System

Abstract

A system is provided.SOLUTION: A system comprising: measuring means for measuring an object in three dimensions; transmitting means for transmitting measurement data acquired by the measuring means; generating means for generating three dimensional data based on the received measurement data; and providing means for providing the generated three dimensional data to a user.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] In recent years, 3D printers have become popular among general consumers, but creating 3D data yourself requires specialized skills and expensive software, making them inaccessible to many consumers. Furthermore, even if you try to use existing 3D data, there is often no data that fits your object, making customization necessary. However, customization also requires specialized knowledge. This makes it difficult for general consumers to easily create 3D data and use 3D printers. [Means for solving the problem]

[0005] The present invention solves this problem by providing a system including a measuring means for measuring an object in three dimensions, a transmitting means for transmitting the measurement data acquired by the measuring means, a generating means for generating three-dimensional data based on the received measurement data, and a providing means for providing the generated three-dimensional data to a user. Specifically, by using a LiDAR sensor as the measuring means, precise three-dimensional data can be easily acquired. Furthermore, the generating means generates three-dimensional data based on requirements entered by the user, making it easy to achieve the customization desired by the user. Furthermore, the providing means provides the generated three-dimensional data as a downloadable link, allowing it to be listed on a content market as needed. This makes it possible to provide a system that allows even ordinary consumers to easily create and use three-dimensional data without requiring specialized knowledge.

[0006] "Measuring means" refers to a device or sensor for measuring an object in three dimensions.

[0007] "Transmission means" refers to a function or module for transmitting data acquired by the measurement means to another device or server.

[0008] "Generation means" refers to the algorithms or software that generate three-dimensional data based on received data.

[0009] "Providing means" refers to the function for providing the generated three-dimensional data to the user in a form that can be referenced, downloaded, or used.

[0010] A "LiDAR sensor" is a device that uses lasers to measure the distance to an object and captures its three-dimensional shape as point cloud data.

[0011] "User" refers to the general consumer or individual who uses this system.

[0012] "Three-dimensional data" refers to digital data used to represent the three-dimensional shape of an object, and is generally saved in a format such as an .obj file.

[0013] "Content Market" refers to an online platform for selling or providing generated 3D data to other users. [Brief explanation of the drawings]

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

[0015] 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.

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

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

[0018] 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.

[0019] 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.

[0020] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0021] 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."

[0022] [First embodiment]

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

[0024] 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.

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

[0026] 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.

[0027] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0028] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0029] 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.

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

[0031] 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.

[0032] 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.

[0033] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

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

[0035] This invention is a system that measures an object in three dimensions, easily generates three-dimensional data, and provides it in a format that can be used by a 3D printer. This system consists of a terminal, a server, and a user. The processing of each element and specific examples are explained below.

[0036] 1. Terminal processing

[0037] Starting the LiDAR Scan Module

[0038] A user opens the smartphone app and activates the LiDAR scanning module, which controls the smartphone's LiDAR sensor to collect three-dimensional data about the object.

[0039] Scanning an object

[0040] Users point their smartphone at the object they want to scan and use the LiDAR sensor to collect point cloud data of the object. This data is displayed on the device in real time, allowing users to see the progress of the scan. Once the scan is complete, the collected data is formatted and ready to be sent.

[0041] Formatting and sending data

[0042] The terminal processes the collected point cloud data and performs preprocessing such as noise removal and smoothing. The processed data is then compressed and sent to the server.

[0043] 2. Server-side processing

[0044] Data Receiving Module

[0045] The server receives the point cloud data sent from the device, passes the integrity check, and performs the necessary processing.

[0046] Generative AI Module

[0047] The server inputs the received data into the generation AI, which generates 3D data according to the user's requirements. The generated data is exported in .obj file format.

[0048] File Creation and Management

[0049] The generated .obj file is stored in the file management system and a file ID is generated, and the server generates a download link for this file and notifies the user.

[0050] 3. User Interface

[0051] Chat Interface

[0052] Users input their requirements (e.g., "Please increase the height of the chair legs by 10 cm") through the chatbot, and this interface provides a convenient way for users to communicate their specifications in detail.

[0053] Check and adjust specifications

[0054] The server displays a preview of the generated .obj file in a chat interface for the user to review and, if necessary, provide corrections.

[0055] 4. File Submission and Market Listing

[0056] Download Module

[0057] The server provides the user with a download link for the generated .obj file. The user clicks the link to download the .obj file, which can then be used with the user's 3D printer.

[0058] Market Linkage Module

[0059] When a user submits a generated .obj file to the Content Marketplace, the server facilitates the process: the user enters the metadata for the marketplace submission (e.g., title, description, price, etc.), which is then uploaded to the marketplace.

[0060] Specific examples

[0061] Example 1: Reproducing furniture parts

[0062] 1. Scan

[0063] A user uses the LiDAR sensor on their smartphone to scan the broken chair leg, and within minutes the point cloud data is collected and reconstructed.

[0064] 2. Generation

[0065] The server receives the point cloud data, and the generation AI generates three-dimensional data according to the user-specified requirements.

[0066] 3. Provide

[0067] The server provides a download link for the generated .obj file, which the user can then download and use to reproduce it on their 3D printer.

[0068] 4. Listing

[0069] When a user puts this data up for sale on the content market, the server assists in the process and the data is sold online.

[0070] This system allows users to easily generate 3D data without requiring specialized skills and use it in a 3D printer. Furthermore, they can earn secondary income by putting the generated data on the market.

[0071] The processing flow will be explained below.

[0072] Program processing flow

[0073] Terminal side processing

[0074] Step 1:

[0075] The user launches the app using their smartphone, which turns on the LiDAR scanning module.

[0076] Step 2:

[0077] The user points the smartphone at the object to be scanned, and the LiDAR sensor collects point cloud data of the object in real time. The device displays the data collection status on the screen, allowing the user to observe the scan until it is complete.

[0078] Step 3:

[0079] Once the user has finished scanning, the device will then shape the collected point cloud data, removing noise and smoothing it.

[0080] Step 4:

[0081] The device compresses the formatted point cloud data and transmits the data to the server.

[0082] Server-side processing

[0083] Step 5:

[0084] The server receives the point cloud data sent from the device and checks the data integrity, making sure there are no missing or corrupted data.

[0085] Step 6:

[0086] The server inputs the received data into the generation AI module, which then generates 3D data according to the user's requirements. The generated data is exported as an .obj file.

[0087] Step 7:

[0088] The server saves the generated .obj file to the file server, generates a file ID, and then generates a download link and notifies the user.

[0089] User Interface Handling

[0090] Step 8:

[0091] The user accesses the chat interface and receives a download link for the generated .obj file. The user downloads the file and uses it in a 3D printer.

[0092] Step 9:

[0093] If a user wants to modify an .obj file, they input their specific requirements through the chatbot, for example, "Please increase the height of the chair legs by 10cm."

[0094] Step 10:

[0095] The server runs the AI ​​module again based on the new requirements, generates the corrected 3D data, and resends the download link for the newly generated .obj file.

[0096] Processing file submissions and market listings

[0097] Step 11:

[0098] Users can download the files and use them in their home 3D printers as needed, or if they want to list their work on the market, the server assists them with the listing process.

[0099] Step 12:

[0100] The user enters the metadata for a marketplace listing (title, description, price, etc.), and the server uploads it to the marketplace.

[0101] Step 13:

[0102] The server sends a listing completion notice to the user and starts selling online.

[0103] This allows users to easily generate three-dimensional data without any specialized knowledge, and use it in a 3D printer or sell it on the market.

[0104] Example 1

[0105] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0106] Conventional methods for generating 3D data of objects require expensive equipment and specialized knowledge, making it difficult for general users to use. Furthermore, the methods for providing the generated data are often limited, restricting the scope of use for users. Therefore, there was a need for a system that could easily acquire and generate high-quality 3D data and enable users to easily use that data.

[0107] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0108] In this invention, the server includes a measuring means for measuring an object in three dimensions through a user interface, a transmitting means for transmitting point cloud data acquired by the measuring means, a generating means for generating three-dimensional data by shaping and preprocessing the received point cloud data, and a providing means for providing the generated three-dimensional data to a user. This makes it possible to easily acquire high-quality three-dimensional data without specialized knowledge and provide it to a user in a format that is easily usable.

[0109] A "user interface" is the means by which a user interacts with a system and inputs instructions and information.

[0110] "Means for measuring an object in three dimensions" refers to a technology or device for measuring the three-dimensional shape or structure of an object and acquiring it as digital data.

[0111] "Point cloud data" is three-dimensional data that represents a collection of multiple points that make up the surface of an object.

[0112] "Transmission means" refers to a technique or device for transmitting acquired data to another system or server.

[0113] "Receiving means" refers to a technique or device for receiving data sent from an external source.

[0114] "Means for shaping and preprocessing" refers to technology or equipment for improving the quality of data by performing processes such as noise removal and smoothing on the acquired raw data.

[0115] "Means for generating 3D data" refers to a technique or device for creating 3D models or shape data from pre-processed data.

[0116] "Providing means" refers to the technology or device for transmitting the generated three-dimensional data to the user or providing it in the form of a download link, etc.

[0117] A "downloadable link" is a web link that allows a user to receive a file or data specified by the user over the Internet.

[0118] The present invention is a system that measures an object in three dimensions, easily generates three-dimensional data, and provides it in a format that can be used by a 3D printer. This system is composed of a user, a terminal, and a server.

[0119] Terminal side processing

[0120] Starting the LiDAR Scan Module

[0121] The user opens the smartphone app and activates the LiDAR scanning module, which controls the smartphone's LiDAR sensor and collects 3D data of the object. The specific hardware used is the smartphone's built-in LiDAR sensor.

[0122] Scanning an object

[0123] Users point their smartphone at the object they want to scan and use the LiDAR sensor to collect point cloud data of the object. This data is displayed on the device in real time, allowing users to see the progress of the scan. Once the scan is complete, the collected data is formatted and ready to be sent.

[0124] Formatting and sending data

[0125] The terminal processes the collected point cloud data and performs preprocessing such as noise removal and smoothing. The processed data is then compressed and sent to the server. Specific software used includes data compression algorithms and noise filtering techniques.

[0126] Server-side processing

[0127] Data Receiving Module

[0128] The server receives the point cloud data sent from the device, passes the integrity check, and performs the necessary processing.

[0129] Generative AI Module

[0130] The server inputs the received data into a generative AI model and generates 3D data according to the user's requirements. The generated data is exported in .obj file format. The generative AI model uses a generative AI engine that analyzes the user's prompt and executes the specified processing.

[0131] File Creation and Management

[0132] The generated .obj file is stored in the file management system and a file ID is generated, and the server generates a download link for this file and notifies the user.

[0133] User Interface

[0134] Chat Interface

[0135] Users input their requirements (e.g., "Please increase the height of the chair legs by 10 cm") through the chatbot, and this interface provides a convenient way for users to communicate their specifications in detail.

[0136] Check and adjust specifications

[0137] The server displays a preview of the generated .obj file in a chat interface for the user to review and, if necessary, provide corrections.

[0138] File submission and market listing

[0139] Download Module

[0140] The server provides the user with a download link for the generated .obj file. The user clicks the link to download the .obj file, which can then be used with the user's 3D printer.

[0141] Market Linkage Module

[0142] When a user submits a generated .obj file to the Content Marketplace, the server facilitates the process: the user enters the metadata for the marketplace submission (e.g., title, description, price, etc.), which is then uploaded to the marketplace.

[0143] Specific examples

[0144] Example 1: Reproducing furniture parts

[0145] 1. Scan

[0146] A user uses the LiDAR sensor on their smartphone to scan the broken chair leg, and within minutes the point cloud data is collected and reconstructed.

[0147] 2. Generation

[0148] The server receives the point cloud data, and the generation AI generates three-dimensional data according to the user-specified requirements.

[0149] 3. Provide

[0150] The server provides a download link for the generated .obj file, which the user can then download and use to reproduce it on their 3D printer.

[0151] 4. Listing

[0152] When a user puts this data up for sale on the content market, the server assists in the process and the data is sold online.

[0153] Prompt Sentence Examples

[0154] Prompt 1: "I scanned a broken chair leg. From the generated data, please generate a version of the chair leg that is 10cm taller."

[0155] Prompt 2: "Generate repair data for the broken vase based on the collected point cloud data."

[0156] This system allows users to easily generate high-quality 3D data without requiring specialized skills and use it in 3D printers. Users can also earn secondary income by listing the generated data on a marketplace.

[0157] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0158] Step 1: Start the LiDAR Scan Module

[0159] The user launches the smartphone app and selects the LiDAR scan module, which causes the device to activate the internal LiDAR sensor and prepare for scanning.

[0160] Input: User action (selection of LiDAR scan module)

[0161] Output: LiDAR sensor wake-up and scan readiness notification

[0162] What it does: When the app launches, the device will begin calibrating the LiDAR sensor and display a "Ready to scan" notification to the user.

[0163] Step 2: Scan the object

[0164] Users point their smartphone at the object they want to scan and press the "Start Scan" button in the app. The device uses its LiDAR sensor to collect point cloud data of the object. This data is displayed on the device's screen in real time, allowing users to track the progress.

[0165] Input: User actions (pressing the start scan button) and data from the LiDAR sensor

[0166] Output: Point cloud data displayed in real time

[0167] Specific operation: During scanning, the device screen will construct 3D data of the object in real time and display a progress bar. When the scanning is complete, the message "Scan complete" will be displayed.

[0168] Step 3: Format and send the data

[0169] The terminal processes the collected point cloud data and performs preprocessing such as noise removal and smoothing. The processed data is then compressed and sent to the server.

[0170] Input: Collected point cloud data

[0171] Output: Formatted and compressed data

[0172] What it does: A denoising algorithm cleans up the data, a smoothing filter is applied, and once complete, the size of the cleaned data is compressed and a progress bar is displayed as the data is sent to the server.

[0173] Step 4: Data receiving module

[0174] The server receives the point cloud data sent from the device, which is first subjected to a consistency check to ensure there is no data corruption or inconsistency.

[0175] Input: Compressed data sent from the terminal

[0176] Output: Data with integrity checked

[0177] Specific operation: The server receives the data, performs a hash check and checks the number of bytes of the data. If consistency is confirmed, it logs "Data reception completed."

[0178] Step 5: Generative AI module

[0179] The server inputs the received data into the generative AI model and generates 3D data according to the user's prompts. The generated data is exported as an .obj file.

[0180] Input: Data that has been validated and a prompt from the user

[0181] Output: Generated .obj file

[0182] Specific behavior: The prompt (e.g., "Please extend the chair legs by 10 cm") is analyzed, and the AI ​​generator executes the specified process. The resulting .obj file is generated.

[0183] Step 6: File Creation and Management

[0184] The generated .obj file is stored in a file management system, where the server generates a file ID and sends the user a download link.

[0185] Input: .obj file

[0186] Output: File ID and download link

[0187] What happens: The server saves the .obj file and generates a unique file ID. It uses this ID to generate a download link and sends a "download link ready" notification to the user.

[0188] Step 7: Enter requirements via chat interface

[0189] Users input their requirements (e.g., "Please increase the height of the chair legs by 10 cm") through the chatbot, and this interface provides a convenient way for users to communicate their specifications in detail.

[0190] Input: User requirements

[0191] Output: Prompt statement based on your requirements

[0192] Specific behavior: The chatbot receives user input and displays a confirmation message to generate a prompt. The user is notified that "the specified requirements have been registered."

[0193] Step 8: Check and adjust specifications

[0194] The server displays a preview of the generated .obj file in a chat interface, allowing the user to check the preview and make corrections if necessary.

[0195] Input: Generated .obj file

[0196] Output: User's correction instructions

[0197] Specific operation: The server generates a low-resolution model for preview, and the chatbot prompts the user to "check the generated results and enter any corrections." The user then enters the corrections into the chat screen.

[0198] Step 9: Download Module

[0199] The server provides the user with a download link for the generated .obj file. The user clicks the link to download the .obj file.

[0200] Input: File ID and download link

[0201] Output: Downloaded .obj file

[0202] What happens: The server generates a download link and sends it to the user's email address or notification system. The user is notified that "Download link has been sent."

[0203] Step 10: Market Linkage Module

[0204] When a user submits a generated .obj file to the Content Marketplace, the server facilitates the process: the user enters the metadata for the marketplace submission (e.g., title, description, price, etc.), which is then uploaded to the marketplace.

[0205] Input: User metadata and .obj file

[0206] Output: Listing on Content Market

[0207] Specific operation: The server provides the user with a listing template and prompts them to enter the necessary metadata. After the user fills in the "title," "description," and "price," the server sends the data to the marketplace and notifies the user that "listing is complete."

[0208] (Application example 1)

[0209] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0210] When a robot or equipment part breaks down in a logistics center, it is necessary to replace or repair the part quickly and accurately. However, conventional methods require time and cost to identify, design, and manufacture the part, resulting in reduced operational efficiency. The objective of this invention is to solve these problems and provide a system that enables efficient and rapid parts management and repair.

[0211] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0212] In this invention, the server includes a measuring means for measuring an object in three dimensions, a transmitting means for transmitting the measurement data acquired by the measuring means, a generating means for generating three-dimensional data based on the received measurement data, a providing means for providing the generated three-dimensional data to a user, and a managing means for supporting preventive maintenance and repairs. This enables the logistics center to quickly identify faulty parts, generate accurate three-dimensional data for the necessary parts, and immediately manufacture them using a 3D printer.

[0213] "Measuring means for measuring an object in three dimensions" refers to equipment or technology for obtaining the shape and dimensions of an object in three-dimensional space.

[0214] The "transmission means for transmitting the measurement data acquired by the measurement means" refers to a mechanism for transferring the data collected by the measurement means to another device or server.

[0215] The "generation means for generating three-dimensional data based on the received measurement data" refers to an algorithm or system for creating a three-dimensional model from the measurement data received via the transmission means.

[0216] "Means for providing generated three-dimensional data to users" refers to technologies and methods for enabling users to access the generated three-dimensional data.

[0217] "Management measures to support preventive maintenance and repair" refers to systems and methods to support the efficient maintenance and management of equipment and robots, as well as the replacement and repair of parts.

[0218] "LiDAR sensor" refers to a sensor technology that uses laser light to precisely measure the distance and shape of an object.

[0219] This invention is a system that measures an object in three dimensions and provides the data in a format that can be used by a 3D printer. The system mainly consists of three elements: a terminal, a server, and a user.

[0220] The device uses a smartphone equipped with a LiDAR sensor as a device for users to measure objects in three dimensions. First, the user opens the smartphone app and activates the LiDAR scanning module. This module controls the LiDAR sensor and collects three-dimensional data of the object. The collected data is displayed on the device in real time, allowing the user to check the progress of the scan. Once the scan is complete, the collected data is formatted and sent from the device to a server.

[0221] The server receives the point cloud data sent from the device. Here, preprocessing such as data consistency checks, noise removal, and smoothing is performed. Using a generative AI model, 3D data based on the user's requirements is generated from the received data. This 3D data is exported as an .obj file and saved in a file management system. The user is then notified of a download link for the generated file.

[0222] In the user interface, the user inputs requirements through a chatbot. For example, a specific request such as "Please increase the height of the chair legs by 10 cm" can be entered in the chat interface. The generative AI model generates 3D data based on this prompt. A preview of the generated .obj file is displayed in the chat interface for the user to review. If necessary, they can also provide instructions for corrections.

[0223] Next, users download the generated .obj file and use it in their own 3D printer. For example, by scanning a broken robot arm part and manufacturing the generated 3D data with a 3D printer, it is possible to quickly and accurately replace or repair the part. The server also supports users in listing the .obj file on the content market. By entering listing metadata, the file can be sold online.

[0224] As a concrete example, if a joint on a robot breaks, the user scans the joint using the LiDAR sensor on their smartphone and sends the data to a server. The generation AI generates three-dimensional data on the joint and provides it as a download link. The user then prints the resulting data on a 3D printer to quickly manufacture a new joint. An example of this prompt could be, "Yesterday, a joint on a robot arm in the warehouse broke. I would like to scan it and print a new joint on a 3D printer. After starting the LiDAR scan, please have the generation AI convert the joint data into 3D data and output it in .obj format."

[0225] In this way, the management and replacement of parts at the logistics center can be made more efficient.

[0226] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0227] Step 1:

[0228] The user opens the smartphone app and activates the LiDAR scanning module, which controls the smartphone's LiDAR sensor and collects 3D data of the object. The input is the user's actions and the target object, and the output is point cloud data acquired in real time.

[0229] Step 2:

[0230] The device processes the collected point cloud data. Specifically, preprocessing such as noise removal and smoothing is performed. The input is the point cloud data, and the output is the processed point cloud data. Once processing is complete, the processed data is compressed and ready for transmission.

[0231] Step 3:

[0232] The terminal sends the formatted point cloud data to the server. Specifically, it sends the data to the server via an HTTP request. The input is the formatted and compressed point cloud data, and the output is a successful data transmission response to the server.

[0233] Step 4:

[0234] The server receives point cloud data sent from the device. The data receiving module checks the integrity of the received data and performs preprocessing such as noise removal and smoothing. The input is the received point cloud data, and the output is the shaped point cloud data.

[0235] Step 5:

[0236] The server's generation AI model generates 3D data based on the shaped point cloud data. It executes the necessary generation process according to the user's prompt. The input is the shaped point cloud data and the user's prompt, and the output is the generated 3D data (.obj file format).

[0237] Step 6:

[0238] The server saves the generated 3D data in a file management system, generates a file ID, and sends a notification to the user to provide a download link. The input is the generated 3D data (.obj file), and the output is a notification of the download link.

[0239] Step 7:

[0240] The user clicks on the provided download link and downloads the generated 3D data (.obj file). This gives the user 3D data that can be used with a 3D printer. The input is the download link, and the output is the downloaded 3D data (.obj file).

[0241] Step 8:

[0242] Users use the downloaded 3D data to manufacture parts using a 3D printer. Specifically, they import the 3D data into the 3D printer and start manufacturing the part. The input is the 3D data (.obj file), and the output is the finished part.

