system

Abstract

We provide the system. [Solution] A recording means for photographing an object from all directions, A communication means for sending captured image data to a server, An analysis means for analyzing image data to estimate three-dimensional shape, A selection method for selecting the components necessary for reproduction based on the shape, An assembly drawing generation means that generates an assembly procedure based on selected components, A system that includes this.

Description

Technical Field

[0005] ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the use of conventional block toys, although a user can reproduce an existing shape based on a specified design drawing or instruction manual, no means has been provided for reproducing an arbitrary object with blocks. Therefore, a high level of expertise and time are required for a user to accurately reproduce an object around them with blocks, which is difficult for general users. Thus, a system for easily and efficiently reproducing an arbitrary object with blocks is demanded.

Means for Solving the Problems

[0005] This invention provides a recording means for the user to photograph an object from all directions, thereby collecting detailed information about the object's shape. Next, a communication means is used to transmit the captured image data to a server, ensuring that the image data is securely and quickly sent to an analysis system. The analysis means on the server uses the received image data to estimate the three-dimensional shape and selects the components necessary for reproduction based on that shape. Furthermore, an assembly diagram generation means generates an assembly procedure based on the selected components, enabling the user to easily reproduce the object using blocks.

[0006] "Object" refers to any three-dimensional object that the user wishes to recreate using blocks.

[0007] "Recording means" refers to a device equipped with the function of acquiring the shape and characteristics of an object as digital data by photographing it from all directions.

[0008] "Communication means" refers to a device equipped with network connectivity for transmitting digital data acquired by recording means to a server.

[0009] "Analysis means" refers to software or algorithms that operate on a server and estimate the three-dimensional shape of an object based on image data received via communication means.

[0010] The "selection means" refers to the function of selecting the optimal components for reproducing an object based on the shape data estimated by the analysis means.

[0011] The "assembly diagram generation means" refers to a function that creates an assembly procedure using the components selected by the selection means so that the user can correctly reproduce the target object.

[0012] A "server" refers to a computer device that performs data processing over a network, including analysis tools, selection tools, and assembly diagram generation tools. [Brief explanation of the drawing]

[0013] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, which incorporates an emotion engine. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0014] An example of an embodiment of the system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0017] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0019] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0020] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0024] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0025] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0026] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0027] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0028] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0031] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0032] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0033] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0034] The primary objective of this invention is for users to photograph an object from all directions and for the data to be analyzed on a server. In this system, users photograph the object from multiple angles using a smartphone or other mobile device. The captured images are organized on the device, converted to an optimal format, and then sent to the server.

[0035] On the server, the received image data is first transferred to the AI ​​analysis unit, where the three-dimensional shape of the object is estimated. This analysis process applies machine learning techniques to reconstruct the shape with high accuracy based on a large database of similar shapes.

[0036] After the shape is estimated, the server selects the appropriate blocks. Here, the optimal block parts necessary for reproduction are selected based on the size and shape of the object. Based on the selected block information, the server automatically generates an assembly procedure. This procedure is organized sequentially, making it easy for the user to follow.

[0037] Ultimately, the device receives assembly instructions sent from the server. The user follows these instructions and uses the blocks they have to recreate the target object. For example, if a user photographs a miniature car on their desk, the server analyzes its shape and selects the appropriate blocks to provide the user with a detailed assembly diagram to recreate the car using the blocks they have. This allows the user to easily and efficiently recreate real objects with blocks.

[0038] In this way, the present invention not only supports users in creative block play, but also has a wide range of applications, from corporate training to design prototyping.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The user uses their smartphone to photograph the object from various angles. This collects multiple images to clearly capture the overall picture of the object.

[0042] Step 2:

[0043] The device organizes the captured images into the appropriate format, optimizes their size, and sends them to the server. During this process, data compression is performed as needed to ensure communication stability.

[0044] Step 3:

[0045] The server sends the received image data to the AI ​​analysis unit. The AI ​​uses machine learning algorithms to estimate the three-dimensional shape of the object from the image data. This analysis includes shape recognition as well as estimation of the relative positions of each part of the object.

[0046] Step 4:

[0047] Based on the analysis results, the server selects the optimal block parts necessary to reproduce the object from the database. This selection takes into account the size, color, and shape of the blocks.

[0048] Step 5:

[0049] The server automatically generates assembly instructions based on the selected block information. These instructions detail the order in which each block should be assembled, as well as any points to note.

[0050] Step 6:

[0051] The server sends the generated assembly instructions and related information to the terminal.

[0052] Step 7:

[0053] The terminal displays the received assembly instructions to the user. The user follows these instructions and uses the blocks at hand to recreate the target object.

[0054] (Example 1)

[0055] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0056] Conventional three-dimensional object reproduction systems have the drawback of being cumbersome and inefficient overall, as each process, from image capture to data analysis and selection of specific assembly parts, is performed individually. Furthermore, there are limited means for ordinary users to easily reproduce three-dimensional objects, and situations often require specialized knowledge and skills.

[0057] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0058] In this invention, the server includes recording means for capturing visual information by photographing an object from various angles, communication means for transferring the acquired visual information to a remote processing device, and analysis means for analyzing the visual information and inferring the three-dimensional structure. This allows users to easily select appropriate parts and generate assembly procedures for reconstructing a three-dimensional object without requiring specialized knowledge.

[0059] "Recording means" refers to devices and technologies for capturing visual information by photographing an object from various angles.

[0060] "Communication means" refers to devices and technologies for transferring acquired visual information to a remote processing unit.

[0061] "Analysis methods" refer to algorithms and techniques for analyzing visual information and inferring three-dimensional structures.

[0062] "Selection method" refers to a technique for selecting the components necessary for reconstruction based on the inferred three-dimensional structure.

[0063] "Assembly diagram generation means" refers to technologies and algorithms for automatically generating assembly procedures based on selected components.

[0064] "Visualization means" refers to devices or technologies for displaying the generated assembly procedure on an information terminal.

[0065] "Output means" refers to the technology or device that allows the user to receive the generated assembly drawing in a predetermined format.

[0066] This invention is a system for efficiently carrying out the process of a user photographing an object from various angles and reconstructing a three-dimensional object based on that data. This system mainly consists of recording means, communication means, analysis means, selection means, assembly drawing generation means, visualization means, and output means.

[0067] Users use smartphones or mobile devices to photograph objects from multiple angles. During this process, the device's camera function acts as a "recording tool," acquiring visual information about the object. The acquired data is transmitted to a server via the internet using a "communication tool." A secure data transmission protocol (e.g., HTTPS) is used for this transmission.

[0068] The server, as an "analysis tool," analyzes the received visual information using an advanced generative AI model to infer a three-dimensional structure. This analysis utilizes machine learning algorithms such as TENSORFLOW® and PyTorch. The inferred structure is then used as a "selection tool" to select the components necessary for reconstruction. For example, this includes selecting parts using a digital 3D model library.

[0069] Based on the selected components, the server automatically generates assembly instructions as an "assembly diagram generation means." 3D modeling software (e.g., Autodesk Tinkercad) is used for this purpose. The generated assembly instructions are organized sequentially and displayed on the terminal's screen by a "visualization means."

[0070] The user checks the assembly instructions displayed on the terminal and physically recreates the object using the blocks they have. By referring to the outputted assembly diagram, the specific assembly steps can be easily understood. The accuracy of the completed object largely depends on the capabilities of the AI ​​model used in the analysis phase.

[0071] For example, if a user takes a picture of a miniature car on their desk, the system will infer the car's three-dimensional shape from the acquired image and select the appropriate components. The user can then recreate the miniature car using the parts they have on hand, based on the assembly diagram provided by the system.

[0072] An example of a prompt to be input to the generating AI model is a specific instruction such as, "Analyze the omnidirectional photograph of the object and generate a block assembly guide." Based on this prompt, the system performs a series of tasks.

[0073] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0074] Step 1:

[0075] The user takes multiple photos of an object from different angles using a smartphone or mobile device. The input consists of multiple images captured by the camera. Specifically, the user operates the device's camera function to take photos of the object from above, below, left, and right, changing the angle. The output is the captured image data.

[0076] Step 2:

[0077] The device organizes the acquired image data and converts it to the optimal format. The input is the raw image data acquired in Step 1. The image editing app converts the data to JPEG or PNG format, adjusts the resolution, and removes noise. The output is the formatted image data.

[0078] Step 3:

[0079] The terminal sends the organized image data to the server. The input is the formatted image data created in step 2. The data is sent to the server using the HTTPS protocol via a communication method. The output is the image data sent to the server.

[0080] Step 4:

[0081] The server transfers the received image data to the AI ​​analysis unit. The input is the image data received in step 3. The program on the server prepares to pass the data to the AI ​​module for 3D shape analysis. The output is the data state passed to the AI ​​module.

[0082] Step 5:

[0083] The server's AI analysis unit uses a generative AI model to infer the three-dimensional shape. The input is the analysis data prepared in step 4. Prompts are given to the TensorFlow or PyTorch-based model to perform shape analysis. The output is the inferred three-dimensional shape data.

[0084] Step 6:

[0085] The server selects appropriate components based on the three-dimensional shape. The input is the three-dimensional shape data inferred in step 5. The server references the data and selects the optimal parts from the digital library. The output is a list of selected components.

[0086] Step 7:

[0087] The server generates assembly instructions based on the selected components. The input is the components selected in step 6. The assembly diagram generation module automatically generates the instructions using 3D modeling software. The output is the generated assembly instructions data.

[0088] Step 8:

[0089] The server sends the generated assembly instructions to the terminal. The input is the assembly instructions data constructed in step 7. The server again uses a communication method to securely transmit the data to the terminal. The output is the assembly instructions sent to the terminal.

[0090] Step 9:

[0091] The terminal displays and provides the received assembly instructions to the user. The input is the assembly instruction data received in step 8. The terminal interprets the instructions using an appropriate interface and displays them in a user-friendly format. The output is the assembly instructions displayed on the terminal screen.

[0092] Step 10:

[0093] The user assembles the object using the parts they have on hand, following the assembly procedure displayed on the terminal. The input is the assembly procedure displayed in step 9. The user physically follows the procedure and assembles the parts in the correct order to form the final shape. The output is the completed three-dimensional object.

[0094] (Application Example 1)

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

[0096] During the prototyping phase of a product, there is a need to reduce the time and effort required to quickly assemble prototypes based on shape data. Traditional methods require a significant amount of manual work and time from prototype design to assembly, which reduces the efficiency of product development.

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

[0098] In this invention, the server includes recording means for capturing images of an object from all directions, communication means for transmitting the captured image data to the server, and analysis means for analyzing the image data to estimate its three-dimensional shape. This makes it possible to quickly and automatically assemble a prototype based on the shape designed by an engineer.

[0099] "Recording means" refers to devices and methods for photographing an object from all directions, and is intended to digitize the shape of a designed product with high precision.

[0100] "Communication methods" refer to the technologies and protocols used to transmit captured image data to a server, enabling the secure and rapid transfer of data.

[0101] "Analysis means" refers to algorithms and techniques for processing image data received on a server and estimating its three-dimensional shape, utilizing advanced machine learning techniques to generate shape data.

[0102] "Selection method" refers to the criteria and methods for selecting the necessary components for reproduction based on the analyzed shape data, and supports the selection of the optimal parts.

[0103] The "assembly diagram generation means" refers to a process that generates assembly procedures based on selected components, and is designed to efficiently visualize the generated procedures.

[0104] "Dispatch means" refers to a method or device for transmitting the generated assembly procedure to a machine and actually performing automated assembly, thereby realizing assembly automation.

[0105] A system implementing this invention mainly consists of a mobile terminal with a high-resolution camera, a server, and mechanical equipment installed in a factory or the like.

[0106] The device captures images of the target object from all directions and transmits these image data to the server using a communication method. Wi-Fi or mobile data communication can be used for this purpose. The smartphone preprocesses the images using OpenCV and converts them to the optimal format. The data is sent to the server using a secure protocol.

[0107] The server processes the received data using analysis tools. These tools include TensorFlow, a Python-based machine learning framework, and OpenCV, an image processing library. Based on the image data, the server estimates the three-dimensional shape of the object and creates a 3D model using the Blender API. Then, based on this model, it identifies the component parts using selection tools and generates the optimal assembly procedure.

[0108] Furthermore, assembly procedures are automatically generated via an assembly drawing generation means. These generated procedures are transmitted to the factory machinery through a feeding means, and the machinery automatically starts assembly. This allows for the efficient construction of prototypes.

[0109] For example, if a part for a new electronic device is designed, the part can be photographed with a device, the data can be analyzed on a server, and a factory robot can quickly assemble the part. This allows engineers to revise the design and immediately test the new prototype.

[0110] An example of a prompt message for a generating AI model would be: "Send new prototyping images to the system and generate an efficient assembly procedure. The next subject to be photographed is a next-generation electronic component."

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

[0112] Step 1:

[0113] The user uses a terminal to photograph the target object from all directions. The terminal uses a high-resolution camera to take photos from multiple angles, and this image data is input to the next processing step. The obtained image data is formatted and subjected to basic image processing using OpenCV on the terminal. This processing optimizes the image quality and prepares it for transmission to the server.

