] Step 8:

[0263] Users follow the process guide displayed on their terminal to complete the project. Each step includes specific instructions, ensuring that users can proceed with the work without getting lost.

[0264] Step 9:

[0265] After completing a project, users input their thoughts and feedback based on the project's results into their device. This feedback is used to propose future projects and improve the artificial intelligence model.

[0266] Step 10:

[0267] The device sends the collected user feedback to the server. The server stores this information in a database and uses it to train future user data.

[0268] This process allows the system to propose the most suitable project to the user and support their creative activities.

[0269] (Example 1)

[0270] 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."

[0271] Traditionally, systems supporting creative work have struggled to present optimal design proposals tailored to individual user preferences and skill levels. Furthermore, there was a lack of mechanisms for efficiently incorporating user feedback into AI models, including providing guidelines for material selection and process execution during project execution. This has created a need for improved user satisfaction and enhanced practical support.

[0272] 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.

[0273] In this invention, the server includes means for collecting information about users and training a knowledge processing model that generates individually optimized design proposals; means for providing design proposals using the knowledge processing model; means for generating information on the selection and procurement of necessary materials; and means for transmitting information to a terminal and visualizing it. This enables the provision of design proposals for creative work optimized for each user, as well as the streamlining of material procurement and processes, and the utilization of user feedback in the AI ​​model.

[0274] A "user" is an individual or group that uses this system to perform creative work.

[0275] "Information" refers to data necessary for optimizing creative work, such as the user's hobbies, skill level, and past activity history.

[0276] A "knowledge processing model" is artificial intelligence trained to generate individually optimized design proposals based on information collected from users.

[0277] A "design proposal" refers to a detailed plan or outline of creative work that is tailored to the user's preferences and skills.

[0278] "Materials" refer to the materials and tools needed to carry out creative work.

[0279] A "terminal" is an electronic device that receives information sent from a server and is used by the user to verify it.

[0280] "Visualization" refers to displaying information in a format that is easy for users to understand.

[0281] "Evaluation information" refers to data on user feedback and user experience provided after a project has been completed.

[0282] To implement this invention, a server is first required. The server trains a knowledge processing model that generates individually optimized design proposals based on information collected from users. The server uses a high-performance database and machine learning libraries to efficiently process large amounts of data. Specifically, it stores information such as hobbies, skill levels, and past activity history entered by users through their terminals in the database. Then, it constructs a knowledge processing model using a deep learning algorithm based on this information.

[0283] The server uses the generated knowledge processing model to provide a design plan that suits the user's individual preferences and skill levels. The provided design plan includes the types and quantities of necessary materials and the implementation procedures of the project. The server further transmits the generated design plan, material information, and data related to the method procedures to the terminal.

[0284] The terminal displays the information received from the server in an easy-to-understand manner for the user. The interface of the terminal is designed so that the user can operate intuitively, and it is easy to select a project and check the progress status. The user can use the terminal to select a project based on the provided design plan, arrange the necessary materials, and proceed with creative work according to the instructed method procedures.

[0285] After the user implements the project, the user provides feedback based on the results and the sense of use. This feedback is transmitted to the server through the terminal and is utilized as additional training data for the knowledge processing model. As a result, the accuracy of the design plan for subsequent times is improved.

[0286] As a specific example, when the user wants to make a pottery work, the server provides a design plan for a vase according to the user's skill level. The server also generates and transmits to the terminal a detailed guideline including the types of clay required, the shaping procedures, and information on the selection of glazes. The user can proceed with the project according to this and provide feedback after completion.

[0287] An example of the prompt text is in the form of "Please propose a pottery project for beginners. The object is a vase, including specific materials and procedures."

[0288] In this way, the present invention provides a system for assisting creative work according to the individual needs of the user.

[0289] The flow of the specific process in Example 1 will be described using FIG. 11.

[0290] Step 1:

[0291] The server receives user input data through the terminal and stores it in a database. This input data includes the user's hobbies, skill level, and past activity history. The server uses this data to prepare the foundational data for providing personalized content to individual users. Specifically, it standardizes data formats, corrects missing data, and saves the prepared data.

[0292] Step 2:

[0293] The server trains a knowledge processing model based on the collected data. This process applies machine learning algorithms to generate design proposals tailored to the user's interests and skills. The input is the user's database information, and the output is a individually optimized AI model. The server repeatedly trains the model through this process to improve its accuracy.

[0294] Step 3:

[0295] The server uses a trained AI model to generate a design proposal optimized for the user. The input is the AI ​​model and user data, and the output is a specific project design proposal. This includes a list of necessary materials, work procedures, and implementation methods. Specifically, the server generates a design proposal based on prompts for each user and sends the results to the terminal.

[0296] Step 4:

[0297] The terminal receives design proposals and related information sent from the server and visualizes them for the user. The input is data from the server, and the output is the project design proposal displayed on the user's screen. The terminal displays information in a format easily understood by the user and supports selection and operation. Specifically, it provides step-by-step guides on the interface, allowing the user to easily understand the next action.

[0298] Step 5:

[0299] Users review design proposals displayed on their devices and select and proceed with projects based on their interests and skills. Inputs are the design proposals received from the device and the user's selections, while output is the actual result of completing the project. Specifically, users procure the necessary materials for the project and create the artwork according to the instructions.

[0300] Step 6:

[0301] After completing a project, users input feedback on the results and their experience into a terminal. This feedback is then sent back to the server, becoming further training data for the knowledge processing model. The input is the user's feedback itself, and the output is the improved AI model. The server uses this to improve the accuracy of future project proposals and increase user satisfaction.

[0302] (Application Example 1)

[0303] 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 glasses 214 will be referred to as the "terminal."

[0304] In recent years, there has been a growing demand in the field of creative activities for the efficient generation of plans and designs tailored to individual needs. However, conventional systems struggle to provide optimal plans that meet the diverse preferences and skills of users. Furthermore, particularly in the industrial sector, there is a lack of guidance on the design of precision machinery and equipment, as well as on the procurement of parts and manufacturing processes.

[0305] 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.

[0306] In this invention, the server includes means for collecting information about a user and training an information processing model that generates a plan optimized for the individual based on the information, means for using the information processing model to provide a plan for a creative task according to the user's preferences and knowledge level, and means for generating information regarding the selection and procurement of necessary materials based on the plan. As a result, it becomes possible for the user to efficiently carry out creative activities that appropriately respond to individual requirements and the design and manufacture of mechanical devices in the industrial field.

[0307] The "information processing model" is an algorithm trained to generate an individually optimized plan or design based on information about the user.

[0308] The "plan" is a specific project plan indicating a creative task or the design of a mechanical device according to the user's preferences and knowledge level.

[0309] "Materials" refers to the materials and parts necessary to realize the plan, and is the target for which information regarding their selection and procurement is generated.

[0310] The "process guide" is a guide that details the procedures and methods for carrying out work based on the plan.

[0311] This invention relates to a system that generates and provides a plan according to various needs of the user. The system mainly consists of a server and a terminal. The server has an information processing model and collects and analyzes information about the user. This information includes the user's preferences, knowledge level, past project history, etc. Using this data, the server trains a generation AI model and creates a plan optimized for the user.

[0312] Furthermore, the server generates information regarding the selection and procurement of materials according to the plan. This process includes information on mechanical parts and other necessary materials. Also, a detailed process guide for implementing the project is created on the server and sent to the terminal.

[0313] The terminal provides a user interface, clearly displaying the draft plan, material information, and process guide sent from the server. Based on this information, users can perform creative work and design machinery and equipment in accordance with the draft plan. After completing a project, users can send feedback to the server via the terminal, which is used to further train the information processing model.

[0314] As a concrete example, consider a scenario where a factory engineer is designing a new packaging robot. By inputting the engineer's past project history, skills, and target product information, the server generates an optimal robot design proposal. A materials list and detailed manufacturing process guide are also generated, allowing the user to efficiently proceed with the design work. An example of a prompt message being used would be, "Generate an optimal product packaging robot design proposal based on this engineer's past project history and target product."

[0315] This invention will improve the efficiency and precision of design and manufacturing processes in creative activities and industrial fields.

[0316] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0317] Step 1:

[0318] The server receives information sent from the user. User preferences, knowledge level, and past project history are provided as input. This data is stored in a database, and an information processing model uses it to build a user profile.

[0319] Step 2:

[0320] The server trains a generation AI model based on the stored user information. This model is tuned to generate individually optimized plan proposals, taking into account skill levels and past history. The input is user information, and the output is the optimized plan proposal generated by the AI ​​model.

[0321] Step 3:

[0322] The server uses a generative AI model to build a plan optimized for the user. This process takes into account the user's preferences and skill level. During plan construction, prompts are used, and the AI ​​generates design proposals and project details. The output is the plan.

[0323] Step 4:

[0324] The server generates a list of necessary materials and procurement information based on the proposed plan. The server analyzes the plan, selects the materials needed for its implementation, and extracts relevant procurement methods from the industrial database. The input is the proposed plan, and the output is a list of materials.

[0325] Step 5:

[0326] The terminal receives the project plan, material list, and process guide sent from the server and displays them through the user interface. The user then uses this information to carry out the project according to the plan. Input is data from the server, and output is the presentation of information to the user.

[0327] Step 6:

[0328] Users complete projects and send feedback from their terminals to the server, including results and opinions from the implementation process. This generates new data, which is then used as training data for subsequent information processing models. The input is user feedback, and the output is an updated user profile.

[0329] 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.

[0330] This invention is a system that supports users' creative activities and provides a more personalized experience, and has the following configuration.