[0243] This series of processing steps creates a system that allows efficient parts management and repairs at the logistics center.

[0244] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0245] This invention improves the user experience by combining a system that measures an object in three dimensions, easily generates three-dimensional data, and provides it in a format that can be used by a 3D printer with an emotion engine that recognizes the user's emotions. This system is composed of a terminal, a server, and a user. The processing of each element and specific examples are explained below.

[0246] 1. Terminal processing

[0247] Starting the LiDAR Scan Module

[0248] A user opens the smartphone app and activates the LiDAR scanning module, which controls the smartphone's LiDAR sensor to collect three-dimensional data about the object.

[0249] Scanning an object

[0250] The user points the smartphone at the object to be scanned, and the LiDAR sensor collects point cloud data of the object in real time. The device displays the data collection status on the screen, allowing the user to observe the scan until it is complete.

[0251] Formatting and sending data

[0252] The device processes the collected point cloud data, removes noise, and smooths it, then compresses the processed data and sends it to the server.

[0253] 2. Server-side processing

[0254] Data Receiving Module

[0255] The server receives the point cloud data sent from the device and checks the data integrity, making sure there are no missing or corrupted data.

[0256] Generative AI Module

[0257] The server inputs the received data into the generation AI, which then generates 3D data according to the user's requirements. The generated data is exported in .obj file format.

[0258] File Creation and Management

[0259] The generated .obj file is stored in the file management system, a file ID is generated, and the server generates a download link and notifies the user.

[0260] 3. User Interface Processing

[0261] Chat Interface

[0262] Users input their requirements (e.g., "Please increase the height of the chair legs by 10 cm") through the chatbot, and this interface provides a convenient way for users to communicate their specifications in detail.

[0263] Check and adjust specifications

[0264] The server displays a preview of the generated .obj file in a chat interface for the user to review and, if necessary, provide corrections.

[0265] 4. Emotional engine processing

[0266] emotion recognition

[0267] The emotion engine recognizes the user's emotions by using technology that analyzes the user's voice, facial expressions, and input content.

[0268] Emotion-Based Adjustment

[0269] The emotion engine automatically adjusts the requirements for the generated 3D data based on the user's emotions, and if the user appears anxious, it displays a dialog asking for further confirmation or provides an easy-to-understand explanation.

[0270] Suggestions and support

[0271] The emotion engine analyzes the user's emotions and provides suggestions and support based on the analysis results. For example, if the user is happy, it will suggest new designs and related options.

[0272] 5. Processing File Submissions and Market Listings

[0273] Download Module

[0274] The server provides the user with a download link for the generated .obj file. The user clicks the link to download the .obj file, which can then be used with the user's 3D printer.

[0275] Market Linkage Module

[0276] When a user submits a generated .obj file to the Content Marketplace, the server facilitates the process: the user enters the metadata for the marketplace submission (e.g., title, description, price, etc.), which is then uploaded to the marketplace.

[0277] Specific examples

[0278] Example 1: Generating and listing furniture parts using an emotion engine

[0279] 1. Scan

[0280] A user uses the LiDAR sensor on their smartphone to scan the broken chair leg, and within minutes the point cloud data is collected and reconstructed.

[0281] 2. Generation

[0282] The server receives the point cloud data, and the generation AI generates three-dimensional data according to the user-specified requirements.

[0283] 3. Emotion recognition

[0284] The emotion engine analyzes the user's facial and vocal responses to determine if they are anxious about the process, and if so, provides additional guidance to resolve the anxiety.

[0285] 4. Provide

[0286] The server provides a download link for the generated .obj file, which the user can then download and use to reproduce it on their 3D printer.

[0287] 5. Listing

[0288] When a user puts this data up for sale on the content market, the server assists in the process and the data is sold online.

[0289] This allows users to easily generate 3D data without any specialized knowledge, and then use it in a 3D printer or sell it on the market. Furthermore, the introduction of an emotion engine improves the user experience, providing a more intuitive and satisfying process.

[0290] The processing flow will be explained below.

[0291] Processing flow of a system that combines emotion engines

[0292] Terminal side processing

[0293] Step 1:

[0294] The user launches the app using their smartphone, which turns on the LiDAR scanning module.

[0295] Step 2:

[0296] The user points the smartphone at the object to be scanned, and the LiDAR sensor collects point cloud data of the object in real time. The device displays the data collection status on the screen, allowing the user to observe the scan until it is complete.

[0297] Step 3:

[0298] Once the user has finished scanning, the device will then shape the collected point cloud data, removing noise and smoothing it.

[0299] Step 4:

[0300] The device compresses the formatted point cloud data and transmits the data to the server.

[0301] Server-side processing

[0302] Step 5:

[0303] The server receives the point cloud data sent from the device and checks the data integrity, making sure there are no missing or corrupted data.

[0304] Step 6:

[0305] The server inputs the received data into the generation AI module, which then generates 3D data according to the user's requirements. The generated data is exported in .obj file format.

[0306] Step 7:

[0307] The server saves the generated .obj file to the file server, generates a file ID, and then generates a download link and notifies the user.

[0308] Emotion engine processing

[0309] Step 8:

[0310] The emotion engine recognizes the user's emotions by analyzing the user's voice, facial expressions, and input through the device or chat interface to determine the user's emotional state.

[0311] Step 9:

[0312] The emotion engine automatically adjusts the requirements of the generated 3D data based on the user's emotions. For example, if the user is anxious, the system will provide more specific guidance. Conversely, if the user is satisfied, it will suggest additional features and options.

[0313] Step 10:

[0314] The emotion engine provides suggestions and support to the user based on the analysis results. For example, if the user is detected as excited, it will suggest creating other related objects.

[0315] User Interface Handling

[0316] Step 11:

[0317] The user accesses the chat interface and receives a download link for the generated .obj file. The user downloads the file and uses it in a 3D printer.

[0318] Step 12:

[0319] If a user wants to modify an .obj file, they input their specific requirements through the chatbot, such as "Please make the chair legs 10cm taller."

[0320] Step 13:

[0321] The server runs the AI ​​module again based on the new requirements, generates the corrected 3D data, and resends the download link for the newly generated .obj file.

[0322] Processing file submissions and market listings

[0323] Step 14:

[0324] The server provides the user with a download link for the generated .obj file. The user clicks the link to download the .obj file, which can then be used with the user's 3D printer.

[0325] Step 15:

[0326] When a user creates an .obj file and submits it to the Content Market, the server assists the process. The user enters the metadata for the market listing (title, description, price, etc.).

[0327] Step 16:

[0328] The server sends a listing completion notice to the user and starts selling online.

[0329] Specific examples

[0330] Example 1: Generating and listing furniture parts using an emotion engine

[0331] Step 1:

[0332] A user launches the app on their smartphone and scans the broken chair leg.

[0333] Step 2:

[0334] The smartphone's LiDAR sensor collects data in real time, and the device formats and compresses the data before sending it to a server.

[0335] Step 3:

[0336] The server receives the data and uses a generative AI module to generate 3D data, which is then exported in .obj file format.

[0337] Step 4:

[0338] The server saves the generated .obj file to a file server and sends the user a download link.

[0339] Step 5:

[0340] The emotion engine analyzes the user's facial expressions and inputs to detect when they are feeling anxious, and provides detailed explanations and additional guidance.

[0341] Step 6:

[0342] The user checks the file and requests additional modifications as needed through the chat interface. The server then runs the generation AI again based on the requested modifications.

[0343] Step 7:

[0344] The server then provides the modified .obj file again, which the user can download and use in their 3D printer.

[0345] Step 8:

[0346] When a user submits a generated .obj file to the marketplace, the server facilitates the process: the user enters metadata and the file is uploaded to the marketplace.

[0347] Step 9:

[0348] The server will send a notice of completion of listing to the user, and online sales will begin.

[0349] This system allows users to easily generate 3D data without any specialized knowledge, and then use it in a 3D printer or sell it on the market. Furthermore, the introduction of an emotion engine improves the user experience, providing a more intuitive and satisfying process.

[0350] Example 2

[0351] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0352] While traditional 3D measurement systems have the ability to generate 3D data of objects, they lack the flexibility to respond to user emotions and requirements, making it difficult to improve the user experience. In particular, there is a lack of appropriate support and explanations when users feel anxious or stressed about the process.

[0353] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0354] In this invention, the server includes a measuring means for measuring an object in three dimensions, a transmitting means for transmitting the measurement data acquired by the measuring means, a generating means for generating three-dimensional data based on the received measurement data, a providing means for providing the generated three-dimensional data to a user, and an emotion recognizing means for recognizing the user's emotion and adjusting the requirements for the three-dimensional data based on the emotion. This enables flexible responses in line with the user's emotion, providing a more satisfying user experience.

[0355] "Object" refers to any three-dimensional object from which three-dimensional data is acquired and measured.

[0356] "Measuring means for three-dimensional measurement" refers to a device or group of devices for obtaining three-dimensional shape information of an object.

[0357] "Measurement data acquired by a measurement means" refers to three-dimensional shape information of an object obtained using a measurement means.

[0358] "Transmitting means" refers to a device or method for transmitting acquired measurement data to another device or system.

[0359] "Received measurement data" refers to data received from data transmitted by a transmitting means.

[0360] The "means for generating three-dimensional data" refers to a device or algorithm for generating three-dimensional shape data based on the received measurement data.

[0361] "Generated three-dimensional data" refers to three-dimensional shape data created by a generating means.

[0362] "Providing means" refers to a device or method for providing the generated three-dimensional data to a user.

[0363] "Emotion recognition means" refers to a device or algorithm that recognizes a user's emotions and adjusts other processes based on that information.

[0364] This invention relates to a system that measures an object in three dimensions, easily generates three-dimensional data, and provides it in a format that can be used by a 3D printer. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the user experience can be improved. This system is composed of a terminal, a server, and a user. The specific processing of each element is described below.

[0365] Terminal side processing

[0366] The user launches the smartphone app and activates the LiDAR scanning module. The LiDAR sensor collects three-dimensional data of the object and generates point cloud data in real time. The device then processes this data with noise reduction and smoothing, compresses it, and sends it to the server.

[0367] Server-side processing

[0368] The server receives the point cloud data sent from the device and verifies the data's integrity. It then generates 3D data using a generative AI model. Examples of such models include OpenAI's GPT series and Codex. This model generates 3D data in the .obj file format, which the server saves in a file management system. The generated data is provided as a download link and notified to the user.

[0369] User Interface Handling

[0370] The user inputs their requirements through the chatbot interface. For example, "Please increase the height of the chair legs by 10 cm." The chatbot then analyzes the input and sends it to the server. A preview of the generated .obj file is displayed in the chat interface, allowing the user to review and make corrections.

[0371] Emotion engine processing

[0372] The emotion engine analyzes the user's voice, facial expressions, and input to recognize their emotions. Based on this information, the system can respond to the user's needs. If the user is anxious, a dialog will appear requesting further explanation or confirmation. If the user is happy, the system will suggest relevant options.

[0373] Processing file submissions and market listings

[0374] The server provides a download link for the generated .obj file, which the user can download and use in a 3D printer. Furthermore, if the user wants to list the generated 3D data on the content market, the server will assist in the process of listing it on the marketplace.

[0375] As a specific example of how it works, a user scans the legs of a broken chair with their smartphone, and new 3D data is generated based on that data. If the emotion engine senses anxiety from the user's facial expression or voice, additional explanations are displayed. Finally, the generated 3D data is provided to the user, who can then print it using a 3D printer. This data can also be put up for sale on an online marketplace.

[0376] These features provide a system that allows users to easily generate and utilize 3D data, even if they do not have specialized knowledge. The introduction of an emotion engine further improves the user experience.

[0377] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0378] System program processing flow

[0379] Step 1:

[0380] The user launches the smartphone app and activates the LiDAR scanning module.

[0381] Input: Smartphone app launch command

[0382] What happens: The app wakes up the LiDAR sensor and begins calibrating it, ready to scan.

[0383] Output: The LiDAR sensor is now active and a ready notification is displayed on the screen.

[0384] Step 2:

[0385] The user points the smartphone at the object to be scanned and performs the scan.

[0386] Input: Smartphone pointed at object and command to start scanning

[0387] How it works: The LiDAR sensor collects point cloud data in real time, and the collection status is displayed on the device screen.

[0388] Output: The collected point cloud data is temporarily saved on the device.

[0389] Step 3:

[0390] The point cloud data collected by the device is shaped, denoised and smoothed.

[0391] Input: Collected point cloud data

[0392] How it works: The algorithm denoises the data and performs a smoothing process, as well as compression.

[0393] Output: Formatted and compressed point cloud data is generated.

[0394] Step 4:

[0395] The terminal transmits the formatted data to the server.

[0396] Input: shaped and compressed point cloud data

[0397] Operation: The data transmission function is activated and data is uploaded to the server.

[0398] Output: Point cloud data sent to the server is received.

[0399] Step 5:

[0400] Check the integrity of the point cloud data received by the server.

[0401] Input: Point cloud data sent to the server

[0402] How it works: Integrity checking algorithms check for errors and missing data.

[0403] Output: Data is saved successfully with integrity checked.

[0404] Step 6:

[0405] The server generates 3D data using a generative AI model (e.g., OpenAI's GPT series).

[0406] Input: Normal point cloud data

[0407] How it works: Input data into the generative AI model and perform calculations to generate 3D shape data.

[0408] Output: The generated 3D data in .obj file format is temporarily saved.

[0409] Step 7:

[0410] The server stores the generated .obj file in a file management system and generates a download link.

[0411] Input: Generated 3D data (.obj file)

[0412] How it works: The file management system saves the data and generates a unique file ID. A download link is created.

[0413] Output: A download link is generated and notified to the user.

[0414] Step 8:

[0415] The user enters their requirements into the chatbot interface (e.g., "Please extend the height of the chair legs by 10 cm").

[0416] Input: Requirement text entered by the user

[0417] How it works: The chatbot parses the text and sends a request to the server.

[0418] Output: The request is sent to the server.

[0419] Step 9:

[0420] The server generates a preview link and sends it to the user.

[0421] Input: User requirements and generated .obj files

[0422] What it does: The preview link generator runs, creates a link, and sends it to the user.

[0423] Output: A preview link is provided to the user.

[0424] Step 10:

[0425] The emotion engine analyzes the user's voice, facial expressions, and input content to recognize emotions.

[0426] Input: User voice, facial expressions, and text input data

[0427] How it works: Emotion recognition algorithms analyze and assess the user's emotional state.

[0428] Output: The user's emotional state is evaluated within the system.

[0429] Step 11:

[0430] An emotion engine adjusts the process based on the user's emotions.

[0431] Input: Emotion recognition results

[0432] Action: The process is adjusted based on the emotion, for example, providing additional explanations to a user who feels anxious.

[0433] Output: The coordinated process is executed and feedback is provided to the user.

[0434] Step 12:

[0435] The user clicks the download link and downloads the generated .obj file.

[0436] Input: Download link provided by the server

[0437] What happens: The user clicks on the link and the file is downloaded.

[0438] Output: The downloaded .obj file is saved to the user's device.

[0439] Step 13:

[0440] The user prints the generated .obj file on a 3D printer.

[0441] Input: Downloaded .obj file

[0442] What it does: Sends a file to a 3D printer and starts the printing process.

[0443] Output: A physical, three-dimensional object is created.

[0444] Step 14:

[0445] The user puts the generated three-dimensional data up for sale on the content market.

[0446] Input: .obj file and marketplace listing metadata (title, description, price, etc.)

[0447] What happens: The server uploads data to the marketplace.

[0448] Output: The submitted 3D data will be published on the marketplace.

[0449] (Application example 2)

[0450] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0451] In modern factories, quickly and accurately repairing broken machine parts is crucial to maintaining production efficiency. However, conventional systems often require specialized technical personnel to acquire 3D data of the broken part and create the appropriate repair part. Furthermore, the lack of mechanisms to reduce user anxiety and stress during the data generation and provision process makes it difficult to provide a satisfactory user experience. Therefore, there is a need for the development of a system that can quickly and easily generate 3D data without specialized knowledge and provide support tailored to the user's emotions.

[0452] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0453] In this invention, the server includes a generating means for generating three-dimensional data of an object, an emotion recognizing means for recognizing a user's emotion, and an adjusting means for adjusting the requirements of the three-dimensional data based on the emotion recognizing means. This makes it possible to quickly and accurately generate three-dimensional data while taking into account the user's emotion, even without specialized knowledge, and to manufacture appropriate repair parts.

[0454] "Measuring means for measuring an object in three dimensions" refers to a device or module that collects three-dimensional data of an object using a LiDAR sensor or the like.

[0455] The "transmission means for transmitting the measurement data acquired by the measurement means" is a device or module having the function of transmitting the acquired three-dimensional data to the server.

[0456] The "means for generating three-dimensional data based on received measurement data" refers to a device or software module that generates a three-dimensional model based on received point cloud data.

[0457] The "means for providing the generated three-dimensional data to the user" is a device or module that has the function of providing the generated three-dimensional model in a format that the user can use.

[0458] The "emotion recognition means for recognizing the user's emotions" is a device or software module that recognizes emotions by analyzing the user's voice, facial expression, input contents, etc.

[0459] The "adjustment means for adjusting the requirements for three-dimensional data based on the emotion recognition means" refers to a device or software module that has the function of adjusting the specifications of the three-dimensional data to be generated and the method of providing it based on the recognized emotion of the user.

[0460] MODE FOR CARRYING OUT THE INVENTION

[0461] The present invention is aimed at improving the efficiency of maintenance and repair work, mainly in factories. Specific embodiments for carrying out the present invention will be described below.

[0462] 1. Hardware Configuration

[0463] Smartphone

[0464] It uses a smartphone equipped with a LiDAR sensor, which can scan broken parts and objects for 3D data with high accuracy.

[0465] Maintenance robot

[0466] Maintenance robots are used to repair and replace parts, and these robots act automatically based on commands from the server.

[0467] server

[0468] It uses a server that includes a generative AI model and an emotion recognition engine. The server receives the scanned data and performs a series of processes: processing, generating, and providing.

[0469] 2. Software Configuration

[0470] LiDAR Scan Module

[0471] It is a software module that is installed on a smartphone and is used to collect three-dimensional data of objects.

[0472] Data Formatting Tools

[0473] This is a tool that processes scanned point cloud data, removes noise, and smooths the data.

[0474] Generative AI Models

[0475] It is an AI model that runs on a server and generates highly accurate 3D models based on formatted 3D data.

[0476] Emotion Recognition Engine

[0477] It is a software module that analyzes the user's voice, facial expressions, and input content to recognize emotions, and utilizes emotional data to improve the user experience.

[0478] 3. Operational flow

[0479] scan

[0480] The device uses its LiDAR sensor to scan broken parts and objects, collecting point cloud data within minutes.

[0481] Data Formatting

[0482] The terminal processes the collected point cloud data, removes noise, and smooths it, then compresses it and sends it to the server.

[0483] 3D data generation

[0484] The server inputs the received data into a generative AI model to generate 3D data according to the user's requirements, which is then output in .obj file format.

[0485] Emotion Recognition and Regulation

[0486] The emotion recognition engine analyzes the user's voice and facial expressions to determine if they are anxious, and if so, the engine provides additional guidance and explanations.

[0487] Parts manufacturing

[0488] The generated 3D data is sent to a maintenance robot, which then produces the repair parts, which are then ready for immediate use.

[0489] 4. Specific Examples

[0490] For example, if part of a robot arm breaks in a factory, repairs are carried out using the following procedure.

[0491] 1. The maintenance robot detects the broken part and scans it using the smartphone's LiDAR sensor. The scan takes about 1-2 minutes.

[0492] 2. The scan data is formatted, denoised and smoothed on the device before being sent to the server.

[0493] 3. The server uses the generative AI model to generate 3D data and outputs it as an .obj file.

[0494] 4. An emotion recognition engine analyzes the operator's emotions and provides additional guidance and support if they are feeling anxious.

[0495] 5. The maintenance robot uses the generated data to 3D print repair parts, making them immediately usable.

[0496] 5. Examples of prompts

[0497] "We propose a new system that scans broken parts in your factory and automatically creates repair parts based on the generated 3D data. It uses a smartphone's LiDAR sensor to create 3D data of the broken part within minutes, and uses an emotion engine to understand and support the operator's emotions. This enables fast and efficient repairs."

[0498] This system is expected to make factory maintenance work more efficient, reduce user stress, and improve overall work efficiency.

[0499] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0500] Step 1:

[0501] The device launches the LiDAR scanning module. The user opens the smartphone app and uses the LiDAR sensor to scan the broken part. This allows 3D data (point cloud data) to be collected in real time. The input is the smartphone's LiDAR data, and the output is the scanned point cloud data.

[0502] Step 2:

[0503] The terminal processes the point cloud data. It removes noise from the collected data and smooths it. This data processing is performed using a software tool. The input is the scanned point cloud data, and the output is the denoised and smoothed point cloud data.

[0504] Step 3:

[0505] The terminal sends the formatted data to the server. The compressed 3D data is sent to the server via the network. The input is the formatted point cloud data, and the output is the data sent to the server.

[0506] Step 4:

[0507] The server processes the data received. It verifies the integrity of the received data and checks for missing or corrupted data. The input is the point cloud data sent to the server, and the output is the point cloud data whose integrity has been verified.

[0508] Step 5:

[0509] The server inputs data into the generated AI model and generates 3D data. A highly accurate 3D model (.obj file) is generated based on the received data. The input is point cloud data whose consistency has been confirmed, and the output is the generated 3D model (.obj file).

[0510] Step 6:

[0511] The server uses an emotion recognition engine to recognize the user's emotions. It analyzes the user's voice, facial expression, and input content to determine their emotional state. The input is the user's voice, facial expression, and input content, and the output is the analyzed emotional data.

[0512] Step 7:

[0513] The server adjusts the requirements for 3D data based on the emotion data. If the user feels anxious, it adjusts the generated data and processes and provides additional guidance and explanations. The input is the analyzed emotion data, and the output is the adjusted 3D data and guidance information.