[0114] Step 2:

[0115] The terminal transmits the formatted image data to the server using a communication method. A secure protocol (e.g., HTTPS) is used for data transmission to ensure data integrity and confidentiality. The server receives the transmitted image data as input.

[0116] Step 3:

[0117] The server analyzes the received image data. Here, using Python's OpenCV and TensorFlow, it first estimates the three-dimensional shape from the image. In this estimation, multiple image data are fused to generate a 3D model, and the result is output to the next processing step.

[0118] Step 4:

[0119] The server selects the necessary components for reproduction based on the generated 3D model using a selection method. The model is further refined using the Blender API, and the necessary component information is provided as output. At this point, a list of parts required for assembly is created.

[0120] Step 5:

[0121] The server generates assembly instructions using an assembly diagram generation mechanism based on the selected components. These instructions are output in a sequential manner to provide clear instructions to the machine. The data created here serves as the instruction manual for the robot.

[0122] Step 6:

[0123] The assembly instructions generated from the server are sent to the machine. The instructions reach the machine via a feeding mechanism, and the robot automatically starts assembly. This automates the actual construction, and the prototype is completed.

[0124] This allows engineers to receive rapid feedback on-site, resulting in a more efficient prototyping process.

[0125] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0126] The system in this invention adds an element of sentiment analysis to the process of a user photographing an object and assembling blocks using a generated assembly diagram. The user uses a smartphone or other mobile device to photograph the object from multiple angles. The captured images are managed and organized on the device and transmitted to a server.

[0127] The server supplies image data to the AI ​​analysis unit, which estimates the three-dimensional shape of the object. This analysis uses shape recognition technology and improves accuracy by comparing it with various databases. Next, the server selects the block parts necessary to reconstruct the object based on the analysis results. This selection process is based on shape information and the user's past operation history.

[0128] Furthermore, this invention uses an emotion engine equipped in the terminal to obtain feedback on the user's emotional state. This emotion engine analyzes the user's facial expressions and tone of voice through the camera and voice input, and sends the results to a server. The server optimizes the assembly procedure based on this emotional information. The purpose of this procedure optimization process is to adjust the difficulty level of the blocks and the presentation procedure so that the user can continue working more comfortably.

[0129] After the assembly procedure is optimized, the device provides the user with a visually and emotionally resonant assembly diagram. This allows the user to proceed with the block assembly with a more positive experience based on the displayed information. For example, if the emotion engine detects signs of stress when the user is tackling a difficult part, the system can suggest a less complex version of the block.

[0130] In this way, by incorporating user emotional information into the program, the system can provide a flexible assembly environment tailored to individual users, comfortably supporting creative activities.

[0131] The following describes the processing flow.

[0132] Step 1:

[0133] The user uses their smartphone to photograph the object from different angles. This collects a series of images that cover the entire 360 ​​degrees.

[0134] Step 2:

[0135] The device formats the captured image appropriately, adjusts the image size, and then sends the image data to the server. This process may involve data compression.

[0136] Step 3:

[0137] The server processes the received image data using an AI analysis unit to estimate a three-dimensional model of the object. This process uses a shape recognition algorithm to extract the object's main features and construct a highly accurate model.

[0138] Step 4:

[0139] The server selects suitable block parts from the database based on a three-dimensional model. Selection criteria include shape, color, and required quantity.

[0140] Step 5:

[0141] An emotion engine built into the device analyzes the user's emotional state from their facial expressions and voice. This data is shared with a server in real time and used to optimize the assembly process.

[0142] Step 6:

[0143] The server dynamically adjusts the assembly procedure while taking into account the user's emotional state. For example, if stress is detected, it may adjust the difficulty of the procedure or include encouraging messages.

[0144] Step 7:

[0145] The server sends optimized assembly instructions to the terminal.

[0146] Step 8:

[0147] The terminal visually displays the received assembly instructions to the user. This display includes concise, step-by-step guidelines, which the user follows to recreate the object using the blocks at hand.

[0148] Step 9:

[0149] As the user proceeds with assembly, the emotion engine continuously acquires emotional data and sends feedback to the server as needed. Based on this feedback, further adjustments may be made.

[0150] (Example 2)

[0151] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0152] In modern society, a challenge exists where users experience stress and reduced enjoyment when engaging in creative assembly activities due to the difficulty of the assembly process. In particular, the selection of appropriate parts based on the shape of the object, and the complexity of the assembly procedure, often do not suit the individual user's skills and emotional state, making it difficult to provide an efficient and satisfying assembly experience.

[0153] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0154] In this invention, the server includes a recording unit and means for photographing an object from multiple viewpoints, a communication unit and means for transmitting the recorded digital data to an information processing device, and a processing unit and means for processing the digital data in the information processing device and estimating its three-dimensional shape. This makes it possible to provide an assembly procedure optimized for each user while taking into account the user's emotional state, thereby realizing a comfortable and creative assembly activity.

[0155] The "object" refers to the object or model that the user uses as the basis for assembly, and is analyzed by the system from multiple perspectives.

[0156] A "recording unit" is a device or component that has the function of capturing an object as digital data in image or video format from multiple viewpoints.

[0157] A "communication unit" is a device or function for transmitting digital data obtained from a recording unit to an information processing device, i.e., a server, via a network.

[0158] A "processing unit" is a device or program that analyzes digital data received by a server and estimates the three-dimensional shape of an object.

[0159] A "decision unit" is a device or function that selects the components necessary to reproduce an object based on the shape generated by the processing unit.

[0160] A "generation unit" is a device or function that creates an optimal assembly procedure, taking into account the components selected by the decision unit and the user's emotional state, and provides it to the user visually.

[0161] An "analysis unit" is a device or program that collects and analyzes user emotional information in real time and optimizes the assembly procedure based on the results.

[0162] This invention is a system that allows users to photograph objects using a mobile device and then uses that information to assist in the assembly of blocks and other materials. Furthermore, it aims to provide a better assembly experience by taking into account the user's emotional state.

[0163] The user uses a device such as a smartphone or tablet to photograph an object from multiple angles. The device's built-in camera and software are used to record image data of the object. This image data is temporarily stored on the device and then transmitted to a server via a communication unit over the network.

[0164] The server analyzes the received image data using a generative AI model or other shape recognition software. Based on the analysis results, it estimates the three-dimensional shape of the object and selects the necessary components accordingly. A database is used for selection, taking into account the compatibility of each part and the user's past history data.

[0165] The device also features an emotion engine that analyzes the user's facial expressions and voice through the camera and microphone. This data is transmitted to a server in real time. Based on the user's emotion information, the server adjusts the assembly procedure and the difficulty level of the parts to generate an appropriate assembly diagram.

[0166] The device displays assembly diagrams that are visually easy to understand and emotionally resonant for the user. This allows users to proceed with the assembly process creatively and without stress.

[0167] As a concrete example, suppose a user is assembling a dinosaur object. This system analyzes photos of the dinosaur from various angles, selects the necessary blocks for assembly, and suggests starting with easier parts if the user is feeling stressed. Examples of prompt messages include, "Starting the dinosaur assembly. Please let us know if you have any questions during assembly. We are also monitoring your emotional state."

[0168] This embodiment of the invention allows users to enjoy a comfortable and efficient assembly experience.

[0169] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0170] Step 1:

[0171] The user uses the camera on their mobile device to photograph an object from multiple angles. The images taken from each angle are recorded on the device as input. These images are then organized within the device, and initial data processing such as noise reduction and resolution adjustment is performed. Finally, the processed images are sent from the device to the server as output.

[0172] Step 2:

[0173] The server receives image data transmitted from the terminal. The image data, as input, is supplied to shape recognition algorithms such as generative AI models within the server. Based on this input, the server estimates the three-dimensional shape of the object using database matching and machine learning techniques. The output of this step is a three-dimensional model data of the object.

[0174] Step 3:

[0175] The server uses an estimated three-dimensional shape model to select the components necessary to reproduce the object. The input consists of 3D model data and a database of components that could potentially fit that model. The server performs a highly accurate selection while considering the user's past operation history and preferences. The output is a list of the selected appropriate components.

[0176] Step 4:

[0177] The device uses an emotion engine to analyze the user's emotional state in real time. Input includes the user's facial expressions and voice, which are then fed into a model using Effective Computing technology. This allows the model to determine the user's emotional state, such as stress or excitement. The output is the analyzed emotion data, which is then sent to a server.

[0178] Step 5:

[0179] The server receives user sentiment data and optimizes the assembly procedure. Inputs include sentiment data from step 4 and a list of components from step 3. Based on these inputs, the server adjusts the difficulty of the procedure to ensure the user is comfortable working with it, generating an optimized assembly procedure. The output is the final assembly diagram, including the user-specific assembly procedure.

[0180] Step 6:

[0181] The terminal presents the user with optimized assembly instructions received from the server. The input is the assembly diagram provided by the server. The terminal displays this information in a visually clear and sequential manner to support the user's assembly work. The output is assembly instructions in a format that the user can visually confirm.

[0182] Through the above process, users can enjoy a comfortable and stress-free assembly experience.

[0183] (Application Example 2)

[0184] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0185] In factory assembly work, prolonged work hours can lead to fatigue and stress, resulting in decreased work efficiency. This situation not only reduces productivity but can also negatively impact workers' health. Therefore, it is necessary to monitor workers' emotional states in real time and adjust the work environment appropriately.

[0186] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0187] In this invention, the server includes recording means for photographing an object from all directions, communication means for transmitting the captured image data to the server, analysis means for analyzing the image data to estimate its three-dimensional shape, emotion information analysis means for analyzing the user's emotional state, and procedure optimization means for optimizing the work procedure based on the emotion information. As a result, the robot performing the assembly work adjusts the work content according to the worker's emotional state, enabling efficient work while reducing the burden on the worker.

[0188] "Recording means" refers to a device or method for photographing an object from all directions and acquiring image data.

[0189] "Communication means" refers to a device or method for performing network communication to transmit captured image data to a server.

[0190] "Analysis means" refers to a device or method for estimating the three-dimensional shape of an object based on received image data.

[0191] "Selection means" refers to an apparatus or method for selecting the components necessary for reproduction based on the analyzed and estimated shape.

[0192] "Assembly drawing generation means" refers to an apparatus or method for generating an assembly procedure based on selected components.

[0193] "Emotional information analysis means" refers to a device or method for analyzing a user's emotional state and acquiring emotional information.

[0194] "Procedure optimization means" refers to a device or method that optimizes work procedures based on acquired emotional information and adjusts the user's work environment.

[0195] The system that realizes this invention is composed of multiple hardware and software components. The server plays a central role and performs various processes. The server is equipped with a high-performance CPU and GPU, enabling it to process large amounts of data in real time.

[0196] First, the user takes photos of the target object from all directions using a smartphone or a dedicated mobile device. The obtained image data is automatically transmitted to the server via a communication method. This communication uses high-speed communication technologies such as Wi-Fi or 4G / 5G. The server analyzes the received image data using image processing libraries such as OpenCV and estimates the three-dimensional shape of the target object. This analysis result is compared with known shape data in a database to improve accuracy.

[0197] Based on the analyzed shape information, the server selects the necessary components. The selection process is optimized by a machine learning algorithm, taking into account the user's past operation history. Based on the selected components, the assembly diagram generation means generates the assembly procedure. This generation utilizes a generation AI model to optimize the required assembly difficulty and sequence.

[0198] Furthermore, the device is equipped with a camera and microphone for emotional information analysis, which analyzes the user's emotional state from their facial expressions and voice. This analysis uses tools such as Librosa for voice data analysis. The emotional data is immediately fed back to the server, and the work procedure is dynamically adjusted by a procedure optimization mechanism.

[0199] As a concrete example, in an electronic component assembly plant, if emotional analysis detects worker fatigue due to prolonged work, the system has the ability to adjust procedures to reduce the complexity of the work at the appropriate time. A concrete example of this prompt sentence would be: "Think of an idea where worker stress is measured in real time, and if stress is detected, a factory robot automatically takes over the work."

[0200] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0201] Step 1:

[0202] The user takes photos of the target object from all directions using a smartphone or dedicated mobile device. The input is image data captured from multiple angles, which is managed and organized within the device. The output is data converted into a communication-compatible format.

[0203] Step 2:

[0204] The device transmits the captured image data to the server. The server receives image data as input, which is done using high-speed communication technology (Wi-Fi / 4G / 5G). The output is the storage of the image data within the server.

[0205] Step 3:

[0206] The server uses OpenCV to analyze received image data and estimate the three-dimensional shape of the object. The input is image data, and the output is estimated three-dimensional shape data. In this process, accuracy is improved by comparing the data with shape data in an existing database.

[0207] Step 4:

[0208] The server selects the necessary components based on estimated shape information. Inputs are 3D shape data and the user's past operation history, while output is information on the selected components. This selection process utilizes machine learning algorithms.

[0209] Step 5:

[0210] The server uses a generative AI model to generate assembly instructions based on selected parts. The input is component information, and the output is the generated assembly instructions. The generation is adjusted to optimize the difficulty and order of the steps.

[0211] Step 6:

[0212] The device's emotional information analysis system uses a camera and microphone to analyze the user's emotional state from their facial expressions and voice. The input is real-time collected facial and voice data, and the output is the analyzed emotional data.

[0213] Step 7:

[0214] The server receives emotional data and adjusts the work procedure using a procedure optimization mechanism. The input is emotional data, and the output is the adjusted work procedure. The adjustment is optimized to reduce user stress.

