system

Abstract

The system according to this embodiment aims to streamline the planning, design, and execution of DIY projects. [Solution] The system according to the embodiment comprises a generation unit, a planning unit, a proposal unit, and a simulation unit. The generation unit generates custom designs based on the user's preferences and skills. The planning unit automates the process during the project planning phase. The proposal unit suggests optimal materials and procedures. The simulation unit simulates the design proposal in a virtual environment.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's 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 the conventional technology, there is not enough support for improving the efficiency of planning, designing, and executing DIY projects, and there is room for improvement.

[0005] The system according to the embodiment aims to improve the efficiency of planning, designing, and executing DIY projects.

Means for Solving the Problems

[0006] The system according to the embodiment includes a generation unit, a planning unit, a proposal unit, and a simulation unit. The generation unit generates a custom design based on the user's preferences and skills. The planning unit automates the processes in the planning stage of the project. The proposal unit proposes the optimal materials and procedures. The simulation unit simulates the design plan in a virtual environment. [Effects of the Invention]

[0007] The system according to this embodiment can streamline the planning, design, and execution of DIY projects. [Brief explanation of the drawing]

[0008] [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. [Modes for carrying out the invention]

[0009] 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.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] 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 only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 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.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice 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 unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (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.

[0022] 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.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 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.

[0025] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The DIY support system according to an embodiment of the present invention is a system that utilizes generative AI to streamline the planning, design, and execution of DIY projects. This DIY support system creates unique designs based on the user's preferences, skills, and requests. It uses generative AI to propose custom designs for DIY projects. When the desired style and dimensions are entered, the generative AI automatically generates interior and furniture design proposals, and automatically generates custom designs based on the individual user's preferences and space constraints. This allows even beginners to obtain professional-quality designs. Next, in the planning stage of a DIY project, the generative AI is used as a service to automate processes and suggest optimal materials and procedures. For example, based on the DIY project the user wants to undertake, the generative AI creates a step-by-step guide. There is also a service that automatically lists the necessary materials and tools based on the project design and presents purchase links and recommended products. This significantly reduces the time spent selecting materials. Furthermore, the design proposal by the generative AI can be simulated in a virtual environment, allowing users to check the appearance and function of the finished product in advance. For example, if the user enters "I want to build a four-tier shelf here," the generative AI generates a design drawing based on that request and lists the necessary materials and tools. Based on the design plans, users can request wood cutting or receive suggestions for ready-made products. In this way, utilizing generative AI streamlines the planning, design, and execution of DIY projects, allowing even beginners to achieve professional-looking results. Furthermore, because the generative AI provides suggestions tailored to the user's skills and experience, it can solve issues such as lack of skills, cost overruns, exceeding deadlines, safety risks, and quality problems. As a result, DIY support systems can generate custom designs based on the user's preferences and skills, streamlining project planning, material selection, and simulation.

[0029] The DIY support system according to this embodiment comprises a generation unit, a planning unit, a proposal unit, and a simulation unit. The generation unit generates custom designs based on the user's preferences and skills. For example, when the user inputs desired style and dimensions, the generation AI automatically generates interior and furniture design proposals. The generation unit can also automatically generate custom designs based on the user's space constraints. For example, when the user inputs "I want to build a four-tier shelf here," the generation unit generates a design drawing based on that request. The planning unit automates the process during the planning stage of a DIY project. For example, the planning unit uses the generation AI to create a step-by-step guide based on the DIY project the user wants to undertake. The planning unit can also automatically list the necessary materials and tools based on the project design and provide purchase links and recommended products. The proposal unit proposes the optimal materials and procedures. For example, the proposal unit uses the generation AI to create a step-by-step guide based on the DIY project the user wants to undertake. The proposal unit can also automatically list the necessary materials and tools based on the project design and provide purchase links and recommended products. The simulation unit simulates the design proposal in a virtual environment. For example, the simulation unit can simulate the design proposal generated by the AI ​​in a virtual environment, allowing users to check the appearance and function of the finished product in advance. For example, if a user inputs "I want to build a four-tier shelf here," the AI ​​generates a design drawing based on that request and lists the necessary materials and tools. As a result, the DIY support system according to this embodiment can generate custom designs based on the user's preferences and skills, and streamline project planning, material selection, and simulation.

[0030] The generation unit generates custom designs based on the user's preferences and skills. For example, when a user inputs their desired style and dimensions, the generation AI automatically generates interior and furniture design proposals. Specifically, when a user inputs their desired design style (e.g., modern, classic, minimalist, etc.) and dimensions (height, width, depth, etc.) through the interface, the generation AI generates the optimal design proposal based on this information. The generation AI learns from past design data and trend information, allowing it to suggest designs that match the user's preferences. The generation unit can also automatically generate custom designs based on the user's space constraints. For example, if a user inputs "I want to build a four-tier shelf here," it will generate a design drawing based on that request. The generation AI analyzes the room dimensions and layout information provided by the user and proposes the optimal shelf design. Furthermore, the generation unit can also generate designs according to the user's skill level. It will suggest simple and easy-to-assemble designs for beginners and complex and challenging designs for advanced users. In this way, the generation unit can efficiently generate custom designs that meet the user's needs and constraints, supporting the success of DIY projects.

[0031] The planning department automates the process during the planning phase of DIY projects. For example, based on a DIY project the user wants to undertake, the planning department's generating AI creates a step-by-step guide. Specifically, when the user inputs a project overview and goals, the generating AI automatically generates detailed work procedures based on that information. Each step includes necessary materials and tools, estimated work time, and points to note, allowing the user to proceed with the work accordingly. The planning department can also automatically list the necessary materials and tools based on the project design, and provide purchase links and recommended products. For example, if a user is planning a project to build a shelf, the generating AI will list the necessary lumber, screws, paint, tools, etc., and provide purchase links for each. Furthermore, the planning department can track the project's progress in real time and notify the user of its progress and the next steps. In this way, the planning department can support users in efficiently and reliably carrying out their DIY projects, increasing the success rate of the project.

[0032] The suggestion section proposes the optimal materials and procedures. For example, based on the DIY project the user wants to undertake, the suggestion section uses a generative AI to create a step-by-step guide. Specifically, when the user inputs project details, the generative AI uses that information to suggest the optimal materials and procedures. For example, if the user wants to build a wooden shelf, the generative AI will suggest the optimal type and size of wood, the necessary tools, and the type of paint. The suggestion section can also automatically list the necessary materials and tools based on the project design and provide purchase links and recommended products. For example, after the user decides on the shelf design, the generative AI will list the necessary materials and tools based on that design and provide purchase links for each. Furthermore, the suggestion section can present multiple options according to the user's budget and preferences. For example, it will suggest cost-effective materials for users with limited budgets and high-quality materials for users who prioritize quality. In this way, the suggestion section can support users in selecting the optimal materials and procedures and proceeding with their DIY projects efficiently and effectively.

[0033] The simulation unit simulates design proposals in a virtual environment. For example, the simulation unit can simulate design proposals generated by AI in a virtual environment, allowing users to check the appearance and function of the finished product in advance. Specifically, the user imports a design proposal created by the AI ​​into the virtual environment and displays it as a 3D model. The user can freely rotate and zoom in and out of the design proposal within the virtual environment to check the details. The simulation unit also performs simulations to evaluate the functionality and practicality of the design proposal. For example, it can simulate the strength and stability of a shelf and evaluate its durability when actually used. Furthermore, the simulation unit can also simulate how the design proposal would look and how the space would be used if placed in an actual room. For example, if the user inputs room dimensions and layout information, the room can be reproduced in the virtual environment and the design proposal can be placed. This allows the user to check in advance how the design proposal will fit into the actual space and modify the design as needed. In this way, the simulation unit can provide support to ensure the success of the project by allowing users to check the appearance and function of the completed design proposal in advance.

[0034] The generation unit can automatically generate custom designs based on user preferences and space constraints. For example, the generation unit's AI can automatically generate interior and furniture design proposals based on user preferences and space constraints. When the user inputs their desired style and dimensions, the generation unit's AI automatically generates a custom design. The generation unit can also have the AI ​​suggest the optimal design based on the user's space constraints. For example, if the user inputs, "I want to build a four-tier shelf here," the generation unit will generate a design drawing based on that request. In this way, by automatically generating custom designs based on user preferences and space constraints, designs that meet individual needs can be provided. Some or all of the above-described processes in the generation unit may be performed using the generation AI, or they may not be performed using the generation AI. For example, the generation unit can generate custom designs using a generation AI model that takes user preferences and space constraints as input and outputs a custom design.