[0331] Regarding server functionality

[0332] The server collects and manages user data, including the user's hobbies, skill level, past project history, and emotional data acquired by the emotion engine. The collected data is used to train an artificial intelligence model, forming the basis for generating user-optimized design proposals. The emotion engine recognizes emotions in real time during user interactions and transmits this data to the server. The server utilizes this emotional data to propose project plans that are tailored not only to past behavioral history but also to the user's emotional state.

[0333] About the device's functions

[0334] The terminal plays a crucial role in displaying design proposals, material information, and process guides sent from the server. The terminal also incorporates or connects an emotion engine to monitor the user's emotions as they interact with the system. It can dynamically change the interface on the terminal in response to emotional changes. For example, if the user is stressed, it can offer simpler projects or simplify the interface, demonstrating flexibility in this regard.

[0335] Regarding user actions

[0336] Users select projects based on information provided on their devices. The options reflect their preferences, skill levels, and emotional states provided by the emotion engine. As users work on projects, the emotion engine continuously monitors their state and sends feedback to the server as the project progresses. This creates a feedback loop for receiving optimized suggestions.

[0337] Specific example

[0338] For example, if a user expresses interest in creating a new pottery piece, the server, in addition to the user's past project data, uses an emotion engine to determine whether the user is experiencing "enjoyment." If the user expresses positive emotions, the server suggests a challenging design that slightly pushes the user's skills. On the other hand, if the user expresses neutral or negative emotions, it suggests a more basic and fulfilling design. After completion, the user provides feedback on their emotional changes and the project, which is then reflected in future suggestions.

[0339] This technology allows users to engage in creative activities not only by improving their skills, but also by increasing their emotional satisfaction. Through this, the system aims to provide a more engaging and sustainable user experience.

[0340] The following describes the processing flow.

[0341] Step 1:

[0342] The server receives data about users' hobbies and skill levels and stores it in a database. This creates a foundation for providing a customized experience for each user.

[0343] Step 2:

[0344] As soon as the user begins interacting with the system, the device activates a built-in or connected emotion engine to monitor the user's emotional state in real time. The emotion engine acquires data through methods such as facial recognition and voice analysis.

[0345] Step 3:

[0346] The emotion engine analyzes the acquired emotion data and sends the results to the server. This allows the server to understand the user's current emotional state.

[0347] Step 4:

[0348] The server updates and trains the artificial intelligence model using collected sentiment data and historical user data. Adjustments are made here to enable the model to suggest projects based on emotions.

[0349] Step 5:

[0350] The server applies a trained artificial intelligence model to generate creative project designs tailored to the user's preferences, skill level, and emotional state.

[0351] Step 6:

[0352] The server sends the generated design proposal, necessary material information, and a step-by-step process guide for the project to the terminal.

[0353] Step 7:

[0354] The device displays the received information in a user interface, allowing the user to select the project they find most appealing.

[0355] Step 8:

[0356] The user selects a project based on the information displayed on their device and begins creative activity following the process guide. Throughout this process, the emotion engine tracks the user's emotions and collects data to improve suggestions based on the situation.

[0357] Step 9:

[0358] After the project is completed, users provide feedback via their devices, including their impressions and changes in feelings. This feedback is stored in a database on the server and used to improve future proposals.

[0359] Step 10:

[0360] The device sends the collected feedback data to the server. The server receives this data and uses it to further train the human model. This successful experience allows the system to continue providing a user-friendly and more personalized user experience.

[0361] (Example 2)

[0362] 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".

[0363] In modern creative activities, there is a demand for providing individually optimized design proposals that take into account each user's hobbies, skill level, and emotional state. However, conventional systems have faced the challenge of not being able to adequately analyze changes in users' emotions and reflect them in project proposals. As a result, it is difficult for users to maintain their interest and engage in creative activities in a sustained manner.

[0364] 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.

[0365] In this invention, the server includes means for collecting information about the user and training a machine learning system for generating individually optimized creative plan proposals based on said information; means for providing plan proposals tailored to the user's preferences and skill level using the machine learning system; and means for analyzing the user's emotional state in real time and reflecting that data in the proposed plan proposals and procedure guides. This makes it possible to provide individually optimized project proposals that accurately reflect the user's emotional state.

[0366] "Information" refers to data about the user, including their hobbies, skill level, emotional state, and past activity history.

[0367] A "machine learning system" refers to a system that has algorithms for analyzing data based on collected user information and generating individually optimized plan proposals.

[0368] A "plan" refers to the design and execution plan of creative activities tailored to the user's preferences and technical level, and includes the projects and procedures provided.

[0369] "Resources" refer to items such as materials and tools that are necessary to carry out creative activities.

[0370] A "procedure guide" refers to a document or display containing explanations and instructions that guide users through each step of a creative activity.

[0371] "Emotional state" refers to data that indicates the user's psychological state, including changes in the user's emotions acquired in real time.

[0372] This invention is a system for supporting users' creative activities. This system mainly consists of servers, terminals, and users who utilize them.

[0373] Server operation:

[0374] The server manages information collected from users and builds a database based on the user's hobbies, skill level, and emotional state. This information is analyzed by machine learning algorithms using generative AI models and forms the basis for generating personalized project plans for each user. The server also analyzes diverse data, including the user's real-time emotional data, and forms instructions for the AI ​​model through prompts. For example, it might generate a prompt such as, "Create a new pottery project proposal based on the user's recent project history and current emotional data. Recommend a challenging design if the emotional state is positive, and a basic design if the emotional state is neutral or negative."

[0375] Device operation:

[0376] The terminal provides the user with a plan, necessary resource information, and procedural guides sent from the server. Furthermore, the terminal has a built-in or connected emotion engine that monitors the user's emotional changes in real time. By dynamically changing the terminal's user interface in response to the emotional data, a more personalized experience can be provided.

[0377] User actions:

[0378] Users select a project proposal that interests them from those displayed on their device and begin their creative activity. The user's feelings towards the selected project are continuously monitored, and their feedback is sent to the server. Based on this feedback, the server further optimizes its next proposal.

[0379] Thus, the system of the present invention, by combining machine learning and real-time sentiment analysis, provides users with an experience that not only promotes creative activity but also enhances emotional satisfaction.

[0380] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0381] Step 1:

[0382] The server collects user hobbies, skill levels, past project history, and real-time sentiment data. Input is diverse information obtained from the user, and output is a database containing this information. Database updates reflect the latest sentiment data, ensuring accurate user profiles.

[0383] Step 2:

[0384] The server uses the collected data to train a generative AI model. The input is user information stored in a database, and the output is the parameters of the AI ​​model used to generate a plan optimized for the user. Specifically, the server executes machine learning algorithms to extract patterns and trends and improve the performance of the AI ​​model.

[0385] Step 3:

[0386] The server uses a trained AI model to generate project plans tailored to the user's preferences and skill level. The input consists of the AI ​​model and user information, while the output is a individually optimized project plan. During this process, the server generates prompts and issues instructions to the AI ​​model.

[0387] Step 4:

[0388] The terminal displays the user with the plan, resource information, and procedure guide received from the server. The input is data from the server, and the output is information presented visually to the user. Specifically, the terminal displays data on its screen and provides an interface that allows the user to easily operate it.

[0389] Step 5:

[0390] The user selects a project of interest based on the proposed plan displayed on the terminal. The input is the proposed plan presented on the terminal, and the output is the user's decision regarding the selected plan.

[0391] Step 6:

[0392] The device initiates creative activities based on a selected plan while collecting real-time emotional data through an emotion engine. The input is the user's emotional changes, and the output is the emotional data sent to the server. During this process, the device adjusts its interface according to the situation to optimize the user experience.

[0393] Step 7:

[0394] The server receives feedback data from users after project completion and incorporates it into proposals for future projects. The input is user feedback and sentiment data, and the output is algorithmic improvements to optimize future proposals. Specifically, it analyzes the feedback and uses the data as training material for an AI model.

[0395] (Application Example 2)

[0396] 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."

[0397] Conventional design proposal generation systems rely solely on user preferences and skills, failing to consider emotional satisfaction. This is a major drawback, as they cannot provide a truly optimal experience for each individual. Furthermore, the inability to flexibly modify the interface to accommodate user emotions can diminish immersion in the project.

[0398] 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.

[0399] In this invention, the server includes means for collecting user data and training an artificial intelligence model that generates individually optimized design proposals; means for providing creative project design proposals tailored to the user's preferences and skill level; and means for acquiring user emotional data in real time and adjusting suggestions or actions based on that data according to the emotional state. This makes it possible to provide users with an optimal project experience that is emotionally satisfying.

[0400] A "personally optimized design proposal" is a design suggestion that has been adjusted by an artificial intelligence model based on the individual user's preferences and skill level.

[0401] An "artificial intelligence model" is a mathematical algorithm that is trained using user data and generates design proposals based on specific requirements.

[0402] "Emotional data" refers to information that indicates a user's emotional state, and is data collected in real time based on facial expressions, tone of voice, and other factors.

[0403] "Adjusting suggestions or actions" refers to the process of dynamically changing the information and content provided in response to the user's emotional state.

[0404] "Dynamically changing" means that the display and content of the user interface, etc., change in real time, so that it responds immediately to the user's situation.

[0405] A "project experience" is a series of activities in which a user engages in creative work, following proposed design plans and guides.

[0406] This invention is implemented by combining various technologies to further optimize the individual user experience.

[0407] The server plays a central role in aggregating user preferences, skill levels, and emotional data. The server uses a database to record users' past behavioral history and current emotional states. Based on this information, an artificial intelligence model is used to train and generate individually optimized design proposals. Typical examples of the AI ​​modeling used here include deep learning frameworks such as TensorFlow.