[0215] Step 8:

[0216] The terminal displays the finalized work procedure to the user. The input is the adjusted work procedure, and the output is an assembly diagram that the user can visually confirm. The display also includes suggestions based on emotional information.

[0217] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0218] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0219] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0220] [Second Embodiment]

[0221] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0222] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0223] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0224] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0225] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0226] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0227] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0228] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0229] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0230] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0231] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0232] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0233] The primary objective of this invention is for users to photograph an object from all directions and for the data to be analyzed on a server. In this system, users photograph the object from multiple angles using a smartphone or other mobile device. The captured images are organized on the device, converted to an optimal format, and then sent to the server.

[0234] On the server, the received image data is first transferred to the AI ​​analysis unit, where the three-dimensional shape of the object is estimated. This analysis process applies machine learning techniques to reconstruct the shape with high accuracy based on a large database of similar shapes.

[0235] After the shape is estimated, the server selects the appropriate blocks. Here, the optimal block parts necessary for reproduction are selected based on the size and shape of the object. Based on the selected block information, the server automatically generates an assembly procedure. This procedure is organized sequentially, making it easy for the user to follow.

[0236] Ultimately, the device receives assembly instructions sent from the server. The user follows these instructions and uses the blocks they have to recreate the target object. For example, if a user photographs a miniature car on their desk, the server analyzes its shape and selects the appropriate blocks to provide the user with a detailed assembly diagram to recreate the car using the blocks they have. This allows the user to easily and efficiently recreate real objects with blocks.

[0237] In this way, the present invention not only supports users in creative block play, but also has a wide range of applications, from corporate training to design prototyping.

[0238] The following describes the processing flow.

[0239] Step 1:

[0240] The user uses their smartphone to photograph the object from various angles. This collects multiple images to clearly capture the overall picture of the object.

[0241] Step 2:

[0242] The device organizes the captured images into the appropriate format, optimizes their size, and sends them to the server. During this process, data compression is performed as needed to ensure communication stability.

[0243] Step 3:

[0244] The server sends the received image data to the AI ​​analysis unit. The AI ​​uses machine learning algorithms to estimate the three-dimensional shape of the object from the image data. This analysis includes shape recognition as well as estimation of the relative positions of each part of the object.

[0245] Step 4:

[0246] Based on the analysis results, the server selects the optimal block parts necessary to reproduce the object from the database. This selection takes into account the size, color, and shape of the blocks.

[0247] Step 5:

[0248] The server automatically generates assembly instructions based on the selected block information. These instructions detail the order in which each block should be assembled, as well as any points to note.

[0249] Step 6:

[0250] The server sends the generated assembly instructions and related information to the terminal.

[0251] Step 7:

[0252] The terminal displays the received assembly instructions to the user. The user follows these instructions and uses the blocks at hand to recreate the target object.

[0253] (Example 1)

[0254] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0255] Conventional three-dimensional object reproduction systems have the drawback of being cumbersome and inefficient overall, as each process, from image capture to data analysis and selection of specific assembly parts, is performed individually. Furthermore, there are limited means for ordinary users to easily reproduce three-dimensional objects, and situations often require specialized knowledge and skills.

[0256] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0257] In this invention, the server includes recording means for capturing visual information by photographing an object from various angles, communication means for transferring the acquired visual information to a remote processing device, and analysis means for analyzing the visual information and inferring the three-dimensional structure. This allows users to easily select appropriate parts and generate assembly procedures for reconstructing a three-dimensional object without requiring specialized knowledge.

[0258] "Recording means" refers to devices and technologies for capturing visual information by photographing an object from various angles.

[0259] "Communication means" refers to devices and technologies for transferring acquired visual information to a remote processing unit.

[0260] "Analysis methods" refer to algorithms and techniques for analyzing visual information and inferring three-dimensional structures.

[0261] "Selection method" refers to a technique for selecting the components necessary for reconstruction based on the inferred three-dimensional structure.

[0262] "Assembly diagram generation means" refers to technologies and algorithms for automatically generating assembly procedures based on selected components.

[0263] "Visualization means" refers to devices or technologies for displaying the generated assembly procedure on an information terminal.

[0264] "Output means" refers to the technology or device that allows the user to receive the generated assembly drawing in a predetermined format.

[0265] This invention is a system for efficiently carrying out the process of a user photographing an object from various angles and reconstructing a three-dimensional object based on that data. This system mainly consists of recording means, communication means, analysis means, selection means, assembly drawing generation means, visualization means, and output means.

[0266] Users use smartphones or mobile devices to photograph objects from multiple angles. During this process, the device's camera function acts as a "recording tool," acquiring visual information about the object. The acquired data is transmitted to a server via the internet using a "communication tool." A secure data transmission protocol (e.g., HTTPS) is used for this transmission.

[0267] The server, as an "analysis tool," analyzes the received visual information using an advanced generative AI model to infer a three-dimensional structure. This analysis utilizes machine learning algorithms using TensorFlow or PyTorch. The inferred structure is then used as a "selection tool" to select the components necessary for reconstruction. For example, this might involve selecting parts using a digital 3D model library.

[0268] Based on the selected components, the server automatically generates assembly instructions as an "assembly diagram generation means." 3D modeling software (e.g., Autodesk Tinkercad) is used for this purpose. The generated assembly instructions are organized sequentially and displayed on the terminal's screen by a "visualization means."

[0269] The user checks the assembly instructions displayed on the terminal and physically recreates the object using the blocks they have. By referring to the outputted assembly diagram, the specific assembly steps can be easily understood. The accuracy of the completed object largely depends on the capabilities of the AI ​​model used in the analysis phase.

[0270] For example, if a user takes a picture of a miniature car on their desk, the system will infer the car's three-dimensional shape from the acquired image and select the appropriate components. The user can then recreate the miniature car using the parts they have on hand, based on the assembly diagram provided by the system.

[0271] An example of a prompt to be input to the generating AI model is a specific instruction such as, "Analyze the omnidirectional photograph of the object and generate a block assembly guide." Based on this prompt, the system performs a series of tasks.

[0272] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0273] Step 1:

[0274] The user takes multiple photos of an object from different angles using a smartphone or mobile device. The input consists of multiple images captured by the camera. Specifically, the user operates the device's camera function to take photos of the object from above, below, left, and right, changing the angle. The output is the captured image data.

[0275] Step 2:

[0276] The device organizes the acquired image data and converts it to the optimal format. The input is the raw image data acquired in Step 1. The image editing app converts the data to JPEG or PNG format, adjusts the resolution, and removes noise. The output is the formatted image data.

[0277] Step 3:

[0278] The terminal sends the organized image data to the server. The input is the formatted image data created in step 2. The data is sent to the server using the HTTPS protocol via a communication method. The output is the image data sent to the server.

[0279] Step 4:

[0280] The server transfers the received image data to the AI analysis unit. The input is the image data received in step 3. The program in the server passes the data to the AI module and prepares for analyzing the three-dimensional shape. The output is the data state passed to the AI module.

[0281] Step 5:

[0282] The AI analysis unit of the server uses the generative AI model to infer the three-dimensional shape. The input is the analysis data prepared in step 4. A prompt sentence is given to the model based on TensorFlow or PyTorch for shape analysis. The output is the inferred three-dimensional shape data.

[0283] Step 6:

[0284] The server selects appropriate components based on the three-dimensional shape. The input is the three-dimensional shape data inferred in step 5. The server performs the process of selecting the optimal parts from the digital library by referring to the data. The output is the selected component list.

[0285] Step 7:

[0286] The server generates an assembly procedure based on the selected components. The input is the components selected in step 6. The assembly drawing generation module uses 3D modeling software to automatically generate the procedure. The output is the generated assembly procedure data.

[0287] Step 8:

[0288] The server transmits the generated assembly procedure to the terminal. The input is the assembly procedure data constructed in step 7. The server uses the communication means again to securely transmit the data to the terminal. The output is the assembly procedure transmitted to the terminal.

[0289] Step 9:

[0290] The terminal displays and provides the received assembly instructions to the user. The input is the assembly instruction data received in step 8. The terminal interprets the instructions using an appropriate interface and displays them in a user-friendly format. The output is the assembly instructions displayed on the terminal screen.

[0291] Step 10:

[0292] The user assembles the object using the parts they have on hand, following the assembly procedure displayed on the terminal. The input is the assembly procedure displayed in step 9. The user physically follows the procedure and assembles the parts in the correct order to form the final shape. The output is the completed three-dimensional object.

[0293] (Application Example 1)

[0294] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0295] During the prototyping phase of a product, there is a need to reduce the time and effort required to quickly assemble prototypes based on shape data. Traditional methods require a significant amount of manual work and time from prototype design to assembly, which reduces the efficiency of product development.

[0296] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0297] In this invention, the server includes recording means for capturing images of an object from all directions, communication means for transmitting the captured image data to the server, and analysis means for analyzing the image data to estimate its three-dimensional shape. This makes it possible to quickly and automatically assemble a prototype based on the shape designed by an engineer.

[0298] "Recording means" refers to devices and methods for photographing an object from all directions, and is intended to digitize the shape of a designed product with high precision.

[0299] "Communication methods" refer to the technologies and protocols used to transmit captured image data to a server, enabling the secure and rapid transfer of data.

[0300] "Analysis means" refers to algorithms and techniques for processing image data received on a server and estimating its three-dimensional shape, utilizing advanced machine learning techniques to generate shape data.

[0301] "Selection method" refers to the criteria and methods for selecting the necessary components for reproduction based on the analyzed shape data, and supports the selection of the optimal parts.

[0302] The "assembly diagram generation means" refers to a process that generates assembly procedures based on selected components, and is designed to efficiently visualize the generated procedures.

[0303] "Dispatch means" refers to a method or device for transmitting the generated assembly procedure to a machine and actually performing automated assembly, thereby realizing assembly automation.

[0304] A system implementing this invention mainly consists of a mobile terminal with a high-resolution camera, a server, and mechanical equipment installed in a factory or the like.

[0305] The device captures images of the target object from all directions and transmits these image data to the server using a communication method. Wi-Fi or mobile data communication can be used for this purpose. The smartphone preprocesses the images using OpenCV and converts them to the optimal format. The data is sent to the server using a secure protocol.

[0306] The server processes the received data using analysis means. Here, what is used is TensorFlow, a machine learning framework based on Python, and OpenCV, an image processing library. Based on the image data, the server estimates the three-dimensional shape of the object and creates a 3D model using the Blender API. Then, based on that model, the component selection means identifies the components and generates an optimal assembly procedure.

[0307] Furthermore, an assembly procedure is automatically generated through the assembly drawing generation means. This generated procedure is transmitted through the sending means to the factory's mechanical equipment, and the machine automatically starts assembling. As a result, the prototype is efficiently constructed.

[0308] As a specific example, when parts of a new electronic device are designed, photographing those parts with a terminal and having the data analyzed by the server enables the factory robot to quickly assemble those parts. As a result, after the engineer modifies the design, they can immediately test the new prototype.

[0309] Examples of prompt sentences for the generated AI model are in the form of "Send new prototyping photographing data to the system and generate an efficient assembly procedure. The next photographing target is the parts of the next-generation electronic device."

[0310] The flow of the specific processing in Application Example 1 will be described using FIG. 12.

[0311] Step 1:

[0312] The user uses the terminal to photograph the object from all directions. The terminal takes photos from multiple angles using a high-resolution camera, and this image data is input into the next processing step. The obtained image data is subjected to format conversion and simple image processing using OpenCV on the terminal. This processing optimizes the image quality and prepares it for transmission to the server.

[0313] Step 2:

[0314] The terminal transmits the formatted image data to the server using a communication method. A secure protocol (e.g., HTTPS) is used for data transmission to ensure data integrity and confidentiality. The server receives the transmitted image data as input.

[0315] Step 3:

[0316] The server analyzes the received image data. Here, using Python's OpenCV and TensorFlow, it first estimates the three-dimensional shape from the image. In this estimation, multiple image data are fused to generate a 3D model, and the result is output to the next processing step.

[0317] Step 4:

[0318] The server selects the necessary components for reproduction based on the generated 3D model using a selection method. The model is further refined using the Blender API, and the necessary component information is provided as output. At this point, a list of parts required for assembly is created.

[0319] Step 5:

[0320] The server generates assembly instructions using an assembly diagram generation mechanism based on the selected components. These instructions are output in a sequential manner to provide clear instructions to the machine. The data created here serves as the instruction manual for the robot.

[0321] Step 6:

[0322] The assembly instructions generated from the server are sent to the machine. The instructions reach the machine via a feeding mechanism, and the robot automatically starts assembly. This automates the actual construction, and the prototype is completed.

[0323] This allows engineers to receive rapid feedback on-site, resulting in a more efficient prototyping process.

[0324] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0325] The system in this invention adds an element of sentiment analysis to the process of a user photographing an object and assembling blocks using a generated assembly diagram. The user uses a smartphone or other mobile device to photograph the object from multiple angles. The captured images are managed and organized on the device and transmitted to a server.

[0326] The server supplies image data to the AI ​​analysis unit, which estimates the three-dimensional shape of the object. This analysis uses shape recognition technology and improves accuracy by comparing it with various databases. Next, the server selects the block parts necessary to reconstruct the object based on the analysis results. This selection process is based on shape information and the user's past operation history.