[0035] The planning department can automate the process of a DIY project. For example, based on the DIY project the user wants to undertake, the planning department's generating AI can create a step-by-step guide. Based on the project design, the planning department's generating AI can automatically list the necessary materials and tools and provide purchase links and recommended products. The planning department can also have the generating AI suggest the optimal process based on the DIY project the user wants to undertake. For example, if the user inputs "I want to build a four-tier shelf here," the planning department can automate the process based on that request. This automates the process of a DIY project, thereby improving work efficiency. Some or all of the above processes in the planning department may be performed using AI or not. For example, the planning department can take the user's DIY project information as input and automate the process using an AI model that automates the process.

[0036] The suggestion section can automatically list the necessary materials and tools based on the project design and provide purchase links and recommended products. For example, the suggestion section can use a generative AI to create a step-by-step guide based on a DIY project the user wants to undertake. The suggestion section can also use a generative AI to automatically list the necessary materials and tools based on the project design and provide purchase links and recommended products. The suggestion section can also use a generative AI to suggest the most suitable materials and tools based on a DIY project the user wants to undertake. For example, if the user inputs "I want to build a four-tier shelf here," the suggestion section will list the necessary materials and tools based on that request. This reduces the time spent selecting materials by automatically listing the necessary materials and tools and providing purchase links and recommended products. Some or all of the above processes in the suggestion section may be performed using generative AI, or they may not. For example, the suggestion section can use a generative AI model that takes the project design as input and outputs the necessary materials and tools to list them.

[0037] The simulation unit simulates design proposals generated by the generation AI in a virtual environment, allowing users to check the appearance and function of the completed product in advance. For example, the simulation unit simulates design proposals generated by the generation AI in a virtual environment, allowing users to check the appearance and function of the completed product in advance. When a user inputs "I want to build a four-tiered shelf here," the simulation unit generates a design drawing based on that request and simulates it in a virtual environment. The simulation unit uses the generation AI to simulate design proposals in a virtual environment, allowing users to check the appearance and function of the completed product in advance. For example, the simulation unit uses the generation AI to perform a simulation in a virtual environment based on the design proposal and provides the user with visual feedback. This allows users to check the appearance and function of the completed product in advance by simulating the design proposal in a virtual environment. Some or all of the above-described processes in the simulation unit may be performed using the generation AI, or they may not be performed using the generation AI. For example, the simulation unit can perform a simulation using a generation AI model that takes a design proposal as input and outputs simulation results in a virtual environment.

[0038] The suggestion function can provide suggestions tailored to the user's skills and experience. For example, the suggestion function can use a generative AI to suggest the optimal materials and tools based on the user's skills and experience. The suggestion function can also use a generative AI to create step-by-step guides based on the user's skills and experience. The suggestion function can also use a generative AI to suggest the optimal procedure based on the user's skills and experience. For example, if the user inputs "I want to build a four-tiered shelf here," the suggestion function will list the optimal materials and tools based on that request. By providing suggestions tailored to the user's skills and experience, it is possible to solve problems such as lack of skills, cost overruns, exceeding deadlines, safety risks, and quality issues. Some or all of the above processes in the suggestion function may be performed using AI or not. For example, the suggestion function can use an AI model that takes the user's skills and experience as input and outputs the optimal suggestion to provide suggestions.

[0039] The generation unit can analyze the user's past project history and propose designs that are best suited to their preferences and skills. For example, the generation unit can use data from projects the user has created in the past to have the generation AI propose designs in a similar style. The generation unit can also use the user's past project history to have the generation AI propose designs that match the user's skill level. The generation unit can also consider the materials and tools the user has used in the past to have the generation AI propose the best design. For example, if the user inputs "I want to build a four-tiered shelf here," the generation unit will propose the best design based on that request. In this way, by analyzing the user's past project history, it can propose designs that are best suited to their preferences and skills. Some or all of the above processes in the generation unit may be performed using the generation AI, or they may not be performed using the generation AI. For example, the generation unit can propose designs using a generation AI model that takes the user's past project data as input and outputs the best design.

[0040] The generation unit can enhance functionality based on the user's lifestyle and intended use during design generation. For example, the generation unit's AI can propose a design suitable for spaces frequently used by the user. The generation unit can also propose a design with enhanced storage functionality based on the user's lifestyle. Depending on the user's intended use, the generation unit's AI can propose a design with specific functions. For example, if the user inputs "I want to build a four-tier shelf here," the generation unit will propose a design with enhanced functionality based on that request. This allows for the provision of more practical designs by enhancing functionality based on the user's lifestyle and intended use. Some or all of the above-described processes in the generation unit may be performed using the generation AI, or they may not be performed using the generation AI. For example, the generation unit can generate designs using a generation AI model that takes the user's lifestyle and intended use as input and outputs a design with enhanced functionality.

[0041] The generation unit can incorporate region-specific design elements by considering the user's geographical location information when generating designs. For example, the generation unit's generation AI can incorporate traditional design elements from the area where the user lives. The generation unit can also have the generation AI propose designs suitable for the local climate based on the user's geographical location information. The generation unit can also have the generation AI propose designs that are in line with the local culture by considering the user's geographical location information. For example, if the user inputs "I want to build a four-tiered shelf here," the generation unit will propose a design incorporating region-specific design elements based on that request. In this way, by incorporating region-specific design elements while considering the user's geographical location information, designs suitable for the region can be provided. Some or all of the above-described processes in the generation unit may be performed using the generation AI, or they may be performed without the generation AI. For example, the generation unit can generate designs using a generation AI model that takes the user's geographical location information as input and outputs region-specific design elements.

[0042] The generation unit can analyze the user's social media activity and propose trend-based designs during design generation. For example, the generation unit's generation AI can incorporate the design styles of influencers the user follows. The generation unit can also propose trend-appropriate designs based on the user's social media "likes" and shares history. The generation unit can analyze the user's social media activity and propose designs that reflect the latest trends. For example, if the user inputs "I want to build a four-tiered shelf here," the generation unit will propose a trend-appropriate design based on that request. In this way, by analyzing the user's social media activity, it is possible to propose trend-based designs. Some or all of the above processes in the generation unit may be performed using the generation AI, or they may not be performed using the generation AI. For example, the generation unit can propose designs using a generation AI model that takes the user's social media activity as input and outputs trend-based designs.

[0043] The planning department can automatically generate the optimal process sequence by referring to past project data. For example, the planning department can propose the most efficient process sequence based on past project data. The planning department can also propose a process sequence with fewer failures based on past project data. The planning department can also analyze past project data and propose a process sequence that can be completed in the shortest time. For example, if a user inputs "I want to build a four-tiered shelf here," the planning department will automatically generate the optimal process sequence based on that request. In this way, the optimal process sequence can be automatically generated by referring to past project data. Some or all of the above processes in the planning department may be performed using AI or not. For example, the planning department can automatically generate a process sequence using an AI model that takes past project data as input and outputs the optimal process sequence.

[0044] The planning unit can customize the plan when creating a project, taking into account the user's schedule and time constraints. For example, the planning unit can propose the optimal work time based on the user's schedule. The planning unit can also propose an efficient project plan considering the user's time constraints. The planning unit can also propose a flexible project plan that fits the user's schedule. For example, if the user inputs "I want to build a four-tiered shelf here," the planning unit will propose the optimal work time based on that request. In this way, by customizing the plan to take into account the user's schedule and time constraints, a more efficient project plan can be provided. Some or all of the above processes in the planning unit may be performed using AI, or not. For example, the planning unit can customize the plan using an AI model that takes the user's schedule and time constraints as input and outputs an optimal project plan.

[0045] The planning department can propose an optimal work environment when planning a project, taking into account the user's geographical location. For example, the planning department can propose an optimal work environment considering the climate of the area where the user lives. The planning department can also propose a work environment suited to the characteristics of the region based on the user's geographical location. The planning department can also propose an optimal work location considering the user's geographical location. For example, if the user inputs "I want to build a four-tiered shelf here," the planning department will propose an optimal work environment based on that request. In this way, by proposing an optimal work environment considering the user's geographical location, it is possible to provide a work environment suitable for the region. Some or all of the above processes in the planning department may be performed using AI, or they may not be performed using AI. For example, the planning department can propose a work environment using an AI model that takes the user's geographical location as input and outputs an optimal work environment.

