system

Abstract

An object of a system according to an embodiment is to enable a user to intuitively operate a CAD system.SOLUTION: A system according to an embodiment includes a generation AI, a chat bot UI, a logic implementing unit, and a design concretizing unit. The generation AI integrates various functions of the CAD system. The chatbot UI allows the user to intuitively operate the CAD system. The logic implementing unit executes a CAD operation based on a user's request received by the chatbot UI. The design concretization unit sets a design element of the chatbot UI.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has the problem that the operation of CAD systems is complicated and it is difficult for users to operate them intuitively.

[0005] The system according to the embodiment aims to enable a user to intuitively operate a CAD system. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation AI, a chatbot UI, a logic implementation unit, and a design realization unit. The generation AI integrates various functions of a CAD system. The chatbot UI allows a user to intuitively operate the CAD system. The logic implementation unit executes CAD operations based on user requests received by the chatbot UI. The design realization unit sets design elements for the chatbot UI. [Effects of the Invention]

[0007] The system according to the embodiment can enable a user to intuitively operate a CAD system. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[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. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) The design support system according to an embodiment of the present invention is a system that uses a generative AI to connect CAD systems and perform design more intuitively and efficiently. This design support system allows the user to intuitively operate the structural calculation and design processes by specifying them with the generative AI. This allows the user to intuitively operate the design support system while performing structural calculations and design.

[0029] A design support system according to an embodiment includes a generation AI, a chatbot UI, a logic implementation unit, and a design realization unit. The generation AI integrates various functions of a CAD system. For example, the generation AI enables centralized operation of basic functions such as creating and editing shapes, entering dimensions, and moving, rotating, and scaling objects. The generation AI also integrates advanced functions such as automatic structural calculation and design generation. The chatbot UI enables users to intuitively operate the CAD system. For example, the chatbot UI arranges elements such as buttons and menus to allow users to easily select functions. The logic implementation unit executes CAD operations based on user requests received by the chatbot UI. For example, when a user instructs the system to "rotate this object," the logic implementation unit analyzes the instruction and executes the operation to rotate the object. The design realization unit sets design elements for the chatbot UI. For example, the design realization unit sets design elements such as color palettes and fonts, and also designs the appearance and icons of the CAD window. As a result, the design support system according to an embodiment provides a CAD system that users can intuitively operate, improving the efficiency of design work.

[0030] Generative AI can analyze a user's past design history and propose optimal design patterns. For example, generative AI can analyze a user's past design history and identify frequently used design patterns and techniques. For example, it can extract specific architectural styles and design elements and propose new design patterns based on them. Generative AI learns the design patterns preferred by the user based on past design data and proposes optimal design patterns. This improves design efficiency by proposing optimal design patterns based on the user's past design history.

[0031] Generative AI can monitor the progress of a design in real time and automatically detect and correct errors and inconsistencies. For example, generative AI can monitor the progress of a design in real time and automatically detect errors and inconsistencies in design drawings. For example, it can identify incorrect dimensions or overlapping objects and suggest corrections. Generative AI implements algorithms that automatically correct errors and inconsistencies that occur as the design progresses. For example, it can detect inconsistencies in design drawings and automatically correct them. This improves the accuracy of the design by automatically correcting errors and inconsistencies as the design progresses.

[0032] Generative AI can automatically ensure data compatibility between different CAD software and achieve seamless integration. For example, generative AI builds a system that analyzes and automatically converts data formats between different CAD software. For example, it ensures data compatibility between AutoCAD and Revit. Generative AI implements algorithms that seamlessly integrate data between different CAD software. For example, it automatically converts data between different CAD software. This ensures data compatibility between different CAD software and achieves seamless integration.

[0033] The generative AI can analyze the user's voice instructions and realize the integration of CAD systems through voice operation. For example, the generative AI analyzes the user's voice instructions and builds a system that realizes the integration of CAD systems through voice operation. For example, it responds to a voice instruction such as "Please rotate this object." The generative AI uses voice recognition technology to analyze the user's voice instructions and executes the corresponding CAD operation. For example, it executes an operation to rotate an object based on the voice instruction. In this way, the generative AI analyzes the user's voice instructions and realizes the integration of CAD systems through voice operation.

[0034] The chatbot UI can analyze the user's operation history and automatically generate the optimal UI layout. For example, the chatbot UI uses generation AI to build a system that analyzes the user's operation history and automatically generates the optimal UI layout. For example, it prioritizes the placement of frequently used functions. The chatbot UI implements an algorithm that proposes the optimal UI layout based on the user's operation history. For example, it places functions that the user frequently uses in a prominent position. This improves operability by automatically generating the optimal UI layout based on the user's operation history.