[0327] Furthermore, this invention uses an emotion engine equipped in the terminal to obtain feedback on the user's emotional state. This emotion engine analyzes the user's facial expressions and tone of voice through the camera and voice input, and sends the results to a server. The server optimizes the assembly procedure based on this emotional information. The purpose of this procedure optimization process is to adjust the difficulty level of the blocks and the presentation procedure so that the user can continue working more comfortably.

[0328] After the assembly procedure is optimized, the device provides the user with a visually and emotionally resonant assembly diagram. This allows the user to proceed with the block assembly with a more positive experience based on the displayed information. For example, if the emotion engine detects signs of stress when the user is tackling a difficult part, the system can suggest a less complex version of the block.

[0329] In this way, by incorporating user emotional information into the program, the system can provide a flexible assembly environment tailored to individual users, comfortably supporting creative activities.

[0330] The following describes the processing flow.

[0331] Step 1:

[0332] The user uses their smartphone to photograph the object from different angles. This collects a series of images that cover the entire 360 ​​degrees.

[0333] Step 2:

[0334] The device formats the captured image appropriately, adjusts the image size, and then sends the image data to the server. This process may involve data compression.

[0335] Step 3:

[0336] The server processes the received image data using an AI analysis unit to estimate a three-dimensional model of the object. This process uses a shape recognition algorithm to extract the object's main features and construct a highly accurate model.

[0337] Step 4:

[0338] The server selects suitable block parts from the database based on a three-dimensional model. Selection criteria include shape, color, and required quantity.

[0339] Step 5:

[0340] An emotion engine built into the device analyzes the user's emotional state from their facial expressions and voice. This data is shared with a server in real time and used to optimize the assembly process.

[0341] Step 6:

[0342] The server dynamically adjusts the assembly procedure while taking into account the user's emotional state. For example, if stress is detected, it may adjust the difficulty of the procedure or include encouraging messages.

[0343] Step 7:

[0344] The server sends optimized assembly instructions to the terminal.

[0345] Step 8:

[0346] The terminal visually displays the received assembly instructions to the user. This display includes concise, step-by-step guidelines, which the user follows to recreate the object using the blocks at hand.

[0347] Step 9:

[0348] As the user proceeds with assembly, the emotion engine continuously acquires emotional data and sends feedback to the server as needed. Based on this feedback, further adjustments may be made.

[0349] (Example 2)

[0350] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0351] In modern society, a challenge exists where users experience stress and reduced enjoyment when engaging in creative assembly activities due to the difficulty of the assembly process. In particular, the selection of appropriate parts based on the shape of the object, and the complexity of the assembly procedure, often do not suit the individual user's skills and emotional state, making it difficult to provide an efficient and satisfying assembly experience.

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

[0353] In this invention, the server includes a recording unit and means for photographing an object from multiple viewpoints, a communication unit and means for transmitting the recorded digital data to an information processing device, and a processing unit and means for processing the digital data in the information processing device and estimating its three-dimensional shape. This makes it possible to provide an assembly procedure optimized for each user while taking into account the user's emotional state, thereby realizing a comfortable and creative assembly activity.

[0354] The "object" refers to the object or model that the user uses as the basis for assembly, and is analyzed by the system from multiple perspectives.

[0355] A "recording unit" is a device or component that has the function of capturing an object as digital data in image or video format from multiple viewpoints.

[0356] A "communication unit" is a device or function for transmitting digital data obtained from a recording unit to an information processing device, i.e., a server, via a network.

[0357] A "processing unit" is a device or program that analyzes digital data received by a server and estimates the three-dimensional shape of an object.

[0358] A "decision unit" is a device or function that selects the components necessary to reproduce an object based on the shape generated by the processing unit.

[0359] A "generation unit" is a device or function that creates an optimal assembly procedure, taking into account the components selected by the decision unit and the user's emotional state, and provides it to the user visually.

[0360] An "analysis unit" is a device or program that collects and analyzes user emotional information in real time and optimizes the assembly procedure based on the results.

[0361] This invention is a system that allows users to photograph objects using a mobile device and then uses that information to assist in the assembly of blocks and other materials. Furthermore, it aims to provide a better assembly experience by taking into account the user's emotional state.

[0362] The user uses a device such as a smartphone or tablet to photograph an object from multiple angles. The device's built-in camera and software are used to record image data of the object. This image data is temporarily stored on the device and then transmitted to a server via a communication unit over the network.

[0363] The server analyzes the received image data using a generative AI model or other shape recognition software. Based on the analysis results, it estimates the three-dimensional shape of the object and selects the necessary components accordingly. A database is used for selection, taking into account the compatibility of each part and the user's past history data.

[0364] The device also features an emotion engine that analyzes the user's facial expressions and voice through the camera and microphone. This data is transmitted to a server in real time. Based on the user's emotion information, the server adjusts the assembly procedure and the difficulty level of the parts to generate an appropriate assembly diagram.

[0365] The device displays assembly diagrams that are visually easy to understand and emotionally resonant for the user. This allows users to proceed with the assembly process creatively and without stress.

[0366] As a concrete example, suppose a user is assembling a dinosaur object. This system analyzes photos of the dinosaur from various angles, selects the necessary blocks for assembly, and suggests starting with easier parts if the user is feeling stressed. Examples of prompt messages include, "Starting the dinosaur assembly. Please let us know if you have any questions during assembly. We are also monitoring your emotional state."

[0367] This embodiment of the invention allows users to enjoy a comfortable and efficient assembly experience.

[0368] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0369] Step 1:

[0370] The user uses the camera on their mobile device to photograph an object from multiple angles. The images taken from each angle are recorded on the device as input. These images are then organized within the device, and initial data processing such as noise reduction and resolution adjustment is performed. Finally, the processed images are sent from the device to the server as output.

[0371] Step 2:

[0372] The server receives image data transmitted from the terminal. The image data, as input, is supplied to shape recognition algorithms such as generative AI models within the server. Based on this input, the server estimates the three-dimensional shape of the object using database matching and machine learning techniques. The output of this step is a three-dimensional model data of the object.

[0373] Step 3:

[0374] The server uses an estimated three-dimensional shape model to select the components necessary to reproduce the object. The input consists of 3D model data and a database of components that could potentially fit that model. The server performs a highly accurate selection while considering the user's past operation history and preferences. The output is a list of the selected appropriate components.

[0375] Step 4:

[0376] The device uses an emotion engine to analyze the user's emotional state in real time. Input includes the user's facial expressions and voice, which are then fed into a model using Effective Computing technology. This allows the model to determine the user's emotional state, such as stress or excitement. The output is the analyzed emotion data, which is then sent to a server.

[0377] Step 5:

[0378] The server receives user sentiment data and optimizes the assembly procedure. Inputs include sentiment data from step 4 and a list of components from step 3. Based on these inputs, the server adjusts the difficulty of the procedure to ensure the user is comfortable working with it, generating an optimized assembly procedure. The output is the final assembly diagram, including the user-specific assembly procedure.

[0379] Step 6:

[0380] The terminal presents the user with optimized assembly instructions received from the server. The input is the assembly diagram provided by the server. The terminal displays this information in a visually clear and sequential manner to support the user's assembly work. The output is assembly instructions in a format that the user can visually confirm.

[0381] Through the above process, users can enjoy a comfortable and stress-free assembly experience.

[0382] (Application Example 2)

[0383] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0384] In factory assembly work, prolonged work hours can lead to fatigue and stress, resulting in decreased work efficiency. This situation not only reduces productivity but can also negatively impact workers' health. Therefore, it is necessary to monitor workers' emotional states in real time and adjust the work environment appropriately.

[0385] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0386] In this invention, the server includes recording means for photographing an object from all directions, communication means for transmitting the captured image data to the server, analysis means for analyzing the image data to estimate its three-dimensional shape, emotion information analysis means for analyzing the user's emotional state, and procedure optimization means for optimizing the work procedure based on the emotion information. As a result, the robot performing the assembly work adjusts the work content according to the worker's emotional state, enabling efficient work while reducing the burden on the worker.

[0387] "Recording means" refers to a device or method for photographing an object from all directions and acquiring image data.

[0388] "Communication means" refers to a device or method for performing network communication to transmit captured image data to a server.

[0389] "Analysis means" refers to a device or method for estimating the three-dimensional shape of an object based on received image data.

[0390] "Selection means" refers to an apparatus or method for selecting the components necessary for reproduction based on the analyzed and estimated shape.

[0391] "Assembly drawing generation means" refers to an apparatus or method for generating an assembly procedure based on selected components.

[0392] "Emotional information analysis means" refers to a device or method for analyzing a user's emotional state and acquiring emotional information.

[0393] "Procedure optimization means" refers to a device or method that optimizes work procedures based on acquired emotional information and adjusts the user's work environment.

[0394] The system that realizes this invention is composed of multiple hardware and software components. The server plays a central role and performs various processes. The server is equipped with a high-performance CPU and GPU, enabling it to process large amounts of data in real time.

[0395] First, the user takes photos of the target object from all directions using a smartphone or a dedicated mobile device. The obtained image data is automatically transmitted to the server via a communication method. This communication uses high-speed communication technologies such as Wi-Fi or 4G / 5G. The server analyzes the received image data using image processing libraries such as OpenCV and estimates the three-dimensional shape of the target object. This analysis result is compared with known shape data in a database to improve accuracy.

[0396] Based on the analyzed shape information, the server selects the necessary components. The selection process is optimized by a machine learning algorithm, taking into account the user's past operation history. Based on the selected components, the assembly diagram generation means generates the assembly procedure. This generation utilizes a generation AI model to optimize the required assembly difficulty and sequence.

[0397] Furthermore, the device is equipped with a camera and microphone for emotional information analysis, which analyzes the user's emotional state from their facial expressions and voice. This analysis uses tools such as Librosa for voice data analysis. The emotional data is immediately fed back to the server, and the work procedure is dynamically adjusted by a procedure optimization mechanism.

[0398] As a concrete example, in an electronic component assembly plant, if emotional analysis detects worker fatigue due to prolonged work, the system has the ability to adjust procedures to reduce the complexity of the work at the appropriate time. A concrete example of this prompt sentence would be: "Think of an idea where worker stress is measured in real time, and if stress is detected, a factory robot automatically takes over the work."

[0399] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0400] Step 1:

[0401] The user takes photos of the target object from all directions using a smartphone or dedicated mobile device. The input is image data captured from multiple angles, which is managed and organized within the device. The output is data converted into a communication-compatible format.

[0402] Step 2:

[0403] The device transmits the captured image data to the server. The server receives image data as input, which is done using high-speed communication technology (Wi-Fi / 4G / 5G). The output is the storage of the image data within the server.

[0404] Step 3:

[0405] The server uses OpenCV to analyze received image data and estimate the three-dimensional shape of the object. The input is image data, and the output is estimated three-dimensional shape data. In this process, accuracy is improved by comparing the data with shape data in an existing database.

[0406] Step 4:

[0407] The server selects the necessary components based on estimated shape information. Inputs are 3D shape data and the user's past operation history, while output is information on the selected components. This selection process utilizes machine learning algorithms.

[0408] Step 5:

[0409] The server uses a generative AI model to generate assembly instructions based on selected parts. The input is component information, and the output is the generated assembly instructions. The generation is adjusted to optimize the difficulty and order of the steps.

[0410] Step 6:

[0411] The device's emotional information analysis system uses a camera and microphone to analyze the user's emotional state from their facial expressions and voice. The input is real-time collected facial and voice data, and the output is the analyzed emotional data.

[0412] Step 7:

[0413] The server receives emotional data and adjusts the work procedure using a procedure optimization mechanism. The input is emotional data, and the output is the adjusted work procedure. The adjustment is optimized to reduce user stress.

[0414] Step 8:

[0415] The terminal displays the finalized work procedure to the user. The input is the adjusted work procedure, and the output is an assembly diagram that the user can visually confirm. The display also includes suggestions based on emotional information.

[0416] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0417] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0418] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0419] [Third Embodiment]

[0420] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0421] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0422] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0423] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0424] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0425] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0426] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0427] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0428] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0429] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0430] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0431] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0432] The primary objective of this invention is for users to photograph an object from all directions and for the data to be analyzed on a server. In this system, users photograph the object from multiple angles using a smartphone or other mobile device. The captured images are organized on the device, converted to an optimal format, and then sent to the server.

[0433] On the server, the received image data is first transferred to the AI ​​analysis unit, where the three-dimensional shape of the object is estimated. This analysis process applies machine learning techniques to reconstruct the shape with high accuracy based on a large database of similar shapes.

[0434] After the shape is estimated, the server selects the appropriate blocks. Here, the optimal block parts necessary for reproduction are selected based on the size and shape of the object. Based on the selected block information, the server automatically generates an assembly procedure. This procedure is organized sequentially, making it easy for the user to follow.

[0435] Ultimately, the device receives assembly instructions sent from the server. The user follows these instructions and uses the blocks they have to recreate the target object. For example, if a user photographs a miniature car on their desk, the server analyzes its shape and selects the appropriate blocks to provide the user with a detailed assembly diagram to recreate the car using the blocks they have. This allows the user to easily and efficiently recreate real objects with blocks.