[0046] The planning department can analyze the user's social media activity and refer to the success stories of other users when planning a project. For example, the planning department can refer to the success stories of DIY projects that the user follows. The planning department can also suggest success stories based on the user's "likes" and shares on social media. The planning department can analyze the user's social media activity and refer to the success stories of other users. For example, if the planning department inputs "I want to build a four-tier shelf here," it will suggest a project plan based on that request and referencing the success stories of other users. In this way, by analyzing the user's social media activity, it is possible to provide a project plan that references the success stories of other users. Some or all of the above processes in the planning department may be performed using AI or not. For example, the planning department can suggest a project plan using an AI model that takes the user's social media activity as input and outputs success stories.

[0047] The suggestion unit can suggest the most suitable materials and tools by referring to the user's past purchase history when making a suggestion. For example, the suggestion unit can make the best suggestion based on the materials and tools the user has purchased in the past. The suggestion unit can also suggest materials and tools of the same brand based on the user's past purchase history. The suggestion unit can also analyze the user's past purchase history and suggest the most suitable materials and tools. For example, if the user inputs "I want to build a four-tier shelf here," the suggestion unit will suggest the most suitable materials and tools based on that request. In this way, the suggestion unit can suggest the most suitable materials and tools by referring to the user's past purchase history. Some or all of the above processing in the suggestion unit may be performed using generative AI, or it may be performed without generative AI. For example, the suggestion unit can make suggestions using a generative AI model that takes the user's past purchase history as input and outputs the most suitable materials and tools.

[0048] The proposal unit can present the optimal options when making a proposal, taking into account the user's budget and cost constraints. For example, the proposal unit can suggest the optimal materials and tools based on the user's budget. The proposal unit can also suggest cost-effective options, taking into account the user's cost constraints. The proposal unit can also present the optimal options based on the user's budget and cost constraints. For example, if the user inputs "I want to build a four-tier shelf here," the proposal unit will present the optimal options based on that request. In this way, the optimal options can be provided by taking the user's budget and cost constraints into consideration. Some or all of the above processing in the proposal unit may be performed using generative AI, or it may be performed without using generative AI. For example, the proposal unit can make a proposal using a generative AI model that takes the user's budget and cost constraints as input and outputs the optimal options.

[0049] The suggestion unit can propose region-specific materials and tools, taking into account the user's geographical location information when making a suggestion. For example, the suggestion unit can suggest local specialties as materials based on the user's location. The suggestion unit can also propose materials suitable for the local climate based on the user's geographical location information. The suggestion unit can also propose tools that are appropriate for the region, taking into account the user's geographical location information. For example, if the user inputs "I want to build a four-tiered shelf here," the suggestion unit will propose region-specific materials and tools based on that request. In this way, by proposing region-specific materials and tools while considering the user's geographical location information, it is possible to provide materials and tools that are appropriate for the region. Some or all of the above processing in the suggestion unit may be performed using generative AI, or it may be performed without using generative AI. For example, the suggestion unit can make suggestions using a generative AI model that takes the user's geographical location information as input and outputs region-specific materials and tools.

[0050] The suggestion function can analyze the user's social media activity and refer to reviews and ratings from other users when making suggestions. For example, the suggestion function can refer to reviews of DIY projects that the user follows. The suggestion function can also refer to reviews based on the user's "likes" and shares on social media. The suggestion function can analyze the user's social media activity and refer to ratings from other users. For example, if the user inputs "I want to build a four-tier shelf here," the suggestion function will make suggestions based on that request and refer to reviews and ratings from other users. In this way, by analyzing the user's social media activity, it is possible to provide suggestions that refer to reviews and ratings from other users. Some or all of the above processing in the suggestion function may be performed using generative AI, or not. For example, the suggestion function can make suggestions using a generative AI model that takes the user's social media activity as input and outputs reviews and ratings.

[0051] The simulation unit can provide an optimal simulation environment by referencing the user's past project data during simulation. For example, the simulation unit can provide an optimal simulation environment based on data from projects the user has created in the past. The simulation unit can also provide a simulation environment that suits the user's preferences from the user's past project data. The simulation unit can also analyze the user's past project data and provide the most efficient simulation environment. For example, if the user inputs "I want to build a four-tiered shelf here," the simulation unit will provide an optimal simulation environment based on that request. In this way, the simulation unit can provide an optimal simulation environment by referencing the user's past project data. Some or all of the above processing in the simulation unit may be performed using generative AI, or it may be performed without using generative AI. For example, the simulation unit can perform a simulation using a generative AI model that takes the user's past project data as input and outputs an optimal simulation environment.

[0052] The simulation unit can enhance functionality during simulation by considering the user's intended use and lifestyle. For example, the simulation unit can provide a simulation suitable for spaces frequently used by the user. The simulation unit can also provide a simulation with enhanced storage functionality based on the user's lifestyle. The simulation unit can also provide simulations with specific functions depending on the user's intended use. For example, if the user inputs "I want to build a four-tier shelf here," the simulation unit will provide a simulation with enhanced functionality based on that request. This allows for the provision of more practical simulations by considering the user's intended use and lifestyle. Some or all of the above processing in the simulation unit may be performed using generative AI, or without generative AI. For example, the simulation unit can perform simulations using a generative AI model that takes the user's intended use and lifestyle as input and outputs a simulation with enhanced functionality.

[0053] The simulation unit can reproduce a region-specific environment by considering the user's geographical location information during simulation. For example, the simulation unit can reproduce an optimal environment by considering the climate of the area where the user lives. The simulation unit can also reproduce an environment that suits the characteristics of the region based on the user's geographical location information. The simulation unit can also reproduce an environment that suits the culture of the region by considering the user's geographical location information. For example, if the user inputs "I want to build a four-tiered shelf here," the simulation unit will reproduce a region-specific environment based on that request. In this way, by reproducing a region-specific environment while considering the user's geographical location information, it is possible to provide an environment that is suitable for the region. Some or all of the above processing in the simulation unit may be performed using generative AI, or it may be performed without using generative AI. For example, the simulation unit can perform a simulation using a generative AI model that takes the user's geographical location information as input and outputs a region-specific environment.

[0054] The simulation unit can analyze the user's social media activity during simulation and refer to the success stories of other users. For example, the simulation unit can refer to the success stories of DIY projects that the user follows. The simulation unit can also refer to success stories based on the user's social media "likes" and shares history. The simulation unit can analyze the user's social media activity and refer to the success stories of other users. For example, if the user inputs "I want to build a four-tier shelf here," the simulation unit will perform a simulation based on that request, referencing the success stories of other users. In this way, by analyzing the user's social media activity, it is possible to provide a simulation that references the success stories of other users. Some or all of the above processing in the simulation unit may be performed using generative AI, or it may be performed without using generative AI. For example, the simulation unit can perform a simulation using a generative AI model that takes the user's social media activity as input and outputs success stories.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The generation unit can analyze the user's past project history and propose designs that are best suited to their preferences and skills. For example, based on data from projects the user has created in the past, the generation AI can propose designs in a similar style. The generation AI can also propose designs that match the user's skill level based on the user's past project history. Furthermore, the generation AI can propose optimal designs by considering the materials and tools the user has used in the past. In this way, by analyzing the user's past project history, it is possible to propose designs that are best suited to their preferences and skills. Some or all of the above processing in the generation unit may be performed using the generation AI, or it may be performed without the generation AI. For example, the generation unit can propose designs using a generation AI model that takes the user's past project data as input and outputs the optimal design.

[0057] The planning department can automatically generate the optimal process sequence by referring to past project data. For example, it can propose the most efficient process sequence based on past project data. It can also propose a process sequence with fewer failures based on past project data. Furthermore, it can analyze past project data and propose a process sequence that can be completed in the shortest time. In this way, the optimal process sequence can be automatically generated by referring to past project data. Some or all of the above processes in the planning department may be performed using AI, or they may not. For example, the planning department can automatically generate the process sequence using an AI model that takes past project data as input and outputs the optimal process sequence.

[0058] The suggestion unit can propose the most suitable materials and tools by referring to the user's past purchase history. For example, it can make optimal suggestions based on the materials and tools the user has purchased in the past. It can also suggest materials and tools of the same brand based on the user's past purchase history. Furthermore, it can analyze the user's past purchase history and propose the most suitable materials and tools. In this way, the optimal materials and tools can be proposed by referring to the user's past purchase history. Some or all of the above processing in the suggestion unit may be performed using generative AI, or it may be performed without generative AI. For example, the suggestion unit can make suggestions using a generative AI model that takes the user's past purchase history as input and outputs the optimal materials and tools.