[0408] The device serves as the primary interface between the user and the system. The device incorporates an emotion recognition engine that collects user emotion data in real time through its camera and microphone. This data is sent to a server and used to generate optimal suggestions for the user. On the device, React Native is used as the UI framework to dynamically change the user interface and display flexible suggestions tailored to the user's emotions.

[0409] As a concrete example, when a user of a smartphone app is browsing a specific virtual product, the emotion recognition engine provides insights to increase the user's interest. For instance, it might display a prompt such as, "You seem to like this product," and then suggest related products. An example of such a prompt would be, "What is the best next action if the user shows positive emotions while browsing a specific product?"

[0410] Users can receive personalized project suggestions on their devices, select projects based on those suggestions, and proceed with them. The server and device work together to create an emotionally satisfying experience for the user.

[0411] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0412] Step 1:

[0413] The server aggregates user preferences, skill levels, past behavioral history, and sentiment data into a database. This provides the foundational data for training an artificial intelligence model. The input is user profile and sentiment measurement data, and the output is a training dataset.

[0414] Step 2:

[0415] The server uses TensorFlow to train an artificial intelligence model and build a model that generates individually optimized design proposals. In this process, it takes a training dataset from a database as input and outputs an optimized AI model. Specifically, the AI ​​model learns an algorithm for generating design proposals based on user profiles.

[0416] Step 3:

[0417] The device uses its camera and microphone to collect real-time emotional data from the user. An emotion recognition engine analyzes this data and quantifies the emotional state. The input is audio and video data, and the output is a score of the user's emotional state.

[0418] Step 4:

[0419] The device sends collected sentiment data to the server. This data is used to adjust project suggestions generated by the AI. The input consists of sentiment scores and contextual information about the user's current interaction, while the output is data used to refine the suggestions.

[0420] Step 5:

[0421] The server uses an AI model to generate optimal project suggestions or actions based on the user's emotional state and sends them to the terminal. The input is a trained AI model and emotional state data, and the output is a customized project suggestion.

[0422] Step 6:

[0423] The terminal displays received project proposals in a user interface, offering the user choices. Here, the interface dynamically changes using the React Native framework. The input is project proposals from the server, and the output is the display of proposals to the user.

[0424] Step 7:

[0425] The user selects a project from the presented options and proceeds to implementation. The user's actions here are crucial for forming a future feedback loop, and the results are returned to the server. This process is part of a continuous cycle of optimizing the user experience. The input is the user's selection action, and the output is the project data being executed.

[0426] 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.

[0427] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0428] 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.

[0429] [Third Embodiment]

[0430] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0431] 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.

[0432] 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).

[0433] 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.

[0434] 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.

[0435] 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).

[0436] 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.

[0437] 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.

[0438] 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.

[0439] 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.

[0440] 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.

[0441] 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".

[0442] This invention is a system for supporting the creative activities of users, and is configured as follows.

[0443] Regarding server functionality

[0444] The server has a database for collecting and storing user data. Users input information such as their hobbies, skill levels, and past project history, and the server stores this data. The server also trains an artificial intelligence model based on this data. The generated AI model has the function of producing optimized design proposals for each user and suggesting project plans based on the user's preferences and skills. Furthermore, the server creates necessary material information based on the generated design proposal and provides information on how to procure them. A process guide for carrying out the project is also generated on the server and sent to the terminal along with related information.

[0445] About the device's functions

[0446] The terminal receives information transmitted from the server and displays it in an easy-to-understand format for the user. This includes design proposals, material information, and process guides. The terminal's interface is designed for intuitive user operation, allowing for easy project selection and access to step-by-step guides. It also includes a function to send user feedback to the server after project completion.

[0447] Regarding user actions

[0448] Users review design proposals provided through their devices and select projects that match their interests and skills. Following the chosen project, they procure the provided materials and proceed with the actual production according to the process guide. The results and insights gained during the production process are sent as feedback to the server via the device, and this is then used again as training data for the AI ​​model.

[0449] Specific example

[0450] For example, suppose a user is interested in pottery and wants to make a beginner-friendly vase. In this case, the server generates a simple vase design based on collected user data, making it easy for beginners to follow. The details include the type and amount of clay needed, the shaping procedure, and what glaze should be used. This design, along with material information and a process guide, is displayed on the user's device, and the user follows the instructions to create the vase. After completion, the user sends a photo of the finished product and feedback on its usability, allowing them to receive more suitable project suggestions in the future.

[0451] Thus, the present invention provides a platform that takes into account the individual needs of users and makes creative activities easier and more fulfilling.

[0452] The following describes the processing flow.

[0453] Step 1:

[0454] The server receives data entered by the user, such as hobbies, skill levels, and past project history, and stores it in a database. This data collection phase forms the basis for individual optimization.

[0455] Step 2:

[0456] The server trains an artificial intelligence model based on accumulated user data. This process learns user preferences and builds a model to generate optimized design proposals for each user.

[0457] Step 3:

[0458] The server uses a trained artificial intelligence model to generate personalized, optimized creative project design proposals for the user. These proposals are then filtered based on factors such as project difficulty and style.

[0459] Step 4:

[0460] Based on the generated design proposal, the server generates the types and quantities of materials needed, as well as their procurement information. This information is essential for project implementation.

[0461] Step 5:

[0462] The server creates a process guide for the user to carry out the project. The guide includes step-by-step instructions and is designed to allow the user to proceed with the work according to the procedure.

[0463] Step 6:

[0464] The terminal provides an intuitive interface that displays design proposals, material information, and process guides received from the server to the user. This interface allows the user to easily select and implement projects.

[0465] Step 7:

[0466] The user selects the project design proposal that interests them most from several options displayed on the device. The selection is based on their current interests and skill level.

[0467] Step 8:

[0468] Users follow the process guide displayed on their terminal to complete the project. Each step includes specific instructions, ensuring that users can proceed with the work without getting lost.

[0469] Step 9:

[0470] After completing a project, users input their thoughts and feedback based on the project's results into their device. This feedback is used to propose future projects and improve the artificial intelligence model.

[0471] Step 10:

[0472] The device sends the collected user feedback to the server. The server stores this information in a database and uses it to train future user data.

[0473] This process allows the system to propose the most suitable project to the user and support their creative activities.

[0474] (Example 1)

[0475] 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."

[0476] Traditionally, systems supporting creative work have struggled to present optimal design proposals tailored to individual user preferences and skill levels. Furthermore, there was a lack of mechanisms for efficiently incorporating user feedback into AI models, including providing guidelines for material selection and process execution during project execution. This has created a need for improved user satisfaction and enhanced practical support.

[0477] 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.

[0478] In this invention, the server includes means for collecting information about users and training a knowledge processing model that generates individually optimized design proposals; means for providing design proposals using the knowledge processing model; means for generating information on the selection and procurement of necessary materials; and means for transmitting information to a terminal and visualizing it. This enables the provision of design proposals for creative work optimized for each user, as well as the streamlining of material procurement and processes, and the utilization of user feedback in the AI ​​model.

[0479] A "user" is an individual or group that uses this system to perform creative work.

[0480] "Information" refers to data necessary for optimizing creative work, such as the user's hobbies, skill level, and past activity history.

[0481] A "knowledge processing model" is artificial intelligence trained to generate individually optimized design proposals based on information collected from users.

[0482] A "design proposal" refers to a detailed plan or outline of creative work that is tailored to the user's preferences and skills.

[0483] "Materials" refer to the materials and tools needed to carry out creative work.

[0484] A "terminal" is an electronic device that receives information sent from a server and is used by the user to verify it.

[0485] "Visualization" refers to displaying information in a format that is easy for users to understand.

[0486] "Evaluation information" refers to data on user feedback and user experience provided after a project has been completed.

[0487] To implement this invention, a server is first required. The server trains a knowledge processing model that generates individually optimized design proposals based on information collected from users. The server uses a high-performance database and machine learning libraries to efficiently process large amounts of data. Specifically, it stores information such as hobbies, skill levels, and past activity history entered by users through their terminals in the database. Then, it constructs a knowledge processing model using a deep learning algorithm based on this information.

[0488] The server uses the generated knowledge processing model to provide design proposals tailored to the user's individual preferences and skill level. These proposals include the types and quantities of necessary materials and the project implementation procedures. The server then transmits the generated design proposals, material information, and method / procedure data to the terminal.

[0489] The terminal displays information received from the server in an easy-to-understand manner for the user. The terminal's interface is designed for intuitive user operation, making it easy to select projects and check their progress. Using the terminal, users can select projects based on the provided design proposals, arrange for necessary materials, and proceed with creative work according to the instructed methods and procedures.

[0490] After completing a project, users provide feedback based on the results and their experience using the system. This feedback is sent to the server via the terminal and used as further training data for the knowledge processing model. This improves the accuracy of future design proposals.

[0491] For example, if a user wants to create a ceramic piece, the server will provide a vase design tailored to the user's skill level. The server will also generate and send detailed guidelines to the terminal, including information on the type of clay needed, molding procedures, and glaze selection. The user can then proceed with the project according to these guidelines and provide feedback upon completion.

[0492] An example of a prompt message would be: "Please propose a beginner-friendly pottery project. The project should involve making a vase, and include specific materials and instructions."

[0493] Thus, the present invention provides a system for supporting creative work tailored to the individual needs of users.

[0494] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0495] Step 1:

[0496] The server receives user input data through the terminal and stores it in a database. This input data includes the user's hobbies, skill level, and past activity history. The server uses this data to prepare the foundational data for providing personalized content to individual users. Specifically, it standardizes data formats, corrects missing data, and saves the prepared data.