[0035] A chatbot UI can provide customization options according to the user's preferences, realizing a personalized experience. For example, the chatbot UI adds customization options to the chatbot's UI, building a system that allows the user to change the UI according to their preferences. For example, it allows the user to select a theme color or font size. The chatbot UI implements an algorithm that provides customization options according to the user's preferences. For example, it suggests a theme color or font size that the user prefers. This allows the user to provide customization options according to their preferences, realizing a personalized experience.

[0036] Chatbot UIs can automatically generate UIs optimized for different devices. For example, a system can be built using generation AI to automatically generate UIs optimized for different devices. For example, it can automatically switch between a UI for smartphones and a UI for PCs. Chatbot UIs implement an algorithm that proposes the optimal UI depending on the characteristics of the device. For example, it adjusts the layout according to the screen size. This improves operability by automatically generating a UI optimized for different devices.

[0037] Chatbot UI can incorporate gesture operations to realize intuitive operation. For example, chatbot UI builds a system that incorporates gesture operations into the chatbot UI to allow users to operate it intuitively. For example, displaying a menu with a swipe or pinch operation. Chatbot UI implements an algorithm that realizes gesture operations. For example, it supports swipe and tap operations. In this way, by incorporating gesture operations, intuitive operation is realized.

[0038] Generative AI can develop design optimization algorithms to minimize material usage and costs. For example, generative AI develops design optimization algorithms and builds systems that minimize material usage. For example, in architectural design, it proposes structures that have maximum strength with the minimum amount of material. Generative AI implements algorithms to minimize material usage and costs. For example, it takes into account the properties of materials and proposes optimal designs. In this way, by developing design optimization algorithms, material usage and costs can be minimized.

[0039] Generative AI can evaluate the progress of a design in real time and propose the optimal design method. For example, generative AI builds a system that evaluates the progress of a design in real time and proposes the optimal design method. For example, it proposes the optimal method in the early stages of design and makes necessary adjustments as the design progresses. Generative AI implements an algorithm that proposes the optimal design method according to the progress of the design. For example, it monitors the progress of the design and proposes the optimal method. This allows the progress of a design to be evaluated in real time and propose the optimal design method.

[0040] Generative AI can automate knowledge sharing between different design projects and improve design efficiency. For example, generative AI builds a system that automates knowledge sharing between different design projects. For example, it applies knowledge gained from past projects to new projects. Generative AI implements algorithms that automate knowledge sharing between different design projects. For example, it automates data sharing between projects. This automates knowledge sharing between different design projects, improving design efficiency.

[0041] The generation AI can analyze the user's voice instructions and realize the execution of advanced functions through voice operation. For example, the generation AI analyzes the user's voice instructions and builds a system that realizes the execution of advanced functions through voice operation. For example, it responds to voice instructions such as "Please perform structural calculations for this building." The generation AI uses voice recognition technology to analyze the user's voice instructions and execute the corresponding advanced function. For example, it executes an operation to perform structural calculations based on the voice instructions. In this way, the generation AI analyzes the user's voice instructions and realizes the execution of advanced functions through voice operation.

[0042] The logic implementation unit can analyze the user's operation history and automatically generate the optimal logic. The logic implementation unit, for example, uses generation AI to analyze the user's operation history and build a system that automatically generates the optimal logic. For example, it automates frequently used operation procedures. The logic implementation unit implements an algorithm that proposes the optimal logic based on the user's operation history. For example, it automates operation procedures that are frequently used by users. In this way, operability is improved by automatically generating the optimal logic based on the user's operation history.

[0043] The logic implementation unit can monitor the execution status of the logic in real time and automatically correct errors and inconsistencies. For example, the logic implementation unit builds a system in which a generative AI monitors the execution status of the logic in real time and automatically detects errors and inconsistencies. For example, it identifies program bugs and inconsistencies and suggests corrections. The logic implementation unit implements an algorithm that monitors the execution status of the logic and automatically corrects errors and inconsistencies. For example, it detects inconsistencies in the logic during execution and automatically corrects them. This allows the execution status of the logic to be monitored in real time and errors and inconsistencies to be automatically corrected.

[0044] The logic implementation unit can automatically ensure logic compatibility between different CAD software and achieve seamless integration. For example, the logic implementation unit builds a system in which generative AI analyzes the logic between different CAD software and automatically converts it. For example, it ensures logic compatibility between AutoCAD and Revit. The logic implementation unit implements an algorithm that seamlessly integrates logic between different CAD software. For example, it automatically converts logic between different CAD software. This ensures logic compatibility between different CAD software and achieves seamless integration.