[0059] The simulation unit can provide an optimal simulation environment by referencing the user's past project data during simulation. For example, it can provide an optimal simulation environment based on data from projects the user has created in the past. It can also provide a simulation environment tailored to the user's preferences based on the user's past project data. Furthermore, it can analyze the user's past project data and provide the most efficient simulation environment. In this way, the optimal simulation environment can be provided by referencing the user's past project data. Some or all of the above processing in the simulation unit may be performed using generative AI, or it may be performed without using generative AI. For example, the simulation unit can perform a simulation using a generative AI model that takes the user's past project data as input and outputs an optimal simulation environment.

[0060] The planning unit can customize the plan during the process planning stage, taking into account the user's schedule and time constraints. For example, it can propose the optimal work time based on the user's schedule. It can also propose an efficient process plan considering the user's time constraints. Furthermore, it can propose a flexible process plan that matches the user's schedule. In this way, by customizing the plan to take into account the user's schedule and time constraints, a more efficient process plan can be provided. Some or all of the above processes in the planning unit may be performed using AI, or they may not. For example, the planning unit can customize the plan using an AI model that takes the user's schedule and time constraints as input and outputs an optimal process plan.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The generation unit generates custom designs based on the user's preferences and skills. For example, if the user inputs their desired style and dimensions, the generation AI will automatically generate interior and furniture design proposals. It can also automatically generate custom designs based on the user's space constraints. For example, if the user inputs "I want to build a four-tier shelf here," the system will generate a design drawing based on that request. Step 2: The planning department automates the process during the planning phase of the DIY project. For example, based on the DIY project the user wants to undertake, the generating AI creates a step-by-step guide. Furthermore, based on the project design, the generating AI can automatically list the necessary materials and tools, and provide purchase links and recommended products. Step 3: The suggestion section proposes the optimal materials and procedures. For example, based on the DIY project the user wants to undertake, the generating AI creates a step-by-step guide. Alternatively, based on the project design, the generating AI can automatically list the necessary materials and tools, and provide purchase links and recommended products. Step 4: The simulation unit simulates the design proposal in a virtual environment. For example, a design proposal generated by AI can be simulated in a virtual environment, allowing for a preview of the final appearance and functionality.

[0063] (Example of form 2) The DIY support system according to an embodiment of the present invention is a system that utilizes generative AI to streamline the planning, design, and execution of DIY projects. This DIY support system creates unique designs based on the user's preferences, skills, and requests. It uses generative AI to propose custom designs for DIY projects. When the desired style and dimensions are entered, the generative AI automatically generates interior and furniture design proposals, and automatically generates custom designs based on the individual user's preferences and space constraints. This allows even beginners to obtain professional-quality designs. Next, in the planning stage of a DIY project, the generative AI is used as a service to automate processes and suggest optimal materials and procedures. For example, based on the DIY project the user wants to undertake, the generative AI creates a step-by-step guide. There is also a service that automatically lists the necessary materials and tools based on the project design and presents purchase links and recommended products. This significantly reduces the time spent selecting materials. Furthermore, the design proposal by the generative AI can be simulated in a virtual environment, allowing users to check the appearance and function of the finished product in advance. For example, if the user enters "I want to build a four-tier shelf here," the generative AI generates a design drawing based on that request and lists the necessary materials and tools. Based on the design plans, users can request wood cutting or receive suggestions for ready-made products. In this way, utilizing generative AI streamlines the planning, design, and execution of DIY projects, allowing even beginners to achieve professional-looking results. Furthermore, because the generative AI provides suggestions tailored to the user's skills and experience, it can solve issues such as lack of skills, cost overruns, exceeding deadlines, safety risks, and quality problems. As a result, DIY support systems can generate custom designs based on the user's preferences and skills, streamlining project planning, material selection, and simulation.

[0064] The DIY support system according to this embodiment comprises a generation unit, a planning unit, a proposal unit, and a simulation unit. The generation unit generates custom designs based on the user's preferences and skills. For example, when the user inputs desired style and dimensions, the generation AI automatically generates interior and furniture design proposals. The generation unit can also automatically generate custom designs based on the user's space constraints. For example, when the user inputs "I want to build a four-tier shelf here," the generation unit generates a design drawing based on that request. The planning unit automates the process during the planning stage of a DIY project. For example, the planning unit uses the generation AI to create a step-by-step guide based on the DIY project the user wants to undertake. The planning unit can also automatically list the necessary materials and tools based on the project design and provide purchase links and recommended products. The proposal unit proposes the optimal materials and procedures. For example, the proposal unit uses the generation AI to create a step-by-step guide based on the DIY project the user wants to undertake. The proposal unit can also automatically list the necessary materials and tools based on the project design and provide purchase links and recommended products. The simulation unit simulates the design proposal in a virtual environment. For example, the simulation unit can simulate the design proposal generated by the AI ​​in a virtual environment, allowing users to check the appearance and function of the finished product in advance. For example, if a user inputs "I want to build a four-tier shelf here," the AI ​​generates a design drawing based on that request and lists the necessary materials and tools. As a result, the DIY support system according to this embodiment can generate custom designs based on the user's preferences and skills, and streamline project planning, material selection, and simulation.

[0065] The generation unit generates custom designs based on the user's preferences and skills. For example, when a user inputs their desired style and dimensions, the generation AI automatically generates interior and furniture design proposals. Specifically, when a user inputs their desired design style (e.g., modern, classic, minimalist, etc.) and dimensions (height, width, depth, etc.) through the interface, the generation AI generates the optimal design proposal based on this information. The generation AI learns from past design data and trend information, allowing it to suggest designs that match the user's preferences. The generation unit can also automatically generate custom designs based on the user's space constraints. For example, if a user inputs "I want to build a four-tier shelf here," it will generate a design drawing based on that request. The generation AI analyzes the room dimensions and layout information provided by the user and proposes the optimal shelf design. Furthermore, the generation unit can also generate designs according to the user's skill level. It will suggest simple and easy-to-assemble designs for beginners and complex and challenging designs for advanced users. In this way, the generation unit can efficiently generate custom designs that meet the user's needs and constraints, supporting the success of DIY projects.

[0066] The planning department automates the process during the planning phase of DIY projects. For example, based on a DIY project the user wants to undertake, the planning department's generating AI creates a step-by-step guide. Specifically, when the user inputs a project overview and goals, the generating AI automatically generates detailed work procedures based on that information. Each step includes necessary materials and tools, estimated work time, and points to note, allowing the user to proceed with the work accordingly. The planning department can also automatically list the necessary materials and tools based on the project design, and provide purchase links and recommended products. For example, if a user is planning a project to build a shelf, the generating AI will list the necessary lumber, screws, paint, tools, etc., and provide purchase links for each. Furthermore, the planning department can track the project's progress in real time and notify the user of its progress and the next steps. In this way, the planning department can support users in efficiently and reliably carrying out their DIY projects, increasing the success rate of the project.

[0067] The suggestion section proposes the optimal materials and procedures. For example, based on the DIY project the user wants to undertake, the suggestion section uses a generative AI to create a step-by-step guide. Specifically, when the user inputs project details, the generative AI uses that information to suggest the optimal materials and procedures. For example, if the user wants to build a wooden shelf, the generative AI will suggest the optimal type and size of wood, the necessary tools, and the type of paint. The suggestion section can also automatically list the necessary materials and tools based on the project design and provide purchase links and recommended products. For example, after the user decides on the shelf design, the generative AI will list the necessary materials and tools based on that design and provide purchase links for each. Furthermore, the suggestion section can present multiple options according to the user's budget and preferences. For example, it will suggest cost-effective materials for users with limited budgets and high-quality materials for users who prioritize quality. In this way, the suggestion section can support users in selecting the optimal materials and procedures and proceeding with their DIY projects efficiently and effectively.

[0068] The simulation unit simulates design proposals in a virtual environment. For example, the simulation unit can simulate design proposals generated by AI in a virtual environment, allowing users to check the appearance and function of the finished product in advance. Specifically, the user imports a design proposal created by the AI ​​into the virtual environment and displays it as a 3D model. The user can freely rotate and zoom in and out of the design proposal within the virtual environment to check the details. The simulation unit also performs simulations to evaluate the functionality and practicality of the design proposal. For example, it can simulate the strength and stability of a shelf and evaluate its durability when actually used. Furthermore, the simulation unit can also simulate how the design proposal would look and how the space would be used if placed in an actual room. For example, if the user inputs room dimensions and layout information, the room can be reproduced in the virtual environment and the design proposal can be placed. This allows the user to check in advance how the design proposal will fit into the actual space and modify the design as needed. In this way, the simulation unit can provide support to ensure the success of the project by allowing users to check the appearance and function of the completed design proposal in advance.