[0497] Step 2:

[0498] The server trains a knowledge processing model based on the collected data. This process applies machine learning algorithms to generate design proposals tailored to the user's interests and skills. The input is the user's database information, and the output is a individually optimized AI model. The server repeatedly trains the model through this process to improve its accuracy.

[0499] Step 3:

[0500] The server uses a trained AI model to generate a design proposal optimized for the user. The input is the AI ​​model and user data, and the output is a specific project design proposal. This includes a list of necessary materials, work procedures, and implementation methods. Specifically, the server generates a design proposal based on prompts for each user and sends the results to the terminal.

[0501] Step 4:

[0502] The terminal receives design proposals and related information sent from the server and visualizes them for the user. The input is data from the server, and the output is the project design proposal displayed on the user's screen. The terminal displays information in a format easily understood by the user and supports selection and operation. Specifically, it provides step-by-step guides on the interface, allowing the user to easily understand the next action.

[0503] Step 5:

[0504] Users review design proposals displayed on their devices and select and proceed with projects based on their interests and skills. Inputs are the design proposals received from the device and the user's selections, while output is the actual result of completing the project. Specifically, users procure the necessary materials for the project and create the artwork according to the instructions.

[0505] Step 6:

[0506] After completing a project, users input feedback on the results and their experience into a terminal. This feedback is then sent back to the server, becoming further training data for the knowledge processing model. The input is the user's feedback itself, and the output is the improved AI model. The server uses this to improve the accuracy of future project proposals and increase user satisfaction.

[0507] (Application Example 1)

[0508] 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."

[0509] In recent years, there has been a growing demand in the field of creative activities for the efficient generation of plans and designs tailored to individual needs. However, conventional systems struggle to provide optimal plans that meet the diverse preferences and skills of users. Furthermore, particularly in the industrial sector, there is a lack of guidance on the design of precision machinery and equipment, as well as on the procurement of parts and manufacturing processes.

[0510] 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.

[0511] In this invention, the server includes means for training an information processing model that collects information about the user and generates individually optimized plans based on said information; means for providing creative work plans tailored to the user's preferences and knowledge level using said information processing model; and means for generating information regarding the selection and procurement of necessary materials based on said plans. This enables users to efficiently engage in creative activities and design and manufacture machinery and equipment in industrial fields that appropriately address their individual needs.

[0512] An "information processing model" is an algorithm trained to generate individually optimized plans and design proposals based on information about the user.

[0513] A "plan" is a specific project proposal that shows creative work or the design of mechanical devices tailored to the user's preferences and knowledge level.

[0514] "Materials" refers to the materials and components necessary to realize the plan, and it is the subject from which information regarding their selection and procurement is generated.

[0515] A "process guide" is a document that provides detailed instructions on the procedures and methods for carrying out work based on a plan.

[0516] This invention relates to a system that generates and provides project plans tailored to the diverse needs of users. The system mainly consists of a server and terminals. The server is equipped with an information processing model that collects and analyzes information about the user. This information includes the user's preferences, knowledge level, and past project history. Using this data, the server trains a generative AI model to create a project plan optimized for the user.

[0517] Furthermore, the server generates information on selecting and procuring materials according to the plan. This process includes information on machine parts and other necessary materials. A detailed process guide for implementing the project is also created on the server and sent to the terminal.

[0518] The terminal provides a user interface, clearly displaying the draft plan, material information, and process guide sent from the server. Based on this information, users can perform creative work and design machinery and equipment in accordance with the draft plan. After completing a project, users can send feedback to the server via the terminal, which is used to further train the information processing model.

[0519] As a concrete example, consider a scenario where a factory engineer is designing a new packaging robot. By inputting the engineer's past project history, skills, and target product information, the server generates an optimal robot design proposal. A materials list and detailed manufacturing process guide are also generated, allowing the user to efficiently proceed with the design work. An example of a prompt message being used would be, "Generate an optimal product packaging robot design proposal based on this engineer's past project history and target product."

[0520] This invention will improve the efficiency and precision of design and manufacturing processes in creative activities and industrial fields.

[0521] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0522] Step 1:

[0523] The server receives information sent from the user. User preferences, knowledge level, and past project history are provided as input. This data is stored in a database, and an information processing model uses it to build a user profile.

[0524] Step 2:

[0525] The server trains a generation AI model based on the stored user information. This model is tuned to generate individually optimized plan proposals, taking into account skill levels and past history. The input is user information, and the output is the optimized plan proposal generated by the AI ​​model.

[0526] Step 3:

[0527] The server uses a generative AI model to build a plan optimized for the user. This process takes into account the user's preferences and skill level. During plan construction, prompts are used, and the AI ​​generates design proposals and project details. The output is the plan.

[0528] Step 4:

[0529] The server generates a list of necessary materials and procurement information based on the proposed plan. The server analyzes the plan, selects the materials needed for its implementation, and extracts relevant procurement methods from the industrial database. The input is the proposed plan, and the output is a list of materials.

[0530] Step 5:

[0531] The terminal receives the project plan, material list, and process guide sent from the server and displays them through the user interface. The user then uses this information to carry out the project according to the plan. Input is data from the server, and output is the presentation of information to the user.

[0532] Step 6:

[0533] Users complete projects and send feedback from their terminals to the server, including results and opinions from the implementation process. This generates new data, which is then used as training data for subsequent information processing models. The input is user feedback, and the output is an updated user profile.

[0534] 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.

[0535] This invention is a system that supports users' creative activities and provides a more personalized experience, and has the following configuration.

[0536] Regarding server functionality

[0537] The server collects and manages user data, including the user's hobbies, skill level, past project history, and emotional data acquired by the emotion engine. The collected data is used to train an artificial intelligence model, forming the basis for generating user-optimized design proposals. The emotion engine recognizes emotions in real time during user interactions and transmits this data to the server. The server utilizes this emotional data to propose project plans that are tailored not only to past behavioral history but also to the user's emotional state.

[0538] About the device's functions

[0539] The terminal plays a crucial role in displaying design proposals, material information, and process guides sent from the server. The terminal also incorporates or connects an emotion engine to monitor the user's emotions as they interact with the system. It can dynamically change the interface on the terminal in response to emotional changes. For example, if the user is stressed, it can offer simpler projects or simplify the interface, demonstrating flexibility in this regard.

[0540] Regarding user actions

[0541] Users select projects based on information provided on their devices. The options reflect their preferences, skill levels, and emotional states provided by the emotion engine. As users work on projects, the emotion engine continuously monitors their state and sends feedback to the server as the project progresses. This creates a feedback loop for receiving optimized suggestions.

[0542] Specific example

[0543] For example, if a user expresses interest in creating a new pottery piece, the server, in addition to the user's past project data, uses an emotion engine to determine whether the user is experiencing "enjoyment." If the user expresses positive emotions, the server suggests a challenging design that slightly pushes the user's skills. On the other hand, if the user expresses neutral or negative emotions, it suggests a more basic and fulfilling design. After completion, the user provides feedback on their emotional changes and the project, which is then reflected in future suggestions.

[0544] This technology allows users to engage in creative activities not only by improving their skills, but also by increasing their emotional satisfaction. Through this, the system aims to provide a more engaging and sustainable user experience.

[0545] The following describes the processing flow.

[0546] Step 1:

[0547] The server receives data about users' hobbies and skill levels and stores it in a database. This creates a foundation for providing a customized experience for each user.

[0548] Step 2:

[0549] As soon as the user begins interacting with the system, the device activates a built-in or connected emotion engine to monitor the user's emotional state in real time. The emotion engine acquires data through methods such as facial recognition and voice analysis.

[0550] Step 3:

[0551] The emotion engine analyzes the acquired emotion data and sends the results to the server. This allows the server to understand the user's current emotional state.

[0552] Step 4:

[0553] The server updates and trains the artificial intelligence model using collected sentiment data and historical user data. Adjustments are made here to enable the model to suggest projects based on emotions.

[0554] Step 5:

[0555] The server applies a trained artificial intelligence model to generate creative project designs tailored to the user's preferences, skill level, and emotional state.

[0556] Step 6:

[0557] The server sends the generated design proposal, necessary material information, and a step-by-step process guide for the project to the terminal.

[0558] Step 7:

[0559] The device displays the received information in a user interface, allowing the user to select the project they find most appealing.

[0560] Step 8:

[0561] The user selects a project based on the information displayed on their device and begins creative activity following the process guide. Throughout this process, the emotion engine tracks the user's emotions and collects data to improve suggestions based on the situation.

[0562] Step 9:

[0563] After the project is completed, users provide feedback via their devices, including their impressions and changes in feelings. This feedback is stored in a database on the server and used to improve future proposals.

[0564] Step 10:

[0565] The device sends the collected feedback data to the server. The server receives this data and uses it to further train the human model. This successful experience allows the system to continue providing a user-friendly and more personalized user experience.

[0566] (Example 2)

[0567] 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."

[0568] In modern creative activities, there is a demand for providing individually optimized design proposals that take into account each user's hobbies, skill level, and emotional state. However, conventional systems have faced the challenge of not being able to adequately analyze changes in users' emotions and reflect them in project proposals. As a result, it is difficult for users to maintain their interest and engage in creative activities in a sustained manner.

[0569] 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.

[0570] In this invention, the server includes means for collecting information about the user and training a machine learning system for generating individually optimized creative plan proposals based on said information; means for providing plan proposals tailored to the user's preferences and skill level using the machine learning system; and means for analyzing the user's emotional state in real time and reflecting that data in the proposed plan proposals and procedure guides. This makes it possible to provide individually optimized project proposals that accurately reflect the user's emotional state.