[0436] In this way, the present invention not only supports users in creative block play, but also has a wide range of applications, from corporate training to design prototyping.

[0437] The following describes the processing flow.

[0438] Step 1:

[0439] The user uses their smartphone to photograph the object from various angles. This collects multiple images to clearly capture the overall picture of the object.

[0440] Step 2:

[0441] The device organizes the captured images into the appropriate format, optimizes their size, and sends them to the server. During this process, data compression is performed as needed to ensure communication stability.

[0442] Step 3:

[0443] The server sends the received image data to the AI ​​analysis unit. The AI ​​uses machine learning algorithms to estimate the three-dimensional shape of the object from the image data. This analysis includes shape recognition as well as estimation of the relative positions of each part of the object.

[0444] Step 4:

[0445] Based on the analysis results, the server selects the optimal block parts necessary to reproduce the object from the database. This selection takes into account the size, color, and shape of the blocks.

[0446] Step 5:

[0447] The server automatically generates assembly instructions based on the selected block information. These instructions detail the order in which each block should be assembled, as well as any points to note.

[0448] Step 6:

[0449] The server sends the generated assembly instructions and related information to the terminal.

[0450] Step 7:

[0451] The terminal displays the received assembly instructions to the user. The user follows these instructions and uses the blocks at hand to recreate the target object.

[0452] (Example 1)

[0453] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0454] Conventional three-dimensional object reproduction systems have the drawback of being cumbersome and inefficient overall, as each process, from image capture to data analysis and selection of specific assembly parts, is performed individually. Furthermore, there are limited means for ordinary users to easily reproduce three-dimensional objects, and situations often require specialized knowledge and skills.

[0455] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0456] In this invention, the server includes recording means for capturing visual information by photographing an object from various angles, communication means for transferring the acquired visual information to a remote processing device, and analysis means for analyzing the visual information and inferring the three-dimensional structure. This allows users to easily select appropriate parts and generate assembly procedures for reconstructing a three-dimensional object without requiring specialized knowledge.

[0457] "Recording means" refers to devices and technologies for capturing visual information by photographing an object from various angles.

[0458] "Communication means" refers to devices and technologies for transferring acquired visual information to a remote processing unit.

[0459] "Analysis methods" refer to algorithms and techniques for analyzing visual information and inferring three-dimensional structures.

[0460] "Selection method" refers to a technique for selecting the components necessary for reconstruction based on the inferred three-dimensional structure.

[0461] "Assembly diagram generation means" refers to technologies and algorithms for automatically generating assembly procedures based on selected components.

[0462] "Visualization means" refers to devices or technologies for displaying the generated assembly procedure on an information terminal.

[0463] "Output means" refers to the technology or device that allows the user to receive the generated assembly drawing in a predetermined format.

[0464] This invention is a system for efficiently carrying out the process of a user photographing an object from various angles and reconstructing a three-dimensional object based on that data. This system mainly consists of recording means, communication means, analysis means, selection means, assembly drawing generation means, visualization means, and output means.

[0465] Users use smartphones or mobile devices to photograph objects from multiple angles. During this process, the device's camera function acts as a "recording tool," acquiring visual information about the object. The acquired data is transmitted to a server via the internet using a "communication tool." A secure data transmission protocol (e.g., HTTPS) is used for this transmission.

[0466] The server, as an "analysis tool," analyzes the received visual information using an advanced generative AI model to infer a three-dimensional structure. This analysis utilizes machine learning algorithms using TensorFlow or PyTorch. The inferred structure is then used as a "selection tool" to select the components necessary for reconstruction. For example, this might involve selecting parts using a digital 3D model library.

[0467] Based on the selected components, the server automatically generates assembly instructions as an "assembly diagram generation means." 3D modeling software (e.g., Autodesk Tinkercad) is used for this purpose. The generated assembly instructions are organized sequentially and displayed on the terminal's screen by a "visualization means."

[0468] The user checks the assembly instructions displayed on the terminal and physically recreates the object using the blocks they have. By referring to the outputted assembly diagram, the specific assembly steps can be easily understood. The accuracy of the completed object largely depends on the capabilities of the AI ​​model used in the analysis phase.

[0469] For example, if a user takes a picture of a miniature car on their desk, the system will infer the car's three-dimensional shape from the acquired image and select the appropriate components. The user can then recreate the miniature car using the parts they have on hand, based on the assembly diagram provided by the system.

[0470] An example of a prompt to be input to the generating AI model is a specific instruction such as, "Analyze the omnidirectional photograph of the object and generate a block assembly guide." Based on this prompt, the system performs a series of tasks.

[0471] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0472] Step 1:

[0473] The user takes multiple photos of an object from different angles using a smartphone or mobile device. The input consists of multiple images captured by the camera. Specifically, the user operates the device's camera function to take photos of the object from above, below, left, and right, changing the angle. The output is the captured image data.

[0474] Step 2:

[0475] The device organizes the acquired image data and converts it to the optimal format. The input is the raw image data acquired in Step 1. The image editing app converts the data to JPEG or PNG format, adjusts the resolution, and removes noise. The output is the formatted image data.

[0476] Step 3:

[0477] The terminal sends the organized image data to the server. The input is the formatted image data created in step 2. The data is sent to the server using the HTTPS protocol via a communication method. The output is the image data sent to the server.

[0478] Step 4:

[0479] The server transfers the received image data to the AI ​​analysis unit. The input is the image data received in step 3. The program on the server prepares to pass the data to the AI ​​module for 3D shape analysis. The output is the data state passed to the AI ​​module.

[0480] Step 5:

[0481] The server's AI analysis unit uses a generative AI model to infer the three-dimensional shape. The input is the analysis data prepared in step 4. Prompts are given to the TensorFlow or PyTorch-based model to perform shape analysis. The output is the inferred three-dimensional shape data.

[0482] Step 6:

[0483] The server selects appropriate components based on the three-dimensional shape. The input is the three-dimensional shape data inferred in step 5. The server references the data and selects the optimal parts from the digital library. The output is a list of selected components.

[0484] Step 7:

[0485] The server generates assembly instructions based on the selected components. The input is the components selected in step 6. The assembly diagram generation module automatically generates the instructions using 3D modeling software. The output is the generated assembly instructions data.

[0486] Step 8:

[0487] The server sends the generated assembly instructions to the terminal. The input is the assembly instructions data constructed in step 7. The server again uses a communication method to securely transmit the data to the terminal. The output is the assembly instructions sent to the terminal.

[0488] Step 9:

[0489] The terminal displays and provides the received assembly instructions to the user. The input is the assembly instruction data received in step 8. The terminal interprets the instructions using an appropriate interface and displays them in a user-friendly format. The output is the assembly instructions displayed on the terminal screen.

[0490] Step 10:

[0491] The user assembles the object using the parts they have on hand, following the assembly procedure displayed on the terminal. The input is the assembly procedure displayed in step 9. The user physically follows the procedure and assembles the parts in the correct order to form the final shape. The output is the completed three-dimensional object.

[0492] (Application Example 1)

[0493] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0494] During the prototyping phase of a product, there is a need to reduce the time and effort required to quickly assemble prototypes based on shape data. Traditional methods require a significant amount of manual work and time from prototype design to assembly, which reduces the efficiency of product development.

[0495] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0496] In this invention, the server includes recording means for capturing images of an object from all directions, communication means for transmitting the captured image data to the server, and analysis means for analyzing the image data to estimate its three-dimensional shape. This makes it possible to quickly and automatically assemble a prototype based on the shape designed by an engineer.

[0497] "Recording means" refers to devices and methods for photographing an object from all directions, and is intended to digitize the shape of a designed product with high precision.

[0498] "Communication methods" refer to the technologies and protocols used to transmit captured image data to a server, enabling the secure and rapid transfer of data.

[0499] "Analysis means" refers to algorithms and techniques for processing image data received on a server and estimating its three-dimensional shape, utilizing advanced machine learning techniques to generate shape data.

[0500] "Selection method" refers to the criteria and methods for selecting the necessary components for reproduction based on the analyzed shape data, and supports the selection of the optimal parts.

[0501] The "assembly diagram generation means" refers to a process that generates assembly procedures based on selected components, and is designed to efficiently visualize the generated procedures.

[0502] "Dispatch means" refers to a method or device for transmitting the generated assembly procedure to a machine and actually performing automated assembly, thereby realizing assembly automation.

[0503] A system implementing this invention mainly consists of a mobile terminal with a high-resolution camera, a server, and mechanical equipment installed in a factory or the like.

[0504] The device captures images of the target object from all directions and transmits these image data to the server using a communication method. Wi-Fi or mobile data communication can be used for this purpose. The smartphone preprocesses the images using OpenCV and converts them to the optimal format. The data is sent to the server using a secure protocol.

[0505] The server processes the received data using analysis tools. These tools include TensorFlow, a Python-based machine learning framework, and OpenCV, an image processing library. Based on the image data, the server estimates the three-dimensional shape of the object and creates a 3D model using the Blender API. Then, based on this model, it identifies the component parts using selection tools and generates the optimal assembly procedure.

[0506] Furthermore, assembly procedures are automatically generated via an assembly drawing generation means. These generated procedures are transmitted to the factory machinery through a feeding means, and the machinery automatically starts assembly. This allows for the efficient construction of prototypes.

[0507] For example, if a part for a new electronic device is designed, the part can be photographed with a device, the data can be analyzed on a server, and a factory robot can quickly assemble the part. This allows engineers to revise the design and immediately test the new prototype.

[0508] An example of a prompt message for a generating AI model would be: "Send new prototyping images to the system and generate an efficient assembly procedure. The next subject to be photographed is a next-generation electronic component."

[0509] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0510] Step 1:

[0511] The user uses a terminal to photograph the target object from all directions. The terminal uses a high-resolution camera to take photos from multiple angles, and this image data is input to the next processing step. The obtained image data is formatted and subjected to basic image processing using OpenCV on the terminal. This processing optimizes the image quality and prepares it for transmission to the server.

[0512] Step 2:

[0513] The terminal transmits the formatted image data to the server using a communication method. A secure protocol (e.g., HTTPS) is used for data transmission to ensure data integrity and confidentiality. The server receives the transmitted image data as input.

[0514] Step 3:

[0515] The server analyzes the received image data. Here, using Python's OpenCV and TensorFlow, it first estimates the three-dimensional shape from the image. In this estimation, multiple image data are fused to generate a 3D model, and the result is output to the next processing step.

[0516] Step 4:

[0517] The server selects the necessary components for reproduction based on the generated 3D model using a selection method. The model is further refined using the Blender API, and the necessary component information is provided as output. At this point, a list of parts required for assembly is created.

[0518] Step 5:

[0519] The server generates assembly instructions using an assembly diagram generation mechanism based on the selected components. These instructions are output in a sequential manner to provide clear instructions to the machine. The data created here serves as the instruction manual for the robot.

[0520] Step 6:

[0521] The assembly instructions generated from the server are sent to the machine. The instructions reach the machine via a feeding mechanism, and the robot automatically starts assembly. This automates the actual construction, and the prototype is completed.

[0522] This allows engineers to receive rapid feedback on-site, resulting in a more efficient prototyping process.

[0523] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0524] The system in this invention adds an element of sentiment analysis to the process of a user photographing an object and assembling blocks using a generated assembly diagram. The user uses a smartphone or other mobile device to photograph the object from multiple angles. The captured images are managed and organized on the device and transmitted to a server.

[0525] The server supplies image data to the AI ​​analysis unit, which estimates the three-dimensional shape of the object. This analysis uses shape recognition technology and improves accuracy by comparing it with various databases. Next, the server selects the block parts necessary to reconstruct the object based on the analysis results. This selection process is based on shape information and the user's past operation history.

[0526] Furthermore, this invention uses an emotion engine equipped in the terminal to obtain feedback on the user's emotional state. This emotion engine analyzes the user's facial expressions and tone of voice through the camera and voice input, and sends the results to a server. The server optimizes the assembly procedure based on this emotional information. The purpose of this procedure optimization process is to adjust the difficulty level of the blocks and the presentation procedure so that the user can continue working more comfortably.

[0527] After the assembly procedure is optimized, the device provides the user with a visually and emotionally resonant assembly diagram. This allows the user to proceed with the block assembly with a more positive experience based on the displayed information. For example, if the emotion engine detects signs of stress when the user is tackling a difficult part, the system can suggest a less complex version of the block.

[0528] In this way, by incorporating user emotional information into the program, the system can provide a flexible assembly environment tailored to individual users, comfortably supporting creative activities.

[0529] The following describes the processing flow.

[0530] Step 1:

[0531] The user uses their smartphone to photograph the object from different angles. This collects a series of images that cover the entire 360 ​​degrees.

[0532] Step 2:

[0533] The device formats the captured image appropriately, adjusts the image size, and then sends the image data to the server. This process may involve data compression.

[0534] Step 3:

[0535] The server processes the received image data using an AI analysis unit to estimate a three-dimensional model of the object. This process uses a shape recognition algorithm to extract the object's main features and construct a highly accurate model.

[0536] Step 4:

[0537] The server selects suitable block parts from the database based on a three-dimensional model. Selection criteria include shape, color, and required quantity.

[0538] Step 5:

[0539] An emotion engine built into the device analyzes the user's emotional state from their facial expressions and voice. This data is shared with a server in real time and used to optimize the assembly process.