[0514] Step 8:

[0515] The server provides the generated 3D data to the user. The generated 3D data is provided to the user as a downloadable link. The input is the adjusted 3D data, and the output is the download link.

[0516] Step 9:

[0517] The user downloads the 3D data using the provided download link. The downloaded data can then be used to produce actual repair parts using the maintenance robot's 3D printer. The input is the download link, and the output is the downloaded 3D data.

[0518] 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.

[0519] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0520] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0521] [Second embodiment]

[0522] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0523] 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.

[0524] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0525] 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.

[0526] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0527] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0528] 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.

[0529] 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.

[0530] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0531] 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.

[0532] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0533] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0534] This invention is a system that measures an object in three dimensions, easily generates three-dimensional data, and provides it in a format that can be used by a 3D printer. This system consists of a terminal, a server, and a user. The processing of each element and specific examples are explained below.

[0535] 1. Terminal processing

[0536] Starting the LiDAR Scan Module

[0537] A user opens the smartphone app and activates the LiDAR scanning module, which controls the smartphone's LiDAR sensor to collect three-dimensional data about the object.

[0538] Scanning an object

[0539] Users point their smartphone at the object they want to scan and use the LiDAR sensor to collect point cloud data of the object. This data is displayed on the device in real time, allowing users to see the progress of the scan. Once the scan is complete, the collected data is formatted and ready to be sent.

[0540] Formatting and sending data

[0541] The terminal processes the collected point cloud data and performs preprocessing such as noise removal and smoothing. The processed data is then compressed and sent to the server.

[0542] 2. Server-side processing

[0543] Data Receiving Module

[0544] The server receives the point cloud data sent from the device, passes the integrity check, and performs the necessary processing.

[0545] Generative AI Module

[0546] The server inputs the received data into the generation AI, which generates 3D data according to the user's requirements. The generated data is exported in .obj file format.

[0547] File Creation and Management

[0548] The generated .obj file is stored in the file management system and a file ID is generated, and the server generates a download link for this file and notifies the user.

[0549] 3. User Interface

[0550] Chat Interface

[0551] Users input their requirements (e.g., "Please increase the height of the chair legs by 10 cm") through the chatbot, and this interface provides a convenient way for users to communicate their specifications in detail.

[0552] Check and adjust specifications

[0553] The server displays a preview of the generated .obj file in a chat interface for the user to review and, if necessary, provide corrections.

[0554] 4. File Submission and Market Listing

[0555] Download Module

[0556] The server provides the user with a download link for the generated .obj file. The user clicks the link to download the .obj file, which can then be used with the user's 3D printer.

[0557] Market Linkage Module

[0558] When a user submits a generated .obj file to the Content Marketplace, the server facilitates the process: the user enters the metadata for the marketplace submission (e.g., title, description, price, etc.), which is then uploaded to the marketplace.

[0559] Specific examples

[0560] Example 1: Reproducing furniture parts

[0561] 1. Scan

[0562] A user uses the LiDAR sensor on their smartphone to scan the broken chair leg, and within minutes the point cloud data is collected and reconstructed.

[0563] 2. Generation

[0564] The server receives the point cloud data, and the generation AI generates three-dimensional data according to the user-specified requirements.

[0565] 3. Provide

[0566] The server provides a download link for the generated .obj file, which the user can then download and use to reproduce it on their 3D printer.

[0567] 4. Listing

[0568] When a user puts this data up for sale on the content market, the server assists in the process and the data is sold online.

[0569] This system allows users to easily generate 3D data without requiring specialized skills and use it in a 3D printer. Furthermore, they can earn secondary income by putting the generated data on the market.

[0570] The processing flow will be explained below.

[0571] Program processing flow

[0572] Terminal side processing

[0573] Step 1:

[0574] The user launches the app using their smartphone, which turns on the LiDAR scanning module.

[0575] Step 2:

[0576] The user points the smartphone at the object to be scanned, and the LiDAR sensor collects point cloud data of the object in real time. The device displays the data collection status on the screen, allowing the user to observe the scan until it is complete.

[0577] Step 3:

[0578] Once the user has finished scanning, the device will then shape the collected point cloud data, removing noise and smoothing it.

[0579] Step 4:

[0580] The device compresses the formatted point cloud data and transmits the data to the server.

[0581] Server-side processing

[0582] Step 5:

[0583] The server receives the point cloud data sent from the device and checks the data integrity, making sure there are no missing or corrupted data.

[0584] Step 6:

[0585] The server inputs the received data into the generation AI module, which then generates 3D data according to the user's requirements. The generated data is exported as an .obj file.

[0586] Step 7:

[0587] The server saves the generated .obj file to the file server, generates a file ID, and then generates a download link and notifies the user.

[0588] User Interface Handling

[0589] Step 8:

[0590] The user accesses the chat interface and receives a download link for the generated .obj file. The user downloads the file and uses it in a 3D printer.

[0591] Step 9:

[0592] If a user wants to modify an .obj file, they input their specific requirements through the chatbot, for example, "Please increase the height of the chair legs by 10cm."

[0593] Step 10:

[0594] The server runs the AI ​​module again based on the new requirements, generates the corrected 3D data, and resends the download link for the newly generated .obj file.

[0595] Processing file submissions and market listings

[0596] Step 11:

[0597] Users can download the files and use them in their home 3D printers as needed, or if they want to list their work on the market, the server assists them with the listing process.

[0598] Step 12:

[0599] The user enters the metadata for a marketplace listing (title, description, price, etc.), and the server uploads it to the marketplace.

[0600] Step 13:

[0601] The server sends a listing completion notice to the user and starts selling online.

[0602] This allows users to easily generate three-dimensional data without any specialized knowledge, and use it in a 3D printer or sell it on the market.

[0603] Example 1

[0604] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0605] Conventional methods for generating 3D data of objects require expensive equipment and specialized knowledge, making it difficult for general users to use. Furthermore, the methods for providing the generated data are often limited, restricting the scope of use for users. Therefore, there was a need for a system that could easily acquire and generate high-quality 3D data and enable users to easily use that data.

[0606] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0607] In this invention, the server includes a measuring means for measuring an object in three dimensions through a user interface, a transmitting means for transmitting point cloud data acquired by the measuring means, a generating means for generating three-dimensional data by shaping and preprocessing the received point cloud data, and a providing means for providing the generated three-dimensional data to a user. This makes it possible to easily acquire high-quality three-dimensional data without specialized knowledge and provide it to a user in a format that is easily usable.

[0608] A "user interface" is the means by which a user interacts with a system and inputs instructions and information.

[0609] "Means for measuring an object in three dimensions" refers to a technology or device for measuring the three-dimensional shape or structure of an object and acquiring it as digital data.

[0610] "Point cloud data" is three-dimensional data that represents a collection of multiple points that make up the surface of an object.

[0611] "Transmission means" refers to a technique or device for transmitting acquired data to another system or server.

[0612] "Receiving means" refers to a technique or device for receiving data sent from an external source.

[0613] "Means for shaping and preprocessing" refers to technology or equipment for improving the quality of data by performing processes such as noise removal and smoothing on the acquired raw data.

[0614] "Means for generating 3D data" refers to a technique or device for creating 3D models or shape data from pre-processed data.

[0615] "Providing means" refers to the technology or device for transmitting the generated three-dimensional data to the user or providing it in the form of a download link, etc.

[0616] A "downloadable link" is a web link that allows a user to receive a file or data specified by the user over the Internet.

[0617] The present invention is a system that measures an object in three dimensions, easily generates three-dimensional data, and provides it in a format that can be used by a 3D printer. This system is composed of a user, a terminal, and a server.

[0618] Terminal side processing

[0619] Starting the LiDAR Scan Module

[0620] The user opens the smartphone app and activates the LiDAR scanning module, which controls the smartphone's LiDAR sensor and collects 3D data of the object. The specific hardware used is the smartphone's built-in LiDAR sensor.

[0621] Scanning an object

[0622] Users point their smartphone at the object they want to scan and use the LiDAR sensor to collect point cloud data of the object. This data is displayed on the device in real time, allowing users to see the progress of the scan. Once the scan is complete, the collected data is formatted and ready to be sent.

[0623] Formatting and sending data

[0624] The terminal processes the collected point cloud data and performs preprocessing such as noise removal and smoothing. The processed data is then compressed and sent to the server. Specific software used includes data compression algorithms and noise filtering techniques.

[0625] Server-side processing

[0626] Data Receiving Module

[0627] The server receives the point cloud data sent from the device, passes the integrity check, and performs the necessary processing.

[0628] Generative AI Module

[0629] The server inputs the received data into a generative AI model and generates 3D data according to the user's requirements. The generated data is exported in .obj file format. The generative AI model uses a generative AI engine that analyzes the user's prompt and executes the specified processing.

[0630] File Creation and Management

[0631] The generated .obj file is stored in the file management system and a file ID is generated, and the server generates a download link for this file and notifies the user.

[0632] User Interface

[0633] Chat Interface

[0634] Users input their requirements (e.g., "Please increase the height of the chair legs by 10 cm") through the chatbot, and this interface provides a convenient way for users to communicate their specifications in detail.

[0635] Check and adjust specifications

[0636] The server displays a preview of the generated .obj file in a chat interface for the user to review and, if necessary, provide corrections.

[0637] File submission and market listing

[0638] Download Module

[0639] The server provides the user with a download link for the generated .obj file. The user clicks the link to download the .obj file, which can then be used with the user's 3D printer.

[0640] Market Linkage Module

[0641] When a user submits a generated .obj file to the Content Marketplace, the server facilitates the process: the user enters the metadata for the marketplace submission (e.g., title, description, price, etc.), which is then uploaded to the marketplace.

[0642] Specific examples

[0643] Example 1: Reproducing furniture parts

[0644] 1. Scan

[0645] A user uses the LiDAR sensor on their smartphone to scan the broken chair leg, and within minutes the point cloud data is collected and reconstructed.

[0646] 2. Generation

[0647] The server receives the point cloud data, and the generation AI generates three-dimensional data according to the user-specified requirements.

[0648] 3. Provide

[0649] The server provides a download link for the generated .obj file, which the user can then download and use to reproduce it on their 3D printer.

[0650] 4. Listing

[0651] When a user puts this data up for sale on the content market, the server assists in the process and the data is sold online.

[0652] Prompt Sentence Examples

[0653] Prompt 1: "I scanned a broken chair leg. From the generated data, please generate a version of the chair leg that is 10cm taller."

[0654] Prompt 2: "Generate repair data for the broken vase based on the collected point cloud data."

[0655] This system allows users to easily generate high-quality 3D data without requiring specialized skills and use it in 3D printers. Users can also earn secondary income by listing the generated data on a marketplace.

[0656] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0657] Step 1: Start the LiDAR Scan Module

[0658] The user launches the smartphone app and selects the LiDAR scan module, which causes the device to activate the internal LiDAR sensor and prepare for scanning.

[0659] Input: User action (selection of LiDAR scan module)

[0660] Output: LiDAR sensor wake-up and scan readiness notification

[0661] What it does: When the app launches, the device will begin calibrating the LiDAR sensor and display a "Ready to scan" notification to the user.

[0662] Step 2: Scan the object

[0663] Users point their smartphone at the object they want to scan and press the "Start Scan" button in the app. The device uses its LiDAR sensor to collect point cloud data of the object. This data is displayed on the device's screen in real time, allowing users to track the progress.

[0664] Input: User actions (pressing the start scan button) and data from the LiDAR sensor

[0665] Output: Point cloud data displayed in real time

[0666] Specific operation: During scanning, the device screen will construct 3D data of the object in real time and display a progress bar. When the scanning is complete, the message "Scan complete" will be displayed.

[0667] Step 3: Format and send the data

[0668] The terminal processes the collected point cloud data and performs preprocessing such as noise removal and smoothing. The processed data is then compressed and sent to the server.

[0669] Input: Collected point cloud data

[0670] Output: Formatted and compressed data

[0671] What it does: A denoising algorithm cleans up the data, a smoothing filter is applied, and once complete, the size of the cleaned data is compressed and a progress bar is displayed as the data is sent to the server.

[0672] Step 4: Data receiving module

[0673] The server receives the point cloud data sent from the device, which is first subjected to a consistency check to ensure there is no data corruption or inconsistency.

[0674] Input: Compressed data sent from the terminal

[0675] Output: Data with integrity checked

[0676] Specific operation: The server receives the data, performs a hash check and checks the number of bytes of the data. If consistency is confirmed, it logs "Data reception completed."

[0677] Step 5: Generative AI module

[0678] The server inputs the received data into the generative AI model and generates 3D data according to the user's prompts. The generated data is exported as an .obj file.

[0679] Input: Data that has been validated and a prompt from the user

[0680] Output: Generated .obj file

[0681] Specific behavior: The prompt (e.g., "Please extend the chair legs by 10 cm") is analyzed, and the AI ​​generator executes the specified process. The resulting .obj file is generated.

[0682] Step 6: File Creation and Management

[0683] The generated .obj file is stored in a file management system, where the server generates a file ID and sends the user a download link.

[0684] Input: .obj file

[0685] Output: File ID and download link

[0686] What happens: The server saves the .obj file and generates a unique file ID. It uses this ID to generate a download link and sends a "download link ready" notification to the user.

[0687] Step 7: Enter requirements via chat interface

[0688] Users input their requirements (e.g., "Please increase the height of the chair legs by 10 cm") through the chatbot, and this interface provides a convenient way for users to communicate their specifications in detail.

[0689] Input: User requirements

[0690] Output: Prompt statement based on your requirements

[0691] Specific behavior: The chatbot receives user input and displays a confirmation message to generate a prompt. The user is notified that "the specified requirements have been registered."

[0692] Step 8: Check and adjust specifications

[0693] The server displays a preview of the generated .obj file in a chat interface, allowing the user to check the preview and make corrections if necessary.

[0694] Input: Generated .obj file

[0695] Output: User's correction instructions

[0696] Specific operation: The server generates a low-resolution model for preview, and the chatbot prompts the user to "check the generated results and enter any corrections." The user then enters the corrections into the chat screen.

[0697] Step 9: Download Module

[0698] The server provides the user with a download link for the generated .obj file. The user clicks the link to download the .obj file.

[0699] Input: File ID and download link

[0700] Output: Downloaded .obj file

[0701] What happens: The server generates a download link and sends it to the user's email address or notification system. The user is notified that "Download link has been sent."

[0702] Step 10: Market Linkage Module

[0703] When a user submits a generated .obj file to the Content Marketplace, the server facilitates the process: the user enters the metadata for the marketplace submission (e.g., title, description, price, etc.), which is then uploaded to the marketplace.

[0704] Input: User metadata and .obj file

[0705] Output: Listing on Content Market

[0706] Specific operation: The server provides the user with a listing template and prompts them to enter the necessary metadata. After the user fills in the "title," "description," and "price," the server sends the data to the marketplace and notifies the user that "listing is complete."

[0707] (Application example 1)

[0708] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0709] When a robot or equipment part breaks down in a logistics center, it is necessary to replace or repair the part quickly and accurately. However, conventional methods require time and cost to identify, design, and manufacture the part, resulting in reduced operational efficiency. The objective of this invention is to solve these problems and provide a system that enables efficient and rapid parts management and repair.

[0710] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0711] In this invention, the server includes a measuring means for measuring an object in three dimensions, a transmitting means for transmitting the measurement data acquired by the measuring means, a generating means for generating three-dimensional data based on the received measurement data, a providing means for providing the generated three-dimensional data to a user, and a managing means for supporting preventive maintenance and repairs. This enables the logistics center to quickly identify faulty parts, generate accurate three-dimensional data for the necessary parts, and immediately manufacture them using a 3D printer.

[0712] "Measuring means for measuring an object in three dimensions" refers to equipment or technology for obtaining the shape and dimensions of an object in three-dimensional space.

[0713] The "transmission means for transmitting the measurement data acquired by the measurement means" refers to a mechanism for transferring the data collected by the measurement means to another device or server.

[0714] The "generation means for generating three-dimensional data based on the received measurement data" refers to an algorithm or system for creating a three-dimensional model from the measurement data received via the transmission means.

[0715] "Means for providing generated three-dimensional data to users" refers to technologies and methods for enabling users to access the generated three-dimensional data.

[0716] "Management measures to support preventive maintenance and repair" refers to systems and methods to support the efficient maintenance and management of equipment and robots, as well as the replacement and repair of parts.

[0717] "LiDAR sensor" refers to a sensor technology that uses laser light to precisely measure the distance and shape of an object.

[0718] This invention is a system that measures an object in three dimensions and provides the data in a format that can be used by a 3D printer. The system mainly consists of three elements: a terminal, a server, and a user.

[0719] The device uses a smartphone equipped with a LiDAR sensor as a device for users to measure objects in three dimensions. First, the user opens the smartphone app and activates the LiDAR scanning module. This module controls the LiDAR sensor and collects three-dimensional data of the object. The collected data is displayed on the device in real time, allowing the user to check the progress of the scan. Once the scan is complete, the collected data is formatted and sent from the device to a server.

[0720] The server receives the point cloud data sent from the device. Here, preprocessing such as data consistency checks, noise removal, and smoothing is performed. Using a generative AI model, 3D data based on the user's requirements is generated from the received data. This 3D data is exported as an .obj file and saved in a file management system. The user is then notified of a download link for the generated file.

[0721] In the user interface, the user inputs requirements through a chatbot. For example, a specific request such as "Please increase the height of the chair legs by 10 cm" can be entered in the chat interface. The generative AI model generates 3D data based on this prompt. A preview of the generated .obj file is displayed in the chat interface for the user to review. If necessary, they can also provide instructions for corrections.

[0722] Next, users download the generated .obj file and use it in their own 3D printer. For example, by scanning a broken robot arm part and manufacturing the generated 3D data with a 3D printer, it is possible to quickly and accurately replace or repair the part. The server also supports users in listing the .obj file on the content market. By entering listing metadata, the file can be sold online.

[0723] As a concrete example, if a joint on a robot breaks, the user scans the joint using the LiDAR sensor on their smartphone and sends the data to a server. The generation AI generates three-dimensional data on the joint and provides it as a download link. The user then prints the resulting data on a 3D printer to quickly manufacture a new joint. An example of this prompt could be, "Yesterday, a joint on a robot arm in the warehouse broke. I would like to scan it and print a new joint on a 3D printer. After starting the LiDAR scan, please have the generation AI convert the joint data into 3D data and output it in .obj format."

[0724] In this way, the management and replacement of parts at the logistics center can be made more efficient.

[0725] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0726] Step 1:

[0727] The user opens the smartphone app and activates the LiDAR scanning module, which controls the smartphone's LiDAR sensor and collects 3D data of the object. The input is the user's actions and the target object, and the output is point cloud data acquired in real time.

[0728] Step 2:

[0729] The device processes the collected point cloud data. Specifically, preprocessing such as noise removal and smoothing is performed. The input is the point cloud data, and the output is the processed point cloud data. Once processing is complete, the processed data is compressed and ready for transmission.

[0730] Step 3:

[0731] The terminal sends the formatted point cloud data to the server. Specifically, it sends the data to the server via an HTTP request. The input is the formatted and compressed point cloud data, and the output is a successful data transmission response to the server.

[0732] Step 4:

[0733] The server receives point cloud data sent from the device. The data receiving module checks the integrity of the received data and performs preprocessing such as noise removal and smoothing. The input is the received point cloud data, and the output is the shaped point cloud data.

[0734] Step 5:

[0735] The server's generation AI model generates 3D data based on the shaped point cloud data. It executes the necessary generation process according to the user's prompt. The input is the shaped point cloud data and the user's prompt, and the output is the generated 3D data (.obj file format).

[0736] Step 6:

[0737] The server saves the generated 3D data in a file management system, generates a file ID, and sends a notification to the user to provide a download link. The input is the generated 3D data (.obj file), and the output is a notification of the download link.

[0738] Step 7:

[0739] The user clicks on the provided download link and downloads the generated 3D data (.obj file). This gives the user 3D data that can be used with a 3D printer. The input is the download link, and the output is the downloaded 3D data (.obj file).

[0740] Step 8:

[0741] Users use the downloaded 3D data to manufacture parts using a 3D printer. Specifically, they import the 3D data into the 3D printer and start manufacturing the part. The input is the 3D data (.obj file), and the output is the finished part.

[0742] This series of processing steps creates a system that allows efficient parts management and repairs at the logistics center.

[0743] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0744] This invention improves the user experience by combining a system that measures an object in three dimensions, easily generates three-dimensional data, and provides it in a format that can be used by a 3D printer with an emotion engine that recognizes the user's emotions. This system is composed of a terminal, a server, and a user. The processing of each element and specific examples are explained below.

[0745] 1. Terminal processing

[0746] Starting the LiDAR Scan Module

[0747] A user opens the smartphone app and activates the LiDAR scanning module, which controls the smartphone's LiDAR sensor to collect three-dimensional data about the object.

[0748] Scanning an object

[0749] The user points the smartphone at the object to be scanned, and the LiDAR sensor collects point cloud data of the object in real time. The device displays the data collection status on the screen, allowing the user to observe the scan until it is complete.

[0750] Formatting and sending data

[0751] The device processes the collected point cloud data, removes noise, and smooths it, then compresses the processed data and sends it to the server.

[0752] 2. Server-side processing

[0753] Data Receiving Module

[0754] The server receives the point cloud data sent from the device and checks the data integrity, making sure there are no missing or corrupted data.

[0755] Generative AI Module

[0756] The server inputs the received data into the generation AI, which then generates 3D data according to the user's requirements. The generated data is exported in .obj file format.

[0757] File Creation and Management

[0758] The generated .obj file is stored in the file management system, a file ID is generated, and the server generates a download link and notifies the user.

[0759] 3. User Interface Processing

[0760] Chat Interface

[0761] Users input their requirements (e.g., "Please increase the height of the chair legs by 10 cm") through the chatbot, and this interface provides a convenient way for users to communicate their specifications in detail.

[0762] Check and adjust specifications

[0763] The server displays a preview of the generated .obj file in a chat interface for the user to review and, if necessary, provide corrections.