[0571] "Information" refers to data about the user, including their hobbies, skill level, emotional state, and past activity history.

[0572] A "machine learning system" refers to a system that has algorithms for analyzing data based on collected user information and generating individually optimized plan proposals.

[0573] A "plan" refers to the design and execution plan of creative activities tailored to the user's preferences and technical level, and includes the projects and procedures provided.

[0574] "Resources" refer to items such as materials and tools that are necessary to carry out creative activities.

[0575] A "procedure guide" refers to a document or display containing explanations and instructions that guide users through each step of a creative activity.

[0576] "Emotional state" refers to data that indicates the user's psychological state, including changes in the user's emotions acquired in real time.

[0577] This invention is a system for supporting users' creative activities. This system mainly consists of servers, terminals, and users who utilize them.

[0578] Server operation:

[0579] The server manages information collected from users and builds a database based on the user's hobbies, skill level, and emotional state. This information is analyzed by machine learning algorithms using generative AI models and forms the basis for generating personalized project plans for each user. The server also analyzes diverse data, including the user's real-time emotional data, and forms instructions for the AI ​​model through prompts. For example, it might generate a prompt such as, "Create a new pottery project proposal based on the user's recent project history and current emotional data. Recommend a challenging design if the emotional state is positive, and a basic design if the emotional state is neutral or negative."

[0580] Device operation:

[0581] The terminal provides the user with a plan, necessary resource information, and procedural guides sent from the server. Furthermore, the terminal has a built-in or connected emotion engine that monitors the user's emotional changes in real time. By dynamically changing the terminal's user interface in response to the emotional data, a more personalized experience can be provided.

[0582] User actions:

[0583] Users select a project proposal that interests them from those displayed on their device and begin their creative activity. The user's feelings towards the selected project are continuously monitored, and their feedback is sent to the server. Based on this feedback, the server further optimizes its next proposal.

[0584] Thus, the system of the present invention, by combining machine learning and real-time sentiment analysis, provides users with an experience that not only promotes creative activity but also enhances emotional satisfaction.

[0585] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0586] Step 1:

[0587] The server collects user hobbies, skill levels, past project history, and real-time sentiment data. Input is diverse information obtained from the user, and output is a database containing this information. Database updates reflect the latest sentiment data, ensuring accurate user profiles.

[0588] Step 2:

[0589] The server uses the collected data to train a generative AI model. The input is user information stored in a database, and the output is the parameters of the AI ​​model used to generate a plan optimized for the user. Specifically, the server executes machine learning algorithms to extract patterns and trends and improve the performance of the AI ​​model.

[0590] Step 3:

[0591] The server uses a trained AI model to generate project plans tailored to the user's preferences and skill level. The input consists of the AI ​​model and user information, while the output is a individually optimized project plan. During this process, the server generates prompts and issues instructions to the AI ​​model.

[0592] Step 4:

[0593] The terminal displays the user with the plan, resource information, and procedure guide received from the server. The input is data from the server, and the output is information presented visually to the user. Specifically, the terminal displays data on its screen and provides an interface that allows the user to easily operate it.

[0594] Step 5:

[0595] The user selects a project of interest based on the proposed plan displayed on the terminal. The input is the proposed plan presented on the terminal, and the output is the user's decision regarding the selected plan.

[0596] Step 6:

[0597] The device initiates creative activities based on a selected plan while collecting real-time emotional data through an emotion engine. The input is the user's emotional changes, and the output is the emotional data sent to the server. During this process, the device adjusts its interface according to the situation to optimize the user experience.

[0598] Step 7:

[0599] The server receives feedback data from users after project completion and incorporates it into proposals for future projects. The input is user feedback and sentiment data, and the output is algorithmic improvements to optimize future proposals. Specifically, it analyzes the feedback and uses the data as training material for an AI model.

[0600] (Application Example 2)

[0601] 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."

[0602] Conventional design proposal generation systems rely solely on user preferences and skills, failing to consider emotional satisfaction. This is a major drawback, as they cannot provide a truly optimal experience for each individual. Furthermore, the inability to flexibly modify the interface to accommodate user emotions can diminish immersion in the project.

[0603] 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.

[0604] In this invention, the server includes means for collecting user data and training an artificial intelligence model that generates individually optimized design proposals; means for providing creative project design proposals tailored to the user's preferences and skill level; and means for acquiring user emotional data in real time and adjusting suggestions or actions based on that data according to the emotional state. This makes it possible to provide users with an optimal project experience that is emotionally satisfying.

[0605] A "personally optimized design proposal" is a design suggestion that has been adjusted by an artificial intelligence model based on the individual user's preferences and skill level.

[0606] An "artificial intelligence model" is a mathematical algorithm that is trained using user data and generates design proposals based on specific requirements.

[0607] "Emotional data" refers to information that indicates a user's emotional state, and is data collected in real time based on facial expressions, tone of voice, and other factors.

[0608] "Adjusting suggestions or actions" refers to the process of dynamically changing the information and content provided in response to the user's emotional state.

[0609] "Dynamically changing" means that the display and content of the user interface, etc., change in real time, so that it responds immediately to the user's situation.

[0610] A "project experience" is a series of activities in which a user engages in creative work, following proposed design plans and guides.

[0611] This invention is implemented by combining various technologies to further optimize the individual user experience.

[0612] The server plays a central role in aggregating user preferences, skill levels, and emotional data. The server uses a database to record users' past behavioral history and current emotional states. Based on this information, an artificial intelligence model is used to train and generate individually optimized design proposals. Typical examples of the AI ​​modeling used here include deep learning frameworks such as TensorFlow.

[0613] The device serves as the primary interface between the user and the system. The device incorporates an emotion recognition engine that collects user emotion data in real time through its camera and microphone. This data is sent to a server and used to generate optimal suggestions for the user. On the device, React Native is used as the UI framework to dynamically change the user interface and display flexible suggestions tailored to the user's emotions.

[0614] As a concrete example, when a user of a smartphone app is browsing a specific virtual product, the emotion recognition engine provides insights to increase the user's interest. For instance, it might display a prompt such as, "You seem to like this product," and then suggest related products. An example of such a prompt would be, "What is the best next action if the user shows positive emotions while browsing a specific product?"

[0615] Users can receive personalized project suggestions on their devices, select projects based on those suggestions, and proceed with them. The server and device work together to create an emotionally satisfying experience for the user.

[0616] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0617] Step 1:

[0618] The server aggregates user preferences, skill levels, past behavioral history, and sentiment data into a database. This provides the foundational data for training an artificial intelligence model. The input is user profile and sentiment measurement data, and the output is a training dataset.

[0619] Step 2:

[0620] The server uses TensorFlow to train an artificial intelligence model and build a model that generates individually optimized design proposals. In this process, it takes a training dataset from a database as input and outputs an optimized AI model. Specifically, the AI ​​model learns an algorithm for generating design proposals based on user profiles.

[0621] Step 3:

[0622] The device uses its camera and microphone to collect real-time emotional data from the user. An emotion recognition engine analyzes this data and quantifies the emotional state. The input is audio and video data, and the output is a score of the user's emotional state.

[0623] Step 4:

[0624] The device sends collected sentiment data to the server. This data is used to adjust project suggestions generated by the AI. The input consists of sentiment scores and contextual information about the user's current interaction, while the output is data used to refine the suggestions.

[0625] Step 5:

[0626] The server uses an AI model to generate optimal project suggestions or actions based on the user's emotional state and sends them to the terminal. The input is a trained AI model and emotional state data, and the output is a customized project suggestion.

[0627] Step 6:

[0628] The terminal displays received project proposals in a user interface, offering the user choices. Here, the interface dynamically changes using the React Native framework. The input is project proposals from the server, and the output is the display of proposals to the user.

[0629] Step 7:

[0630] The user selects a project from the presented options and proceeds to implementation. The user's actions here are crucial for forming a future feedback loop, and the results are returned to the server. This process is part of a continuous cycle of optimizing the user experience. The input is the user's selection action, and the output is the project data being executed.

[0631] 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.

[0632] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0633] 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.

[0634] [Fourth Embodiment]

[0635] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0636] 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.

[0637] 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).

[0638] 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.

[0639] 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.

[0640] 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).

[0641] 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.

[0642] 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.

[0643] 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.

[0644] 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.

[0645] 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.

[0646] 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.

[0647] 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".

[0648] This invention is a system for supporting the creative activities of users, and is configured as follows.

[0649] Regarding server functionality

[0650] The server has a database for collecting and storing user data. Users input information such as their hobbies, skill levels, and past project history, and the server stores this data. The server also trains an artificial intelligence model based on this data. The generated AI model has the function of producing optimized design proposals for each user and suggesting project plans based on the user's preferences and skills. Furthermore, the server creates necessary material information based on the generated design proposal and provides information on how to procure them. A process guide for carrying out the project is also generated on the server and sent to the terminal along with related information.

[0651] About the device's functions

[0652] The terminal receives information transmitted from the server and displays it in an easy-to-understand format for the user. This includes design proposals, material information, and process guides. The terminal's interface is designed for intuitive user operation, allowing for easy project selection and access to step-by-step guides. It also includes a function to send user feedback to the server after project completion.

[0653] Regarding user actions

[0654] Users review design proposals provided through their devices and select projects that match their interests and skills. Following the chosen project, they procure the provided materials and proceed with the actual production according to the process guide. The results and insights gained during the production process are sent as feedback to the server via the device, and this is then used again as training data for the AI ​​model.