[0540] Step 6:

[0541] The server dynamically adjusts the assembly procedure while taking into account the user's emotional state. For example, if stress is detected, it may adjust the difficulty of the procedure or include encouraging messages.

[0542] Step 7:

[0543] The server sends optimized assembly instructions to the terminal.

[0544] Step 8:

[0545] The terminal visually displays the received assembly instructions to the user. This display includes concise, step-by-step guidelines, which the user follows to recreate the object using the blocks at hand.

[0546] Step 9:

[0547] As the user proceeds with assembly, the emotion engine continuously acquires emotional data and sends feedback to the server as needed. Based on this feedback, further adjustments may be made.

[0548] (Example 2)

[0549] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0550] In modern society, a challenge exists where users experience stress and reduced enjoyment when engaging in creative assembly activities due to the difficulty of the assembly process. In particular, the selection of appropriate parts based on the shape of the object, and the complexity of the assembly procedure, often do not suit the individual user's skills and emotional state, making it difficult to provide an efficient and satisfying assembly experience.

[0551] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0552] In this invention, the server includes a recording unit and means for photographing an object from multiple viewpoints, a communication unit and means for transmitting the recorded digital data to an information processing device, and a processing unit and means for processing the digital data in the information processing device and estimating its three-dimensional shape. This makes it possible to provide an assembly procedure optimized for each user while taking into account the user's emotional state, thereby realizing a comfortable and creative assembly activity.

[0553] The "object" refers to the object or model that the user uses as the basis for assembly, and is analyzed by the system from multiple perspectives.

[0554] A "recording unit" is a device or component that has the function of capturing an object as digital data in image or video format from multiple viewpoints.

[0555] A "communication unit" is a device or function for transmitting digital data obtained from a recording unit to an information processing device, i.e., a server, via a network.

[0556] A "processing unit" is a device or program that analyzes digital data received by a server and estimates the three-dimensional shape of an object.

[0557] A "decision unit" is a device or function that selects the components necessary to reproduce an object based on the shape generated by the processing unit.

[0558] A "generation unit" is a device or function that creates an optimal assembly procedure, taking into account the components selected by the decision unit and the user's emotional state, and provides it to the user visually.

[0559] An "analysis unit" is a device or program that collects and analyzes user emotional information in real time and optimizes the assembly procedure based on the results.

[0560] This invention is a system that allows users to photograph objects using a mobile device and then uses that information to assist in the assembly of blocks and other materials. Furthermore, it aims to provide a better assembly experience by taking into account the user's emotional state.

[0561] The user uses a device such as a smartphone or tablet to photograph an object from multiple angles. The device's built-in camera and software are used to record image data of the object. This image data is temporarily stored on the device and then transmitted to a server via a communication unit over the network.

[0562] The server analyzes the received image data using a generative AI model or other shape recognition software. Based on the analysis results, it estimates the three-dimensional shape of the object and selects the necessary components accordingly. A database is used for selection, taking into account the compatibility of each part and the user's past history data.

[0563] The device also features an emotion engine that analyzes the user's facial expressions and voice through the camera and microphone. This data is transmitted to a server in real time. Based on the user's emotion information, the server adjusts the assembly procedure and the difficulty level of the parts to generate an appropriate assembly diagram.

[0564] The device displays assembly diagrams that are visually easy to understand and emotionally resonant for the user. This allows users to proceed with the assembly process creatively and without stress.

[0565] As a concrete example, suppose a user is assembling a dinosaur object. This system analyzes photos of the dinosaur from various angles, selects the necessary blocks for assembly, and suggests starting with easier parts if the user is feeling stressed. Examples of prompt messages include, "Starting the dinosaur assembly. Please let us know if you have any questions during assembly. We are also monitoring your emotional state."

[0566] This embodiment of the invention allows users to enjoy a comfortable and efficient assembly experience.

[0567] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0568] Step 1:

[0569] The user uses the camera on their mobile device to photograph an object from multiple angles. The images taken from each angle are recorded on the device as input. These images are then organized within the device, and initial data processing such as noise reduction and resolution adjustment is performed. Finally, the processed images are sent from the device to the server as output.

[0570] Step 2:

[0571] The server receives image data transmitted from the terminal. The image data, as input, is supplied to shape recognition algorithms such as generative AI models within the server. Based on this input, the server estimates the three-dimensional shape of the object using database matching and machine learning techniques. The output of this step is a three-dimensional model data of the object.

[0572] Step 3:

[0573] The server uses an estimated three-dimensional shape model to select the components necessary to reproduce the object. The input consists of 3D model data and a database of components that could potentially fit that model. The server performs a highly accurate selection while considering the user's past operation history and preferences. The output is a list of the selected appropriate components.

[0574] Step 4:

[0575] The device uses an emotion engine to analyze the user's emotional state in real time. Input includes the user's facial expressions and voice, which are then fed into a model using Effective Computing technology. This allows the model to determine the user's emotional state, such as stress or excitement. The output is the analyzed emotion data, which is then sent to a server.

[0576] Step 5:

[0577] The server receives user sentiment data and optimizes the assembly procedure. Inputs include sentiment data from step 4 and a list of components from step 3. Based on these inputs, the server adjusts the difficulty of the procedure to ensure the user is comfortable working with it, generating an optimized assembly procedure. The output is the final assembly diagram, including the user-specific assembly procedure.

[0578] Step 6:

[0579] The terminal presents the user with optimized assembly instructions received from the server. The input is the assembly diagram provided by the server. The terminal displays this information in a visually clear and sequential manner to support the user's assembly work. The output is assembly instructions in a format that the user can visually confirm.

[0580] Through the above process, users can enjoy a comfortable and stress-free assembly experience.

[0581] (Application Example 2)

[0582] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0583] In factory assembly work, prolonged work hours can lead to fatigue and stress, resulting in decreased work efficiency. This situation not only reduces productivity but can also negatively impact workers' health. Therefore, it is necessary to monitor workers' emotional states in real time and adjust the work environment appropriately.

[0584] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0585] In this invention, the server includes recording means for photographing an object from all directions, communication means for transmitting the captured image data to the server, analysis means for analyzing the image data to estimate its three-dimensional shape, emotion information analysis means for analyzing the user's emotional state, and procedure optimization means for optimizing the work procedure based on the emotion information. As a result, the robot performing the assembly work adjusts the work content according to the worker's emotional state, enabling efficient work while reducing the burden on the worker.

[0586] "Recording means" refers to a device or method for photographing an object from all directions and acquiring image data.

[0587] "Communication means" refers to a device or method for performing network communication to transmit captured image data to a server.

[0588] "Analysis means" refers to a device or method for estimating the three-dimensional shape of an object based on received image data.

[0589] "Selection means" refers to an apparatus or method for selecting the components necessary for reproduction based on the analyzed and estimated shape.

[0590] "Assembly drawing generation means" refers to an apparatus or method for generating an assembly procedure based on selected components.

[0591] "Emotional information analysis means" refers to a device or method for analyzing a user's emotional state and acquiring emotional information.

[0592] "Procedure optimization means" refers to a device or method that optimizes work procedures based on acquired emotional information and adjusts the user's work environment.

[0593] The system that realizes this invention is composed of multiple hardware and software components. The server plays a central role and performs various processes. The server is equipped with a high-performance CPU and GPU, enabling it to process large amounts of data in real time.

[0594] First, the user takes photos of the target object from all directions using a smartphone or a dedicated mobile device. The obtained image data is automatically transmitted to the server via a communication method. This communication uses high-speed communication technologies such as Wi-Fi or 4G / 5G. The server analyzes the received image data using image processing libraries such as OpenCV and estimates the three-dimensional shape of the target object. This analysis result is compared with known shape data in a database to improve accuracy.

[0595] Based on the analyzed shape information, the server selects the necessary components. The selection process is optimized by a machine learning algorithm, taking into account the user's past operation history. Based on the selected components, the assembly diagram generation means generates the assembly procedure. This generation utilizes a generation AI model to optimize the required assembly difficulty and sequence.

[0596] Furthermore, the device is equipped with a camera and microphone for emotional information analysis, which analyzes the user's emotional state from their facial expressions and voice. This analysis uses tools such as Librosa for voice data analysis. The emotional data is immediately fed back to the server, and the work procedure is dynamically adjusted by a procedure optimization mechanism.

[0597] As a concrete example, in an electronic component assembly plant, if emotional analysis detects worker fatigue due to prolonged work, the system has the ability to adjust procedures to reduce the complexity of the work at the appropriate time. A concrete example of this prompt sentence would be: "Think of an idea where worker stress is measured in real time, and if stress is detected, a factory robot automatically takes over the work."

[0598] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0599] Step 1:

[0600] The user takes photos of the target object from all directions using a smartphone or dedicated mobile device. The input is image data captured from multiple angles, which is managed and organized within the device. The output is data converted into a communication-compatible format.

[0601] Step 2:

[0602] The device transmits the captured image data to the server. The server receives image data as input, which is done using high-speed communication technology (Wi-Fi / 4G / 5G). The output is the storage of the image data within the server.

[0603] Step 3:

[0604] The server uses OpenCV to analyze received image data and estimate the three-dimensional shape of the object. The input is image data, and the output is estimated three-dimensional shape data. In this process, accuracy is improved by comparing the data with shape data in an existing database.

[0605] Step 4:

[0606] The server selects the necessary components based on estimated shape information. Inputs are 3D shape data and the user's past operation history, while output is information on the selected components. This selection process utilizes machine learning algorithms.

[0607] Step 5:

[0608] The server uses a generative AI model to generate assembly instructions based on selected parts. The input is component information, and the output is the generated assembly instructions. The generation is adjusted to optimize the difficulty and order of the steps.

[0609] Step 6:

[0610] The device's emotional information analysis system uses a camera and microphone to analyze the user's emotional state from their facial expressions and voice. The input is real-time collected facial and voice data, and the output is the analyzed emotional data.

[0611] Step 7:

[0612] The server receives emotional data and adjusts the work procedure using a procedure optimization mechanism. The input is emotional data, and the output is the adjusted work procedure. The adjustment is optimized to reduce user stress.

[0613] Step 8:

[0614] The terminal displays the finalized work procedure to the user. The input is the adjusted work procedure, and the output is an assembly diagram that the user can visually confirm. The display also includes suggestions based on emotional information.

[0615] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0616] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0617] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0618] [Fourth Embodiment]

[0619] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0620] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0621] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0622] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0623] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0624] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0625] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0626] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors in the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0627] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0628] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0629] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0630] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0631] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0632] The primary objective of this invention is for users to photograph an object from all directions and for the data to be analyzed on a server. In this system, users photograph the object from multiple angles using a smartphone or other mobile device. The captured images are organized on the device, converted to an optimal format, and then sent to the server.

[0633] On the server, the received image data is first transferred to the AI ​​analysis unit, where the three-dimensional shape of the object is estimated. This analysis process applies machine learning techniques to reconstruct the shape with high accuracy based on a large database of similar shapes.

[0634] After the shape is estimated, the server selects the appropriate blocks. Here, the optimal block parts necessary for reproduction are selected based on the size and shape of the object. Based on the selected block information, the server automatically generates an assembly procedure. This procedure is organized sequentially, making it easy for the user to follow.

[0635] Ultimately, the device receives assembly instructions sent from the server. The user follows these instructions and uses the blocks they have to recreate the target object. For example, if a user photographs a miniature car on their desk, the server analyzes its shape and selects the appropriate blocks to provide the user with a detailed assembly diagram to recreate the car using the blocks they have. This allows the user to easily and efficiently recreate real objects with blocks.

[0636] In this way, the present invention not only supports users in creative block play, but also has a wide range of applications, from corporate training to design prototyping.

[0637] The following describes the processing flow.

[0638] Step 1:

[0639] The user uses their smartphone to photograph the object from various angles. This collects multiple images to clearly capture the overall picture of the object.

[0640] Step 2:

[0641] The device organizes the captured images into the appropriate format, optimizes their size, and sends them to the server. During this process, data compression is performed as needed to ensure communication stability.

[0642] Step 3:

[0643] The server sends the received image data to the AI ​​analysis unit. The AI ​​uses machine learning algorithms to estimate the three-dimensional shape of the object from the image data. This analysis includes shape recognition as well as estimation of the relative positions of each part of the object.

[0644] Step 4:

[0645] Based on the analysis results, the server selects the optimal block parts necessary to reproduce the object from the database. This selection takes into account the size, color, and shape of the blocks.

[0646] Step 5:

[0647] The server automatically generates assembly instructions based on the selected block information. These instructions detail the order in which each block should be assembled, as well as any points to note.

[0648] Step 6:

[0649] The server sends the generated assembly instructions and related information to the terminal.

[0650] Step 7:

[0651] The terminal displays the received assembly instructions to the user. The user follows these instructions and uses the blocks at hand to recreate the target object.

[0652] (Example 1)

[0653] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0654] Conventional three-dimensional object reproduction systems have the drawback of being cumbersome and inefficient overall, as each process, from image capture to data analysis and selection of specific assembly parts, is performed individually. Furthermore, there are limited means for ordinary users to easily reproduce three-dimensional objects, and situations often require specialized knowledge and skills.