[0764] 4. Emotional engine processing

[0765] emotion recognition

[0766] The emotion engine recognizes the user's emotions by using technology that analyzes the user's voice, facial expressions, and input content.

[0767] Emotion-Based Adjustment

[0768] The emotion engine automatically adjusts the requirements for the generated 3D data based on the user's emotions, and if the user appears anxious, it displays a dialog asking for further confirmation or provides an easy-to-understand explanation.

[0769] Suggestions and support

[0770] The emotion engine analyzes the user's emotions and provides suggestions and support based on the analysis results. For example, if the user is happy, it will suggest new designs and related options.

[0771] 5. Processing File Submissions and Market Listings

[0772] Download Module

[0773] The server provides the user with a download link for the generated .obj file. The user clicks the link to download the .obj file, which can then be used with the user's 3D printer.

[0774] Market Linkage Module

[0775] When a user submits a generated .obj file to the Content Marketplace, the server facilitates the process: the user enters the metadata for the marketplace submission (e.g., title, description, price, etc.), which is then uploaded to the marketplace.

[0776] Specific examples

[0777] Example 1: Generating and listing furniture parts using an emotion engine

[0778] 1. Scan

[0779] A user uses the LiDAR sensor on their smartphone to scan the broken chair leg, and within minutes the point cloud data is collected and reconstructed.

[0780] 2. Generation

[0781] The server receives the point cloud data, and the generation AI generates three-dimensional data according to the user-specified requirements.

[0782] 3. Emotion recognition

[0783] The emotion engine analyzes the user's facial and vocal responses to determine if they are anxious about the process, and if so, provides additional guidance to resolve the anxiety.

[0784] 4. Provide

[0785] The server provides a download link for the generated .obj file, which the user can then download and use to reproduce it on their 3D printer.

[0786] 5. Listing

[0787] When a user puts this data up for sale on the content market, the server assists in the process and the data is sold online.

[0788] This allows users to easily generate 3D data without any specialized knowledge, and then use it in a 3D printer or sell it on the market. Furthermore, the introduction of an emotion engine improves the user experience, providing a more intuitive and satisfying process.

[0789] The processing flow will be explained below.

[0790] Processing flow of a system that combines emotion engines

[0791] Terminal side processing

[0792] Step 1:

[0793] The user launches the app using their smartphone, which turns on the LiDAR scanning module.

[0794] Step 2:

[0795] The user points the smartphone at the object to be scanned, and the LiDAR sensor collects point cloud data of the object in real time. The device displays the data collection status on the screen, allowing the user to observe the scan until it is complete.

[0796] Step 3:

[0797] Once the user has finished scanning, the device will then shape the collected point cloud data, removing noise and smoothing it.

[0798] Step 4:

[0799] The device compresses the formatted point cloud data and transmits the data to the server.

[0800] Server-side processing

[0801] Step 5:

[0802] The server receives the point cloud data sent from the device and checks the data integrity, making sure there are no missing or corrupted data.

[0803] Step 6:

[0804] The server inputs the received data into the generation AI module, which then generates 3D data according to the user's requirements. The generated data is exported in .obj file format.

[0805] Step 7:

[0806] The server saves the generated .obj file to the file server, generates a file ID, and then generates a download link and notifies the user.

[0807] Emotion engine processing

[0808] Step 8:

[0809] The emotion engine recognizes the user's emotions by analyzing the user's voice, facial expressions, and input through the device or chat interface to determine the user's emotional state.

[0810] Step 9:

[0811] The emotion engine automatically adjusts the requirements of the generated 3D data based on the user's emotions. For example, if the user is anxious, the system will provide more specific guidance. Conversely, if the user is satisfied, it will suggest additional features and options.

[0812] Step 10:

[0813] The emotion engine provides suggestions and support to the user based on the analysis results. For example, if the user is detected as excited, it will suggest creating other related objects.

[0814] User Interface Handling

[0815] Step 11:

[0816] The user accesses the chat interface and receives a download link for the generated .obj file. The user downloads the file and uses it in a 3D printer.

[0817] Step 12:

[0818] If a user wants to modify an .obj file, they input their specific requirements through the chatbot, such as "Please make the chair legs 10cm taller."

[0819] Step 13:

[0820] The server runs the AI ​​module again based on the new requirements, generates the corrected 3D data, and resends the download link for the newly generated .obj file.

[0821] Processing file submissions and market listings

[0822] Step 14:

[0823] The server provides the user with a download link for the generated .obj file. The user clicks the link to download the .obj file, which can then be used with the user's 3D printer.

[0824] Step 15:

[0825] When a user creates an .obj file and submits it to the Content Market, the server assists the process. The user enters the metadata for the market listing (title, description, price, etc.).

[0826] Step 16:

[0827] The server sends a listing completion notice to the user and starts selling online.

[0828] Specific examples

[0829] Example 1: Generating and listing furniture parts using an emotion engine

[0830] Step 1:

[0831] A user launches the app on their smartphone and scans the broken chair leg.

[0832] Step 2:

[0833] The smartphone's LiDAR sensor collects data in real time, and the device formats and compresses the data before sending it to a server.

[0834] Step 3:

[0835] The server receives the data and uses a generative AI module to generate 3D data, which is then exported in .obj file format.

[0836] Step 4:

[0837] The server saves the generated .obj file to a file server and sends the user a download link.

[0838] Step 5:

[0839] The emotion engine analyzes the user's facial expressions and inputs to detect when they are feeling anxious, and provides detailed explanations and additional guidance.

[0840] Step 6:

[0841] The user checks the file and requests additional modifications as needed through the chat interface. The server then runs the generation AI again based on the requested modifications.

[0842] Step 7:

[0843] The server then provides the modified .obj file again, which the user can download and use in their 3D printer.

[0844] Step 8:

[0845] When a user submits a generated .obj file to the marketplace, the server facilitates the process: the user enters metadata and the file is uploaded to the marketplace.

[0846] Step 9:

[0847] The server will send a notice of completion of listing to the user, and online sales will begin.

[0848] This system allows users to easily generate 3D data without any specialized knowledge, and then use it in a 3D printer or sell it on the market. Furthermore, the introduction of an emotion engine improves the user experience, providing a more intuitive and satisfying process.

[0849] Example 2

[0850] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0851] While traditional 3D measurement systems have the ability to generate 3D data of objects, they lack the flexibility to respond to user emotions and requirements, making it difficult to improve the user experience. In particular, there is a lack of appropriate support and explanations when users feel anxious or stressed about the process.

[0852] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0853] In this invention, the server includes a measuring means for measuring an object in three dimensions, a transmitting means for transmitting the measurement data acquired by the measuring means, a generating means for generating three-dimensional data based on the received measurement data, a providing means for providing the generated three-dimensional data to a user, and an emotion recognizing means for recognizing the user's emotion and adjusting the requirements for the three-dimensional data based on the emotion. This enables flexible responses in line with the user's emotion, providing a more satisfying user experience.

[0854] "Object" refers to any three-dimensional object from which three-dimensional data is acquired and measured.

[0855] "Measuring means for three-dimensional measurement" refers to a device or group of devices for obtaining three-dimensional shape information of an object.

[0856] "Measurement data acquired by a measurement means" refers to three-dimensional shape information of an object obtained using a measurement means.

[0857] "Transmitting means" refers to a device or method for transmitting acquired measurement data to another device or system.

[0858] "Received measurement data" refers to data received from data transmitted by a transmitting means.

[0859] The "means for generating three-dimensional data" refers to a device or algorithm for generating three-dimensional shape data based on the received measurement data.

[0860] "Generated three-dimensional data" refers to three-dimensional shape data created by a generating means.

[0861] "Providing means" refers to a device or method for providing the generated three-dimensional data to a user.

[0862] "Emotion recognition means" refers to a device or algorithm that recognizes a user's emotions and adjusts other processes based on that information.

[0863] This invention relates to a system that measures an object in three dimensions, easily generates three-dimensional data, and provides it in a format that can be used by a 3D printer. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the user experience can be improved. This system is composed of a terminal, a server, and a user. The specific processing of each element is described below.

[0864] Terminal side processing

[0865] The user launches the smartphone app and activates the LiDAR scanning module. The LiDAR sensor collects three-dimensional data of the object and generates point cloud data in real time. The device then processes this data with noise reduction and smoothing, compresses it, and sends it to the server.

[0866] Server-side processing

[0867] The server receives the point cloud data sent from the device and verifies the data's integrity. It then generates 3D data using a generative AI model. Examples of such models include OpenAI's GPT series and Codex. This model generates 3D data in the .obj file format, which the server saves in a file management system. The generated data is provided as a download link and notified to the user.

[0868] User Interface Handling

[0869] The user inputs their requirements through the chatbot interface. For example, "Please increase the height of the chair legs by 10 cm." The chatbot then analyzes the input and sends it to the server. A preview of the generated .obj file is displayed in the chat interface, allowing the user to review and make corrections.

[0870] Emotion engine processing

[0871] The emotion engine analyzes the user's voice, facial expressions, and input to recognize their emotions. Based on this information, the system can respond to the user's needs. If the user is anxious, a dialog will appear requesting further explanation or confirmation. If the user is happy, the system will suggest relevant options.

[0872] Processing file submissions and market listings

[0873] The server provides a download link for the generated .obj file, which the user can download and use in a 3D printer. Furthermore, if the user wants to list the generated 3D data on the content market, the server will assist in the process of listing it on the marketplace.

[0874] As a specific example of how it works, a user scans the legs of a broken chair with their smartphone, and new 3D data is generated based on that data. If the emotion engine senses anxiety from the user's facial expression or voice, additional explanations are displayed. Finally, the generated 3D data is provided to the user, who can then print it using a 3D printer. This data can also be put up for sale on an online marketplace.

[0875] These features provide a system that allows users to easily generate and utilize 3D data, even if they do not have specialized knowledge. The introduction of an emotion engine further improves the user experience.

[0876] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0877] System program processing flow

[0878] Step 1:

[0879] The user launches the smartphone app and activates the LiDAR scanning module.

[0880] Input: Smartphone app launch command

[0881] What happens: The app wakes up the LiDAR sensor and begins calibrating it, ready to scan.

[0882] Output: The LiDAR sensor is now active and a ready notification is displayed on the screen.

[0883] Step 2:

[0884] The user points the smartphone at the object to be scanned and performs the scan.

[0885] Input: Smartphone pointed at object and command to start scanning

[0886] How it works: The LiDAR sensor collects point cloud data in real time, and the collection status is displayed on the device screen.

[0887] Output: The collected point cloud data is temporarily saved on the device.

[0888] Step 3:

[0889] The point cloud data collected by the device is shaped, denoised and smoothed.

[0890] Input: Collected point cloud data

[0891] How it works: The algorithm denoises the data and performs a smoothing process, as well as compression.

[0892] Output: Formatted and compressed point cloud data is generated.

[0893] Step 4:

[0894] The terminal transmits the formatted data to the server.

[0895] Input: shaped and compressed point cloud data

[0896] Operation: The data transmission function is activated and data is uploaded to the server.

[0897] Output: Point cloud data sent to the server is received.

[0898] Step 5:

[0899] Check the integrity of the point cloud data received by the server.

[0900] Input: Point cloud data sent to the server

[0901] How it works: Integrity checking algorithms check for errors and missing data.

[0902] Output: Data is saved successfully with integrity checked.

[0903] Step 6:

[0904] The server generates 3D data using a generative AI model (e.g., OpenAI's GPT series).

[0905] Input: Normal point cloud data

[0906] How it works: Input data into the generative AI model and perform calculations to generate 3D shape data.

[0907] Output: The generated 3D data in .obj file format is temporarily saved.

[0908] Step 7:

[0909] The server stores the generated .obj file in a file management system and generates a download link.

[0910] Input: Generated 3D data (.obj file)

[0911] How it works: The file management system saves the data and generates a unique file ID. A download link is created.

[0912] Output: A download link is generated and notified to the user.

[0913] Step 8:

[0914] The user enters their requirements into the chatbot interface (e.g., "Please extend the height of the chair legs by 10 cm").

[0915] Input: Requirement text entered by the user

[0916] How it works: The chatbot parses the text and sends a request to the server.

[0917] Output: The request is sent to the server.

[0918] Step 9:

[0919] The server generates a preview link and sends it to the user.

[0920] Input: User requirements and generated .obj files

[0921] What it does: The preview link generator runs, creates a link, and sends it to the user.

[0922] Output: A preview link is provided to the user.

[0923] Step 10:

[0924] The emotion engine analyzes the user's voice, facial expressions, and input content to recognize emotions.

[0925] Input: User voice, facial expressions, and text input data

[0926] How it works: Emotion recognition algorithms analyze and assess the user's emotional state.

[0927] Output: The user's emotional state is evaluated within the system.

[0928] Step 11:

[0929] An emotion engine adjusts the process based on the user's emotions.

[0930] Input: Emotion recognition results

[0931] Action: The process is adjusted based on the emotion, for example, providing additional explanations to a user who feels anxious.

[0932] Output: The coordinated process is executed and feedback is provided to the user.

[0933] Step 12:

[0934] The user clicks the download link and downloads the generated .obj file.

[0935] Input: Download link provided by the server

[0936] What happens: The user clicks on the link and the file is downloaded.

[0937] Output: The downloaded .obj file is saved to the user's device.

[0938] Step 13:

[0939] The user prints the generated .obj file on a 3D printer.

[0940] Input: Downloaded .obj file

[0941] What it does: Sends a file to a 3D printer and starts the printing process.

[0942] Output: A physical, three-dimensional object is created.

[0943] Step 14:

[0944] The user puts the generated three-dimensional data up for sale on the content market.

[0945] Input: .obj file and marketplace listing metadata (title, description, price, etc.)

[0946] What happens: The server uploads data to the marketplace.

[0947] Output: The submitted 3D data will be published on the marketplace.

[0948] (Application example 2)

[0949] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0950] In modern factories, quickly and accurately repairing broken machine parts is crucial to maintaining production efficiency. However, conventional systems often require specialized technical personnel to acquire 3D data of the broken part and create the appropriate repair part. Furthermore, the lack of mechanisms to reduce user anxiety and stress during the data generation and provision process makes it difficult to provide a satisfactory user experience. Therefore, there is a need for the development of a system that can quickly and easily generate 3D data without specialized knowledge and provide support tailored to the user's emotions.

[0951] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0952] In this invention, the server includes a generating means for generating three-dimensional data of an object, an emotion recognizing means for recognizing a user's emotion, and an adjusting means for adjusting the requirements of the three-dimensional data based on the emotion recognizing means. This makes it possible to quickly and accurately generate three-dimensional data while taking into account the user's emotion, even without specialized knowledge, and to manufacture appropriate repair parts.

[0953] "Measuring means for measuring an object in three dimensions" refers to a device or module that collects three-dimensional data of an object using a LiDAR sensor or the like.

[0954] The "transmission means for transmitting the measurement data acquired by the measurement means" is a device or module having the function of transmitting the acquired three-dimensional data to the server.

[0955] The "means for generating three-dimensional data based on received measurement data" refers to a device or software module that generates a three-dimensional model based on received point cloud data.

[0956] The "means for providing the generated three-dimensional data to the user" is a device or module that has the function of providing the generated three-dimensional model in a format that the user can use.

[0957] The "emotion recognition means for recognizing the user's emotions" is a device or software module that recognizes emotions by analyzing the user's voice, facial expression, input contents, etc.

[0958] The "adjustment means for adjusting the requirements for three-dimensional data based on the emotion recognition means" refers to a device or software module that has the function of adjusting the specifications of the three-dimensional data to be generated and the method of providing it based on the recognized emotion of the user.

[0959] MODE FOR CARRYING OUT THE INVENTION

[0960] The present invention is aimed at improving the efficiency of maintenance and repair work, mainly in factories. Specific embodiments for carrying out the present invention will be described below.

[0961] 1. Hardware Configuration

[0962] Smartphone

[0963] It uses a smartphone equipped with a LiDAR sensor, which can scan broken parts and objects for 3D data with high accuracy.

[0964] Maintenance robot

[0965] Maintenance robots are used to repair and replace parts, and these robots act automatically based on commands from the server.

[0966] server

[0967] It uses a server that includes a generative AI model and an emotion recognition engine. The server receives the scanned data and performs a series of processes: processing, generating, and providing.

[0968] 2. Software Configuration

[0969] LiDAR Scan Module

[0970] It is a software module that is installed on a smartphone and is used to collect three-dimensional data of objects.

[0971] Data Formatting Tools

[0972] This is a tool that processes scanned point cloud data, removes noise, and smooths the data.

[0973] Generative AI Models

[0974] It is an AI model that runs on a server and generates highly accurate 3D models based on formatted 3D data.

[0975] Emotion Recognition Engine

[0976] It is a software module that analyzes the user's voice, facial expressions, and input content to recognize emotions, and utilizes emotional data to improve the user experience.

[0977] 3. Operational flow

[0978] scan

[0979] The device uses its LiDAR sensor to scan broken parts and objects, collecting point cloud data within minutes.

[0980] Data Formatting

[0981] The terminal processes the collected point cloud data, removes noise, and smooths it, then compresses it and sends it to the server.

[0982] 3D data generation

[0983] The server inputs the received data into a generative AI model to generate 3D data according to the user's requirements, which is then output in .obj file format.

[0984] Emotion Recognition and Regulation

[0985] The emotion recognition engine analyzes the user's voice and facial expressions to determine if they are anxious, and if so, the engine provides additional guidance and explanations.

[0986] Parts manufacturing

[0987] The generated 3D data is sent to a maintenance robot, which then produces the repair parts, which are then ready for immediate use.

[0988] 4. Specific Examples

[0989] For example, if part of a robot arm breaks in a factory, repairs are carried out using the following procedure.

[0990] 1. The maintenance robot detects the broken part and scans it using the smartphone's LiDAR sensor. The scan takes about 1-2 minutes.

[0991] 2. The scan data is formatted, denoised and smoothed on the device before being sent to the server.

[0992] 3. The server uses the generative AI model to generate 3D data and outputs it as an .obj file.

[0993] 4. An emotion recognition engine analyzes the operator's emotions and provides additional guidance and support if they are feeling anxious.

[0994] 5. The maintenance robot uses the generated data to 3D print repair parts, making them immediately usable.

[0995] 5. Examples of prompts

[0996] "We propose a new system that scans broken parts in your factory and automatically creates repair parts based on the generated 3D data. It uses a smartphone's LiDAR sensor to create 3D data of the broken part within minutes, and uses an emotion engine to understand and support the operator's emotions. This enables fast and efficient repairs."

[0997] This system is expected to make factory maintenance work more efficient, reduce user stress, and improve overall work efficiency.

[0998] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0999] Step 1:

[1000] The device launches the LiDAR scanning module. The user opens the smartphone app and uses the LiDAR sensor to scan the broken part. This allows 3D data (point cloud data) to be collected in real time. The input is the smartphone's LiDAR data, and the output is the scanned point cloud data.

[1001] Step 2:

[1002] The terminal processes the point cloud data. It removes noise from the collected data and smooths it. This data processing is performed using a software tool. The input is the scanned point cloud data, and the output is the denoised and smoothed point cloud data.

[1003] Step 3:

[1004] The terminal sends the formatted data to the server. The compressed 3D data is sent to the server via the network. The input is the formatted point cloud data, and the output is the data sent to the server.

[1005] Step 4:

[1006] The server processes the data received. It verifies the integrity of the received data and checks for missing or corrupted data. The input is the point cloud data sent to the server, and the output is the point cloud data whose integrity has been verified.

[1007] Step 5:

[1008] The server inputs data into the generated AI model and generates 3D data. A highly accurate 3D model (.obj file) is generated based on the received data. The input is point cloud data whose consistency has been confirmed, and the output is the generated 3D model (.obj file).

[1009] Step 6:

[1010] The server uses an emotion recognition engine to recognize the user's emotions. It analyzes the user's voice, facial expression, and input content to determine their emotional state. The input is the user's voice, facial expression, and input content, and the output is the analyzed emotional data.

[1011] Step 7:

[1012] The server adjusts the requirements for 3D data based on the emotion data. If the user feels anxious, it adjusts the generated data and processes and provides additional guidance and explanations. The input is the analyzed emotion data, and the output is the adjusted 3D data and guidance information.

[1013] Step 8:

[1014] The server provides the generated 3D data to the user. The generated 3D data is provided to the user as a downloadable link. The input is the adjusted 3D data, and the output is the download link.

[1015] Step 9:

[1016] The user downloads the 3D data using the provided download link. The downloaded data can then be used to produce actual repair parts using the maintenance robot's 3D printer. The input is the download link, and the output is the downloaded 3D data.

[1017] 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.

[1018] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1019] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[1020] [Third embodiment]

[1021] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1022] 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.

[1023] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1024] 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.

[1025] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1026] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1027] 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.

[1028] 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.

[1029] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1030] 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.

[1031] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1032] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[1033] This invention is a system that measures an object in three dimensions, easily generates three-dimensional data, and provides it in a format that can be used by a 3D printer. This system consists of a terminal, a server, and a user. The processing of each element and specific examples are explained below.

[1034] 1. Terminal processing

[1035] Starting the LiDAR Scan Module

[1036] A user opens the smartphone app and activates the LiDAR scanning module, which controls the smartphone's LiDAR sensor to collect three-dimensional data about the object.

[1037] Scanning an object

[1038] Users point their smartphone at the object they want to scan and use the LiDAR sensor to collect point cloud data of the object. This data is displayed on the device in real time, allowing users to see the progress of the scan. Once the scan is complete, the collected data is formatted and ready to be sent.

[1039] Formatting and sending data

[1040] The terminal processes the collected point cloud data and performs preprocessing such as noise removal and smoothing. The processed data is then compressed and sent to the server.

[1041] 2. Server-side processing

[1042] Data Receiving Module

[1043] The server receives the point cloud data sent from the device, passes the integrity check, and performs the necessary processing.

[1044] Generative AI Module

[1045] The server inputs the received data into the generation AI, which generates 3D data according to the user's requirements. The generated data is exported in .obj file format.