[0069] The generation unit can automatically generate custom designs based on user preferences and space constraints. For example, the generation unit's AI can automatically generate interior and furniture design proposals based on user preferences and space constraints. When the user inputs their desired style and dimensions, the generation unit's AI automatically generates a custom design. The generation unit can also have the AI ​​suggest the optimal design based on the user's space constraints. For example, if the user inputs, "I want to build a four-tier shelf here," the generation unit will generate a design drawing based on that request. In this way, by automatically generating custom designs based on user preferences and space constraints, designs that meet individual needs can be provided. Some or all of the above-described processes in the generation unit may be performed using the generation AI, or they may not be performed using the generation AI. For example, the generation unit can generate custom designs using a generation AI model that takes user preferences and space constraints as input and outputs a custom design.

[0070] The planning department can automate the process of a DIY project. For example, based on the DIY project the user wants to undertake, the planning department's generating AI can create a step-by-step guide. Based on the project design, the planning department's generating AI can automatically list the necessary materials and tools and provide purchase links and recommended products. The planning department can also have the generating AI suggest the optimal process based on the DIY project the user wants to undertake. For example, if the user inputs "I want to build a four-tier shelf here," the planning department can automate the process based on that request. This automates the process of a DIY project, thereby improving work efficiency. Some or all of the above processes in the planning department may be performed using AI or not. For example, the planning department can take the user's DIY project information as input and automate the process using an AI model that automates the process.

[0071] The suggestion section can automatically list the necessary materials and tools based on the project design and provide purchase links and recommended products. For example, the suggestion section can use a generative AI to create a step-by-step guide based on a DIY project the user wants to undertake. The suggestion section can also use a generative AI to automatically list the necessary materials and tools based on the project design and provide purchase links and recommended products. The suggestion section can also use a generative AI to suggest the most suitable materials and tools based on a DIY project the user wants to undertake. For example, if the user inputs "I want to build a four-tier shelf here," the suggestion section will list the necessary materials and tools based on that request. This reduces the time spent selecting materials by automatically listing the necessary materials and tools and providing purchase links and recommended products. Some or all of the above processes in the suggestion section may be performed using generative AI, or they may not. For example, the suggestion section can use a generative AI model that takes the project design as input and outputs the necessary materials and tools to list them.

[0072] The simulation unit simulates design proposals generated by the generation AI in a virtual environment, allowing users to check the appearance and function of the completed product in advance. For example, the simulation unit simulates design proposals generated by the generation AI in a virtual environment, allowing users to check the appearance and function of the completed product in advance. When a user inputs "I want to build a four-tiered shelf here," the simulation unit generates a design drawing based on that request and simulates it in a virtual environment. The simulation unit uses the generation AI to simulate design proposals in a virtual environment, allowing users to check the appearance and function of the completed product in advance. For example, the simulation unit uses the generation AI to perform a simulation in a virtual environment based on the design proposal and provides the user with visual feedback. This allows users to check the appearance and function of the completed product in advance by simulating the design proposal in a virtual environment. Some or all of the above-described processes in the simulation unit may be performed using the generation AI, or they may not be performed using the generation AI. For example, the simulation unit can perform a simulation using a generation AI model that takes a design proposal as input and outputs simulation results in a virtual environment.

[0073] The suggestion function can provide suggestions tailored to the user's skills and experience. For example, the suggestion function can use a generative AI to suggest the optimal materials and tools based on the user's skills and experience. The suggestion function can also use a generative AI to create step-by-step guides based on the user's skills and experience. The suggestion function can also use a generative AI to suggest the optimal procedure based on the user's skills and experience. For example, if the user inputs "I want to build a four-tiered shelf here," the suggestion function will list the optimal materials and tools based on that request. By providing suggestions tailored to the user's skills and experience, it is possible to solve problems such as lack of skills, cost overruns, exceeding deadlines, safety risks, and quality issues. Some or all of the above processes in the suggestion function may be performed using AI or not. For example, the suggestion function can use an AI model that takes the user's skills and experience as input and outputs the optimal suggestion to provide suggestions.

[0074] The generation unit can estimate the user's emotions and adjust the design style and colors based on the estimated emotions. For example, if the user is relaxed, the generation AI may suggest a design with calming colors. If the user is excited, the generation AI may suggest a design using vibrant colors. If the user is stressed, the generation AI may suggest a simple and visually calming design. By adjusting the design style and colors based on the user's emotions, it is possible to provide designs that better suit individual needs. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generation AI or not. For example, the generation unit can adjust the design using a generation AI model that takes user emotion data as input and outputs design styles and colors.

[0075] The generation unit can analyze the user's past project history and propose designs that are best suited to their preferences and skills. For example, the generation unit can use data from projects the user has created in the past to have the generation AI propose designs in a similar style. The generation unit can also use the user's past project history to have the generation AI propose designs that match the user's skill level. The generation unit can also consider the materials and tools the user has used in the past to have the generation AI propose the best design. For example, if the user inputs "I want to build a four-tiered shelf here," the generation unit will propose the best design based on that request. In this way, by analyzing the user's past project history, it can propose designs that are best suited to their preferences and skills. Some or all of the above processes in the generation unit may be performed using the generation AI, or they may not be performed using the generation AI. For example, the generation unit can propose designs using a generation AI model that takes the user's past project data as input and outputs the best design.

[0076] The generation unit can enhance functionality based on the user's lifestyle and intended use during design generation. For example, the generation unit's AI can propose a design suitable for spaces frequently used by the user. The generation unit can also propose a design with enhanced storage functionality based on the user's lifestyle. Depending on the user's intended use, the generation unit's AI can propose a design with specific functions. For example, if the user inputs "I want to build a four-tier shelf here," the generation unit will propose a design with enhanced functionality based on that request. This allows for the provision of more practical designs by enhancing functionality based on the user's lifestyle and intended use. Some or all of the above-described processes in the generation unit may be performed using the generation AI, or they may not be performed using the generation AI. For example, the generation unit can generate designs using a generation AI model that takes the user's lifestyle and intended use as input and outputs a design with enhanced functionality.

[0077] The generation unit can estimate the user's emotions and adjust the complexity of the design based on the estimated emotions. For example, if the user is relaxed, the generation unit can suggest a complex design using the generation AI. If the user is stressed, the generation unit can suggest a simple design using the generation AI. If the user is excited, the generation unit can suggest a visually stimulating design using the generation AI. This allows for the provision of designs that better suit individual needs by adjusting the complexity of the design based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using or without the generation AI. For example, the generation unit can adjust the design using a generation AI model that takes user emotion data as input and outputs design complexity.

[0078] The generation unit can incorporate region-specific design elements by considering the user's geographical location information when generating designs. For example, the generation unit's generation AI can incorporate traditional design elements from the area where the user lives. The generation unit can also have the generation AI propose designs suitable for the local climate based on the user's geographical location information. The generation unit can also have the generation AI propose designs that are in line with the local culture by considering the user's geographical location information. For example, if the user inputs "I want to build a four-tiered shelf here," the generation unit will propose a design incorporating region-specific design elements based on that request. In this way, by incorporating region-specific design elements while considering the user's geographical location information, designs suitable for the region can be provided. Some or all of the above-described processes in the generation unit may be performed using the generation AI, or they may be performed without the generation AI. For example, the generation unit can generate designs using a generation AI model that takes the user's geographical location information as input and outputs region-specific design elements.

[0079] The generation unit can analyze the user's social media activity and propose trend-based designs during design generation. For example, the generation unit's generation AI can incorporate the design styles of influencers the user follows. The generation unit can also propose trend-appropriate designs based on the user's social media "likes" and shares history. The generation unit can analyze the user's social media activity and propose designs that reflect the latest trends. For example, if the user inputs "I want to build a four-tiered shelf here," the generation unit will propose a trend-appropriate design based on that request. In this way, by analyzing the user's social media activity, it is possible to propose trend-based designs. Some or all of the above processes in the generation unit may be performed using the generation AI, or they may not be performed using the generation AI. For example, the generation unit can propose designs using a generation AI model that takes the user's social media activity as input and outputs trend-based designs.