[0655] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0656] In this invention, the server includes recording means for capturing visual information by photographing an object from various angles, communication means for transferring the acquired visual information to a remote processing device, and analysis means for analyzing the visual information and inferring the three-dimensional structure. This allows users to easily select appropriate parts and generate assembly procedures for reconstructing a three-dimensional object without requiring specialized knowledge.

[0657] "Recording means" refers to devices and technologies for capturing visual information by photographing an object from various angles.

[0658] "Communication means" refers to devices and technologies for transferring acquired visual information to a remote processing unit.

[0659] "Analysis methods" refer to algorithms and techniques for analyzing visual information and inferring three-dimensional structures.

[0660] "Selection method" refers to a technique for selecting the components necessary for reconstruction based on the inferred three-dimensional structure.

[0661] "Assembly diagram generation means" refers to technologies and algorithms for automatically generating assembly procedures based on selected components.

[0662] "Visualization means" refers to devices or technologies for displaying the generated assembly procedure on an information terminal.

[0663] "Output means" refers to the technology or device that allows the user to receive the generated assembly drawing in a predetermined format.

[0664] This invention is a system for efficiently carrying out the process of a user photographing an object from various angles and reconstructing a three-dimensional object based on that data. This system mainly consists of recording means, communication means, analysis means, selection means, assembly drawing generation means, visualization means, and output means.

[0665] Users use smartphones or mobile devices to photograph objects from multiple angles. During this process, the device's camera function acts as a "recording tool," acquiring visual information about the object. The acquired data is transmitted to a server via the internet using a "communication tool." A secure data transmission protocol (e.g., HTTPS) is used for this transmission.

[0666] The server, as an "analysis tool," analyzes the received visual information using an advanced generative AI model to infer a three-dimensional structure. This analysis utilizes machine learning algorithms using TensorFlow or PyTorch. The inferred structure is then used as a "selection tool" to select the components necessary for reconstruction. For example, this might involve selecting parts using a digital 3D model library.

[0667] Based on the selected components, the server automatically generates assembly instructions as an "assembly diagram generation means." 3D modeling software (e.g., Autodesk Tinkercad) is used for this purpose. The generated assembly instructions are organized sequentially and displayed on the terminal's screen by a "visualization means."

[0668] The user checks the assembly instructions displayed on the terminal and physically recreates the object using the blocks they have. By referring to the outputted assembly diagram, the specific assembly steps can be easily understood. The accuracy of the completed object largely depends on the capabilities of the AI ​​model used in the analysis phase.

[0669] For example, if a user takes a picture of a miniature car on their desk, the system will infer the car's three-dimensional shape from the acquired image and select the appropriate components. The user can then recreate the miniature car using the parts they have on hand, based on the assembly diagram provided by the system.

[0670] An example of a prompt to be input to the generating AI model is a specific instruction such as, "Analyze the omnidirectional photograph of the object and generate a block assembly guide." Based on this prompt, the system performs a series of tasks.

[0671] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0672] Step 1:

[0673] The user takes multiple photos of an object from different angles using a smartphone or mobile device. The input consists of multiple images captured by the camera. Specifically, the user operates the device's camera function to take photos of the object from above, below, left, and right, changing the angle. The output is the captured image data.

[0674] Step 2:

[0675] The device organizes the acquired image data and converts it to the optimal format. The input is the raw image data acquired in Step 1. The image editing app converts the data to JPEG or PNG format, adjusts the resolution, and removes noise. The output is the formatted image data.

[0676] Step 3:

[0677] The terminal sends the organized image data to the server. The input is the formatted image data created in step 2. The data is sent to the server using the HTTPS protocol via a communication method. The output is the image data sent to the server.

[0678] Step 4:

[0679] The server transfers the received image data to the AI ​​analysis unit. The input is the image data received in step 3. The program on the server prepares to pass the data to the AI ​​module for 3D shape analysis. The output is the data state passed to the AI ​​module.

[0680] Step 5:

[0681] The server's AI analysis unit uses a generative AI model to infer the three-dimensional shape. The input is the analysis data prepared in step 4. Prompts are given to the TensorFlow or PyTorch-based model to perform shape analysis. The output is the inferred three-dimensional shape data.

[0682] Step 6:

[0683] The server selects appropriate components based on the three-dimensional shape. The input is the three-dimensional shape data inferred in step 5. The server references the data and selects the optimal parts from the digital library. The output is a list of selected components.

[0684] Step 7:

[0685] The server generates assembly instructions based on the selected components. The input is the components selected in step 6. The assembly diagram generation module automatically generates the instructions using 3D modeling software. The output is the generated assembly instructions data.

[0686] Step 8:

[0687] The server sends the generated assembly instructions to the terminal. The input is the assembly instructions data constructed in step 7. The server again uses a communication method to securely transmit the data to the terminal. The output is the assembly instructions sent to the terminal.

[0688] Step 9:

[0689] The terminal displays and provides the received assembly instructions to the user. The input is the assembly instruction data received in step 8. The terminal interprets the instructions using an appropriate interface and displays them in a user-friendly format. The output is the assembly instructions displayed on the terminal screen.

[0690] Step 10:

[0691] The user assembles the object using the parts they have on hand, following the assembly procedure displayed on the terminal. The input is the assembly procedure displayed in step 9. The user physically follows the procedure and assembles the parts in the correct order to form the final shape. The output is the completed three-dimensional object.

[0692] (Application Example 1)

[0693] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0694] During the prototyping phase of a product, there is a need to reduce the time and effort required to quickly assemble prototypes based on shape data. Traditional methods require a significant amount of manual work and time from prototype design to assembly, which reduces the efficiency of product development.

[0695] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0696] In this invention, the server includes recording means for capturing images of an object from all directions, communication means for transmitting the captured image data to the server, and analysis means for analyzing the image data to estimate its three-dimensional shape. This makes it possible to quickly and automatically assemble a prototype based on the shape designed by an engineer.

[0697] "Recording means" refers to devices and methods for photographing an object from all directions, and is intended to digitize the shape of a designed product with high precision.

[0698] "Communication methods" refer to the technologies and protocols used to transmit captured image data to a server, enabling the secure and rapid transfer of data.

[0699] "Analysis means" refers to algorithms and techniques for processing image data received on a server and estimating its three-dimensional shape, utilizing advanced machine learning techniques to generate shape data.

[0700] "Selection method" refers to the criteria and methods for selecting the necessary components for reproduction based on the analyzed shape data, and supports the selection of the optimal parts.

[0701] The "assembly diagram generation means" refers to a process that generates assembly procedures based on selected components, and is designed to efficiently visualize the generated procedures.

[0702] "Dispatch means" refers to a method or device for transmitting the generated assembly procedure to a machine and actually performing automated assembly, thereby realizing assembly automation.

[0703] A system implementing this invention mainly consists of a mobile terminal with a high-resolution camera, a server, and mechanical equipment installed in a factory or the like.

[0704] The device captures images of the target object from all directions and transmits these image data to the server using a communication method. Wi-Fi or mobile data communication can be used for this purpose. The smartphone preprocesses the images using OpenCV and converts them to the optimal format. The data is sent to the server using a secure protocol.

[0705] The server processes the received data using analysis tools. These tools include TensorFlow, a Python-based machine learning framework, and OpenCV, an image processing library. Based on the image data, the server estimates the three-dimensional shape of the object and creates a 3D model using the Blender API. Then, based on this model, it identifies the component parts using selection tools and generates the optimal assembly procedure.

[0706] Furthermore, assembly procedures are automatically generated via an assembly drawing generation means. These generated procedures are transmitted to the factory machinery through a feeding means, and the machinery automatically starts assembly. This allows for the efficient construction of prototypes.

[0707] For example, if a part for a new electronic device is designed, the part can be photographed with a device, the data can be analyzed on a server, and a factory robot can quickly assemble the part. This allows engineers to revise the design and immediately test the new prototype.

[0708] An example of a prompt message for a generating AI model would be: "Send new prototyping images to the system and generate an efficient assembly procedure. The next subject to be photographed is a next-generation electronic component."

[0709] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0710] Step 1:

[0711] The user uses a terminal to photograph the target object from all directions. The terminal uses a high-resolution camera to take photos from multiple angles, and this image data is input to the next processing step. The obtained image data is formatted and subjected to basic image processing using OpenCV on the terminal. This processing optimizes the image quality and prepares it for transmission to the server.

[0712] Step 2:

[0713] The terminal transmits the formatted image data to the server using a communication method. A secure protocol (e.g., HTTPS) is used for data transmission to ensure data integrity and confidentiality. The server receives the transmitted image data as input.

[0714] Step 3:

[0715] The server analyzes the received image data. Here, using Python's OpenCV and TensorFlow, it first estimates the three-dimensional shape from the image. In this estimation, multiple image data are fused to generate a 3D model, and the result is output to the next processing step.

[0716] Step 4:

[0717] The server selects the necessary components for reproduction based on the generated 3D model using a selection method. The model is further refined using the Blender API, and the necessary component information is provided as output. At this point, a list of parts required for assembly is created.

[0718] Step 5:

[0719] The server generates assembly instructions using an assembly diagram generation mechanism based on the selected components. These instructions are output in a sequential manner to provide clear instructions to the machine. The data created here serves as the instruction manual for the robot.

[0720] Step 6:

[0721] The assembly instructions generated from the server are sent to the machine. The instructions reach the machine via a feeding mechanism, and the robot automatically starts assembly. This automates the actual construction, and the prototype is completed.

[0722] This allows engineers to receive rapid feedback on-site, resulting in a more efficient prototyping process.

[0723] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0724] The system in this invention adds an element of sentiment analysis to the process of a user photographing an object and assembling blocks using a generated assembly diagram. The user uses a smartphone or other mobile device to photograph the object from multiple angles. The captured images are managed and organized on the device and transmitted to a server.

[0725] The server supplies image data to the AI ​​analysis unit, which estimates the three-dimensional shape of the object. This analysis uses shape recognition technology and improves accuracy by comparing it with various databases. Next, the server selects the block parts necessary to reconstruct the object based on the analysis results. This selection process is based on shape information and the user's past operation history.

[0726] Furthermore, this invention uses an emotion engine equipped in the terminal to obtain feedback on the user's emotional state. This emotion engine analyzes the user's facial expressions and tone of voice through the camera and voice input, and sends the results to a server. The server optimizes the assembly procedure based on this emotional information. The purpose of this procedure optimization process is to adjust the difficulty level of the blocks and the presentation procedure so that the user can continue working more comfortably.

[0727] After the assembly procedure is optimized, the device provides the user with a visually and emotionally resonant assembly diagram. This allows the user to proceed with the block assembly with a more positive experience based on the displayed information. For example, if the emotion engine detects signs of stress when the user is tackling a difficult part, the system can suggest a less complex version of the block.

[0728] In this way, by incorporating user emotional information into the program, the system can provide a flexible assembly environment tailored to individual users, comfortably supporting creative activities.

[0729] The following describes the processing flow.

[0730] Step 1:

[0731] The user uses their smartphone to photograph the object from different angles. This collects a series of images that cover the entire 360 ​​degrees.

[0732] Step 2:

[0733] The device formats the captured image appropriately, adjusts the image size, and then sends the image data to the server. This process may involve data compression.

[0734] Step 3:

[0735] The server processes the received image data using an AI analysis unit to estimate a three-dimensional model of the object. This process uses a shape recognition algorithm to extract the object's main features and construct a highly accurate model.

[0736] Step 4:

[0737] The server selects suitable block parts from the database based on a three-dimensional model. Selection criteria include shape, color, and required quantity.

[0738] Step 5:

[0739] An emotion engine built into the device analyzes the user's emotional state from their facial expressions and voice. This data is shared with a server in real time and used to optimize the assembly process.

[0740] Step 6:

[0741] The server dynamically adjusts the assembly procedure while taking into account the user's emotional state. For example, if stress is detected, it may adjust the difficulty of the procedure or include encouraging messages.

[0742] Step 7:

[0743] The server sends optimized assembly instructions to the terminal.

[0744] Step 8:

[0745] The terminal visually displays the received assembly instructions to the user. This display includes concise, step-by-step guidelines, which the user follows to recreate the object using the blocks at hand.

[0746] Step 9:

[0747] As the user proceeds with assembly, the emotion engine continuously acquires emotional data and sends feedback to the server as needed. Based on this feedback, further adjustments may be made.

[0748] (Example 2)

[0749] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0750] In modern society, a challenge exists where users experience stress and reduced enjoyment when engaging in creative assembly activities due to the difficulty of the assembly process. In particular, the selection of appropriate parts based on the shape of the object, and the complexity of the assembly procedure, often do not suit the individual user's skills and emotional state, making it difficult to provide an efficient and satisfying assembly experience.

[0751] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0752] In this invention, the server includes a recording unit and means for photographing an object from multiple viewpoints, a communication unit and means for transmitting the recorded digital data to an information processing device, and a processing unit and means for processing the digital data in the information processing device and estimating its three-dimensional shape. This makes it possible to provide an assembly procedure optimized for each user while taking into account the user's emotional state, thereby realizing a comfortable and creative assembly activity.

[0753] The "object" refers to the object or model that the user uses as the basis for assembly, and is analyzed by the system from multiple perspectives.

[0754] A "recording unit" is a device or component that has the function of capturing an object as digital data in image or video format from multiple viewpoints.