[0045] The logic implementation unit can analyze the user's voice instructions and realize the execution of logic through voice operation. For example, the logic implementation unit constructs a system in which a generation AI analyzes the user's voice instructions and realizes the execution of logic through voice operation. For example, it responds to a voice instruction such as "Please rotate this object." The logic implementation unit uses voice recognition technology to analyze the user's voice instructions and execute the corresponding logic. For example, it executes an operation to rotate an object based on the voice instruction. In this way, the user's voice instructions are analyzed and the execution of logic through voice operation is realized.

[0046] The design materialization unit can analyze the user's design history and automatically generate optimal design elements. The design materialization unit, for example, uses generation AI to analyze the user's design history and build a system that automatically generates optimal design elements. For example, it identifies frequently used design elements and styles. The design materialization unit implements an algorithm that suggests optimal design elements based on the user's design history. For example, it suggests color palettes and fonts that the user frequently uses. This improves design efficiency by automatically generating optimal design elements based on the user's design history.

[0047] The design embodiment unit can monitor the progress of the design in real time and automatically detect and correct errors and inconsistencies. The design embodiment unit, for example, builds a system in which a generative AI monitors the progress of the design in real time and automatically detects errors and inconsistencies in the design. For example, it identifies color inconsistencies and layout disruptions and suggests corrections. The design embodiment unit implements an algorithm that monitors the progress of the design and automatically corrects errors and inconsistencies. For example, it detects inconsistencies in the design as it progresses and automatically corrects them. This allows the progress of the design to be monitored in real time and errors and inconsistencies to be automatically corrected.

[0048] The design materialization unit automates knowledge sharing between different design projects, improving design efficiency. For example, the design materialization unit builds a system in which generative AI automates knowledge sharing between different design projects. For example, it applies knowledge gained from past projects to new projects. The design materialization unit implements an algorithm that automates knowledge sharing between different design projects. For example, it automates data sharing between projects. This automates knowledge sharing between different design projects, improving design efficiency.

[0049] The design embodiment unit can analyze the user's voice instructions and realize the realization of the design through voice operation. For example, the design embodiment unit constructs a system in which a generation AI analyzes the user's voice instructions and realizes the realization of the design through voice operation. For example, it responds to a voice instruction such as "Please change the color of this window." The design embodiment unit uses voice recognition technology to analyze the user's voice instructions and executes the corresponding design operation. For example, it executes an operation to change the color of a window based on the voice instruction. In this way, the user's voice instructions are analyzed and the realization of the design through voice operation is realized.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The design support system may further include a trend analysis unit that analyzes design trends based on the user's past design history. The trend analysis unit may, for example, analyze design data from the past few years to identify current design trends. This allows the user to create designs based on the latest trends. The trend analysis unit may also analyze design trends in specific regions or industries and provide the results to the user. For example, it may suggest architectural styles that are popular in specific cities. This allows the user to create designs that meet market needs.

[0052] The design support system may further include a voice support unit that analyzes the user's voice instructions and supports the progress of the design through voice operation. For example, when the user instructs, "Please proceed to the next step," the voice support unit automatically moves the design progress to the next step. This allows the user to proceed with the design work without using their hands. The voice support unit can also dynamically adjust the progress of the design based on the user's voice instructions. For example, when the user instructs, "Please correct this part," the voice support unit can automatically correct the relevant part. This supports the progress of the design based on the user's voice instructions and improves work efficiency.

[0053] The design support system may further include a prediction unit that predicts the progress of the design based on the user's operation history. The prediction unit, for example, analyzes design operations performed by the user in the past and predicts the next operation to be performed. This allows the user to proceed without hesitation about the next operation to be performed. The prediction unit may also dynamically adjust the progress of the design based on the user's operation history. For example, it may prioritize and suggest operations that the user frequently performs. This makes it possible to predict the progress of the design based on the user's operation history and improve work efficiency.

[0054] The design support system may further include an optimization unit that optimizes the design progress based on the user's operation history. The optimization unit, for example, analyzes the design operations performed by the user in the past and proposes the optimal operation procedure. This allows the user to proceed with the design work efficiently. The optimization unit may also dynamically adjust the design progress based on the user's operation history. For example, it may automate operations that the user frequently performs. This optimizes the design progress based on the user's operation history and improves work efficiency.

[0055] The design support system may further include a guide unit that guides the progress of the design based on the user's operation history. The guide unit, for example, analyzes design operations performed by the user in the past and guides the user on the next operation to be performed. This allows the user to proceed without hesitation about the next operation to be performed. The guide unit may also dynamically adjust the progress of the design based on the user's operation history. For example, it may suggest operations that the user frequently performs with priority. This allows the design progress to be guided based on the user's operation history, improving work efficiency.