[0080] The planning unit can estimate the user's emotions and adjust the process speed based on the estimated emotions. For example, if the user is relaxed, the planning unit may suggest a relaxed pace. If the user is in a hurry, the planning unit may suggest a faster pace. If the user is stressed, the planning unit may suggest a pace that includes appropriate breaks. This allows for a more appropriate pace by adjusting the process speed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the planning unit may be performed using AI or not. For example, the planning unit can adjust the speed using an AI model that takes user emotion data as input and outputs the process speed.

[0081] The planning department can automatically generate the optimal process sequence by referring to past project data. For example, the planning department can propose the most efficient process sequence based on past project data. The planning department can also propose a process sequence with fewer failures based on past project data. The planning department can also analyze past project data and propose a process sequence that can be completed in the shortest time. For example, if a user inputs "I want to build a four-tiered shelf here," the planning department will automatically generate the optimal process sequence based on that request. In this way, the optimal process sequence can be automatically generated by referring to past project data. Some or all of the above processes in the planning department may be performed using AI or not. For example, the planning department can automatically generate a process sequence using an AI model that takes past project data as input and outputs the optimal process sequence.

[0082] The planning unit can customize the plan when creating a project, taking into account the user's schedule and time constraints. For example, the planning unit can propose the optimal work time based on the user's schedule. The planning unit can also propose an efficient project plan considering the user's time constraints. The planning unit can also propose a flexible project plan that fits the user's schedule. For example, if the user inputs "I want to build a four-tiered shelf here," the planning unit will propose the optimal work time based on that request. In this way, by customizing the plan to take into account the user's schedule and time constraints, a more efficient project plan can be provided. Some or all of the above processes in the planning unit may be performed using AI, or not. For example, the planning unit can customize the plan using an AI model that takes the user's schedule and time constraints as input and outputs an optimal project plan.

[0083] The planning unit can estimate the user's emotions and determine process priorities based on those emotions. For example, if the user is relaxed, the planning unit may prioritize complex processes. If the user is stressed, the planning unit may prioritize simpler processes. If the user is in a hurry, the planning unit may postpone time-consuming processes. This allows for more appropriate prioritization by determining process priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the planning unit may be performed using AI or not. For example, the planning unit may determine priorities using an AI model that takes user emotion data as input and outputs process priorities.

[0084] The planning department can propose an optimal work environment when planning a project, taking into account the user's geographical location. For example, the planning department can propose an optimal work environment considering the climate of the area where the user lives. The planning department can also propose a work environment suited to the characteristics of the region based on the user's geographical location. The planning department can also propose an optimal work location considering the user's geographical location. For example, if the user inputs "I want to build a four-tiered shelf here," the planning department will propose an optimal work environment based on that request. In this way, by proposing an optimal work environment considering the user's geographical location, it is possible to provide a work environment suitable for the region. Some or all of the above processes in the planning department may be performed using AI, or they may not be performed using AI. For example, the planning department can propose a work environment using an AI model that takes the user's geographical location as input and outputs an optimal work environment.

[0085] The planning department can analyze the user's social media activity and refer to the success stories of other users when planning a project. For example, the planning department can refer to the success stories of DIY projects that the user follows. The planning department can also suggest success stories based on the user's "likes" and shares on social media. The planning department can analyze the user's social media activity and refer to the success stories of other users. For example, if the planning department inputs "I want to build a four-tier shelf here," it will suggest a project plan based on that request and referencing the success stories of other users. In this way, by analyzing the user's social media activity, it is possible to provide a project plan that references the success stories of other users. Some or all of the above processes in the planning department may be performed using AI or not. For example, the planning department can suggest a project plan using an AI model that takes the user's social media activity as input and outputs success stories.

[0086] The suggestion unit can estimate the user's emotions and adjust the suggestions based on those emotions. For example, if the user is relaxed, the suggestion unit can provide detailed suggestions. If the user is in a hurry, the suggestion unit can provide concise suggestions. If the user is stressed, the suggestion unit can provide simple suggestions. By adjusting the suggestions based on the user's emotions, it is possible to provide more appropriate suggestions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using AI or not. For example, the suggestion unit can adjust the suggestions using an AI model that takes user emotion data as input and outputs suggestions.

[0087] The suggestion unit can suggest the most suitable materials and tools by referring to the user's past purchase history when making a suggestion. For example, the suggestion unit can make the best suggestion based on the materials and tools the user has purchased in the past. The suggestion unit can also suggest materials and tools of the same brand based on the user's past purchase history. The suggestion unit can also analyze the user's past purchase history and suggest the most suitable materials and tools. For example, if the user inputs "I want to build a four-tier shelf here," the suggestion unit will suggest the most suitable materials and tools based on that request. In this way, the suggestion unit can suggest the most suitable materials and tools by referring to the user's past purchase history. Some or all of the above processing in the suggestion unit may be performed using generative AI, or it may be performed without generative AI. For example, the suggestion unit can make suggestions using a generative AI model that takes the user's past purchase history as input and outputs the most suitable materials and tools.

[0088] The proposal unit can present the optimal options when making a proposal, taking into account the user's budget and cost constraints. For example, the proposal unit can suggest the optimal materials and tools based on the user's budget. The proposal unit can also suggest cost-effective options, taking into account the user's cost constraints. The proposal unit can also present the optimal options based on the user's budget and cost constraints. For example, if the user inputs "I want to build a four-tier shelf here," the proposal unit will present the optimal options based on that request. In this way, the optimal options can be provided by taking the user's budget and cost constraints into consideration. Some or all of the above processing in the proposal unit may be performed using generative AI, or it may be performed without using generative AI. For example, the proposal unit can make a proposal using a generative AI model that takes the user's budget and cost constraints as input and outputs the optimal options.

[0089] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on the estimated emotions. For example, if the user is relaxed, the suggestion unit may prioritize complex suggestions. If the user is stressed, the suggestion unit may also prioritize simple suggestions. If the user is in a hurry, the suggestion unit may also prioritize suggestions that can be acted upon quickly. This allows for more appropriate prioritization by determining the priority of suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using generative AI or not. For example, the suggestion unit may determine priorities using a generative AI model that takes user emotion data as input and outputs the priority of suggestions.

[0090] The suggestion unit can propose region-specific materials and tools, taking into account the user's geographical location information when making a suggestion. For example, the suggestion unit can suggest local specialties as materials based on the user's location. The suggestion unit can also propose materials suitable for the local climate based on the user's geographical location information. The suggestion unit can also propose tools that are appropriate for the region, taking into account the user's geographical location information. For example, if the user inputs "I want to build a four-tiered shelf here," the suggestion unit will propose region-specific materials and tools based on that request. In this way, by proposing region-specific materials and tools while considering the user's geographical location information, it is possible to provide materials and tools that are appropriate for the region. Some or all of the above processing in the suggestion unit may be performed using generative AI, or it may be performed without using generative AI. For example, the suggestion unit can make suggestions using a generative AI model that takes the user's geographical location information as input and outputs region-specific materials and tools.

[0091] The suggestion function can analyze the user's social media activity and refer to reviews and ratings from other users when making suggestions. For example, the suggestion function can refer to reviews of DIY projects that the user follows. The suggestion function can also refer to reviews based on the user's "likes" and shares on social media. The suggestion function can analyze the user's social media activity and refer to ratings from other users. For example, if the user inputs "I want to build a four-tier shelf here," the suggestion function will make suggestions based on that request and refer to reviews and ratings from other users. In this way, by analyzing the user's social media activity, it is possible to provide suggestions that refer to reviews and ratings from other users. Some or all of the above processing in the suggestion function may be performed using generative AI, or not. For example, the suggestion function can make suggestions using a generative AI model that takes the user's social media activity as input and outputs reviews and ratings.

[0092] The simulation unit can estimate the user's emotions and adjust the display method of the simulation based on the estimated user emotions. For example, if the user is relaxed, the simulation unit can provide a detailed simulation. If the user is in a hurry, the simulation unit can also provide a concise simulation. If the user is stressed, the simulation unit can also provide a simple simulation. This allows for the provision of a more appropriate simulation by adjusting the display method of the simulation based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the simulation unit may be performed using generative AI or not. For example, the simulation unit can adjust the display method using a generative AI model that takes user emotion data as input and outputs the display method of the simulation.