[0755] A "communication unit" is a device or function for transmitting digital data obtained from a recording unit to an information processing device, i.e., a server, via a network.

[0756] A "processing unit" is a device or program that analyzes digital data received by a server and estimates the three-dimensional shape of an object.

[0757] A "decision unit" is a device or function that selects the components necessary to reproduce an object based on the shape generated by the processing unit.

[0758] A "generation unit" is a device or function that creates an optimal assembly procedure, taking into account the components selected by the decision unit and the user's emotional state, and provides it to the user visually.

[0759] An "analysis unit" is a device or program that collects and analyzes user emotional information in real time and optimizes the assembly procedure based on the results.

[0760] This invention is a system that allows users to photograph objects using a mobile device and then uses that information to assist in the assembly of blocks and other materials. Furthermore, it aims to provide a better assembly experience by taking into account the user's emotional state.

[0761] The user uses a device such as a smartphone or tablet to photograph an object from multiple angles. The device's built-in camera and software are used to record image data of the object. This image data is temporarily stored on the device and then transmitted to a server via a communication unit over the network.

[0762] The server analyzes the received image data using a generative AI model or other shape recognition software. Based on the analysis results, it estimates the three-dimensional shape of the object and selects the necessary components accordingly. A database is used for selection, taking into account the compatibility of each part and the user's past history data.

[0763] The device also features an emotion engine that analyzes the user's facial expressions and voice through the camera and microphone. This data is transmitted to a server in real time. Based on the user's emotion information, the server adjusts the assembly procedure and the difficulty level of the parts to generate an appropriate assembly diagram.

[0764] The device displays assembly diagrams that are visually easy to understand and emotionally resonant for the user. This allows users to proceed with the assembly process creatively and without stress.

[0765] As a concrete example, suppose a user is assembling a dinosaur object. This system analyzes photos of the dinosaur from various angles, selects the necessary blocks for assembly, and suggests starting with easier parts if the user is feeling stressed. Examples of prompt messages include, "Starting the dinosaur assembly. Please let us know if you have any questions during assembly. We are also monitoring your emotional state."

[0766] This embodiment of the invention allows users to enjoy a comfortable and efficient assembly experience.

[0767] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0768] Step 1:

[0769] The user uses the camera on their mobile device to photograph an object from multiple angles. The images taken from each angle are recorded on the device as input. These images are then organized within the device, and initial data processing such as noise reduction and resolution adjustment is performed. Finally, the processed images are sent from the device to the server as output.

[0770] Step 2:

[0771] The server receives image data transmitted from the terminal. The image data, as input, is supplied to shape recognition algorithms such as generative AI models within the server. Based on this input, the server estimates the three-dimensional shape of the object using database matching and machine learning techniques. The output of this step is a three-dimensional model data of the object.

[0772] Step 3:

[0773] The server uses an estimated three-dimensional shape model to select the components necessary to reproduce the object. The input consists of 3D model data and a database of components that could potentially fit that model. The server performs a highly accurate selection while considering the user's past operation history and preferences. The output is a list of the selected appropriate components.

[0774] Step 4:

[0775] The device uses an emotion engine to analyze the user's emotional state in real time. Input includes the user's facial expressions and voice, which are then fed into a model using Effective Computing technology. This allows the model to determine the user's emotional state, such as stress or excitement. The output is the analyzed emotion data, which is then sent to a server.

[0776] Step 5:

[0777] The server receives user sentiment data and optimizes the assembly procedure. Inputs include sentiment data from step 4 and a list of components from step 3. Based on these inputs, the server adjusts the difficulty of the procedure to ensure the user is comfortable working with it, generating an optimized assembly procedure. The output is the final assembly diagram, including the user-specific assembly procedure.

[0778] Step 6:

[0779] The terminal presents the user with optimized assembly instructions received from the server. The input is the assembly diagram provided by the server. The terminal displays this information in a visually clear and sequential manner to support the user's assembly work. The output is assembly instructions in a format that the user can visually confirm.

[0780] Through the above process, users can enjoy a comfortable and stress-free assembly experience.

[0781] (Application Example 2)

[0782] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0783] In factory assembly work, prolonged work hours can lead to fatigue and stress, resulting in decreased work efficiency. This situation not only reduces productivity but can also negatively impact workers' health. Therefore, it is necessary to monitor workers' emotional states in real time and adjust the work environment appropriately.

[0784] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0785] In this invention, the server includes recording means for photographing an object from all directions, communication means for transmitting the captured image data to the server, analysis means for analyzing the image data to estimate its three-dimensional shape, emotion information analysis means for analyzing the user's emotional state, and procedure optimization means for optimizing the work procedure based on the emotion information. As a result, the robot performing the assembly work adjusts the work content according to the worker's emotional state, enabling efficient work while reducing the burden on the worker.

[0786] "Recording means" refers to a device or method for photographing an object from all directions and acquiring image data.

[0787] "Communication means" refers to a device or method for performing network communication to transmit captured image data to a server.

[0788] "Analysis means" refers to a device or method for estimating the three-dimensional shape of an object based on received image data.

[0789] "Selection means" refers to an apparatus or method for selecting the components necessary for reproduction based on the analyzed and estimated shape.

[0790] "Assembly drawing generation means" refers to an apparatus or method for generating an assembly procedure based on selected components.

[0791] "Emotional information analysis means" refers to a device or method for analyzing a user's emotional state and acquiring emotional information.

[0792] "Procedure optimization means" refers to a device or method that optimizes work procedures based on acquired emotional information and adjusts the user's work environment.

[0793] The system that realizes this invention is composed of multiple hardware and software components. The server plays a central role and performs various processes. The server is equipped with a high-performance CPU and GPU, enabling it to process large amounts of data in real time.

[0794] First, the user takes photos of the target object from all directions using a smartphone or a dedicated mobile device. The obtained image data is automatically transmitted to the server via a communication method. This communication uses high-speed communication technologies such as Wi-Fi or 4G / 5G. The server analyzes the received image data using image processing libraries such as OpenCV and estimates the three-dimensional shape of the target object. This analysis result is compared with known shape data in a database to improve accuracy.

[0795] Based on the analyzed shape information, the server selects the necessary components. The selection process is optimized by a machine learning algorithm, taking into account the user's past operation history. Based on the selected components, the assembly diagram generation means generates the assembly procedure. This generation utilizes a generation AI model to optimize the required assembly difficulty and sequence.

[0796] Furthermore, the device is equipped with a camera and microphone for emotional information analysis, which analyzes the user's emotional state from their facial expressions and voice. This analysis uses tools such as Librosa for voice data analysis. The emotional data is immediately fed back to the server, and the work procedure is dynamically adjusted by a procedure optimization mechanism.

[0797] As a concrete example, in an electronic component assembly plant, if emotional analysis detects worker fatigue due to prolonged work, the system has the ability to adjust procedures to reduce the complexity of the work at the appropriate time. A concrete example of this prompt sentence would be: "Think of an idea where worker stress is measured in real time, and if stress is detected, a factory robot automatically takes over the work."

[0798] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0799] Step 1:

[0800] The user takes photos of the target object from all directions using a smartphone or dedicated mobile device. The input is image data captured from multiple angles, which is managed and organized within the device. The output is data converted into a communication-compatible format.

[0801] Step 2:

[0802] The device transmits the captured image data to the server. The server receives image data as input, which is done using high-speed communication technology (Wi-Fi / 4G / 5G). The output is the storage of the image data within the server.

[0803] Step 3:

[0804] The server uses OpenCV to analyze received image data and estimate the three-dimensional shape of the object. The input is image data, and the output is estimated three-dimensional shape data. In this process, accuracy is improved by comparing the data with shape data in an existing database.

[0805] Step 4:

[0806] The server selects the necessary components based on estimated shape information. Inputs are 3D shape data and the user's past operation history, while output is information on the selected components. This selection process utilizes machine learning algorithms.

[0807] Step 5:

[0808] The server uses a generative AI model to generate assembly instructions based on selected parts. The input is component information, and the output is the generated assembly instructions. The generation is adjusted to optimize the difficulty and order of the steps.

[0809] Step 6:

[0810] The device's emotional information analysis system uses a camera and microphone to analyze the user's emotional state from their facial expressions and voice. The input is real-time collected facial and voice data, and the output is the analyzed emotional data.

[0811] Step 7:

[0812] The server receives emotional data and adjusts the work procedure using a procedure optimization mechanism. The input is emotional data, and the output is the adjusted work procedure. The adjustment is optimized to reduce user stress.

[0813] Step 8:

[0814] The terminal displays the finalized work procedure to the user. The input is the adjusted work procedure, and the output is an assembly diagram that the user can visually confirm. The display also includes suggestions based on emotional information.

[0815] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0816] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet Search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0817] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0818] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0819] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0820] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0821] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0822] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0823] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0824] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0825] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0826] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0827] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0828] 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.

[0829] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0830] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0831] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0832] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0833] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0834] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0835] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0836] The following is further disclosed regarding the embodiments described above.

[0837] (Claim 1)

[0838] A recording means for photographing an object from all directions,

[0839] A communication means for sending captured image data to a server,

[0840] An analysis means for analyzing image data to estimate three-dimensional shape,

[0841] A selection method for selecting the components necessary for reproduction based on the shape,

[0842] An assembly drawing generation means that generates an assembly procedure based on selected components,

[0843] A system that includes this.

[0844] (Claim 2)

[0845] The system according to claim 1, further comprising a display means for displaying the generated assembly procedure on a terminal.

[0846] (Claim 3)

[0847] The system according to claim 1, further comprising output means for the user to receive the generated assembly drawing in a predetermined format.

[0848] "Example 1"

[0849] (Claim 1)

[0850] A recording means that captures visual information by photographing an object from various angles,

[0851] A communication means for transferring acquired visual information to a remote processing unit,

[0852] An analytical means for analyzing visual information and inferring a three-dimensional structure,

[0853] A selection means for selecting the components necessary for reconstruction based on the inferred structure,

[0854] An assembly diagram generation means that automatically generates an assembly procedure based on selected components,

[0855] A system that includes this.

[0856] (Claim 2)

[0857] The system according to claim 1, further comprising visualization means for displaying the generated assembly procedure on an information terminal.

[0858] (Claim 3)

[0859] The system according to claim 1, further comprising output means for a user to receive the generated assembly drawing in a predetermined format.

[0860] "Application Example 1"

[0861] (Claim 1)

[0862] A recording means for photographing an object from all directions,

[0863] A communication means for sending captured image data to a server,

[0864] An analysis means for analyzing image data to estimate three-dimensional shape,

[0865] A selection method for selecting the components necessary for reproduction based on the shape,

[0866] An assembly drawing generation means that generates an assembly procedure based on selected components,

[0867] A sending means that transmits the generated assembly procedure to a machine and performs automatic assembly,

[0868] A system that includes this.

[0869] (Claim 2)

[0870] The system according to claim 1, further comprising a display means for displaying the generated assembly procedure on a terminal.

[0871] (Claim 3)

[0872] The system according to claim 1, further comprising output means for the user to receive the generated assembly drawing in a predetermined format.

[0873] "Example 2 of combining an emotion engine"

[0874] (Claim 1)

[0875] A recording unit and means for photographing an object from multiple viewpoints,

[0876] A communication unit and means for transmitting recorded digital data to an information processing device,

[0877] A processing unit and means for processing digital data using an information processing device and estimating a three-dimensional shape,

[0878] A decision unit and means for selecting necessary components based on the estimated shape,

[0879] A generation unit means that generates an assembly procedure based on the determined components and the user's emotional state,

[0880] A system that includes this.

[0881] (Claim 2)

[0882] The system according to claim 1, further comprising a display unit that displays the generated assembly procedure on a portable device.

[0883] (Claim 3)

[0884] The system according to claim 1, comprising an analysis unit that analyzes user sentiment information and provides an optimized assembly procedure.

[0885] "Application example 2 when combining with an emotional engine"

[0886] (Claim 1)

[0887] A recording means for photographing an object from all directions,

[0888] A communication means for sending captured image data to a server,

[0889] An analysis means for analyzing image data to estimate three-dimensional shape,

[0890] A selection method for selecting the components necessary for reproduction based on the shape,

[0891] An assembly drawing generation means that generates an assembly procedure based on selected components,

[0892] An emotional information analysis tool for analyzing the user's emotional state,

[0893] A procedure optimization means that optimizes work procedures based on emotional information,

[0894] A system that includes this.

[0895] (Claim 2)

[0896] The system according to claim 1, further comprising a display means that displays the generated assembly procedure on a terminal and makes suggestions based on emotional information.

[0897] (Claim 3)

[0898] The system according to claim 1, comprising output means for the user to receive the generated assembly drawing in a predetermined format, and outputting difficulty adjustment suggestions using emotion analysis results. [Explanation of Symbols]

[0899] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A recording means for photographing an object from all directions, A communication means for sending captured image data to a server, An analysis means for analyzing image data to estimate three-dimensional shape, A selection method for selecting the components necessary for reproduction based on the shape, An assembly drawing generation means that generates an assembly procedure based on selected components, A system that includes this.

2. The system according to claim 1, further comprising a display means for displaying the generated assembly procedure on a terminal.

3. The system according to claim 1, further comprising output means for the user to receive the generated assembly drawing in a predetermined format.

Images