[0655] Specific example

[0656] For example, suppose a user is interested in pottery and wants to make a beginner-friendly vase. In this case, the server generates a simple vase design based on collected user data, making it easy for beginners to follow. The details include the type and amount of clay needed, the shaping procedure, and what glaze should be used. This design, along with material information and a process guide, is displayed on the user's device, and the user follows the instructions to create the vase. After completion, the user sends a photo of the finished product and feedback on its usability, allowing them to receive more suitable project suggestions in the future.

[0657] Thus, the present invention provides a platform that takes into account the individual needs of users and makes creative activities easier and more fulfilling.

[0658] The following describes the processing flow.

[0659] Step 1:

[0660] The server receives data entered by the user, such as hobbies, skill levels, and past project history, and stores it in a database. This data collection phase forms the basis for individual optimization.

[0661] Step 2:

[0662] The server trains an artificial intelligence model based on accumulated user data. This process learns user preferences and builds a model to generate optimized design proposals for each user.

[0663] Step 3:

[0664] The server uses a trained artificial intelligence model to generate personalized, optimized creative project design proposals for the user. These proposals are then filtered based on factors such as project difficulty and style.

[0665] Step 4:

[0666] Based on the generated design proposal, the server generates the types and quantities of materials needed, as well as their procurement information. This information is essential for project implementation.

[0667] Step 5:

[0668] The server creates a process guide for the user to carry out the project. The guide includes step-by-step instructions and is designed to allow the user to proceed with the work according to the procedure.

[0669] Step 6:

[0670] The terminal provides an intuitive interface that displays design proposals, material information, and process guides received from the server to the user. This interface allows the user to easily select and implement projects.

[0671] Step 7:

[0672] The user selects the project design proposal that interests them most from several options displayed on the device. The selection is based on their current interests and skill level.

[0673] Step 8:

[0674] Users follow the process guide displayed on their terminal to complete the project. Each step includes specific instructions, ensuring that users can proceed with the work without getting lost.

[0675] Step 9:

[0676] After completing a project, users input their thoughts and feedback based on the project's results into their device. This feedback is used to propose future projects and improve the artificial intelligence model.

[0677] Step 10:

[0678] The device sends the collected user feedback to the server. The server stores this information in a database and uses it to train future user data.

[0679] This process allows the system to propose the most suitable project to the user and support their creative activities.

[0680] (Example 1)

[0681] 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".

[0682] Traditionally, systems supporting creative work have struggled to present optimal design proposals tailored to individual user preferences and skill levels. Furthermore, there was a lack of mechanisms for efficiently incorporating user feedback into AI models, including providing guidelines for material selection and process execution during project execution. This has created a need for improved user satisfaction and enhanced practical support.

[0683] 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.

[0684] In this invention, the server includes means for collecting information about users and training a knowledge processing model that generates individually optimized design proposals; means for providing design proposals using the knowledge processing model; means for generating information on the selection and procurement of necessary materials; and means for transmitting information to a terminal and visualizing it. This enables the provision of design proposals for creative work optimized for each user, as well as the streamlining of material procurement and processes, and the utilization of user feedback in the AI ​​model.

[0685] A "user" is an individual or group that uses this system to perform creative work.

[0686] "Information" refers to data necessary for optimizing creative work, such as the user's hobbies, skill level, and past activity history.

[0687] A "knowledge processing model" is artificial intelligence trained to generate individually optimized design proposals based on information collected from users.

[0688] A "design proposal" refers to a detailed plan or outline of creative work that is tailored to the user's preferences and skills.

[0689] "Materials" refer to the materials and tools needed to carry out creative work.

[0690] A "terminal" is an electronic device that receives information sent from a server and is used by the user to verify it.

[0691] "Visualization" refers to displaying information in a format that is easy for users to understand.

[0692] "Evaluation information" refers to data on user feedback and user experience provided after a project has been completed.

[0693] To implement this invention, a server is first required. The server trains a knowledge processing model that generates individually optimized design proposals based on information collected from users. The server uses a high-performance database and machine learning libraries to efficiently process large amounts of data. Specifically, it stores information such as hobbies, skill levels, and past activity history entered by users through their terminals in the database. Then, it constructs a knowledge processing model using a deep learning algorithm based on this information.

[0694] The server uses the generated knowledge processing model to provide design proposals tailored to the user's individual preferences and skill level. These proposals include the types and quantities of necessary materials and the project implementation procedures. The server then transmits the generated design proposals, material information, and method / procedure data to the terminal.

[0695] The terminal displays information received from the server in an easy-to-understand manner for the user. The terminal's interface is designed for intuitive user operation, making it easy to select projects and check their progress. Using the terminal, users can select projects based on the provided design proposals, arrange for necessary materials, and proceed with creative work according to the instructed methods and procedures.

[0696] After completing a project, users provide feedback based on the results and their experience using the system. This feedback is sent to the server via the terminal and used as further training data for the knowledge processing model. This improves the accuracy of future design proposals.

[0697] For example, if a user wants to create a ceramic piece, the server will provide a vase design tailored to the user's skill level. The server will also generate and send detailed guidelines to the terminal, including information on the type of clay needed, molding procedures, and glaze selection. The user can then proceed with the project according to these guidelines and provide feedback upon completion.

[0698] An example of a prompt message would be: "Please propose a beginner-friendly pottery project. The project should involve making a vase, and include specific materials and instructions."

[0699] Thus, the present invention provides a system for supporting creative work tailored to the individual needs of users.

[0700] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0701] Step 1:

[0702] The server receives user input data through the terminal and stores it in a database. This input data includes the user's hobbies, skill level, and past activity history. The server uses this data to prepare the foundational data for providing personalized content to individual users. Specifically, it standardizes data formats, corrects missing data, and saves the prepared data.

[0703] Step 2:

[0704] The server trains a knowledge processing model based on the collected data. This process applies machine learning algorithms to generate design proposals tailored to the user's interests and skills. The input is the user's database information, and the output is a individually optimized AI model. The server repeatedly trains the model through this process to improve its accuracy.

[0705] Step 3:

[0706] The server uses a trained AI model to generate a design proposal optimized for the user. The input is the AI ​​model and user data, and the output is a specific project design proposal. This includes a list of necessary materials, work procedures, and implementation methods. Specifically, the server generates a design proposal based on prompts for each user and sends the results to the terminal.

[0707] Step 4:

[0708] The terminal receives design proposals and related information sent from the server and visualizes them for the user. The input is data from the server, and the output is the project design proposal displayed on the user's screen. The terminal displays information in a format easily understood by the user and supports selection and operation. Specifically, it provides step-by-step guides on the interface, allowing the user to easily understand the next action.

[0709] Step 5:

[0710] Users review design proposals displayed on their devices and select and proceed with projects based on their interests and skills. Inputs are the design proposals received from the device and the user's selections, while output is the actual result of completing the project. Specifically, users procure the necessary materials for the project and create the artwork according to the instructions.

[0711] Step 6:

[0712] After completing a project, users input feedback on the results and their experience into a terminal. This feedback is then sent back to the server, becoming further training data for the knowledge processing model. The input is the user's feedback itself, and the output is the improved AI model. The server uses this to improve the accuracy of future project proposals and increase user satisfaction.

[0713] (Application Example 1)

[0714] 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".

[0715] In recent years, there has been a growing demand in the field of creative activities for the efficient generation of plans and designs tailored to individual needs. However, conventional systems struggle to provide optimal plans that meet the diverse preferences and skills of users. Furthermore, particularly in the industrial sector, there is a lack of guidance on the design of precision machinery and equipment, as well as on the procurement of parts and manufacturing processes.

[0716] 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.

[0717] In this invention, the server includes means for training an information processing model that collects information about the user and generates individually optimized plans based on said information; means for providing creative work plans tailored to the user's preferences and knowledge level using said information processing model; and means for generating information regarding the selection and procurement of necessary materials based on said plans. This enables users to efficiently engage in creative activities and design and manufacture machinery and equipment in industrial fields that appropriately address their individual needs.

[0718] An "information processing model" is an algorithm trained to generate individually optimized plans and design proposals based on information about the user.

[0719] A "plan" is a specific project proposal that shows creative work or the design of mechanical devices tailored to the user's preferences and knowledge level.

[0720] "Materials" refers to the materials and components necessary to realize the plan, and it is the subject from which information regarding their selection and procurement is generated.

[0721] A "process guide" is a document that provides detailed instructions on the procedures and methods for carrying out work based on a plan.

[0722] This invention relates to a system that generates and provides project plans tailored to the diverse needs of users. The system mainly consists of a server and terminals. The server is equipped with an information processing model that collects and analyzes information about the user. This information includes the user's preferences, knowledge level, and past project history. Using this data, the server trains a generative AI model to create a project plan optimized for the user.

[0723] Furthermore, the server generates information on selecting and procuring materials according to the plan. This process includes information on machine parts and other necessary materials. A detailed process guide for implementing the project is also created on the server and sent to the terminal.

[0724] The terminal provides a user interface, clearly displaying the draft plan, material information, and process guide sent from the server. Based on this information, users can perform creative work and design machinery and equipment in accordance with the draft plan. After completing a project, users can send feedback to the server via the terminal, which is used to further train the information processing model.

[0725] As a concrete example, consider a scenario where a factory engineer is designing a new packaging robot. By inputting the engineer's past project history, skills, and target product information, the server generates an optimal robot design proposal. A materials list and detailed manufacturing process guide are also generated, allowing the user to efficiently proceed with the design work. An example of a prompt message being used would be, "Generate an optimal product packaging robot design proposal based on this engineer's past project history and target product."

[0726] This invention will improve the efficiency and precision of design and manufacturing processes in creative activities and industrial fields.