[0093] The simulation unit can provide an optimal simulation environment by referencing the user's past project data during simulation. For example, the simulation unit can provide an optimal simulation environment based on data from projects the user has created in the past. The simulation unit can also provide a simulation environment that suits the user's preferences from the user's past project data. The simulation unit can also analyze the user's past project data and provide the most efficient simulation environment. For example, if the user inputs "I want to build a four-tiered shelf here," the simulation unit will provide an optimal simulation environment based on that request. In this way, the simulation unit can provide an optimal simulation environment by referencing the user's past project data. Some or all of the above processing in the simulation unit may be performed using generative AI, or it may be performed without using generative AI. For example, the simulation unit can perform a simulation using a generative AI model that takes the user's past project data as input and outputs an optimal simulation environment.

[0094] The simulation unit can enhance functionality during simulation by considering the user's intended use and lifestyle. For example, the simulation unit can provide a simulation suitable for spaces frequently used by the user. The simulation unit can also provide a simulation with enhanced storage functionality based on the user's lifestyle. The simulation unit can also provide simulations with specific functions depending on the user's intended use. For example, if the user inputs "I want to build a four-tier shelf here," the simulation unit will provide a simulation with enhanced functionality based on that request. This allows for the provision of more practical simulations by considering the user's intended use and lifestyle. Some or all of the above processing in the simulation unit may be performed using generative AI, or without generative AI. For example, the simulation unit can perform simulations using a generative AI model that takes the user's intended use and lifestyle as input and outputs a simulation with enhanced functionality.

[0095] The simulation unit can estimate the user's emotions and determine the priority of simulations based on the estimated emotions. For example, if the user is relaxed, the simulation unit may prioritize complex simulations. If the user is stressed, the simulation unit may also prioritize simpler simulations. If the user is in a hurry, the simulation unit may also prioritize simulations that can be executed quickly. This allows for more appropriate prioritization by determining simulation priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the simulation unit may be performed using generative AI or not. For example, the simulation unit may determine priorities using a generative AI model that takes user emotion data as input and outputs simulation priorities.

[0096] The simulation unit can reproduce a region-specific environment by considering the user's geographical location information during simulation. For example, the simulation unit can reproduce an optimal environment by considering the climate of the area where the user lives. The simulation unit can also reproduce an environment that suits the characteristics of the region based on the user's geographical location information. The simulation unit can also reproduce an environment that suits the culture of the region by considering the user's geographical location information. For example, if the user inputs "I want to build a four-tiered shelf here," the simulation unit will reproduce a region-specific environment based on that request. In this way, by reproducing a region-specific environment while considering the user's geographical location information, it is possible to provide an environment that is suitable for the region. Some or all of the above processing in the simulation unit may be performed using generative AI, or it may be performed without using generative AI. For example, the simulation unit can perform a simulation using a generative AI model that takes the user's geographical location information as input and outputs a region-specific environment.

[0097] The simulation unit can analyze the user's social media activity during simulation and refer to the success stories of other users. For example, the simulation unit can refer to the success stories of DIY projects that the user follows. The simulation unit can also refer to success stories based on the user's social media "likes" and shares history. The simulation unit can analyze the user's social media activity and refer to the success stories of other users. For example, if the user inputs "I want to build a four-tier shelf here," the simulation unit will perform a simulation based on that request, referencing the success stories of other users. In this way, by analyzing the user's social media activity, it is possible to provide a simulation that references the success stories of other users. Some or all of the above processing in the simulation unit may be performed using generative AI, or it may be performed without using generative AI. For example, the simulation unit can perform a simulation using a generative AI model that takes the user's social media activity as input and outputs success stories.

[0098] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0099] The generation unit can estimate the user's emotions and adjust the design style and colors based on the estimated emotions. For example, if the user is relaxed, the generation AI can suggest a design with calming colors. If the user is excited, the generation AI can also suggest a design using vibrant colors. Furthermore, if the user is stressed, the generation AI can suggest a simple and visually calming design. By adjusting the design style and colors based on the user's emotions, it is possible to provide designs that better suit individual needs. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generation AI or not. For example, the generation unit can adjust the design using a generation AI model that takes user emotion data as input and outputs design styles and colors.

[0100] The planning unit can estimate the user's emotions and adjust the process speed based on the estimated emotions. For example, if the user is relaxed, the planning unit can suggest a relaxed pace. If the user is in a hurry, the planning unit can suggest a faster pace. Furthermore, if the user is stressed, the planning unit can suggest a pace that includes appropriate breaks. This allows for a more appropriate pace to be provided by adjusting the process speed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the planning unit may be performed using AI or not. For example, the planning unit can adjust the pace using an AI model that takes user emotion data as input and outputs a process speed.

[0101] The suggestion unit can estimate the user's emotions and adjust the suggestions based on those emotions. For example, if the user is relaxed, it can provide detailed suggestions. If the user is in a hurry, it can provide concise suggestions. Furthermore, if the user is stressed, it can provide simple suggestions. By adjusting the suggestions based on the user's emotions, it is possible to provide more appropriate suggestions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit can adjust the suggestions using an AI model that takes user emotion data as input and outputs suggestions.

[0102] The simulation unit can estimate the user's emotions and adjust the simulation display method based on the estimated user emotions. For example, if the user is relaxed, it can provide a detailed simulation. If the user is in a hurry, it can provide a concise simulation. Furthermore, if the user is stressed, it can provide a simple simulation. In this way, by adjusting the simulation display method based on the user's emotions, a more appropriate simulation can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the simulation unit may be performed using generative AI or not. For example, the simulation unit can adjust the display method using a generative AI model that takes user emotion data as input and outputs the simulation display method.

[0103] The suggestion unit can estimate the user's emotions and determine the priority of suggestions based on those emotions. For example, if the user is relaxed, it may prioritize complex suggestions. If the user is stressed, it may prioritize simple suggestions. Furthermore, if the user is in a hurry, it may prioritize suggestions that can be executed quickly. This allows for more appropriate prioritization by determining the priority of suggestions based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the suggestion unit may be performed using generative AI or not. For example, the suggestion unit can determine priorities using a generative AI model that takes user emotion data as input and outputs the priority of suggestions.

[0104] The generation unit can analyze the user's past project history and propose designs that are best suited to their preferences and skills. For example, based on data from projects the user has created in the past, the generation AI can propose designs in a similar style. The generation AI can also propose designs that match the user's skill level based on the user's past project history. Furthermore, the generation AI can propose optimal designs by considering the materials and tools the user has used in the past. In this way, by analyzing the user's past project history, it is possible to propose designs that are best suited to their preferences and skills. Some or all of the above processing in the generation unit may be performed using the generation AI, or it may be performed without the generation AI. For example, the generation unit can propose designs using a generation AI model that takes the user's past project data as input and outputs the optimal design.

[0105] The planning department can automatically generate the optimal process sequence by referring to past project data. For example, it can propose the most efficient process sequence based on past project data. It can also propose a process sequence with fewer failures based on past project data. Furthermore, it can analyze past project data and propose a process sequence that can be completed in the shortest time. In this way, the optimal process sequence can be automatically generated by referring to past project data. Some or all of the above processes in the planning department may be performed using AI, or they may not. For example, the planning department can automatically generate the process sequence using an AI model that takes past project data as input and outputs the optimal process sequence.

[0106] The suggestion unit can propose the most suitable materials and tools by referring to the user's past purchase history. For example, it can make optimal suggestions based on the materials and tools the user has purchased in the past. It can also suggest materials and tools of the same brand based on the user's past purchase history. Furthermore, it can analyze the user's past purchase history and propose the most suitable materials and tools. In this way, the optimal materials and tools can be proposed by referring to the user's past purchase history. Some or all of the above processing in the suggestion unit may be performed using generative AI, or it may be performed without generative AI. For example, the suggestion unit can make suggestions using a generative AI model that takes the user's past purchase history as input and outputs the optimal materials and tools.

[0107] The simulation unit can provide an optimal simulation environment by referencing the user's past project data during simulation. For example, it can provide an optimal simulation environment based on data from projects the user has created in the past. It can also provide a simulation environment tailored to the user's preferences based on the user's past project data. Furthermore, it can analyze the user's past project data and provide the most efficient simulation environment. In this way, the optimal simulation environment can be provided by referencing the user's past project data. Some or all of the above processing in the simulation unit may be performed using generative AI, or it may be performed without using generative AI. For example, the simulation unit can perform a simulation using a generative AI model that takes the user's past project data as input and outputs an optimal simulation environment.

[0108] The planning unit can customize the plan during the process planning stage, taking into account the user's schedule and time constraints. For example, it can propose the optimal work time based on the user's schedule. It can also propose an efficient process plan considering the user's time constraints. Furthermore, it can propose a flexible process plan that matches the user's schedule. In this way, by customizing the plan to take into account the user's schedule and time constraints, a more efficient process plan can be provided. Some or all of the above processes in the planning unit may be performed using AI, or they may not. For example, the planning unit can customize the plan using an AI model that takes the user's schedule and time constraints as input and outputs an optimal process plan.