[0056] The design support system may further include an evaluation unit that evaluates the progress of the design based on the user's operation history. The evaluation unit, for example, analyzes the design operations performed by the user in the past and evaluates the progress of the design. This allows the user to understand the progress of their design work. The evaluation unit may also dynamically adjust the progress of the design based on the user's operation history. For example, it may suggest operations that the user frequently performs with priority. This allows the design progress to be evaluated based on the user's operation history, improving work efficiency.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: Generative AI integrates various functions of the CAD system. For example, generative AI allows for unified operation of basic functions such as creating and editing shapes, entering dimensions, and moving, rotating, and scaling objects. It also integrates advanced functions such as automatic structural calculations and design generation. Step 2: The chatbot UI allows users to intuitively operate the CAD system. For example, the chatbot UI arranges elements such as buttons and menus to allow users to easily select functions. Step 3: The logic implementation unit executes CAD operations based on the user request received by the chatbot UI. For example, if the user requests, "Please rotate this object," the logic implementation unit analyzes the request and executes the operation to rotate the object. Step 4: The design implementation department sets the design elements of the chatbot UI, such as the color palette and font, and also designs the appearance and icons of the CAD window.

[0059] (Example 2) The design support system according to an embodiment of the present invention is a system that uses a generative AI to connect CAD systems and perform design more intuitively and efficiently. This design support system allows the user to intuitively operate the structural calculation and design processes by specifying them with the generative AI. This allows the user to intuitively operate the design support system while performing structural calculations and design.

[0060] A design support system according to an embodiment includes a generation AI, a chatbot UI, a logic implementation unit, and a design realization unit. The generation AI integrates various functions of a CAD system. For example, the generation AI enables centralized operation of basic functions such as creating and editing shapes, entering dimensions, and moving, rotating, and scaling objects. The generation AI also integrates advanced functions such as automatic structural calculation and design generation. The chatbot UI enables users to intuitively operate the CAD system. For example, the chatbot UI arranges elements such as buttons and menus to allow users to easily select functions. The logic implementation unit executes CAD operations based on user requests received by the chatbot UI. For example, when a user instructs the system to "rotate this object," the logic implementation unit analyzes the instruction and executes the operation to rotate the object. The design realization unit sets design elements for the chatbot UI. For example, the design realization unit sets design elements such as color palettes and fonts, and also designs the appearance and icons of the CAD window. As a result, the design support system according to an embodiment provides a CAD system that users can intuitively operate, improving the efficiency of design work.

[0061] Generative AI can analyze a user's past design history and propose optimal design patterns. For example, generative AI can analyze a user's past design history and identify frequently used design patterns and techniques. For example, it can extract specific architectural styles and design elements and propose new design patterns based on them. Generative AI learns the design patterns preferred by the user based on past design data and proposes optimal design patterns. This improves design efficiency by proposing optimal design patterns based on the user's past design history.

[0062] Generative AI can monitor the progress of a design in real time and automatically detect and correct errors and inconsistencies. For example, generative AI can monitor the progress of a design in real time and automatically detect errors and inconsistencies in design drawings. For example, it can identify incorrect dimensions or overlapping objects and suggest corrections. Generative AI implements algorithms that automatically correct errors and inconsistencies that occur as the design progresses. For example, it can detect inconsistencies in design drawings and automatically correct them. This improves the accuracy of the design by automatically correcting errors and inconsistencies as the design progresses.

[0063] The generative AI can use its emotion estimation function to provide design assistance according to the user's emotional state, reducing stress. For example, the generative AI can use its emotion estimation function to analyze the user's emotional state in real time and provide design assistance that helps them relax when stress levels rise. For example, if it determines that the user is tired, it will suggest a simple operation. The generative AI dynamically changes the content of the design assistance according to the user's emotional state. For example, if the user is feeling stressed, it will provide design assistance that helps them relax. In this way, design assistance that responds to the user's emotional state reduces stress.

[0064] Generative AI can automatically ensure data compatibility between different CAD software and achieve seamless integration. For example, generative AI builds a system that analyzes and automatically converts data formats between different CAD software. For example, it ensures data compatibility between AutoCAD and Revit. Generative AI implements algorithms that seamlessly integrate data between different CAD software. For example, it automatically converts data between different CAD software. This ensures data compatibility between different CAD software and achieves seamless integration.

[0065] The generative AI can analyze the user's voice instructions and realize the integration of CAD systems through voice operation. For example, the generative AI analyzes the user's voice instructions and builds a system that realizes the integration of CAD systems through voice operation. For example, it responds to a voice instruction such as "Please rotate this object." The generative AI uses voice recognition technology to analyze the user's voice instructions and executes the corresponding CAD operation. For example, it executes an operation to rotate an object based on the voice instruction. In this way, the generative AI analyzes the user's voice instructions and realizes the integration of CAD systems through voice operation.