[1046] File Creation and Management

[1047] The generated .obj file is stored in the file management system and a file ID is generated, and the server generates a download link for this file and notifies the user.

[1048] 3. User Interface

[1049] Chat Interface

[1050] Users input their requirements (e.g., "Please increase the height of the chair legs by 10 cm") through the chatbot, and this interface provides a convenient way for users to communicate their specifications in detail.

[1051] Check and adjust specifications

[1052] The server displays a preview of the generated .obj file in a chat interface for the user to review and, if necessary, provide corrections.

[1053] 4. File Submission and Market Listing

[1054] Download Module

[1055] The server provides the user with a download link for the generated .obj file. The user clicks the link to download the .obj file, which can then be used with the user's 3D printer.

[1056] Market Linkage Module

[1057] When a user submits a generated .obj file to the Content Marketplace, the server facilitates the process: the user enters the metadata for the marketplace submission (e.g., title, description, price, etc.), which is then uploaded to the marketplace.

[1058] Specific examples

[1059] Example 1: Reproducing furniture parts

[1060] 1. Scan

[1061] A user uses the LiDAR sensor on their smartphone to scan the broken chair leg, and within minutes the point cloud data is collected and reconstructed.

[1062] 2. Generation

[1063] The server receives the point cloud data, and the generation AI generates three-dimensional data according to the user-specified requirements.

[1064] 3. Provide

[1065] The server provides a download link for the generated .obj file, which the user can then download and use to reproduce it on their 3D printer.

[1066] 4. Listing

[1067] When a user puts this data up for sale on the content market, the server assists in the process and the data is sold online.

[1068] This system allows users to easily generate 3D data without requiring specialized skills and use it in a 3D printer. Furthermore, they can earn secondary income by putting the generated data on the market.

[1069] The processing flow will be explained below.

[1070] Program processing flow

[1071] Terminal side processing

[1072] Step 1:

[1073] The user launches the app using their smartphone, which turns on the LiDAR scanning module.

[1074] Step 2:

[1075] The user points the smartphone at the object to be scanned, and the LiDAR sensor collects point cloud data of the object in real time. The device displays the data collection status on the screen, allowing the user to observe the scan until it is complete.

[1076] Step 3:

[1077] Once the user has finished scanning, the device will then shape the collected point cloud data, removing noise and smoothing it.

[1078] Step 4:

[1079] The device compresses the formatted point cloud data and transmits the data to the server.

[1080] Server-side processing

[1081] Step 5:

[1082] The server receives the point cloud data sent from the device and checks the data integrity, making sure there are no missing or corrupted data.

[1083] Step 6:

[1084] The server inputs the received data into the generation AI module, which then generates 3D data according to the user's requirements. The generated data is exported as an .obj file.

[1085] Step 7:

[1086] The server saves the generated .obj file to the file server, generates a file ID, and then generates a download link and notifies the user.

[1087] User Interface Handling

[1088] Step 8:

[1089] The user accesses the chat interface and receives a download link for the generated .obj file. The user downloads the file and uses it in a 3D printer.

[1090] Step 9:

[1091] If a user wants to modify an .obj file, they input their specific requirements through the chatbot, for example, "Please increase the height of the chair legs by 10cm."

[1092] Step 10:

[1093] The server runs the AI ​​module again based on the new requirements, generates the corrected 3D data, and resends the download link for the newly generated .obj file.

[1094] Processing file submissions and market listings

[1095] Step 11:

[1096] Users can download the files and use them in their home 3D printers as needed, or if they want to list their work on the market, the server assists them with the listing process.

[1097] Step 12:

[1098] The user enters the metadata for a marketplace listing (title, description, price, etc.), and the server uploads it to the marketplace.

[1099] Step 13:

[1100] The server sends a listing completion notice to the user and starts selling online.

[1101] This allows users to easily generate three-dimensional data without any specialized knowledge, and use it in a 3D printer or sell it on the market.

[1102] Example 1

[1103] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1104] Conventional methods for generating 3D data of objects require expensive equipment and specialized knowledge, making it difficult for general users to use. Furthermore, the methods for providing the generated data are often limited, restricting the scope of use for users. Therefore, there was a need for a system that could easily acquire and generate high-quality 3D data and enable users to easily use that data.

[1105] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1106] In this invention, the server includes a measuring means for measuring an object in three dimensions through a user interface, a transmitting means for transmitting point cloud data acquired by the measuring means, a generating means for generating three-dimensional data by shaping and preprocessing the received point cloud data, and a providing means for providing the generated three-dimensional data to a user. This makes it possible to easily acquire high-quality three-dimensional data without specialized knowledge and provide it to a user in a format that is easily usable.

[1107] A "user interface" is the means by which a user interacts with a system and inputs instructions and information.

[1108] "Means for measuring an object in three dimensions" refers to a technology or device for measuring the three-dimensional shape or structure of an object and acquiring it as digital data.

[1109] "Point cloud data" is three-dimensional data that represents a collection of multiple points that make up the surface of an object.

[1110] "Transmission means" refers to a technique or device for transmitting acquired data to another system or server.

[1111] "Receiving means" refers to a technique or device for receiving data sent from an external source.

[1112] "Means for shaping and preprocessing" refers to technology or equipment for improving the quality of data by performing processes such as noise removal and smoothing on the acquired raw data.

[1113] "Means for generating 3D data" refers to a technique or device for creating 3D models or shape data from pre-processed data.

[1114] "Providing means" refers to the technology or device for transmitting the generated three-dimensional data to the user or providing it in the form of a download link, etc.

[1115] A "downloadable link" is a web link that allows a user to receive a file or data specified by the user over the Internet.

[1116] The present invention is a system that measures an object in three dimensions, easily generates three-dimensional data, and provides it in a format that can be used by a 3D printer. This system is composed of a user, a terminal, and a server.

[1117] Terminal side processing

[1118] Starting the LiDAR Scan Module

[1119] The user opens the smartphone app and activates the LiDAR scanning module, which controls the smartphone's LiDAR sensor and collects 3D data of the object. The specific hardware used is the smartphone's built-in LiDAR sensor.

[1120] Scanning an object

[1121] Users point their smartphone at the object they want to scan and use the LiDAR sensor to collect point cloud data of the object. This data is displayed on the device in real time, allowing users to see the progress of the scan. Once the scan is complete, the collected data is formatted and ready to be sent.

[1122] Formatting and sending data

[1123] The terminal processes the collected point cloud data and performs preprocessing such as noise removal and smoothing. The processed data is then compressed and sent to the server. Specific software used includes data compression algorithms and noise filtering techniques.

[1124] Server-side processing

[1125] Data Receiving Module

[1126] The server receives the point cloud data sent from the device, passes the integrity check, and performs the necessary processing.

[1127] Generative AI Module

[1128] The server inputs the received data into a generative AI model and generates 3D data according to the user's requirements. The generated data is exported in .obj file format. The generative AI model uses a generative AI engine that analyzes the user's prompt and executes the specified processing.

[1129] File Creation and Management

[1130] The generated .obj file is stored in the file management system and a file ID is generated, and the server generates a download link for this file and notifies the user.

[1131] User Interface

[1132] Chat Interface

[1133] Users input their requirements (e.g., "Please increase the height of the chair legs by 10 cm") through the chatbot, and this interface provides a convenient way for users to communicate their specifications in detail.

[1134] Check and adjust specifications

[1135] The server displays a preview of the generated .obj file in a chat interface for the user to review and, if necessary, provide corrections.

[1136] File submission and market listing

[1137] Download Module

[1138] The server provides the user with a download link for the generated .obj file. The user clicks the link to download the .obj file, which can then be used with the user's 3D printer.

[1139] Market Linkage Module

[1140] When a user submits a generated .obj file to the Content Marketplace, the server facilitates the process: the user enters the metadata for the marketplace submission (e.g., title, description, price, etc.), which is then uploaded to the marketplace.

[1141] Specific examples

[1142] Example 1: Reproducing furniture parts

[1143] 1. Scan

[1144] A user uses the LiDAR sensor on their smartphone to scan the broken chair leg, and within minutes the point cloud data is collected and reconstructed.

[1145] 2. Generation

[1146] The server receives the point cloud data, and the generation AI generates three-dimensional data according to the user-specified requirements.

[1147] 3. Provide

[1148] The server provides a download link for the generated .obj file, which the user can then download and use to reproduce it on their 3D printer.

[1149] 4. Listing

[1150] When a user puts this data up for sale on the content market, the server assists in the process and the data is sold online.

[1151] Prompt Sentence Examples

[1152] Prompt 1: "I scanned a broken chair leg. From the generated data, please generate a version of the chair leg that is 10cm taller."

[1153] Prompt 2: "Generate repair data for the broken vase based on the collected point cloud data."

[1154] This system allows users to easily generate high-quality 3D data without requiring specialized skills and use it in 3D printers. Users can also earn secondary income by listing the generated data on a marketplace.

[1155] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1156] Step 1: Start the LiDAR Scan Module

[1157] The user launches the smartphone app and selects the LiDAR scan module, which causes the device to activate the internal LiDAR sensor and prepare for scanning.

[1158] Input: User action (selection of LiDAR scan module)

[1159] Output: LiDAR sensor wake-up and scan readiness notification

[1160] What it does: When the app launches, the device will begin calibrating the LiDAR sensor and display a "Ready to scan" notification to the user.

[1161] Step 2: Scan the object

[1162] Users point their smartphone at the object they want to scan and press the "Start Scan" button in the app. The device uses its LiDAR sensor to collect point cloud data of the object. This data is displayed on the device's screen in real time, allowing users to track the progress.

[1163] Input: User actions (pressing the start scan button) and data from the LiDAR sensor

[1164] Output: Point cloud data displayed in real time

[1165] Specific operation: During scanning, the device screen will construct 3D data of the object in real time and display a progress bar. When the scanning is complete, the message "Scan complete" will be displayed.

[1166] Step 3: Format and send the data

[1167] The terminal processes the collected point cloud data and performs preprocessing such as noise removal and smoothing. The processed data is then compressed and sent to the server.

[1168] Input: Collected point cloud data

[1169] Output: Formatted and compressed data

[1170] What it does: A denoising algorithm cleans up the data, a smoothing filter is applied, and once complete, the size of the cleaned data is compressed and a progress bar is displayed as the data is sent to the server.

[1171] Step 4: Data receiving module

[1172] The server receives the point cloud data sent from the device, which is first subjected to a consistency check to ensure there is no data corruption or inconsistency.

[1173] Input: Compressed data sent from the terminal

[1174] Output: Data with integrity checked

[1175] Specific operation: The server receives the data, performs a hash check and checks the number of bytes of the data. If consistency is confirmed, it logs "Data reception completed."

[1176] Step 5: Generative AI module

[1177] The server inputs the received data into the generative AI model and generates 3D data according to the user's prompts. The generated data is exported as an .obj file.

[1178] Input: Data that has been validated and a prompt from the user

[1179] Output: Generated .obj file

[1180] Specific behavior: The prompt (e.g., "Please extend the chair legs by 10 cm") is analyzed, and the AI ​​generator executes the specified process. The resulting .obj file is generated.

[1181] Step 6: File Creation and Management

[1182] The generated .obj file is stored in a file management system, where the server generates a file ID and sends the user a download link.

[1183] Input: .obj file

[1184] Output: File ID and download link

[1185] What happens: The server saves the .obj file and generates a unique file ID. It uses this ID to generate a download link and sends a "download link ready" notification to the user.

[1186] Step 7: Enter requirements via chat interface

[1187] Users input their requirements (e.g., "Please increase the height of the chair legs by 10 cm") through the chatbot, and this interface provides a convenient way for users to communicate their specifications in detail.

[1188] Input: User requirements

[1189] Output: Prompt statement based on your requirements

[1190] Specific behavior: The chatbot receives user input and displays a confirmation message to generate a prompt. The user is notified that "the specified requirements have been registered."

[1191] Step 8: Check and adjust specifications

[1192] The server displays a preview of the generated .obj file in a chat interface, allowing the user to check the preview and make corrections if necessary.

[1193] Input: Generated .obj file

[1194] Output: User's correction instructions

[1195] Specific operation: The server generates a low-resolution model for preview, and the chatbot prompts the user to "check the generated results and enter any corrections." The user then enters the corrections into the chat screen.

[1196] Step 9: Download Module

[1197] The server provides the user with a download link for the generated .obj file. The user clicks the link to download the .obj file.

[1198] Input: File ID and download link

[1199] Output: Downloaded .obj file

[1200] What happens: The server generates a download link and sends it to the user's email address or notification system. The user is notified that "Download link has been sent."

[1201] Step 10: Market Linkage Module

[1202] When a user submits a generated .obj file to the Content Marketplace, the server facilitates the process: the user enters the metadata for the marketplace submission (e.g., title, description, price, etc.), which is then uploaded to the marketplace.

[1203] Input: User metadata and .obj file

[1204] Output: Listing on Content Market

[1205] Specific operation: The server provides the user with a listing template and prompts them to enter the necessary metadata. After the user fills in the "title," "description," and "price," the server sends the data to the marketplace and notifies the user that "listing is complete."

[1206] (Application example 1)

[1207] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1208] When a robot or equipment part breaks down in a logistics center, it is necessary to replace or repair the part quickly and accurately. However, conventional methods require time and cost to identify, design, and manufacture the part, resulting in reduced operational efficiency. The objective of this invention is to solve these problems and provide a system that enables efficient and rapid parts management and repair.

[1209] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1210] In this invention, the server includes a measuring means for measuring an object in three dimensions, a transmitting means for transmitting the measurement data acquired by the measuring means, a generating means for generating three-dimensional data based on the received measurement data, a providing means for providing the generated three-dimensional data to a user, and a managing means for supporting preventive maintenance and repairs. This enables the logistics center to quickly identify faulty parts, generate accurate three-dimensional data for the necessary parts, and immediately manufacture them using a 3D printer.

[1211] "Measuring means for measuring an object in three dimensions" refers to equipment or technology for obtaining the shape and dimensions of an object in three-dimensional space.

[1212] The "transmission means for transmitting the measurement data acquired by the measurement means" refers to a mechanism for transferring the data collected by the measurement means to another device or server.

[1213] The "generation means for generating three-dimensional data based on the received measurement data" refers to an algorithm or system for creating a three-dimensional model from the measurement data received via the transmission means.

[1214] "Means for providing generated three-dimensional data to users" refers to technologies and methods for enabling users to access the generated three-dimensional data.

[1215] "Management measures to support preventive maintenance and repair" refers to systems and methods to support the efficient maintenance and management of equipment and robots, as well as the replacement and repair of parts.

[1216] "LiDAR sensor" refers to a sensor technology that uses laser light to precisely measure the distance and shape of an object.

[1217] This invention is a system that measures an object in three dimensions and provides the data in a format that can be used by a 3D printer. The system mainly consists of three elements: a terminal, a server, and a user.

[1218] The device uses a smartphone equipped with a LiDAR sensor as a device for users to measure objects in three dimensions. First, the user opens the smartphone app and activates the LiDAR scanning module. This module controls the LiDAR sensor and collects three-dimensional data of the object. The collected data is displayed on the device in real time, allowing the user to check the progress of the scan. Once the scan is complete, the collected data is formatted and sent from the device to a server.

[1219] The server receives the point cloud data sent from the device. Here, preprocessing such as data consistency checks, noise removal, and smoothing is performed. Using a generative AI model, 3D data based on the user's requirements is generated from the received data. This 3D data is exported as an .obj file and saved in a file management system. The user is then notified of a download link for the generated file.

[1220] In the user interface, the user inputs requirements through a chatbot. For example, a specific request such as "Please increase the height of the chair legs by 10 cm" can be entered in the chat interface. The generative AI model generates 3D data based on this prompt. A preview of the generated .obj file is displayed in the chat interface for the user to review. If necessary, they can also provide instructions for corrections.

[1221] Next, users download the generated .obj file and use it in their own 3D printer. For example, by scanning a broken robot arm part and manufacturing the generated 3D data with a 3D printer, it is possible to quickly and accurately replace or repair the part. The server also supports users in listing the .obj file on the content market. By entering listing metadata, the file can be sold online.

[1222] As a concrete example, if a joint on a robot breaks, the user scans the joint using the LiDAR sensor on their smartphone and sends the data to a server. The generation AI generates three-dimensional data on the joint and provides it as a download link. The user then prints the resulting data on a 3D printer to quickly manufacture a new joint. An example of this prompt could be, "Yesterday, a joint on a robot arm in the warehouse broke. I would like to scan it and print a new joint on a 3D printer. After starting the LiDAR scan, please have the generation AI convert the joint data into 3D data and output it in .obj format."

[1223] In this way, the management and replacement of parts at the logistics center can be made more efficient.

[1224] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1225] Step 1:

[1226] The user opens the smartphone app and activates the LiDAR scanning module, which controls the smartphone's LiDAR sensor and collects 3D data of the object. The input is the user's actions and the target object, and the output is point cloud data acquired in real time.

[1227] Step 2:

[1228] The device processes the collected point cloud data. Specifically, preprocessing such as noise removal and smoothing is performed. The input is the point cloud data, and the output is the processed point cloud data. Once processing is complete, the processed data is compressed and ready for transmission.

[1229] Step 3:

[1230] The terminal sends the formatted point cloud data to the server. Specifically, it sends the data to the server via an HTTP request. The input is the formatted and compressed point cloud data, and the output is a successful data transmission response to the server.

[1231] Step 4:

[1232] The server receives point cloud data sent from the device. The data receiving module checks the integrity of the received data and performs preprocessing such as noise removal and smoothing. The input is the received point cloud data, and the output is the shaped point cloud data.

[1233] Step 5:

[1234] The server's generation AI model generates 3D data based on the shaped point cloud data. It executes the necessary generation process according to the user's prompt. The input is the shaped point cloud data and the user's prompt, and the output is the generated 3D data (.obj file format).

[1235] Step 6:

[1236] The server saves the generated 3D data in a file management system, generates a file ID, and sends a notification to the user to provide a download link. The input is the generated 3D data (.obj file), and the output is a notification of the download link.

[1237] Step 7:

[1238] The user clicks on the provided download link and downloads the generated 3D data (.obj file). This gives the user 3D data that can be used with a 3D printer. The input is the download link, and the output is the downloaded 3D data (.obj file).

[1239] Step 8:

[1240] Users use the downloaded 3D data to manufacture parts using a 3D printer. Specifically, they import the 3D data into the 3D printer and start manufacturing the part. The input is the 3D data (.obj file), and the output is the finished part.

[1241] This series of processing steps creates a system that allows efficient parts management and repairs at the logistics center.

[1242] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1243] This invention improves the user experience by combining a system that measures an object in three dimensions, easily generates three-dimensional data, and provides it in a format that can be used by a 3D printer with an emotion engine that recognizes the user's emotions. This system is composed of a terminal, a server, and a user. The processing of each element and specific examples are explained below.

[1244] 1. Terminal processing

[1245] Starting the LiDAR Scan Module

[1246] A user opens the smartphone app and activates the LiDAR scanning module, which controls the smartphone's LiDAR sensor to collect three-dimensional data about the object.

[1247] Scanning an object

[1248] The user points the smartphone at the object to be scanned, and the LiDAR sensor collects point cloud data of the object in real time. The device displays the data collection status on the screen, allowing the user to observe the scan until it is complete.

[1249] Formatting and sending data

[1250] The device processes the collected point cloud data, removes noise, and smooths it, then compresses the processed data and sends it to the server.

[1251] 2. Server-side processing

[1252] Data Receiving Module

[1253] The server receives the point cloud data sent from the device and checks the data integrity, making sure there are no missing or corrupted data.

[1254] Generative AI Module

[1255] The server inputs the received data into the generation AI, which then generates 3D data according to the user's requirements. The generated data is exported in .obj file format.

[1256] File Creation and Management

[1257] The generated .obj file is stored in the file management system, a file ID is generated, and the server generates a download link and notifies the user.

[1258] 3. User Interface Processing

[1259] Chat Interface

[1260] Users input their requirements (e.g., "Please increase the height of the chair legs by 10 cm") through the chatbot, and this interface provides a convenient way for users to communicate their specifications in detail.

[1261] Check and adjust specifications

[1262] The server displays a preview of the generated .obj file in a chat interface for the user to review and, if necessary, provide corrections.

[1263] 4. Emotional engine processing

[1264] emotion recognition

[1265] The emotion engine recognizes the user's emotions by using technology that analyzes the user's voice, facial expressions, and input content.

[1266] Emotion-Based Adjustment

[1267] The emotion engine automatically adjusts the requirements for the generated 3D data based on the user's emotions, and if the user appears anxious, it displays a dialog asking for further confirmation or provides an easy-to-understand explanation.

[1268] Suggestions and support

[1269] The emotion engine analyzes the user's emotions and provides suggestions and support based on the analysis results. For example, if the user is happy, it will suggest new designs and related options.

[1270] 5. Processing File Submissions and Market Listings

[1271] Download Module

[1272] The server provides the user with a download link for the generated .obj file. The user clicks the link to download the .obj file, which can then be used with the user's 3D printer.

[1273] Market Linkage Module

[1274] When a user submits a generated .obj file to the Content Marketplace, the server facilitates the process: the user enters the metadata for the marketplace submission (e.g., title, description, price, etc.), which is then uploaded to the marketplace.

[1275] Specific examples

[1276] Example 1: Generating and listing furniture parts using an emotion engine

[1277] 1. Scan

[1278] A user uses the LiDAR sensor on their smartphone to scan the broken chair leg, and within minutes the point cloud data is collected and reconstructed.

[1279] 2. Generation

[1280] The server receives the point cloud data, and the generation AI generates three-dimensional data according to the user-specified requirements.

[1281] 3. Emotion recognition

[1282] The emotion engine analyzes the user's facial and vocal responses to determine if they are anxious about the process, and if so, provides additional guidance to resolve the anxiety.

[1283] 4. Provide

[1284] The server provides a download link for the generated .obj file, which the user can then download and use to reproduce it on their 3D printer.