[0109] The following briefly describes the processing flow for example form 2.

[0110] Step 1: The generation unit generates custom designs based on the user's preferences and skills. For example, if the user inputs their desired style and dimensions, the generation AI will automatically generate interior and furniture design proposals. It can also automatically generate custom designs based on the user's space constraints. For example, if the user inputs "I want to build a four-tier shelf here," the system will generate a design drawing based on that request. Step 2: The planning department automates the process during the planning phase of the DIY project. For example, based on the DIY project the user wants to undertake, the generating AI creates a step-by-step guide. Furthermore, based on the project design, the generating AI can automatically list the necessary materials and tools, and provide purchase links and recommended products. Step 3: The suggestion section proposes the optimal materials and procedures. For example, based on the DIY project the user wants to undertake, the generating AI creates a step-by-step guide. Alternatively, based on the project design, the generating AI can automatically list the necessary materials and tools, and provide purchase links and recommended products. Step 4: The simulation unit simulates the design proposal in a virtual environment. For example, a design proposal generated by AI can be simulated in a virtual environment, allowing for a preview of the final appearance and functionality.

[0111] 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.

[0112] Data generation model 58 is a form of 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> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. 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 (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0113] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0114] Each of the multiple elements described above, including the generation unit, planning unit, proposal unit, and simulation unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the generation unit is implemented by the control unit 46A of the smart device 14 and generates a custom design based on the user's preferences and skills. The planning unit is implemented by the specific processing unit 290 of the data processing unit 12 and automates the process during the planning stage of a DIY project. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the optimal materials and procedures. The simulation unit is implemented by the control unit 46A of the smart device 14 and simulates the design proposal in a virtual environment. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0115] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0116] 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.

[0117] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0118] 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.

[0119] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0120] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0121] 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.

[0122] 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 by the processor 28. The storage 32 stores the specific processing program 56.

[0123] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0124] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0125] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0126] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0127] 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.

[0128] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0129] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0130] Each of the multiple elements described above, including the generation unit, planning unit, proposal unit, and simulation unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the generation unit is implemented by the control unit 46A of the smart glasses 214 and generates a custom design based on the user's preferences and skills. The planning unit is implemented by the specific processing unit 290 of the data processing unit 12 and automates the process during the planning stage of a DIY project. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the optimal materials and procedures. The simulation unit is implemented by the control unit 46A of the smart glasses 214 and simulates the design proposal in a virtual environment. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0131] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0132] 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.

[0133] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0134] 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.

[0135] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0136] 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, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0137] 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.

[0138] 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.

[0139] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0140] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0141] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0142] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0143] 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.

[0144] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0145] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0146] Each of the multiple elements described above, including the generation unit, planning unit, proposal unit, and simulation unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the generation unit is implemented by the control unit 46A of the headset terminal 314 and generates a custom design based on the user's preferences and skills. The planning unit is implemented by the specific processing unit 290 of the data processing unit 12 and automates the process during the planning stage of a DIY project. The proposal unit is implemented by the specific processing unit 290 of the data processing unit 12 and proposes the optimal materials and procedures. The simulation unit is implemented by the control unit 46A of the headset terminal 314 and simulates the design proposal in a virtual environment. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0147] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0148] 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.

[0149] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. 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 and / or LAN.

[0150] 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.

[0151] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, 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.

[0152] 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 image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0153] 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.

[0154] 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. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0155] 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.

[0156] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0157] 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. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0158] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0159] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0160] 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.

[0161] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. 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 inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0162] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0163] Each of the multiple elements described above, including the generation unit, planning unit, proposal unit, and simulation unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the generation unit is implemented by the control unit 46A of the robot 414 and generates a custom design based on the user's preferences and skills. The planning unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automates the process during the planning phase of a DIY project. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes the optimal materials and procedures. The simulation unit is implemented by, for example, the control unit 46A of the robot 414 and simulates the design proposal in a virtual environment. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0164] 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.

[0165] Figure 9 shows the 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.

[0166] 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.

[0167] 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.

[0168] 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, and motorcycles, 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 based, for example, 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.

[0169] 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."

[0170] 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.

[0171] 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 method for the specific process may be used, which includes computer 22 and multiple other computers.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0180] 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 other things 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.

[0181] 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.

[0182] (Note 1) A generation unit that generates custom designs based on the user's preferences and skills, The planning department automates the process during the project planning phase, The proposal department suggests the optimal materials and procedures, It includes a simulation unit that simulates design proposals in a virtual environment. A system characterized by the following features. (Note 2) The generating unit is Automatically generates custom designs based on user preferences and space constraints. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned planning department, Automate the process of a DIY project. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, Based on the project design, it automatically lists the necessary materials and tools, and provides purchase links and recommended products. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned simulation unit, The AI-generated design proposals are simulated in a virtual environment, allowing users to check the appearance and functionality of the final product in advance. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned proposal section is, We provide suggestions tailored to the user's skills and experience. The system described in Appendix 1, characterized by the features described herein. (Note 7) The generating unit is It estimates the user's emotions and adjusts the design style and colors based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The generating unit is We analyze the user's past project history and suggest designs that are best suited to their preferences and skills. The system described in Appendix 1, characterized by the features described herein. (Note 9) The generating unit is During design generation, functionality is enhanced based on the user's lifestyle and intended use. The system described in Appendix 1, characterized by the features described herein. (Note 10) The generating unit is It estimates the user's emotions and adjusts the complexity of the design based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is When generating designs, the design incorporates region-specific design elements, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is During the design generation process, we analyze users' social media activity and propose designs based on current trends. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned planning department, It estimates the user's emotions and adjusts the process speed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned planning department, By referring to past project data, the optimal process sequence is automatically generated. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned planning department, During the process planning stage, customize the plan to take into account the user's schedule and time constraints. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned planning department, The system estimates the user's emotions and determines the priority of processes based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned planning department, When planning the process, we propose the optimal work environment by taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned planning department, During the process planning stage, analyze users' social media activity and refer to the success stories of other users. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, It estimates the user's emotions and adjusts the suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, we refer to the user's past purchase history to suggest the most suitable materials and tools. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, When making a proposal, we will present the best option while considering the user's budget and cost constraints. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, It estimates the user's emotions and determines the priority of suggestions based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making a proposal, we take the user's geographical location into consideration and suggest region-specific materials and tools. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned proposal section is, When making a proposal, we analyze the user's social media activity and refer to reviews and ratings from other users. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned simulation unit, It estimates the user's emotions and adjusts how the simulation is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned simulation unit, During simulation, the system provides an optimal simulation environment by referencing the user's past project data. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned simulation unit, During simulation, functionality is enhanced by considering the user's intended use and lifestyle. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned simulation unit, It estimates the user's emotions and determines the priority of simulations based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned simulation unit, During simulation, the system takes into account the user's geographical location to reproduce the region-specific environment. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned simulation unit, During the simulation, we analyze the user's social media activity and refer to the success stories of other users. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0183] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A generation unit that generates custom designs based on the user's preferences and skills, The planning department automates the process during the project planning phase, The proposal department suggests the optimal materials and procedures, It includes a simulation unit that simulates design proposals in a virtual environment. A system characterized by the following features.

2. The generating unit is Automatically generates custom designs based on user preferences and space constraints. The system according to feature 1.

3. The aforementioned planning department, Automate the process of a DIY project. The system according to feature 1.

4. The aforementioned proposal section is, Based on the project design, it automatically lists the necessary materials and tools, and provides purchase links and recommended products. The system according to feature 1.

5. The aforementioned simulation unit, The AI-generated design proposals are simulated in a virtual environment, allowing for a preview of the final appearance and functionality. The system according to feature 1.

6. The aforementioned proposal section is, We provide suggestions tailored to the user's skills and experience. The system according to feature 1.

7. The generating unit is It estimates the user's emotions and adjusts the design style and colors based on those estimated emotions. The system according to feature 1.

8. The generating unit is We analyze the user's past project history and suggest designs that are best suited to their preferences and skills. The system according to feature 1.

9. The generating unit is During design generation, functionality is enhanced based on the user's lifestyle and intended use. The system according to feature 1.

10. The generating unit is It estimates the user's emotions and adjusts the complexity of the design based on those estimated emotions. The system according to feature 1.

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