[0066] The generative AI can use the emotion estimation function to learn the design style that most satisfies the user and make suggestions based on that style. For example, the generative AI can use the emotion estimation function to build a system that learns the design style that most satisfies the user. For example, it can analyze the user's past design history and emotional data to identify the optimal design style. The generative AI can implement an algorithm that suggests the optimal design style based on the user's emotional data. For example, it can learn the design style that most satisfies the user and make suggestions based on that style.

[0067] The chatbot UI can analyze the user's operation history and automatically generate the optimal UI layout. For example, the chatbot UI uses generation AI to build a system that analyzes the user's operation history and automatically generates the optimal UI layout. For example, it prioritizes the placement of frequently used functions. The chatbot UI implements an algorithm that proposes the optimal UI layout based on the user's operation history. For example, it places functions that the user frequently uses in a prominent position. This improves operability by automatically generating the optimal UI layout based on the user's operation history.

[0068] A chatbot UI can provide customization options according to the user's preferences, realizing a personalized experience. For example, the chatbot UI adds customization options to the chatbot's UI, building a system that allows the user to change the UI according to their preferences. For example, it allows the user to select a theme color or font size. The chatbot UI implements an algorithm that provides customization options according to the user's preferences. For example, it suggests a theme color or font size that the user prefers. This allows the user to provide customization options according to their preferences, realizing a personalized experience.

[0069] A chatbot UI can improve operability by dynamically changing the UI design according to the user's emotional state using an emotion estimation function. For example, a chatbot UI can use the emotion estimation function to analyze the user's emotional state in real time and build a system that dynamically changes the UI design. For example, if the user is feeling stressed, the design can be changed to a simpler one. The chatbot UI implements an algorithm that dynamically changes the UI design according to the user's emotional state. For example, the design can be changed to one that helps the user relax. This improves operability by dynamically changing the UI design according to the user's emotional state.

[0070] Chatbot UIs can automatically generate UIs optimized for different devices. For example, a system can be built using generation AI to automatically generate UIs optimized for different devices. For example, it can automatically switch between a UI for smartphones and a UI for PCs. Chatbot UIs implement an algorithm that proposes the optimal UI depending on the characteristics of the device. For example, it adjusts the layout according to the screen size. This improves operability by automatically generating a UI optimized for different devices.

[0071] Chatbot UI can incorporate gesture operations to realize intuitive operation. For example, chatbot UI builds a system that incorporates gesture operations into the chatbot UI to allow users to operate it intuitively. For example, displaying a menu with a swipe or pinch operation. Chatbot UI implements an algorithm that realizes gesture operations. For example, it supports swipe and tap operations. In this way, by incorporating gesture operations, intuitive operation is realized.

[0072] The chatbot UI can use an emotion estimation function to suggest a UI theme that will make the user feel most relaxed, thereby reducing stress. For example, the chatbot UI uses the emotion estimation function to build a system that suggests a UI theme that will make the user feel most relaxed. For example, a color palette with a relaxing effect is suggested based on the user's emotion data. The chatbot UI implements an algorithm that suggests the optimal UI theme depending on the user's emotional state. For example, a theme that will make the user feel relaxed is suggested. This reduces stress by suggesting a UI theme that will make the user feel most relaxed.

[0073] Generative AI can develop design optimization algorithms to minimize material usage and costs. For example, generative AI develops design optimization algorithms and builds systems that minimize material usage. For example, in architectural design, it proposes structures that have maximum strength with the minimum amount of material. Generative AI implements algorithms to minimize material usage and costs. For example, it takes into account the properties of materials and proposes optimal designs. In this way, by developing design optimization algorithms, material usage and costs can be minimized.

[0074] Generative AI can evaluate the progress of a design in real time and propose the optimal design method. For example, generative AI builds a system that evaluates the progress of a design in real time and proposes the optimal design method. For example, it proposes the optimal method in the early stages of design and makes necessary adjustments as the design progresses. Generative AI implements an algorithm that proposes the optimal design method according to the progress of the design. For example, it monitors the progress of the design and proposes the optimal method. This allows the progress of a design to be evaluated in real time and propose the optimal design method.

[0075] Generative AI can use its emotion estimation function to provide design assistance according to the user's emotional state, improving work efficiency. For example, generative AI can use its emotion estimation function to analyze the user's emotional state in real time and build a system that provides design assistance. For example, if the user is tired, it can suggest simple operations. Generative AI dynamically changes the content of the design assistance according to the user's emotional state. For example, it can provide design assistance that helps the user relax. This improves work efficiency by providing design assistance according to the user's emotional state.