[1285] 5. Listing

[1286] When a user puts this data up for sale on the content market, the server assists in the process and the data is sold online.

[1287] This allows users to easily generate 3D data without any specialized knowledge, and then use it in a 3D printer or sell it on the market. Furthermore, the introduction of an emotion engine improves the user experience, providing a more intuitive and satisfying process.

[1288] The processing flow will be explained below.

[1289] Processing flow of a system that combines emotion engines

[1290] Terminal side processing

[1291] Step 1:

[1292] The user launches the app using their smartphone, which turns on the LiDAR scanning module.

[1293] Step 2:

[1294] The user points the smartphone at the object to be scanned, and the LiDAR sensor collects point cloud data of the object in real time. The device displays the data collection status on the screen, allowing the user to observe the scan until it is complete.

[1295] Step 3:

[1296] Once the user has finished scanning, the device will then shape the collected point cloud data, removing noise and smoothing it.

[1297] Step 4:

[1298] The device compresses the formatted point cloud data and transmits the data to the server.

[1299] Server-side processing

[1300] Step 5:

[1301] The server receives the point cloud data sent from the device and checks the data integrity, making sure there are no missing or corrupted data.

[1302] Step 6:

[1303] The server inputs the received data into the generation AI module, which then generates 3D data according to the user's requirements. The generated data is exported in .obj file format.

[1304] Step 7:

[1305] The server saves the generated .obj file to the file server, generates a file ID, and then generates a download link and notifies the user.

[1306] Emotion engine processing

[1307] Step 8:

[1308] The emotion engine recognizes the user's emotions by analyzing the user's voice, facial expressions, and input through the device or chat interface to determine the user's emotional state.

[1309] Step 9:

[1310] The emotion engine automatically adjusts the requirements of the generated 3D data based on the user's emotions. For example, if the user is anxious, the system will provide more specific guidance. Conversely, if the user is satisfied, it will suggest additional features and options.

[1311] Step 10:

[1312] The emotion engine provides suggestions and support to the user based on the analysis results. For example, if the user is detected as excited, it will suggest creating other related objects.

[1313] User Interface Handling

[1314] Step 11:

[1315] The user accesses the chat interface and receives a download link for the generated .obj file. The user downloads the file and uses it in a 3D printer.

[1316] Step 12:

[1317] If a user wants to modify an .obj file, they input their specific requirements through the chatbot, such as "Please make the chair legs 10cm taller."

[1318] Step 13:

[1319] The server runs the AI ​​module again based on the new requirements, generates the corrected 3D data, and resends the download link for the newly generated .obj file.

[1320] Processing file submissions and market listings

[1321] Step 14:

[1322] The server provides the user with a download link for the generated .obj file. The user clicks the link to download the .obj file, which can then be used with the user's 3D printer.

[1323] Step 15:

[1324] When a user creates an .obj file and submits it to the Content Market, the server assists the process. The user enters the metadata for the market listing (title, description, price, etc.).

[1325] Step 16:

[1326] The server sends a listing completion notice to the user and starts selling online.

[1327] Specific examples

[1328] Example 1: Generating and listing furniture parts using an emotion engine

[1329] Step 1:

[1330] A user launches the app on their smartphone and scans the broken chair leg.

[1331] Step 2:

[1332] The smartphone's LiDAR sensor collects data in real time, and the device formats and compresses the data before sending it to a server.

[1333] Step 3:

[1334] The server receives the data and uses a generative AI module to generate 3D data, which is then exported in .obj file format.

[1335] Step 4:

[1336] The server saves the generated .obj file to a file server and sends the user a download link.

[1337] Step 5:

[1338] The emotion engine analyzes the user's facial expressions and inputs to detect when they are feeling anxious, and provides detailed explanations and additional guidance.

[1339] Step 6:

[1340] The user checks the file and requests additional modifications as needed through the chat interface. The server then runs the generation AI again based on the requested modifications.

[1341] Step 7:

[1342] The server then provides the modified .obj file again, which the user can download and use in their 3D printer.

[1343] Step 8:

[1344] When a user submits a generated .obj file to the marketplace, the server facilitates the process: the user enters metadata and the file is uploaded to the marketplace.

[1345] Step 9:

[1346] The server will send a notice of completion of listing to the user, and online sales will begin.

[1347] This system allows users to easily generate 3D data without any specialized knowledge, and then use it in a 3D printer or sell it on the market. Furthermore, the introduction of an emotion engine improves the user experience, providing a more intuitive and satisfying process.

[1348] Example 2

[1349] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1350] While traditional 3D measurement systems have the ability to generate 3D data of objects, they lack the flexibility to respond to user emotions and requirements, making it difficult to improve the user experience. In particular, there is a lack of appropriate support and explanations when users feel anxious or stressed about the process.

[1351] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1352] In this invention, the server includes a measuring means for measuring an object in three dimensions, a transmitting means for transmitting the measurement data acquired by the measuring means, a generating means for generating three-dimensional data based on the received measurement data, a providing means for providing the generated three-dimensional data to a user, and an emotion recognizing means for recognizing the user's emotion and adjusting the requirements for the three-dimensional data based on the emotion. This enables flexible responses in line with the user's emotion, providing a more satisfying user experience.

[1353] "Object" refers to any three-dimensional object from which three-dimensional data is acquired and measured.

[1354] "Measuring means for three-dimensional measurement" refers to a device or group of devices for obtaining three-dimensional shape information of an object.

[1355] "Measurement data acquired by a measurement means" refers to three-dimensional shape information of an object obtained using a measurement means.

[1356] "Transmitting means" refers to a device or method for transmitting acquired measurement data to another device or system.

[1357] "Received measurement data" refers to data received from data transmitted by a transmitting means.

[1358] The "means for generating three-dimensional data" refers to a device or algorithm for generating three-dimensional shape data based on the received measurement data.

[1359] "Generated three-dimensional data" refers to three-dimensional shape data created by a generating means.

[1360] "Providing means" refers to a device or method for providing the generated three-dimensional data to a user.

[1361] "Emotion recognition means" refers to a device or algorithm that recognizes a user's emotions and adjusts other processes based on that information.

[1362] This invention relates to a system that measures an object in three dimensions, easily generates three-dimensional data, and provides it in a format that can be used by a 3D printer. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the user experience can be improved. This system is composed of a terminal, a server, and a user. The specific processing of each element is described below.

[1363] Terminal side processing

[1364] The user launches the smartphone app and activates the LiDAR scanning module. The LiDAR sensor collects three-dimensional data of the object and generates point cloud data in real time. The device then processes this data with noise reduction and smoothing, compresses it, and sends it to the server.

[1365] Server-side processing

[1366] The server receives the point cloud data sent from the device and verifies the data's integrity. It then generates 3D data using a generative AI model. Examples of such models include OpenAI's GPT series and Codex. This model generates 3D data in the .obj file format, which the server saves in a file management system. The generated data is provided as a download link and notified to the user.

[1367] User Interface Handling

[1368] The user inputs their requirements through the chatbot interface. For example, "Please increase the height of the chair legs by 10 cm." The chatbot then analyzes the input and sends it to the server. A preview of the generated .obj file is displayed in the chat interface, allowing the user to review and make corrections.

[1369] Emotion engine processing

[1370] The emotion engine analyzes the user's voice, facial expressions, and input to recognize their emotions. Based on this information, the system can respond to the user's needs. If the user is anxious, a dialog will appear requesting further explanation or confirmation. If the user is happy, the system will suggest relevant options.

[1371] Processing file submissions and market listings

[1372] The server provides a download link for the generated .obj file, which the user can download and use in a 3D printer. Furthermore, if the user wants to list the generated 3D data on the content market, the server will assist in the process of listing it on the marketplace.

[1373] As a specific example of how it works, a user scans the legs of a broken chair with their smartphone, and new 3D data is generated based on that data. If the emotion engine senses anxiety from the user's facial expression or voice, additional explanations are displayed. Finally, the generated 3D data is provided to the user, who can then print it using a 3D printer. This data can also be put up for sale on an online marketplace.

[1374] These features provide a system that allows users to easily generate and utilize 3D data, even if they do not have specialized knowledge. The introduction of an emotion engine further improves the user experience.

[1375] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1376] System program processing flow

[1377] Step 1:

[1378] The user launches the smartphone app and activates the LiDAR scanning module.

[1379] Input: Smartphone app launch command

[1380] What happens: The app wakes up the LiDAR sensor and begins calibrating it, ready to scan.

[1381] Output: The LiDAR sensor is now active and a ready notification is displayed on the screen.

[1382] Step 2:

[1383] The user points the smartphone at the object to be scanned and performs the scan.

[1384] Input: Smartphone pointed at object and command to start scanning

[1385] How it works: The LiDAR sensor collects point cloud data in real time, and the collection status is displayed on the device screen.

[1386] Output: The collected point cloud data is temporarily saved on the device.

[1387] Step 3:

[1388] The point cloud data collected by the device is shaped, denoised and smoothed.

[1389] Input: Collected point cloud data

[1390] How it works: The algorithm denoises the data and performs a smoothing process, as well as compression.

[1391] Output: Formatted and compressed point cloud data is generated.

[1392] Step 4:

[1393] The terminal transmits the formatted data to the server.

[1394] Input: shaped and compressed point cloud data

[1395] Operation: The data transmission function is activated and data is uploaded to the server.

[1396] Output: Point cloud data sent to the server is received.

[1397] Step 5:

[1398] Check the integrity of the point cloud data received by the server.

[1399] Input: Point cloud data sent to the server

[1400] How it works: Integrity checking algorithms check for errors and missing data.

[1401] Output: Data is saved successfully with integrity checked.

[1402] Step 6:

[1403] The server generates 3D data using a generative AI model (e.g., OpenAI's GPT series).

[1404] Input: Normal point cloud data

[1405] How it works: Input data into the generative AI model and perform calculations to generate 3D shape data.

[1406] Output: The generated 3D data in .obj file format is temporarily saved.

[1407] Step 7:

[1408] The server stores the generated .obj file in a file management system and generates a download link.

[1409] Input: Generated 3D data (.obj file)

[1410] How it works: The file management system saves the data and generates a unique file ID. A download link is created.

[1411] Output: A download link is generated and notified to the user.

[1412] Step 8:

[1413] The user enters their requirements into the chatbot interface (e.g., "Please extend the height of the chair legs by 10 cm").

[1414] Input: Requirement text entered by the user

[1415] How it works: The chatbot parses the text and sends a request to the server.

[1416] Output: The request is sent to the server.

[1417] Step 9:

[1418] The server generates a preview link and sends it to the user.

[1419] Input: User requirements and generated .obj files

[1420] What it does: The preview link generator runs, creates a link, and sends it to the user.

[1421] Output: A preview link is provided to the user.

[1422] Step 10:

[1423] The emotion engine analyzes the user's voice, facial expressions, and input content to recognize emotions.

[1424] Input: User voice, facial expressions, and text input data

[1425] How it works: Emotion recognition algorithms analyze and assess the user's emotional state.

[1426] Output: The user's emotional state is evaluated within the system.

[1427] Step 11:

[1428] An emotion engine adjusts the process based on the user's emotions.

[1429] Input: Emotion recognition results

[1430] Action: The process is adjusted based on the emotion, for example, providing additional explanations to a user who feels anxious.

[1431] Output: The coordinated process is executed and feedback is provided to the user.

[1432] Step 12:

[1433] The user clicks the download link and downloads the generated .obj file.

[1434] Input: Download link provided by the server

[1435] What happens: The user clicks on the link and the file is downloaded.

[1436] Output: The downloaded .obj file is saved to the user's device.

[1437] Step 13:

[1438] The user prints the generated .obj file on a 3D printer.

[1439] Input: Downloaded .obj file

[1440] What it does: Sends a file to a 3D printer and starts the printing process.

[1441] Output: A physical, three-dimensional object is created.

[1442] Step 14:

[1443] The user puts the generated three-dimensional data up for sale on the content market.

[1444] Input: .obj file and marketplace listing metadata (title, description, price, etc.)

[1445] What happens: The server uploads data to the marketplace.

[1446] Output: The submitted 3D data will be published on the marketplace.

[1447] (Application example 2)

[1448] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1449] In modern factories, quickly and accurately repairing broken machine parts is crucial to maintaining production efficiency. However, conventional systems often require specialized technical personnel to acquire 3D data of the broken part and create the appropriate repair part. Furthermore, the lack of mechanisms to reduce user anxiety and stress during the data generation and provision process makes it difficult to provide a satisfactory user experience. Therefore, there is a need for the development of a system that can quickly and easily generate 3D data without specialized knowledge and provide support tailored to the user's emotions.

[1450] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1451] In this invention, the server includes a generating means for generating three-dimensional data of an object, an emotion recognizing means for recognizing a user's emotion, and an adjusting means for adjusting the requirements of the three-dimensional data based on the emotion recognizing means. This makes it possible to quickly and accurately generate three-dimensional data while taking into account the user's emotion, even without specialized knowledge, and to manufacture appropriate repair parts.

[1452] "Measuring means for measuring an object in three dimensions" refers to a device or module that collects three-dimensional data of an object using a LiDAR sensor or the like.

[1453] The "transmission means for transmitting the measurement data acquired by the measurement means" is a device or module having the function of transmitting the acquired three-dimensional data to the server.

[1454] The "means for generating three-dimensional data based on received measurement data" refers to a device or software module that generates a three-dimensional model based on received point cloud data.

[1455] The "means for providing the generated three-dimensional data to the user" is a device or module that has the function of providing the generated three-dimensional model in a format that the user can use.

[1456] The "emotion recognition means for recognizing the user's emotions" is a device or software module that recognizes emotions by analyzing the user's voice, facial expression, input contents, etc.

[1457] The "adjustment means for adjusting the requirements for three-dimensional data based on the emotion recognition means" refers to a device or software module that has the function of adjusting the specifications of the three-dimensional data to be generated and the method of providing it based on the recognized emotion of the user.

[1458] MODE FOR CARRYING OUT THE INVENTION

[1459] The present invention is aimed at improving the efficiency of maintenance and repair work, mainly in factories. Specific embodiments for carrying out the present invention will be described below.

[1460] 1. Hardware Configuration

[1461] Smartphone

[1462] It uses a smartphone equipped with a LiDAR sensor, which can scan broken parts and objects for 3D data with high accuracy.

[1463] Maintenance robot

[1464] Maintenance robots are used to repair and replace parts, and these robots act automatically based on commands from the server.

[1465] server

[1466] It uses a server that includes a generative AI model and an emotion recognition engine. The server receives the scanned data and performs a series of processes: processing, generating, and providing.

[1467] 2. Software Configuration

[1468] LiDAR Scan Module

[1469] It is a software module that is installed on a smartphone and is used to collect three-dimensional data of objects.

[1470] Data Formatting Tools

[1471] This is a tool that processes scanned point cloud data, removes noise, and smooths the data.

[1472] Generative AI Models

[1473] It is an AI model that runs on a server and generates highly accurate 3D models based on formatted 3D data.

[1474] Emotion Recognition Engine

[1475] It is a software module that analyzes the user's voice, facial expressions, and input content to recognize emotions, and utilizes emotional data to improve the user experience.

[1476] 3. Operational flow

[1477] scan

[1478] The device uses its LiDAR sensor to scan broken parts and objects, collecting point cloud data within minutes.

[1479] Data Formatting

[1480] The terminal processes the collected point cloud data, removes noise, and smooths it, then compresses it and sends it to the server.

[1481] 3D data generation

[1482] The server inputs the received data into a generative AI model to generate 3D data according to the user's requirements, which is then output in .obj file format.

[1483] Emotion Recognition and Regulation

[1484] The emotion recognition engine analyzes the user's voice and facial expressions to determine if they are anxious, and if so, the engine provides additional guidance and explanations.

[1485] Parts manufacturing

[1486] The generated 3D data is sent to a maintenance robot, which then produces the repair parts, which are then ready for immediate use.

[1487] 4. Specific Examples

[1488] For example, if part of a robot arm breaks in a factory, repairs are carried out using the following procedure.

[1489] 1. The maintenance robot detects the broken part and scans it using the smartphone's LiDAR sensor. The scan takes about 1-2 minutes.

[1490] 2. The scan data is formatted, denoised and smoothed on the device before being sent to the server.

[1491] 3. The server uses the generative AI model to generate 3D data and outputs it as an .obj file.

[1492] 4. An emotion recognition engine analyzes the operator's emotions and provides additional guidance and support if they are feeling anxious.

[1493] 5. The maintenance robot uses the generated data to 3D print repair parts, making them immediately usable.

[1494] 5. Examples of prompts

[1495] "We propose a new system that scans broken parts in your factory and automatically creates repair parts based on the generated 3D data. It uses a smartphone's LiDAR sensor to create 3D data of the broken part within minutes, and uses an emotion engine to understand and support the operator's emotions. This enables fast and efficient repairs."

[1496] This system is expected to make factory maintenance work more efficient, reduce user stress, and improve overall work efficiency.

[1497] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1498] Step 1:

[1499] The device launches the LiDAR scanning module. The user opens the smartphone app and uses the LiDAR sensor to scan the broken part. This allows 3D data (point cloud data) to be collected in real time. The input is the smartphone's LiDAR data, and the output is the scanned point cloud data.

[1500] Step 2:

[1501] The terminal processes the point cloud data. It removes noise from the collected data and smooths it. This data processing is performed using a software tool. The input is the scanned point cloud data, and the output is the denoised and smoothed point cloud data.

[1502] Step 3:

[1503] The terminal sends the formatted data to the server. The compressed 3D data is sent to the server via the network. The input is the formatted point cloud data, and the output is the data sent to the server.

[1504] Step 4:

[1505] The server processes the data received. It verifies the integrity of the received data and checks for missing or corrupted data. The input is the point cloud data sent to the server, and the output is the point cloud data whose integrity has been verified.

[1506] Step 5:

[1507] The server inputs data into the generated AI model and generates 3D data. A highly accurate 3D model (.obj file) is generated based on the received data. The input is point cloud data whose consistency has been confirmed, and the output is the generated 3D model (.obj file).

[1508] Step 6:

[1509] The server uses an emotion recognition engine to recognize the user's emotions. It analyzes the user's voice, facial expression, and input content to determine their emotional state. The input is the user's voice, facial expression, and input content, and the output is the analyzed emotional data.

[1510] Step 7:

[1511] The server adjusts the requirements for 3D data based on the emotion data. If the user feels anxious, it adjusts the generated data and processes and provides additional guidance and explanations. The input is the analyzed emotion data, and the output is the adjusted 3D data and guidance information.

[1512] Step 8:

[1513] The server provides the generated 3D data to the user. The generated 3D data is provided to the user as a downloadable link. The input is the adjusted 3D data, and the output is the download link.

[1514] Step 9:

[1515] The user downloads the 3D data using the provided download link. The downloaded data can then be used to produce actual repair parts using the maintenance robot's 3D printer. The input is the download link, and the output is the downloaded 3D data.

[1516] 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.

[1517] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1518] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1519] [Fourth embodiment]

[1520] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1521] 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.

[1522] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1523] 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.

[1524] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1525] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1526] 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.

[1527] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1528] 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.

[1529] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1530] 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.

[1531] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1532] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1533] This invention is a system that measures an object in three dimensions, easily generates three-dimensional data, and provides it in a format that can be used by a 3D printer. This system consists of a terminal, a server, and a user. The processing of each element and specific examples are explained below.

[1534] 1. Terminal processing

[1535] Starting the LiDAR Scan Module

[1536] A user opens the smartphone app and activates the LiDAR scanning module, which controls the smartphone's LiDAR sensor to collect three-dimensional data about the object.

[1537] Scanning an object

[1538] Users point their smartphone at the object they want to scan and use the LiDAR sensor to collect point cloud data of the object. This data is displayed on the device in real time, allowing users to see the progress of the scan. Once the scan is complete, the collected data is formatted and ready to be sent.

[1539] Formatting and sending data

[1540] The terminal processes the collected point cloud data and performs preprocessing such as noise removal and smoothing. The processed data is then compressed and sent to the server.

[1541] 2. Server-side processing

[1542] Data Receiving Module

[1543] The server receives the point cloud data sent from the device, passes the integrity check, and performs the necessary processing.

[1544] Generative AI Module

[1545] The server inputs the received data into the generation AI, which generates 3D data according to the user's requirements. The generated data is exported in .obj file format.

[1546] File Creation and Management

[1547] The generated .obj file is stored in the file management system and a file ID is generated, and the server generates a download link for this file and notifies the user.

[1548] 3. User Interface

[1549] Chat Interface

[1550] Users input their requirements (e.g., "Please increase the height of the chair legs by 10 cm") through the chatbot, and this interface provides a convenient way for users to communicate their specifications in detail.

[1551] Check and adjust specifications

[1552] The server displays a preview of the generated .obj file in a chat interface for the user to review and, if necessary, provide corrections.

[1553] 4. File Submission and Market Listing

[1554] Download Module

[1555] The server provides the user with a download link for the generated .obj file. The user clicks the link to download the .obj file, which can then be used with the user's 3D printer.

[1556] Market Linkage Module

[1557] When a user submits a generated .obj file to the Content Marketplace, the server facilitates the process: the user enters the metadata for the marketplace submission (e.g., title, description, price, etc.), which is then uploaded to the marketplace.

[1558] Specific examples

[1559] Example 1: Reproducing furniture parts

[1560] 1. Scan

[1561] A user uses the LiDAR sensor on their smartphone to scan the broken chair leg, and within minutes the point cloud data is collected and reconstructed.

[1562] 2. Generation

[1563] The server receives the point cloud data, and the generation AI generates three-dimensional data according to the user-specified requirements.

[1564] 3. Provide

[1565] The server provides a download link for the generated .obj file, which the user can then download and use to reproduce it on their 3D printer.

[1566] 4. Listing

[1567] When a user puts this data up for sale on the content market, the server assists in the process and the data is sold online.

[1568] This system allows users to easily generate 3D data without requiring specialized skills and use it in a 3D printer. Furthermore, they can earn secondary income by putting the generated data on the market.