[0727] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0728] Step 1:

[0729] The server receives information sent from the user. User preferences, knowledge level, and past project history are provided as input. This data is stored in a database, and an information processing model uses it to build a user profile.

[0730] Step 2:

[0731] The server trains a generation AI model based on the stored user information. This model is tuned to generate individually optimized plan proposals, taking into account skill levels and past history. The input is user information, and the output is the optimized plan proposal generated by the AI ​​model.

[0732] Step 3:

[0733] The server uses a generative AI model to build a plan optimized for the user. This process takes into account the user's preferences and skill level. During plan construction, prompts are used, and the AI ​​generates design proposals and project details. The output is the plan.

[0734] Step 4:

[0735] The server generates a list of necessary materials and procurement information based on the proposed plan. The server analyzes the plan, selects the materials needed for its implementation, and extracts relevant procurement methods from the industrial database. The input is the proposed plan, and the output is a list of materials.

[0736] Step 5:

[0737] The terminal receives the project plan, material list, and process guide sent from the server and displays them through the user interface. The user then uses this information to carry out the project according to the plan. Input is data from the server, and output is the presentation of information to the user.

[0738] Step 6:

[0739] Users complete projects and send feedback from their terminals to the server, including results and opinions from the implementation process. This generates new data, which is then used as training data for subsequent information processing models. The input is user feedback, and the output is an updated user profile.

[0740] 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.

[0741] This invention is a system that supports users' creative activities and provides a more personalized experience, and has the following configuration.

[0742] Regarding server functionality

[0743] The server collects and manages user data, including the user's hobbies, skill level, past project history, and emotional data acquired by the emotion engine. The collected data is used to train an artificial intelligence model, forming the basis for generating user-optimized design proposals. The emotion engine recognizes emotions in real time during user interactions and transmits this data to the server. The server utilizes this emotional data to propose project plans that are tailored not only to past behavioral history but also to the user's emotional state.

[0744] About the device's functions

[0745] The terminal plays a crucial role in displaying design proposals, material information, and process guides sent from the server. The terminal also incorporates or connects an emotion engine to monitor the user's emotions as they interact with the system. It can dynamically change the interface on the terminal in response to emotional changes. For example, if the user is stressed, it can offer simpler projects or simplify the interface, demonstrating flexibility in this regard.

[0746] Regarding user actions

[0747] Users select projects based on information provided on their devices. The options reflect their preferences, skill levels, and emotional states provided by the emotion engine. As users work on projects, the emotion engine continuously monitors their state and sends feedback to the server as the project progresses. This creates a feedback loop for receiving optimized suggestions.

[0748] Specific example

[0749] For example, if a user expresses interest in creating a new pottery piece, the server, in addition to the user's past project data, uses an emotion engine to determine whether the user is experiencing "enjoyment." If the user expresses positive emotions, the server suggests a challenging design that slightly pushes the user's skills. On the other hand, if the user expresses neutral or negative emotions, it suggests a more basic and fulfilling design. After completion, the user provides feedback on their emotional changes and the project, which is then reflected in future suggestions.

[0750] This technology allows users to engage in creative activities not only by improving their skills, but also by increasing their emotional satisfaction. Through this, the system aims to provide a more engaging and sustainable user experience.

[0751] The following describes the processing flow.

[0752] Step 1:

[0753] The server receives data about users' hobbies and skill levels and stores it in a database. This creates a foundation for providing a customized experience for each user.

[0754] Step 2:

[0755] As soon as the user begins interacting with the system, the device activates a built-in or connected emotion engine to monitor the user's emotional state in real time. The emotion engine acquires data through methods such as facial recognition and voice analysis.

[0756] Step 3:

[0757] The emotion engine analyzes the acquired emotion data and sends the results to the server. This allows the server to understand the user's current emotional state.

[0758] Step 4:

[0759] The server updates and trains the artificial intelligence model using collected sentiment data and historical user data. Adjustments are made here to enable the model to suggest projects based on emotions.

[0760] Step 5:

[0761] The server applies a trained artificial intelligence model to generate creative project designs tailored to the user's preferences, skill level, and emotional state.

[0762] Step 6:

[0763] The server sends the generated design proposal, necessary material information, and a step-by-step process guide for the project to the terminal.

[0764] Step 7:

[0765] The device displays the received information in a user interface, allowing the user to select the project they find most appealing.

[0766] Step 8:

[0767] The user selects a project based on the information displayed on their device and begins creative activity following the process guide. Throughout this process, the emotion engine tracks the user's emotions and collects data to improve suggestions based on the situation.

[0768] Step 9:

[0769] After the project is completed, users provide feedback via their devices, including their impressions and changes in feelings. This feedback is stored in a database on the server and used to improve future proposals.

[0770] Step 10:

[0771] The device sends the collected feedback data to the server. The server receives this data and uses it to further train the human model. This successful experience allows the system to continue providing a user-friendly and more personalized user experience.

[0772] (Example 2)

[0773] 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".

[0774] In modern creative activities, there is a demand for providing individually optimized design proposals that take into account each user's hobbies, skill level, and emotional state. However, conventional systems have faced the challenge of not being able to adequately analyze changes in users' emotions and reflect them in project proposals. As a result, it is difficult for users to maintain their interest and engage in creative activities in a sustained manner.

[0775] 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.

[0776] In this invention, the server includes means for collecting information about the user and training a machine learning system for generating individually optimized creative plan proposals based on said information; means for providing plan proposals tailored to the user's preferences and skill level using the machine learning system; and means for analyzing the user's emotional state in real time and reflecting that data in the proposed plan proposals and procedure guides. This makes it possible to provide individually optimized project proposals that accurately reflect the user's emotional state.

[0777] "Information" refers to data about the user, including their hobbies, skill level, emotional state, and past activity history.

[0778] A "machine learning system" refers to a system that has algorithms for analyzing data based on collected user information and generating individually optimized plan proposals.

[0779] A "plan" refers to the design and execution plan of creative activities tailored to the user's preferences and technical level, and includes the projects and procedures provided.

[0780] "Resources" refer to items such as materials and tools that are necessary to carry out creative activities.

[0781] A "procedure guide" refers to a document or display containing explanations and instructions that guide users through each step of a creative activity.

[0782] "Emotional state" refers to data that indicates the user's psychological state, including changes in the user's emotions acquired in real time.

[0783] This invention is a system for supporting users' creative activities. This system mainly consists of servers, terminals, and users who utilize them.

[0784] Server operation:

[0785] The server manages information collected from users and builds a database based on the user's hobbies, skill level, and emotional state. This information is analyzed by machine learning algorithms using generative AI models and forms the basis for generating personalized project plans for each user. The server also analyzes diverse data, including the user's real-time emotional data, and forms instructions for the AI ​​model through prompts. For example, it might generate a prompt such as, "Create a new pottery project proposal based on the user's recent project history and current emotional data. Recommend a challenging design if the emotional state is positive, and a basic design if the emotional state is neutral or negative."

[0786] Device operation:

[0787] The terminal provides the user with a plan, necessary resource information, and procedural guides sent from the server. Furthermore, the terminal has a built-in or connected emotion engine that monitors the user's emotional changes in real time. By dynamically changing the terminal's user interface in response to the emotional data, a more personalized experience can be provided.

[0788] User actions:

[0789] Users select a project proposal that interests them from those displayed on their device and begin their creative activity. The user's feelings towards the selected project are continuously monitored, and their feedback is sent to the server. Based on this feedback, the server further optimizes its next proposal.

[0790] Thus, the system of the present invention, by combining machine learning and real-time sentiment analysis, provides users with an experience that not only promotes creative activity but also enhances emotional satisfaction.

[0791] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0792] Step 1:

[0793] The server collects user hobbies, skill levels, past project history, and real-time sentiment data. Input is diverse information obtained from the user, and output is a database containing this information. Database updates reflect the latest sentiment data, ensuring accurate user profiles.

[0794] Step 2:

[0795] The server uses the collected data to train a generative AI model. The input is user information stored in a database, and the output is the parameters of the AI ​​model used to generate a plan optimized for the user. Specifically, the server executes machine learning algorithms to extract patterns and trends and improve the performance of the AI ​​model.

[0796] Step 3:

[0797] The server uses a trained AI model to generate project plans tailored to the user's preferences and skill level. The input consists of the AI ​​model and user information, while the output is a individually optimized project plan. During this process, the server generates prompts and issues instructions to the AI ​​model.

[0798] Step 4:

[0799] The terminal displays the user with the plan, resource information, and procedure guide received from the server. The input is data from the server, and the output is information presented visually to the user. Specifically, the terminal displays data on its screen and provides an interface that allows the user to easily operate it.

[0800] Step 5:

[0801] The user selects a project of interest based on the proposed plan displayed on the terminal. The input is the proposed plan presented on the terminal, and the output is the user's decision regarding the selected plan.

[0802] Step 6:

[0803] The device initiates creative activities based on a selected plan while collecting real-time emotional data through an emotion engine. The input is the user's emotional changes, and the output is the emotional data sent to the server. During this process, the device adjusts its interface according to the situation to optimize the user experience.

[0804] Step 7:

[0805] The server receives feedback data from users after project completion and incorporates it into proposals for future projects. The input is user feedback and sentiment data, and the output is algorithmic improvements to optimize future proposals. Specifically, it analyzes the feedback and uses the data as training material for an AI model.

[0806] (Application Example 2)

[0807] 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".

[0808] Conventional design proposal generation systems rely solely on user preferences and skills, failing to consider emotional satisfaction. This is a major drawback, as they cannot provide a truly optimal experience for each individual. Furthermore, the inability to flexibly modify the interface to accommodate user emotions can diminish immersion in the project.

[0809] 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.