[0076] Generative AI can automate knowledge sharing between different design projects and improve design efficiency. For example, generative AI builds a system that automates knowledge sharing between different design projects. For example, it applies knowledge gained from past projects to new projects. Generative AI implements algorithms that automate knowledge sharing between different design projects. For example, it automates data sharing between projects. This automates knowledge sharing between different design projects, improving design efficiency.

[0077] The generation AI can analyze the user's voice instructions and realize the execution of advanced functions through voice operation. For example, the generation AI analyzes the user's voice instructions and builds a system that realizes the execution of advanced functions through voice operation. For example, it responds to voice instructions such as "Please perform structural calculations for this building." The generation AI uses voice recognition technology to analyze the user's voice instructions and execute the corresponding advanced function. For example, it executes an operation to perform structural calculations based on the voice instructions. In this way, the generation AI analyzes the user's voice instructions and realizes the execution of advanced functions through voice operation.

[0078] The generative AI can use the emotion estimation function to learn the design method that most satisfies the user and make suggestions based on that method. For example, the generative AI can use the emotion estimation function to build a system that learns the design method that most satisfies the user. For example, it can analyze the user's past design history and emotional data to identify the optimal design method. The generative AI can implement an algorithm that suggests the optimal design method based on the user's emotional data. For example, it can learn the design method that most satisfies the user and make suggestions based on that method.

[0079] The logic implementation unit can analyze the user's operation history and automatically generate the optimal logic. The logic implementation unit, for example, uses generation AI to analyze the user's operation history and build a system that automatically generates the optimal logic. For example, it automates frequently used operation procedures. The logic implementation unit implements an algorithm that proposes the optimal logic based on the user's operation history. For example, it automates operation procedures that are frequently used by users. In this way, operability is improved by automatically generating the optimal logic based on the user's operation history.

[0080] The logic implementation unit can monitor the execution status of the logic in real time and automatically correct errors and inconsistencies. For example, the logic implementation unit builds a system in which a generative AI monitors the execution status of the logic in real time and automatically detects errors and inconsistencies. For example, it identifies program bugs and inconsistencies and suggests corrections. The logic implementation unit implements an algorithm that monitors the execution status of the logic and automatically corrects errors and inconsistencies. For example, it detects inconsistencies in the logic during execution and automatically corrects them. This allows the execution status of the logic to be monitored in real time and errors and inconsistencies to be automatically corrected.

[0081] The logic implementation unit uses the emotion estimation function to execute logic according to the user's emotional state, thereby reducing stress. The logic implementation unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and provide logic that allows the user to relax when stress increases. For example, if it is determined that the user is tired, it suggests a simple operation. The logic implementation unit dynamically changes the execution content of the logic according to the user's emotional state. For example, it provides logic that allows the user to relax. In this way, stress is reduced by executing logic according to the user's emotional state.

[0082] The logic implementation unit can automatically ensure logic compatibility between different CAD software and achieve seamless integration. For example, the logic implementation unit builds a system in which generative AI analyzes the logic between different CAD software and automatically converts it. For example, it ensures logic compatibility between AutoCAD and Revit. The logic implementation unit implements an algorithm that seamlessly integrates logic between different CAD software. For example, it automatically converts logic between different CAD software. This ensures logic compatibility between different CAD software and achieves seamless integration.

[0083] The logic implementation unit can analyze the user's voice instructions and realize the execution of logic through voice operation. For example, the logic implementation unit constructs a system in which a generation AI analyzes the user's voice instructions and realizes the execution of logic through voice operation. For example, it responds to a voice instruction such as "Please rotate this object." The logic implementation unit uses voice recognition technology to analyze the user's voice instructions and execute the corresponding logic. For example, it executes an operation to rotate an object based on the voice instruction. In this way, the user's voice instructions are analyzed and the execution of logic through voice operation is realized.

[0084] The logic implementation unit can use the emotion estimation function to learn the logic that most satisfies the user and make suggestions based on that logic. The logic implementation unit, for example, uses the emotion estimation function to build a system that learns the logic that most satisfies the user. For example, it analyzes the user's past operation history and emotion data to identify the optimal logic. The logic implementation unit implements an algorithm that suggests the optimal logic based on the user's emotion data. For example, it learns the logic that most satisfies the user and makes suggestions based on that logic. In this way, it learns the logic that most satisfies the user and makes suggestions based on that logic.

[0085] The design materialization unit can analyze the user's design history and automatically generate optimal design elements. The design materialization unit, for example, uses generation AI to analyze the user's design history and build a system that automatically generates optimal design elements. For example, it identifies frequently used design elements and styles. The design materialization unit implements an algorithm that suggests optimal design elements based on the user's design history. For example, it suggests color palettes and fonts that the user frequently uses. This improves design efficiency by automatically generating optimal design elements based on the user's design history.