[1569] The processing flow will be explained below.

[1570] Program processing flow

[1571] Terminal side processing

[1572] Step 1:

[1573] The user launches the app using their smartphone, which turns on the LiDAR scanning module.

[1574] Step 2:

[1575] The user points the smartphone at the object to be scanned, and the LiDAR sensor collects point cloud data of the object in real time. The device displays the data collection status on the screen, allowing the user to observe the scan until it is complete.

[1576] Step 3:

[1577] Once the user has finished scanning, the device will then shape the collected point cloud data, removing noise and smoothing it.

[1578] Step 4:

[1579] The device compresses the formatted point cloud data and transmits the data to the server.

[1580] Server-side processing

[1581] Step 5:

[1582] The server receives the point cloud data sent from the device and checks the data integrity, making sure there are no missing or corrupted data.

[1583] Step 6:

[1584] The server inputs the received data into the generation AI module, which then generates 3D data according to the user's requirements. The generated data is exported as an .obj file.

[1585] Step 7:

[1586] The server saves the generated .obj file to the file server, generates a file ID, and then generates a download link and notifies the user.

[1587] User Interface Handling

[1588] Step 8:

[1589] The user accesses the chat interface and receives a download link for the generated .obj file. The user downloads the file and uses it in a 3D printer.

[1590] Step 9:

[1591] If a user wants to modify an .obj file, they input their specific requirements through the chatbot, for example, "Please increase the height of the chair legs by 10cm."

[1592] Step 10:

[1593] The server runs the AI ​​module again based on the new requirements, generates the corrected 3D data, and resends the download link for the newly generated .obj file.

[1594] Processing file submissions and market listings

[1595] Step 11:

[1596] Users can download the files and use them in their home 3D printers as needed, or if they want to list their work on the market, the server assists them with the listing process.

[1597] Step 12:

[1598] The user enters the metadata for a marketplace listing (title, description, price, etc.), and the server uploads it to the marketplace.

[1599] Step 13:

[1600] The server sends a listing completion notice to the user and starts selling online.

[1601] This allows users to easily generate three-dimensional data without any specialized knowledge, and use it in a 3D printer or sell it on the market.

[1602] Example 1

[1603] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1604] Conventional methods for generating 3D data of objects require expensive equipment and specialized knowledge, making it difficult for general users to use. Furthermore, the methods for providing the generated data are often limited, restricting the scope of use for users. Therefore, there was a need for a system that could easily acquire and generate high-quality 3D data and enable users to easily use that data.

[1605] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1606] In this invention, the server includes a measuring means for measuring an object in three dimensions through a user interface, a transmitting means for transmitting point cloud data acquired by the measuring means, a generating means for generating three-dimensional data by shaping and preprocessing the received point cloud data, and a providing means for providing the generated three-dimensional data to a user. This makes it possible to easily acquire high-quality three-dimensional data without specialized knowledge and provide it to a user in a format that is easily usable.

[1607] A "user interface" is the means by which a user interacts with a system and inputs instructions and information.

[1608] "Means for measuring an object in three dimensions" refers to a technology or device for measuring the three-dimensional shape or structure of an object and acquiring it as digital data.

[1609] "Point cloud data" is three-dimensional data that represents a collection of multiple points that make up the surface of an object.

[1610] "Transmission means" refers to a technique or device for transmitting acquired data to another system or server.

[1611] "Receiving means" refers to a technique or device for receiving data sent from an external source.

[1612] "Means for shaping and preprocessing" refers to technology or equipment for improving the quality of data by performing processes such as noise removal and smoothing on the acquired raw data.

[1613] "Means for generating 3D data" refers to a technique or device for creating 3D models or shape data from pre-processed data.

[1614] "Providing means" refers to the technology or device for transmitting the generated three-dimensional data to the user or providing it in the form of a download link, etc.

[1615] A "downloadable link" is a web link that allows a user to receive a file or data specified by the user over the Internet.

[1616] The present invention is a system that measures an object in three dimensions, easily generates three-dimensional data, and provides it in a format that can be used by a 3D printer. This system is composed of a user, a terminal, and a server.

[1617] Terminal side processing

[1618] Starting the LiDAR Scan Module

[1619] The user opens the smartphone app and activates the LiDAR scanning module, which controls the smartphone's LiDAR sensor and collects 3D data of the object. The specific hardware used is the smartphone's built-in LiDAR sensor.

[1620] Scanning an object

[1621] Users point their smartphone at the object they want to scan and use the LiDAR sensor to collect point cloud data of the object. This data is displayed on the device in real time, allowing users to see the progress of the scan. Once the scan is complete, the collected data is formatted and ready to be sent.

[1622] Formatting and sending data

[1623] The terminal processes the collected point cloud data and performs preprocessing such as noise removal and smoothing. The processed data is then compressed and sent to the server. Specific software used includes data compression algorithms and noise filtering techniques.

[1624] Server-side processing

[1625] Data Receiving Module

[1626] The server receives the point cloud data sent from the device, passes the integrity check, and performs the necessary processing.

[1627] Generative AI Module

[1628] The server inputs the received data into a generative AI model and generates 3D data according to the user's requirements. The generated data is exported in .obj file format. The generative AI model uses a generative AI engine that analyzes the user's prompt and executes the specified processing.

[1629] File Creation and Management

[1630] The generated .obj file is stored in the file management system and a file ID is generated, and the server generates a download link for this file and notifies the user.

[1631] User Interface

[1632] Chat Interface

[1633] Users input their requirements (e.g., "Please increase the height of the chair legs by 10 cm") through the chatbot, and this interface provides a convenient way for users to communicate their specifications in detail.

[1634] Check and adjust specifications

[1635] The server displays a preview of the generated .obj file in a chat interface for the user to review and, if necessary, provide corrections.

[1636] File submission and market listing

[1637] Download Module

[1638] The server provides the user with a download link for the generated .obj file. The user clicks the link to download the .obj file, which can then be used with the user's 3D printer.

[1639] Market Linkage Module

[1640] When a user submits a generated .obj file to the Content Marketplace, the server facilitates the process: the user enters the metadata for the marketplace submission (e.g., title, description, price, etc.), which is then uploaded to the marketplace.

[1641] Specific examples

[1642] Example 1: Reproducing furniture parts

[1643] 1. Scan

[1644] A user uses the LiDAR sensor on their smartphone to scan the broken chair leg, and within minutes the point cloud data is collected and reconstructed.

[1645] 2. Generation

[1646] The server receives the point cloud data, and the generation AI generates three-dimensional data according to the user-specified requirements.

[1647] 3. Provide

[1648] The server provides a download link for the generated .obj file, which the user can then download and use to reproduce it on their 3D printer.

[1649] 4. Listing

[1650] When a user puts this data up for sale on the content market, the server assists in the process and the data is sold online.

[1651] Prompt Sentence Examples

[1652] Prompt 1: "I scanned a broken chair leg. From the generated data, please generate a version of the chair leg that is 10cm taller."

[1653] Prompt 2: "Generate repair data for the broken vase based on the collected point cloud data."

[1654] This system allows users to easily generate high-quality 3D data without requiring specialized skills and use it in 3D printers. Users can also earn secondary income by listing the generated data on a marketplace.

[1655] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1656] Step 1: Start the LiDAR Scan Module

[1657] The user launches the smartphone app and selects the LiDAR scan module, which causes the device to activate the internal LiDAR sensor and prepare for scanning.

[1658] Input: User action (selection of LiDAR scan module)

[1659] Output: LiDAR sensor wake-up and scan readiness notification

[1660] What it does: When the app launches, the device will begin calibrating the LiDAR sensor and display a "Ready to scan" notification to the user.

[1661] Step 2: Scan the object

[1662] Users point their smartphone at the object they want to scan and press the "Start Scan" button in the app. The device uses its LiDAR sensor to collect point cloud data of the object. This data is displayed on the device's screen in real time, allowing users to track the progress.

[1663] Input: User actions (pressing the start scan button) and data from the LiDAR sensor

[1664] Output: Point cloud data displayed in real time

[1665] Specific operation: During scanning, the device screen will construct 3D data of the object in real time and display a progress bar. When the scanning is complete, the message "Scan complete" will be displayed.

[1666] Step 3: Format and send the data

[1667] The terminal processes the collected point cloud data and performs preprocessing such as noise removal and smoothing. The processed data is then compressed and sent to the server.

[1668] Input: Collected point cloud data

[1669] Output: Formatted and compressed data

[1670] What it does: A denoising algorithm cleans up the data, a smoothing filter is applied, and once complete, the size of the cleaned data is compressed and a progress bar is displayed as the data is sent to the server.

[1671] Step 4: Data receiving module

[1672] The server receives the point cloud data sent from the device, which is first subjected to a consistency check to ensure there is no data corruption or inconsistency.

[1673] Input: Compressed data sent from the terminal

[1674] Output: Data with integrity checked

[1675] Specific operation: The server receives the data, performs a hash check and checks the number of bytes of the data. If consistency is confirmed, it logs "Data reception completed."

[1676] Step 5: Generative AI module

[1677] The server inputs the received data into the generative AI model and generates 3D data according to the user's prompts. The generated data is exported as an .obj file.

[1678] Input: Data that has been validated and a prompt from the user

[1679] Output: Generated .obj file

[1680] Specific behavior: The prompt (e.g., "Please extend the chair legs by 10 cm") is analyzed, and the AI ​​generator executes the specified process. The resulting .obj file is generated.

[1681] Step 6: File Creation and Management

[1682] The generated .obj file is stored in a file management system, where the server generates a file ID and sends the user a download link.

[1683] Input: .obj file

[1684] Output: File ID and download link

[1685] What happens: The server saves the .obj file and generates a unique file ID. It uses this ID to generate a download link and sends a "download link ready" notification to the user.

[1686] Step 7: Enter requirements via chat interface

[1687] Users input their requirements (e.g., "Please increase the height of the chair legs by 10 cm") through the chatbot, and this interface provides a convenient way for users to communicate their specifications in detail.

[1688] Input: User requirements

[1689] Output: Prompt statement based on your requirements

[1690] Specific behavior: The chatbot receives user input and displays a confirmation message to generate a prompt. The user is notified that "the specified requirements have been registered."

[1691] Step 8: Check and adjust specifications

[1692] The server displays a preview of the generated .obj file in a chat interface, allowing the user to check the preview and make corrections if necessary.

[1693] Input: Generated .obj file

[1694] Output: User's correction instructions

[1695] Specific operation: The server generates a low-resolution model for preview, and the chatbot prompts the user to "check the generated results and enter any corrections." The user then enters the corrections into the chat screen.

[1696] Step 9: Download Module

[1697] The server provides the user with a download link for the generated .obj file. The user clicks the link to download the .obj file.

[1698] Input: File ID and download link

[1699] Output: Downloaded .obj file

[1700] What happens: The server generates a download link and sends it to the user's email address or notification system. The user is notified that "Download link has been sent."

[1701] Step 10: Market Linkage Module

[1702] When a user submits a generated .obj file to the Content Marketplace, the server facilitates the process: the user enters the metadata for the marketplace submission (e.g., title, description, price, etc.), which is then uploaded to the marketplace.

[1703] Input: User metadata and .obj file

[1704] Output: Listing on Content Market

[1705] Specific operation: The server provides the user with a listing template and prompts them to enter the necessary metadata. After the user fills in the "title," "description," and "price," the server sends the data to the marketplace and notifies the user that "listing is complete."

[1706] (Application example 1)

[1707] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1708] When a robot or equipment part breaks down in a logistics center, it is necessary to replace or repair the part quickly and accurately. However, conventional methods require time and cost to identify, design, and manufacture the part, resulting in reduced operational efficiency. The objective of this invention is to solve these problems and provide a system that enables efficient and rapid parts management and repair.

[1709] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1710] In this invention, the server includes a measuring means for measuring an object in three dimensions, a transmitting means for transmitting the measurement data acquired by the measuring means, a generating means for generating three-dimensional data based on the received measurement data, a providing means for providing the generated three-dimensional data to a user, and a managing means for supporting preventive maintenance and repairs. This enables the logistics center to quickly identify faulty parts, generate accurate three-dimensional data for the necessary parts, and immediately manufacture them using a 3D printer.

[1711] "Measuring means for measuring an object in three dimensions" refers to equipment or technology for obtaining the shape and dimensions of an object in three-dimensional space.

[1712] The "transmission means for transmitting the measurement data acquired by the measurement means" refers to a mechanism for transferring the data collected by the measurement means to another device or server.

[1713] The "generation means for generating three-dimensional data based on the received measurement data" refers to an algorithm or system for creating a three-dimensional model from the measurement data received via the transmission means.

[1714] "Means for providing generated three-dimensional data to users" refers to technologies and methods for enabling users to access the generated three-dimensional data.

[1715] "Management measures to support preventive maintenance and repair" refers to systems and methods to support the efficient maintenance and management of equipment and robots, as well as the replacement and repair of parts.

[1716] "LiDAR sensor" refers to a sensor technology that uses laser light to precisely measure the distance and shape of an object.

[1717] This invention is a system that measures an object in three dimensions and provides the data in a format that can be used by a 3D printer. The system mainly consists of three elements: a terminal, a server, and a user.

[1718] The device uses a smartphone equipped with a LiDAR sensor as a device for users to measure objects in three dimensions. First, the user opens the smartphone app and activates the LiDAR scanning module. This module controls the LiDAR sensor and collects three-dimensional data of the object. The collected data is displayed on the device in real time, allowing the user to check the progress of the scan. Once the scan is complete, the collected data is formatted and sent from the device to a server.

[1719] The server receives the point cloud data sent from the device. Here, preprocessing such as data consistency checks, noise removal, and smoothing is performed. Using a generative AI model, 3D data based on the user's requirements is generated from the received data. This 3D data is exported as an .obj file and saved in a file management system. The user is then notified of a download link for the generated file.

[1720] In the user interface, the user inputs requirements through a chatbot. For example, a specific request such as "Please increase the height of the chair legs by 10 cm" can be entered in the chat interface. The generative AI model generates 3D data based on this prompt. A preview of the generated .obj file is displayed in the chat interface for the user to review. If necessary, they can also provide instructions for corrections.

[1721] Next, users download the generated .obj file and use it in their own 3D printer. For example, by scanning a broken robot arm part and manufacturing the generated 3D data with a 3D printer, it is possible to quickly and accurately replace or repair the part. The server also supports users in listing the .obj file on the content market. By entering listing metadata, the file can be sold online.

[1722] As a concrete example, if a joint on a robot breaks, the user scans the joint using the LiDAR sensor on their smartphone and sends the data to a server. The generation AI generates three-dimensional data on the joint and provides it as a download link. The user then prints the resulting data on a 3D printer to quickly manufacture a new joint. An example of this prompt could be, "Yesterday, a joint on a robot arm in the warehouse broke. I would like to scan it and print a new joint on a 3D printer. After starting the LiDAR scan, please have the generation AI convert the joint data into 3D data and output it in .obj format."

[1723] In this way, the management and replacement of parts at the logistics center can be made more efficient.

[1724] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1725] Step 1:

[1726] The user opens the smartphone app and activates the LiDAR scanning module, which controls the smartphone's LiDAR sensor and collects 3D data of the object. The input is the user's actions and the target object, and the output is point cloud data acquired in real time.

[1727] Step 2:

[1728] The device processes the collected point cloud data. Specifically, preprocessing such as noise removal and smoothing is performed. The input is the point cloud data, and the output is the processed point cloud data. Once processing is complete, the processed data is compressed and ready for transmission.

[1729] Step 3:

[1730] The terminal sends the formatted point cloud data to the server. Specifically, it sends the data to the server via an HTTP request. The input is the formatted and compressed point cloud data, and the output is a successful data transmission response to the server.

[1731] Step 4:

[1732] The server receives point cloud data sent from the device. The data receiving module checks the integrity of the received data and performs preprocessing such as noise removal and smoothing. The input is the received point cloud data, and the output is the shaped point cloud data.

[1733] Step 5:

[1734] The server's generation AI model generates 3D data based on the shaped point cloud data. It executes the necessary generation process according to the user's prompt. The input is the shaped point cloud data and the user's prompt, and the output is the generated 3D data (.obj file format).

[1735] Step 6:

[1736] The server saves the generated 3D data in a file management system, generates a file ID, and sends a notification to the user to provide a download link. The input is the generated 3D data (.obj file), and the output is a notification of the download link.

[1737] Step 7:

[1738] The user clicks on the provided download link and downloads the generated 3D data (.obj file). This gives the user 3D data that can be used with a 3D printer. The input is the download link, and the output is the downloaded 3D data (.obj file).

[1739] Step 8:

[1740] Users use the downloaded 3D data to manufacture parts using a 3D printer. Specifically, they import the 3D data into the 3D printer and start manufacturing the part. The input is the 3D data (.obj file), and the output is the finished part.

[1741] This series of processing steps creates a system that allows efficient parts management and repairs at the logistics center.

[1742] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1743] This invention improves the user experience by combining a system that measures an object in three dimensions, easily generates three-dimensional data, and provides it in a format that can be used by a 3D printer with an emotion engine that recognizes the user's emotions. This system is composed of a terminal, a server, and a user. The processing of each element and specific examples are explained below.

[1744] 1. Terminal processing

[1745] Starting the LiDAR Scan Module

[1746] A user opens the smartphone app and activates the LiDAR scanning module, which controls the smartphone's LiDAR sensor to collect three-dimensional data about the object.

[1747] Scanning an object

[1748] The user points the smartphone at the object to be scanned, and the LiDAR sensor collects point cloud data of the object in real time. The device displays the data collection status on the screen, allowing the user to observe the scan until it is complete.

[1749] Formatting and sending data

[1750] The device processes the collected point cloud data, removes noise, and smooths it, then compresses the processed data and sends it to the server.

[1751] 2. Server-side processing

[1752] Data Receiving Module

[1753] The server receives the point cloud data sent from the device and checks the data integrity, making sure there are no missing or corrupted data.

[1754] Generative AI Module

[1755] The server inputs the received data into the generation AI, which then generates 3D data according to the user's requirements. The generated data is exported in .obj file format.

[1756] File Creation and Management

[1757] The generated .obj file is stored in the file management system, a file ID is generated, and the server generates a download link and notifies the user.

[1758] 3. User Interface Processing

[1759] Chat Interface

[1760] Users input their requirements (e.g., "Please increase the height of the chair legs by 10 cm") through the chatbot, and this interface provides a convenient way for users to communicate their specifications in detail.

[1761] Check and adjust specifications

[1762] The server displays a preview of the generated .obj file in a chat interface for the user to review and, if necessary, provide corrections.

[1763] 4. Emotional engine processing

[1764] emotion recognition

[1765] The emotion engine recognizes the user's emotions by using technology that analyzes the user's voice, facial expressions, and input content.

[1766] Emotion-Based Adjustment

[1767] The emotion engine automatically adjusts the requirements for the generated 3D data based on the user's emotions, and if the user appears anxious, it displays a dialog asking for further confirmation or provides an easy-to-understand explanation.

[1768] Suggestions and support

[1769] The emotion engine analyzes the user's emotions and provides suggestions and support based on the analysis results. For example, if the user is happy, it will suggest new designs and related options.

[1770] 5. Processing File Submissions and Market Listings

[1771] Download Module

[1772] The server provides the user with a download link for the generated .obj file. The user clicks the link to download the .obj file, which can then be used with the user's 3D printer.

[1773] Market Linkage Module

[1774] When a user submits a generated .obj file to the Content Marketplace, the server facilitates the process: the user enters the metadata for the marketplace submission (e.g., title, description, price, etc.), which is then uploaded to the marketplace.

[1775] Specific examples

[1776] Example 1: Generating and listing furniture parts using an emotion engine

[1777] 1. Scan

[1778] A user uses the LiDAR sensor on their smartphone to scan the broken chair leg, and within minutes the point cloud data is collected and reconstructed.

[1779] 2. Generation

[1780] The server receives the point cloud data, and the generation AI generates three-dimensional data according to the user-specified requirements.

[1781] 3. Emotion recognition

[1782] The emotion engine analyzes the user's facial and vocal responses to determine if they are anxious about the process, and if so, provides additional guidance to resolve the anxiety.

[1783] 4. Provide

[1784] The server provides a download link for the generated .obj file, which the user can then download and use to reproduce it on their 3D printer.

[1785] 5. Listing

[1786] When a user puts this data up for sale on the content market, the server assists in the process and the data is sold online.

[1787] This allows users to easily generate 3D data without any specialized knowledge, and then use it in a 3D printer or sell it on the market. Furthermore, the introduction of an emotion engine improves the user experience, providing a more intuitive and satisfying process.

[1788] The processing flow will be explained below.

[1789] Processing flow of a system that combines emotion engines

[1790] Terminal side processing

[1791] Step 1:

[1792] The user launches the app using their smartphone, which turns on the LiDAR scanning module.

[1793] Step 2:

[1794] The user points the smartphone at the object to be scanned, and the LiDAR sensor collects point cloud data of the object in real time. The device displays the data collection status on the screen, allowing the user to observe the scan until it is complete.

[1795] Step 3:

[1796] Once the user has finished scanning, the device will then shape the collected point cloud data, removing noise and smoothing it.

[1797] Step 4:

[1798] The device compresses the formatted point cloud data and transmits the data to the server.

[1799] Server-side processing

[1800] Step 5:

[1801] The server receives the point cloud data sent from the device and checks the data integrity, making sure there are no missing or corrupted data.

[1802] Step 6:

[1803] The server inputs the received data into the generation AI module, which then generates 3D data according to the user's requirements. The generated data is exported in .obj file format.

[1804] Step 7:

[1805] The server saves the generated .obj file to the file server, generates a file ID, and then generates a download link and notifies the user.

[1806] Emotion engine processing

[1807] Step 8:

[1808] The emotion engine recognizes the user's emotions by analyzing the user's voice, facial expressions, and input through the device or chat interface to determine the user's emotional state.

[1809] Step 9:

[1810] The emotion engine automatically adjusts the requirements of the generated 3D data based on the user's emotions. For example, if the user is anxious, the system will provide more specific guidance. Conversely, if the user is satisfied, it will suggest additional features and options.