[0810] In this invention, the server includes means for collecting user data and training an artificial intelligence model that generates individually optimized design proposals; means for providing creative project design proposals tailored to the user's preferences and skill level; and means for acquiring user emotional data in real time and adjusting suggestions or actions based on that data according to the emotional state. This makes it possible to provide users with an optimal project experience that is emotionally satisfying.

[0811] A "personally optimized design proposal" is a design suggestion that has been adjusted by an artificial intelligence model based on the individual user's preferences and skill level.

[0812] An "artificial intelligence model" is a mathematical algorithm that is trained using user data and generates design proposals based on specific requirements.

[0813] "Emotional data" refers to information that indicates a user's emotional state, and is data collected in real time based on facial expressions, tone of voice, and other factors.

[0814] "Adjusting suggestions or actions" refers to the process of dynamically changing the information and content provided in response to the user's emotional state.

[0815] "Dynamically changing" means that the display and content of the user interface, etc., change in real time, so that it responds immediately to the user's situation.

[0816] A "project experience" is a series of activities in which a user engages in creative work, following proposed design plans and guides.

[0817] This invention is implemented by combining various technologies to further optimize the individual user experience.

[0818] The server plays a central role in aggregating user preferences, skill levels, and emotional data. The server uses a database to record users' past behavioral history and current emotional states. Based on this information, an artificial intelligence model is used to train and generate individually optimized design proposals. Typical examples of the AI ​​modeling used here include deep learning frameworks such as TensorFlow.

[0819] The device serves as the primary interface between the user and the system. The device incorporates an emotion recognition engine that collects user emotion data in real time through its camera and microphone. This data is sent to a server and used to generate optimal suggestions for the user. On the device, React Native is used as the UI framework to dynamically change the user interface and display flexible suggestions tailored to the user's emotions.

[0820] As a concrete example, when a user of a smartphone app is browsing a specific virtual product, the emotion recognition engine provides insights to increase the user's interest. For instance, it might display a prompt such as, "You seem to like this product," and then suggest related products. An example of such a prompt would be, "What is the best next action if the user shows positive emotions while browsing a specific product?"

[0821] Users can receive personalized project suggestions on their devices, select projects based on those suggestions, and proceed with them. The server and device work together to create an emotionally satisfying experience for the user.

[0822] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0823] Step 1:

[0824] The server aggregates user preferences, skill levels, past behavioral history, and sentiment data into a database. This provides the foundational data for training an artificial intelligence model. The input is user profile and sentiment measurement data, and the output is a training dataset.

[0825] Step 2:

[0826] The server uses TensorFlow to train an artificial intelligence model and build a model that generates individually optimized design proposals. In this process, it takes a training dataset from a database as input and outputs an optimized AI model. Specifically, the AI ​​model learns an algorithm for generating design proposals based on user profiles.

[0827] Step 3:

[0828] The device uses its camera and microphone to collect real-time emotional data from the user. An emotion recognition engine analyzes this data and quantifies the emotional state. The input is audio and video data, and the output is a score of the user's emotional state.

[0829] Step 4:

[0830] The device sends collected sentiment data to the server. This data is used to adjust project suggestions generated by the AI. The input consists of sentiment scores and contextual information about the user's current interaction, while the output is data used to refine the suggestions.

[0831] Step 5:

[0832] The server uses an AI model to generate optimal project suggestions or actions based on the user's emotional state and sends them to the terminal. The input is a trained AI model and emotional state data, and the output is a customized project suggestion.

[0833] Step 6:

[0834] The terminal displays received project proposals in a user interface, offering the user choices. Here, the interface dynamically changes using the React Native framework. The input is project proposals from the server, and the output is the display of proposals to the user.

[0835] Step 7:

[0836] The user selects a project from the presented options and proceeds to implementation. The user's actions here are crucial for forming a future feedback loop, and the results are returned to the server. This process is part of a continuous cycle of optimizing the user experience. The input is the user's selection action, and the output is the project data being executed.

[0837] 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.

[0838] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An 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.

[0839] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0840] 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.

[0841] 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.

[0842] 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.

[0843] 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.

[0844] 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.

[0845] 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."

[0846] 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.

[0847] 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.

[0848] 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.

[0849] 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.

[0850] 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.

[0851] 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.

[0852] 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.

[0853] 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.

[0854] 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.

[0855] 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.

[0856] 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.

[0857] 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.

[0858] The following is further disclosed regarding the embodiments described above.

[0859] (Claim 1)

[0860] A means for training an artificial intelligence model that collects user data and generates individually optimized design proposals based on said data,

[0861] A means for providing creative project design proposals tailored to the user's preferences and skill level, utilizing the aforementioned artificial intelligence model.

[0862] A means for generating information regarding the selection and procurement of necessary materials based on the aforementioned design proposal,

[0863] A means for generating a process guide for implementing a project corresponding to the aforementioned design proposal,

[0864] A system that includes this.

[0865] (Claim 2)

[0866] The system according to claim 1, further comprising means for collecting feedback after project implementation based on the design proposal selected by the user and using it to train the artificial intelligence model.

[0867] (Claim 3)

[0868] The system according to claim 1, further comprising means for providing a user interface and displaying the design proposal, material information and process guide.

[0869] "Example 1"

[0870] (Claim 1)

[0871] A means for training a knowledge processing model that collects information about users and generates individually optimized design proposals based on that information,

[0872] A means for providing creative work design proposals tailored to the user's preferences and skill level, utilizing the aforementioned knowledge processing model.

[0873] A means for generating information regarding the selection and procurement of necessary materials based on the aforementioned design proposal,

[0874] Means for generating methods and procedures for carrying out work corresponding to the aforementioned design proposal,

[0875] A means of sending information to a terminal and making it visible,

[0876] A system that includes this.

[0877] (Claim 2)

[0878] The system according to claim 1, further comprising means for collecting evaluation information after the work has been performed based on the design proposal selected by the user and using it to train the knowledge processing model.

[0879] (Claim 3)

[0880] The system according to claim 1, further comprising means for providing a user-facing interface and displaying the design proposal, material information, and method procedure.

[0881] "Application Example 1"

[0882] (Claim 1)

[0883] A means for training an information processing model that collects information about users and generates individually optimized plan proposals based on that information,

[0884] A means for providing a creative work plan tailored to the user's preferences and knowledge level, utilizing the aforementioned information processing model.

[0885] A means for generating information regarding the selection and procurement of necessary materials based on the aforementioned plan,

[0886] Means for generating a process guide for carrying out the work corresponding to the aforementioned plan,

[0887] A means for generating design proposals for machinery and equipment, selecting and procuring related parts, and providing detailed manufacturing process guidance,

[0888] A system that includes this.

[0889] (Claim 2)

[0890] The system according to claim 1, further comprising means for collecting opinion information after the implementation of an action based on the plan selected by the user and using it to train the information processing model.

[0891] (Claim 3)

[0892] The system according to claim 1, further comprising means for providing a user interface and displaying the plan, material information and process guide.

[0893] "Example 2 of combining an emotion engine"

[0894] (Claim 1)

[0895] A means for collecting information about users and training a machine learning system to generate individually optimized creative plans based on said information,

[0896] A means for providing a plan tailored to the user's preferences and technical level using the aforementioned machine learning system,

[0897] Based on the aforementioned plan, means for generating information regarding the selection and acquisition of necessary resources,

[0898] A means for generating a procedural guide for carrying out activities corresponding to the aforementioned plan,

[0899] A means for analyzing the user's emotional state in real time and reflecting that data in the proposed plan and procedure guide,

[0900] A system that includes this.

[0901] (Claim 2)

[0902] The system according to claim 1, further comprising means for collecting evaluations after the implementation of an activity based on the plan selected by the user and using them to train the machine learning system.

[0903] (Claim 3)

[0904] The system according to claim 1, further comprising means for providing input / output means for the user and for displaying the plan, resource information and procedure guide.

[0905] "Application example 2 when combining with an emotional engine"

[0906] (Claim 1)

[0907] A means for training an artificial intelligence model that collects user data and generates individually optimized design proposals based on said data,

[0908] A means for providing creative project design proposals tailored to the user's preferences and skill level, utilizing the aforementioned artificial intelligence model.

[0909] A means of acquiring user emotional data in real time and adjusting suggestions or actions according to the emotional state based on that data,

[0910] A means for generating information regarding the selection and procurement of necessary materials based on the aforementioned design proposal,

[0911] A means for generating a process guide for implementing a project corresponding to the aforementioned design proposal,

[0912] A means of dynamically changing the user interface and adjusting the content presented according to the user's emotional state,

[0913] A system that includes this.

[0914] (Claim 2)

[0915] The system according to claim 1, further comprising means for collecting feedback after project implementation based on the design proposal selected by the user and using it to train the artificial intelligence model.

[0916] (Claim 3)

[0917] The system according to claim 1, further comprising means for providing a user interface, displaying the design proposal, material information, and process guide, and customizing the content according to the user's sentiment. [Explanation of symbols]

[0918] 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 for training an artificial intelligence model that collects user data and generates individually optimized design proposals based on said data, A means for providing creative project design proposals tailored to the user's preferences and skill level, utilizing the aforementioned artificial intelligence model. A means for generating information regarding the selection and procurement of necessary materials based on the aforementioned design proposal, A means for generating a process guide for implementing a project corresponding to the aforementioned design proposal, A system that includes this.

2. The system according to claim 1, further comprising means for collecting feedback after project implementation based on the design proposal selected by the user and using it for training the artificial intelligence model.

3. The system according to claim 1, further comprising means for providing a user interface and displaying the design proposal, material information and process guide.

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