[0086] The design embodiment unit can monitor the progress of the design in real time and automatically detect and correct errors and inconsistencies. The design embodiment unit, for example, builds a system in which a generative AI monitors the progress of the design in real time and automatically detects errors and inconsistencies in the design. For example, it identifies color inconsistencies and layout disruptions and suggests corrections. The design embodiment unit implements an algorithm that monitors the progress of the design and automatically corrects errors and inconsistencies. For example, it detects inconsistencies in the design as it progresses and automatically corrects them. This allows the progress of the design to be monitored in real time and errors and inconsistencies to be automatically corrected.

[0087] The design embodiment unit uses the emotion estimation function to provide design assistance according to the user's emotional state, thereby improving work efficiency. The design embodiment unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and build a system that provides design assistance. For example, if the user is tired, it suggests a simple operation. The design embodiment unit dynamically changes the content of the design assistance according to the user's emotional state. For example, it provides design assistance that allows the user to relax. In this way, work efficiency is improved by providing design assistance according to the user's emotional state.

[0088] The design materialization unit automates knowledge sharing between different design projects, improving design efficiency. For example, the design materialization unit builds a system in which generative AI automates knowledge sharing between different design projects. For example, it applies knowledge gained from past projects to new projects. The design materialization unit implements an algorithm that automates knowledge sharing between different design projects. For example, it automates data sharing between projects. This automates knowledge sharing between different design projects, improving design efficiency.

[0089] The design embodiment unit can analyze the user's voice instructions and realize the realization of the design through voice operation. For example, the design embodiment unit constructs a system in which a generation AI analyzes the user's voice instructions and realizes the realization of the design through voice operation. For example, it responds to a voice instruction such as "Please change the color of this window." The design embodiment unit uses voice recognition technology to analyze the user's voice instructions and executes the corresponding design operation. For example, it executes an operation to change the color of a window based on the voice instruction. In this way, the user's voice instructions are analyzed and the realization of the design through voice operation is realized.

[0090] The design embodiment unit can use the emotion estimation function to learn the design style that most satisfies the user and make suggestions based on that style. The design embodiment unit, for example, uses the emotion estimation function to build a system that learns the design style that most satisfies the user. For example, it analyzes the user's past design history and emotion data to identify the optimal design style. The design embodiment unit implements an algorithm that suggests the optimal design style based on the user's emotion data. For example, it learns the design style that most satisfies the user and makes suggestions based on that style. In this way, it learns the design style that most satisfies the user and makes suggestions based on that style.

[0091] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0092] The design support system may further include a biometric information acquisition unit that acquires the user's biometric information. The biometric information acquisition unit, for example, measures the user's heart rate and skin temperature and monitors the user's physical condition in real time during design work. This makes it possible to suggest appropriate breaks if the user is tired or stressed. The system may also adjust the work environment based on the user's biometric information. For example, if the user's heart rate is high, it may suggest lowering the room temperature. This allows the system to provide support according to the user's physical condition and improve work efficiency.

[0093] The design support system may further include a trend analysis unit that analyzes design trends based on the user's past design history. The trend analysis unit may, for example, analyze design data from the past few years to identify current design trends. This allows the user to create designs based on the latest trends. The trend analysis unit may also analyze design trends in specific regions or industries and provide the results to the user. For example, it may suggest architectural styles that are popular in specific cities. This allows the user to create designs that meet market needs.

[0094] The design support system may further include an evaluation unit that evaluates the progress of the design based on the user's emotional state. For example, if the user is feeling stressed, the evaluation unit may temporarily stop the design progress and suggest a relaxing activity. This allows the user to resume the design work in a refreshed state. The evaluation unit may also dynamically adjust the progress of the design according to the user's emotional state. For example, if the user is concentrating, a more complex design task may be suggested. This supports the progress of the design according to the user's emotional state and improves work efficiency.

[0095] The design support system can further include a guide unit that guides the progress of the design based on the user's emotional state. For example, if the user feels anxious, the guide unit guides the user step by step through the progress of the design, allowing the user to proceed with the work with peace of mind. This allows the user to perform the design work with confidence. The guide unit can also provide advice to support the progress of the design according to the user's emotional state. For example, if the user is unsure, the guide unit can suggest the next step to take. This supports the progress of the design according to the user's emotional state and improves work efficiency.

[0096] The design support system may further include an optimization unit that optimizes the progress of the design based on the user's emotional state. For example, if the user is tired, the optimization unit may temporarily slow down the progress of the design and suggest an easier task, allowing the user to continue working without straining themselves. The optimization unit may also dynamically adjust the progress of the design based on the user's emotional state. For example, if the user is concentrating, a more complex design task may be suggested. This allows the design progress to be optimized according to the user's emotional state, improving work efficiency.