[1811] Step 10:

[1812] The emotion engine provides suggestions and support to the user based on the analysis results. For example, if the user is detected as excited, it will suggest creating other related objects.

[1813] User Interface Handling

[1814] Step 11:

[1815] The user accesses the chat interface and receives a download link for the generated .obj file. The user downloads the file and uses it in a 3D printer.

[1816] Step 12:

[1817] If a user wants to modify an .obj file, they input their specific requirements through the chatbot, such as "Please make the chair legs 10cm taller."

[1818] Step 13:

[1819] The server runs the AI ​​module again based on the new requirements, generates the corrected 3D data, and resends the download link for the newly generated .obj file.

[1820] Processing file submissions and market listings

[1821] Step 14:

[1822] The server provides the user with a download link for the generated .obj file. The user clicks the link to download the .obj file, which can then be used with the user's 3D printer.

[1823] Step 15:

[1824] When a user creates an .obj file and submits it to the Content Market, the server assists the process. The user enters the metadata for the market listing (title, description, price, etc.).

[1825] Step 16:

[1826] The server sends a listing completion notice to the user and starts selling online.

[1827] Specific examples

[1828] Example 1: Generating and listing furniture parts using an emotion engine

[1829] Step 1:

[1830] A user launches the app on their smartphone and scans the broken chair leg.

[1831] Step 2:

[1832] The smartphone's LiDAR sensor collects data in real time, and the device formats and compresses the data before sending it to a server.

[1833] Step 3:

[1834] The server receives the data and uses a generative AI module to generate 3D data, which is then exported in .obj file format.

[1835] Step 4:

[1836] The server saves the generated .obj file to a file server and sends the user a download link.

[1837] Step 5:

[1838] The emotion engine analyzes the user's facial expressions and inputs to detect when they are feeling anxious, and provides detailed explanations and additional guidance.

[1839] Step 6:

[1840] The user checks the file and requests additional modifications as needed through the chat interface. The server then runs the generation AI again based on the requested modifications.

[1841] Step 7:

[1842] The server then provides the modified .obj file again, which the user can download and use in their 3D printer.

[1843] Step 8:

[1844] When a user submits a generated .obj file to the marketplace, the server facilitates the process: the user enters metadata and the file is uploaded to the marketplace.

[1845] Step 9:

[1846] The server will send a notice of completion of listing to the user, and online sales will begin.

[1847] This system allows users to easily generate 3D data without any specialized knowledge, and then use it in a 3D printer or sell it on the market. Furthermore, the introduction of an emotion engine improves the user experience, providing a more intuitive and satisfying process.

[1848] Example 2

[1849] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1850] While traditional 3D measurement systems have the ability to generate 3D data of objects, they lack the flexibility to respond to user emotions and requirements, making it difficult to improve the user experience. In particular, there is a lack of appropriate support and explanations when users feel anxious or stressed about the process.

[1851] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1852] In this invention, the server includes a measuring means for measuring an object in three dimensions, a transmitting means for transmitting the measurement data acquired by the measuring means, a generating means for generating three-dimensional data based on the received measurement data, a providing means for providing the generated three-dimensional data to a user, and an emotion recognizing means for recognizing the user's emotion and adjusting the requirements for the three-dimensional data based on the emotion. This enables flexible responses in line with the user's emotion, providing a more satisfying user experience.

[1853] "Object" refers to any three-dimensional object from which three-dimensional data is acquired and measured.

[1854] "Measuring means for three-dimensional measurement" refers to a device or group of devices for obtaining three-dimensional shape information of an object.

[1855] "Measurement data acquired by a measurement means" refers to three-dimensional shape information of an object obtained using a measurement means.

[1856] "Transmitting means" refers to a device or method for transmitting acquired measurement data to another device or system.

[1857] "Received measurement data" refers to data received from data transmitted by a transmitting means.

[1858] The "means for generating three-dimensional data" refers to a device or algorithm for generating three-dimensional shape data based on the received measurement data.

[1859] "Generated three-dimensional data" refers to three-dimensional shape data created by a generating means.

[1860] "Providing means" refers to a device or method for providing the generated three-dimensional data to a user.

[1861] "Emotion recognition means" refers to a device or algorithm that recognizes a user's emotions and adjusts other processes based on that information.

[1862] This invention relates to a system that measures an object in three dimensions, easily generates three-dimensional data, and provides it in a format that can be used by a 3D printer. Furthermore, by combining it with an emotion engine that recognizes the user's emotions, the user experience can be improved. This system is composed of a terminal, a server, and a user. The specific processing of each element is described below.

[1863] Terminal side processing

[1864] The user launches the smartphone app and activates the LiDAR scanning module. The LiDAR sensor collects three-dimensional data of the object and generates point cloud data in real time. The device then processes this data with noise reduction and smoothing, compresses it, and sends it to the server.

[1865] Server-side processing

[1866] The server receives the point cloud data sent from the device and verifies the data's integrity. It then generates 3D data using a generative AI model. Examples of such models include OpenAI's GPT series and Codex. This model generates 3D data in the .obj file format, which the server saves in a file management system. The generated data is provided as a download link and notified to the user.

[1867] User Interface Handling

[1868] The user inputs their requirements through the chatbot interface. For example, "Please increase the height of the chair legs by 10 cm." The chatbot then analyzes the input and sends it to the server. A preview of the generated .obj file is displayed in the chat interface, allowing the user to review and make corrections.

[1869] Emotion engine processing

[1870] The emotion engine analyzes the user's voice, facial expressions, and input to recognize their emotions. Based on this information, the system can respond to the user's needs. If the user is anxious, a dialog will appear requesting further explanation or confirmation. If the user is happy, the system will suggest relevant options.

[1871] Processing file submissions and market listings

[1872] The server provides a download link for the generated .obj file, which the user can download and use in a 3D printer. Furthermore, if the user wants to list the generated 3D data on the content market, the server will assist in the process of listing it on the marketplace.

[1873] As a specific example of how it works, a user scans the legs of a broken chair with their smartphone, and new 3D data is generated based on that data. If the emotion engine senses anxiety from the user's facial expression or voice, additional explanations are displayed. Finally, the generated 3D data is provided to the user, who can then print it using a 3D printer. This data can also be put up for sale on an online marketplace.

[1874] These features provide a system that allows users to easily generate and utilize 3D data, even if they do not have specialized knowledge. The introduction of an emotion engine further improves the user experience.

[1875] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1876] System program processing flow

[1877] Step 1:

[1878] The user launches the smartphone app and activates the LiDAR scanning module.

[1879] Input: Smartphone app launch command

[1880] What happens: The app wakes up the LiDAR sensor and begins calibrating it, ready to scan.

[1881] Output: The LiDAR sensor is now active and a ready notification is displayed on the screen.

[1882] Step 2:

[1883] The user points the smartphone at the object to be scanned and performs the scan.

[1884] Input: Smartphone pointed at object and command to start scanning

[1885] How it works: The LiDAR sensor collects point cloud data in real time, and the collection status is displayed on the device screen.

[1886] Output: The collected point cloud data is temporarily saved on the device.

[1887] Step 3:

[1888] The point cloud data collected by the device is shaped, denoised and smoothed.

[1889] Input: Collected point cloud data

[1890] How it works: The algorithm denoises the data and performs a smoothing process, as well as compression.

[1891] Output: Formatted and compressed point cloud data is generated.

[1892] Step 4:

[1893] The terminal transmits the formatted data to the server.

[1894] Input: shaped and compressed point cloud data

[1895] Operation: The data transmission function is activated and data is uploaded to the server.

[1896] Output: Point cloud data sent to the server is received.

[1897] Step 5:

[1898] Check the integrity of the point cloud data received by the server.

[1899] Input: Point cloud data sent to the server

[1900] How it works: Integrity checking algorithms check for errors and missing data.

[1901] Output: Data is saved successfully with integrity checked.

[1902] Step 6:

[1903] The server generates 3D data using a generative AI model (e.g., OpenAI's GPT series).

[1904] Input: Normal point cloud data

[1905] How it works: Input data into the generative AI model and perform calculations to generate 3D shape data.

[1906] Output: The generated 3D data in .obj file format is temporarily saved.

[1907] Step 7:

[1908] The server stores the generated .obj file in a file management system and generates a download link.

[1909] Input: Generated 3D data (.obj file)

[1910] How it works: The file management system saves the data and generates a unique file ID. A download link is created.

[1911] Output: A download link is generated and notified to the user.

[1912] Step 8:

[1913] The user enters their requirements into the chatbot interface (e.g., "Please extend the height of the chair legs by 10 cm").

[1914] Input: Requirement text entered by the user

[1915] How it works: The chatbot parses the text and sends a request to the server.

[1916] Output: The request is sent to the server.

[1917] Step 9:

[1918] The server generates a preview link and sends it to the user.

[1919] Input: User requirements and generated .obj files

[1920] What it does: The preview link generator runs, creates a link, and sends it to the user.

[1921] Output: A preview link is provided to the user.

[1922] Step 10:

[1923] The emotion engine analyzes the user's voice, facial expressions, and input content to recognize emotions.

[1924] Input: User voice, facial expressions, and text input data

[1925] How it works: Emotion recognition algorithms analyze and assess the user's emotional state.

[1926] Output: The user's emotional state is evaluated within the system.

[1927] Step 11:

[1928] An emotion engine adjusts the process based on the user's emotions.

[1929] Input: Emotion recognition results

[1930] Action: The process is adjusted based on the emotion, for example, providing additional explanations to a user who feels anxious.

[1931] Output: The coordinated process is executed and feedback is provided to the user.

[1932] Step 12:

[1933] The user clicks the download link and downloads the generated .obj file.

[1934] Input: Download link provided by the server

[1935] What happens: The user clicks on the link and the file is downloaded.

[1936] Output: The downloaded .obj file is saved to the user's device.

[1937] Step 13:

[1938] The user prints the generated .obj file on a 3D printer.

[1939] Input: Downloaded .obj file

[1940] What it does: Sends a file to a 3D printer and starts the printing process.

[1941] Output: A physical, three-dimensional object is created.

[1942] Step 14:

[1943] The user puts the generated three-dimensional data up for sale on the content market.

[1944] Input: .obj file and marketplace listing metadata (title, description, price, etc.)

[1945] What happens: The server uploads data to the marketplace.

[1946] Output: The submitted 3D data will be published on the marketplace.

[1947] (Application example 2)

[1948] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1949] In modern factories, quickly and accurately repairing broken machine parts is crucial to maintaining production efficiency. However, conventional systems often require specialized technical personnel to acquire 3D data of the broken part and create the appropriate repair part. Furthermore, the lack of mechanisms to reduce user anxiety and stress during the data generation and provision process makes it difficult to provide a satisfactory user experience. Therefore, there is a need for the development of a system that can quickly and easily generate 3D data without specialized knowledge and provide support tailored to the user's emotions.

[1950] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1951] In this invention, the server includes a generating means for generating three-dimensional data of an object, an emotion recognizing means for recognizing a user's emotion, and an adjusting means for adjusting the requirements of the three-dimensional data based on the emotion recognizing means. This makes it possible to quickly and accurately generate three-dimensional data while taking into account the user's emotion, even without specialized knowledge, and to manufacture appropriate repair parts.

[1952] "Measuring means for measuring an object in three dimensions" refers to a device or module that collects three-dimensional data of an object using a LiDAR sensor or the like.

[1953] The "transmission means for transmitting the measurement data acquired by the measurement means" is a device or module having the function of transmitting the acquired three-dimensional data to the server.

[1954] The "means for generating three-dimensional data based on received measurement data" refers to a device or software module that generates a three-dimensional model based on received point cloud data.

[1955] The "means for providing the generated three-dimensional data to the user" is a device or module that has the function of providing the generated three-dimensional model in a format that the user can use.

[1956] The "emotion recognition means for recognizing the user's emotions" is a device or software module that recognizes emotions by analyzing the user's voice, facial expression, input contents, etc.

[1957] The "adjustment means for adjusting the requirements for three-dimensional data based on the emotion recognition means" refers to a device or software module that has the function of adjusting the specifications of the three-dimensional data to be generated and the method of providing it based on the recognized emotion of the user.

[1958] MODE FOR CARRYING OUT THE INVENTION

[1959] The present invention is aimed at improving the efficiency of maintenance and repair work, mainly in factories. Specific embodiments for carrying out the present invention will be described below.

[1960] 1. Hardware Configuration

[1961] Smartphone

[1962] It uses a smartphone equipped with a LiDAR sensor, which can scan broken parts and objects for 3D data with high accuracy.

[1963] Maintenance robot

[1964] Maintenance robots are used to repair and replace parts, and these robots act automatically based on commands from the server.

[1965] server

[1966] It uses a server that includes a generative AI model and an emotion recognition engine. The server receives the scanned data and performs a series of processes: processing, generating, and providing.

[1967] 2. Software Configuration

[1968] LiDAR Scan Module

[1969] It is a software module that is installed on a smartphone and is used to collect three-dimensional data of objects.

[1970] Data Formatting Tools

[1971] This is a tool that processes scanned point cloud data, removes noise, and smooths the data.

[1972] Generative AI Models

[1973] It is an AI model that runs on a server and generates highly accurate 3D models based on formatted 3D data.

[1974] Emotion Recognition Engine

[1975] It is a software module that analyzes the user's voice, facial expressions, and input content to recognize emotions, and utilizes emotional data to improve the user experience.

[1976] 3. Operational flow

[1977] scan

[1978] The device uses its LiDAR sensor to scan broken parts and objects, collecting point cloud data within minutes.

[1979] Data Formatting

[1980] The terminal processes the collected point cloud data, removes noise, and smooths it, then compresses it and sends it to the server.

[1981] 3D data generation

[1982] The server inputs the received data into a generative AI model to generate 3D data according to the user's requirements, which is then output in .obj file format.

[1983] Emotion Recognition and Regulation

[1984] The emotion recognition engine analyzes the user's voice and facial expressions to determine if they are anxious, and if so, the engine provides additional guidance and explanations.

[1985] Parts manufacturing

[1986] The generated 3D data is sent to a maintenance robot, which then produces the repair parts, which are then ready for immediate use.

[1987] 4. Specific Examples

[1988] For example, if part of a robot arm breaks in a factory, repairs are carried out using the following procedure.

[1989] 1. The maintenance robot detects the broken part and scans it using the smartphone's LiDAR sensor. The scan takes about 1-2 minutes.

[1990] 2. The scan data is formatted, denoised and smoothed on the device before being sent to the server.

[1991] 3. The server uses the generative AI model to generate 3D data and outputs it as an .obj file.

[1992] 4. An emotion recognition engine analyzes the operator's emotions and provides additional guidance and support if they are feeling anxious.

[1993] 5. The maintenance robot uses the generated data to 3D print repair parts, making them immediately usable.

[1994] 5. Examples of prompts

[1995] "We propose a new system that scans broken parts in your factory and automatically creates repair parts based on the generated 3D data. It uses a smartphone's LiDAR sensor to create 3D data of the broken part within minutes, and uses an emotion engine to understand and support the operator's emotions. This enables fast and efficient repairs."

[1996] This system is expected to make factory maintenance work more efficient, reduce user stress, and improve overall work efficiency.

[1997] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1998] Step 1:

[1999] The device launches the LiDAR scanning module. The user opens the smartphone app and uses the LiDAR sensor to scan the broken part. This allows 3D data (point cloud data) to be collected in real time. The input is the smartphone's LiDAR data, and the output is the scanned point cloud data.

[2000] Step 2:

[2001] The terminal processes the point cloud data. It removes noise from the collected data and smooths it. This data processing is performed using a software tool. The input is the scanned point cloud data, and the output is the denoised and smoothed point cloud data.

[2002] Step 3:

[2003] The terminal sends the formatted data to the server. The compressed 3D data is sent to the server via the network. The input is the formatted point cloud data, and the output is the data sent to the server.

[2004] Step 4:

[2005] The server processes the data received. It verifies the integrity of the received data and checks for missing or corrupted data. The input is the point cloud data sent to the server, and the output is the point cloud data whose integrity has been verified.

[2006] Step 5:

[2007] The server inputs data into the generated AI model and generates 3D data. A highly accurate 3D model (.obj file) is generated based on the received data. The input is point cloud data whose consistency has been confirmed, and the output is the generated 3D model (.obj file).

[2008] Step 6:

[2009] The server uses an emotion recognition engine to recognize the user's emotions. It analyzes the user's voice, facial expression, and input content to determine their emotional state. The input is the user's voice, facial expression, and input content, and the output is the analyzed emotional data.

[2010] Step 7:

[2011] The server adjusts the requirements for 3D data based on the emotion data. If the user feels anxious, it adjusts the generated data and processes and provides additional guidance and explanations. The input is the analyzed emotion data, and the output is the adjusted 3D data and guidance information.

[2012] Step 8:

[2013] The server provides the generated 3D data to the user. The generated 3D data is provided to the user as a downloadable link. The input is the adjusted 3D data, and the output is the download link.

[2014] Step 9:

[2015] The user downloads the 3D data using the provided download link. The downloaded data can then be used to produce actual repair parts using the maintenance robot's 3D printer. The input is the download link, and the output is the downloaded 3D data.

[2016] 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.

[2017] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[2018] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[2019] 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.

[2020] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[2021] 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.

[2022] 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).

[2023] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[2024] 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."

[2025] 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.

[2026] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[2027] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[2028] 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.

[2029] 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.

[2030] 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.

[2031] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[2032] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[2033] 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.

[2034] 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.

[2035] 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.

[2036] 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.

[2037] The following is further disclosed regarding the above embodiment.

[2038] (Claim 1)

[2039] a measuring means for measuring an object in three dimensions;

[2040] a transmitting means for transmitting the measurement data acquired by the measuring means;

[2041] generating means for generating three-dimensional data based on the received measurement data;

[2042] a providing means for providing the generated three-dimensional data to a user;

[2043] A system including:

[2044] (Claim 2)

[2045] 10. The system of claim 1,

[2046] The system wherein the measuring means is a LiDAR sensor.

[2047] (Claim 3)

[2048] 10. The system of claim 1,

[2049] The system wherein the providing means includes means for providing the generated three-dimensional data as a downloadable link.

[2050] (Claim 4)

[2051] 10. The system of claim 1,

[2052] The system wherein the providing means includes a means for putting the generated three-dimensional data up for sale on a content market.

[2053] (Claim 5)

[2054] 10. The system of claim 1,

[2055] The system wherein the generating means includes means for generating three-dimensional data based on requirements input by a user.

[2056] "Example 1"

[2057] (Claim 1)

[2058] a measuring means for measuring an object in three dimensions through a user interface;

[2059] a transmitting means for transmitting the point cloud data acquired by the measuring means;

[2060] generating means for generating three-dimensional data by shaping and preprocessing the received point cloud data;

[2061] providing means for providing the generated three-dimensional data to a user;

[2062] A system including:

[2063] (Claim 2)

[2064] The system of claim 1, wherein the measuring means is a terminal equipped with a LiDAR sensor.

[2065] (Claim 3)

[2066] 2. The system of claim 1, wherein the providing means provides the generated three-dimensional data as a downloadable link.

[2067] "Application Example 1"

[2068] (Claim 1)

[2069] a measuring means for measuring an object in three dimensions;

[2070] a transmitting means for transmitting the measurement data acquired by the measuring means;

[2071] generating means for generating three-dimensional data based on the received measurement data;

[2072] a providing means for providing the generated three-dimensional data to a user;

[2073] Control measures to support preventative maintenance and repair;

[2074] A system including:

[2075] (Claim 2)

[2076] The system of claim 1 , wherein the measuring means is a LiDAR sensor.

[2077] (Claim 3)

[2078] 2. The system according to claim 1, wherein the providing means includes means for providing the generated three-dimensional data as a downloadable link.

[2079] "Example 2: Combining Emotion Engines"

[2080] (Claim 1)

[2081] a measuring means for measuring an object in three dimensions;

[2082] a transmitting means for transmitting the measurement data acquired by the measuring means;

[2083] generating means for generating three-dimensional data based on the received measurement data;

[2084] a providing means for providing the generated three-dimensional data to a user;

[2085] emotion recognition means for recognizing an emotion of a user and adjusting requirements for the three-dimensional data based on the emotion;

[2086] A system including:

[2087] (Claim 2)

[2088] 2. The system of claim 1, wherein the measuring means is a three-dimensional measuring sensor.

[2089] (Claim 3)

[2090] 2. The system according to claim 1, wherein the providing means includes means for providing the generated three-dimensional data as a downloadable link.

[2091] "Application example 2 when combining emotion engines"

[2092] (Claim 1)

[2093] a measuring means for measuring an object in three dimensions;

[2094] a transmitting means for transmitting the measurement data acquired by the measuring means;

[2095] generating means for generating three-dimensional data based on the received measurement data;

[2096] a providing means for providing the generated three-dimensional data to a user;

[2097] an emotion recognition means for recognizing an emotion of a user;

[2098] an adjusting means for adjusting the requirements of the three-dimensional data based on the emotion recognition means;

[2099] A system including:

[2100] (Claim 2)

[2101] The system of claim 1 , wherein the measuring means is a LiDAR sensor.

[2102] (Claim 3)

[2103] 2. The system according to claim 1, wherein the providing means includes means for providing the generated three-dimensional data as a downloadable link. [Explanation of symbols]

[2104] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. a measuring means for measuring an object in three dimensions; a transmitting means for transmitting the measurement data acquired by the measuring means; generating means for generating three-dimensional data based on the received measurement data; a providing means for providing the generated three-dimensional data to a user; A system including:

2. 10. The system of claim 1, The system wherein the measuring means is a LiDAR sensor.

3. 10. The system of claim 1, The system wherein the providing means includes means for providing the generated three-dimensional data as a downloadable link.

4. 10. The system of claim 1, The system wherein the providing means includes a means for putting the generated three-dimensional data up for sale on a content market.

5. 10. The system of claim 1, The system wherein the generating means includes means for generating three-dimensional data based on requirements input by a user.

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