[0097] The design support system may further include a voice support unit that analyzes the user's voice instructions and supports the progress of the design through voice operation. For example, when the user instructs, "Please proceed to the next step," the voice support unit automatically moves the design progress to the next step. This allows the user to proceed with the design work without using their hands. The voice support unit can also dynamically adjust the progress of the design based on the user's voice instructions. For example, when the user instructs, "Please correct this part," the voice support unit can automatically correct the relevant part. This supports the progress of the design based on the user's voice instructions and improves work efficiency.

[0098] The design support system may further include a prediction unit that predicts the progress of the design based on the user's operation history. The prediction unit, for example, analyzes design operations performed by the user in the past and predicts the next operation to be performed. This allows the user to proceed without hesitation about the next operation to be performed. The prediction unit may also dynamically adjust the progress of the design based on the user's operation history. For example, it may prioritize and suggest operations that the user frequently performs. This makes it possible to predict the progress of the design based on the user's operation history and improve work efficiency.

[0099] The design support system may further include an optimization unit that optimizes the design progress based on the user's operation history. The optimization unit, for example, analyzes the design operations performed by the user in the past and proposes the optimal operation procedure. This allows the user to proceed with the design work efficiently. The optimization unit may also dynamically adjust the design progress based on the user's operation history. For example, it may automate operations that the user frequently performs. This optimizes the design progress based on the user's operation history and improves work efficiency.

[0100] The design support system may further include a guide unit that guides the progress of the design based on the user's operation history. The guide unit, for example, analyzes design operations performed by the user in the past and guides the user on the next operation to be performed. This allows the user to proceed without hesitation about the next operation to be performed. The guide unit may also dynamically adjust the progress of the design based on the user's operation history. For example, it may suggest operations that the user frequently performs with priority. This allows the design progress to be guided based on the user's operation history, improving work efficiency.

[0101] The design support system may further include an evaluation unit that evaluates the progress of the design based on the user's operation history. The evaluation unit, for example, analyzes the design operations performed by the user in the past and evaluates the progress of the design. This allows the user to understand the progress of their design work. The evaluation unit may also dynamically adjust the progress of the design based on the user's operation history. For example, it may suggest operations that the user frequently performs with priority. This allows the design progress to be evaluated based on the user's operation history, improving work efficiency.

[0102] The processing flow of the second embodiment will be briefly explained below.

[0103] Step 1: Generative AI integrates various functions of the CAD system. For example, generative AI allows for unified operation of basic functions such as creating and editing shapes, entering dimensions, and moving, rotating, and scaling objects. It also integrates advanced functions such as automatic structural calculations and design generation. Step 2: The chatbot UI allows users to intuitively operate the CAD system. For example, the chatbot UI arranges elements such as buttons and menus to allow users to easily select functions. Step 3: The logic implementation unit executes CAD operations based on the user request received by the chatbot UI. For example, if the user requests, "Please rotate this object," the logic implementation unit analyzes the request and executes the operation to rotate the object. Step 4: The design implementation department sets the design elements of the chatbot UI, such as the color palette and font, and also designs the appearance and icons of the CAD window.

[0104] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0105] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0109] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0115] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0118] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0122] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0123] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0124] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0130] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0131] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0132] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0133] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0134] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0135] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0136] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0137] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0138] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0139] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0140] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0141] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0143] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0144] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0145] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0146] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0147] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0148] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0149] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0150] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0151] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0152] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0153] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0154] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0155] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0156] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0157] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0158] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0159] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0160] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0161] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0162] 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.

[0163] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0164] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0165] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0166] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0167] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0168] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0169] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0170] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0171] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. Generative AI integrates various functions of CAD systems using generative AI, A chatbot UI that allows users to intuitively operate the CAD system, a logic implementation unit that executes CAD operations based on a user request received by the chatbot UI; a design realization unit that sets design elements of the chatbot UI; A system characterized by:

2. The generated AI is Monitor design progress in real time and automatically detect and correct errors and inconsistencies 2. The system of claim 1.

3. The generated AI is Automatically ensures data compatibility between different CAD software and realizes seamless integration 2. The system of claim 1.

4. The chatbot UI is: Analyzing the user's operation history and automatically generating the optimal UI layout 2. The system of claim 1.

5. The logic implementation unit Monitor logic execution in real time and automatically correct errors and inconsistencies 2. The system of claim 1.

6. The design realization unit Providing design assistance according to the user's emotional state, thereby improving work efficiency 2. The system of claim 1.

7. The generated AI is Design assistance is provided according to the emotional state of the user, thereby reducing stress for the user.

2. The system of claim 1.

8. The chatbot UI is: The UI design is dynamically changed according to the emotional state of the user, thereby improving operability.

2. The system of claim 1.

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