system

Abstract

Provide a system. [Solution] A means for a user to input design requests for a mobile device; means for transmitting design requests for the mobile device to a server; A means for analyzing the design request received by the server and generating multiple design proposals using an algorithm; A means for the server to present the generated design proposal to the user; a means for users to select from multiple design options; a means for storing the selected design proposal in a server and outputting it in a format that can be used in a manufacturing process; A system including:

Description

[Technical Field]

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

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

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

[0004] When selecting a design for a mobile device, conventional methods have made it difficult for users to find a design that specifically reflects their preferences. Furthermore, there has been a lack of efficient means for directly incorporating user requirements into the manufacturing process. For example, it is difficult to quickly provide a product that accurately reflects the functions and design elements desired by the user, which can lead to a decrease in user satisfaction. Therefore, there is a need for a system that automatically generates mobile device designs based on detailed user requirements and efficiently applies them to the manufacturing process. [Means for solving the problem]

[0005] To solve the above problems, the present invention provides the following means: a system including a means for a user to input design requirements, a means for transmitting the design requirements from a mobile device to a server, a means for the server to analyze the requirements and generate multiple design proposals using an artificial intelligence algorithm, a means for the server to present the generated design proposals to the user, a means for the user to select from the multiple design proposals, and a means for the server to save the selected design proposal and output it in a format usable in the manufacturing process. This system makes it possible to efficiently generate and present design proposals that reflect the user's requirements in detail and quickly apply them to the manufacturing process.

[0006] A "user" is a person who uses the system to input and select design requirements for a mobile device.

[0007] "Design requests" refer to detailed requirements such as the shape, color, and functions of the mobile device desired by the user.

[0008] A "mobile terminal" is a portable electronic device used by a user, including, for example, a smartphone or tablet.

[0009] "Server" means a computer system that receives design requests sent by users, analyzes them, generates design proposals using algorithms, and stores and outputs them.

[0010] An "algorithm" refers to a series of calculation procedures for automatically generating multiple design proposals for mobile devices based on design requests, and includes artificial intelligence technology.

[0011] "Design proposals" refer to specific design contents for multiple mobile terminals that are generated based on the user's design requests.

[0012] The "manufacturing process" refers to the process of actually producing a mobile device based on the selected design proposal.

[0013] A "3D model" is a three-dimensional digital representation of the generated design proposal. [Brief explanation of the drawings]

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

[0015] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

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

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

[0018] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0019] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

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

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0022] [First embodiment]

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

[0024] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

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

[0026] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

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

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

[0029] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0031] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0032] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

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

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

[0035] This invention provides a system in which a user inputs design requirements on a mobile device, and a server automatically generates multiple design proposals based on those requirements, and presents, selects, saves, and outputs them. This system consists of three main components: the device, the server, and the user.

[0036] Processing flow

[0037] The user inputs their design requirements

[0038] Users input their desired mobile device features using a dedicated application on their device or a webpage, including specific design requirements such as a 6-inch screen size, red color, curved display, and dual-lens camera.

[0039] The device sends the request to the server

[0040] After the user has finished entering their design requirements, the device sends them to the server via a communication method such as an HTTP POST request.

[0041] The server analyzes the request and generates design proposals using an AI algorithm.

[0042] The server receives and analyzes the design requests sent from the device. Based on the analyzed requests, the server uses an AI algorithm to generate multiple design proposals. These proposals include a 3D model as a stereoscopic image and various specifications.

[0043] The server presents the generated design to the user.

[0044] The generated design proposals are sent from the server to the terminal and presented to the user, who can then freely browse these design proposals.

[0045] The user selects a design option

[0046] The user selects the design they like from the multiple designs presented, and the selected design is sent back to the server from the device.

[0047] The server saves and outputs the selected design

[0048] The selected design proposal is stored in a database by the server, and then output in a format (e.g., CAD data) suitable for the manufacturing process. This format can be used directly for manufacturing.

[0049] Specific examples

[0050] 1. The user enters their design requirements

[0051] Users launch the application and input their preferences, such as "screen size 6 inches, color red, curved display, dual lens camera."

[0052] 2. The device sends the request to the server

[0053] Once the request is finalized, the device sends it to the server, which receives the data.

[0054] 3. The server analyzes the request and generates a design proposal

[0055] The server analyzes the received design request and uses AI algorithms to generate multiple design proposals, such as a 3D model of a 6-inch mobile device with a red curved display.

[0056] 4. The server presents the generated design to the user.

[0057] The generated design proposals are sent from the server to the terminal and presented to the user, who can then check them in detail.

[0058] 5. User selects design options

[0059] The user selects the design that best suits their needs from the multiple design proposals presented.

[0060] 6. The device notifies the server of the selection result

[0061] The terminal notifies the server of the user's selection, which is then stored by the server.

[0062] 7. The server saves and outputs the selected design

[0063] The selected design is stored in a database by the server and output in a format that can be used in the manufacturing process, for example as a CAD file, and sent to the manufacturing line.

[0064] In this way, the system of the present invention automatically generates and presents optimal mobile device design proposals based on the user's detailed design requests, enabling them to be quickly applied to the manufacturing process.

[0065] The processing flow will be explained below.

[0066] Step 1:

[0067] The user inputs the design requirements.

[0068] Users open an application or web page on their device and fill out a design request form detailing the features of their desired mobile device, including screen size, color, display type, camera specifications, and other details.

[0069] Step 2:

[0070] The terminal transmits the request to the server.

[0071] When the user completes the input and presses the submit button, the device sends the request to the server. The HTTP POST request is used to format the design request data and send it to the server.

[0072] Step 3:

[0073] The server analyzes the request and generates design proposals using an AI algorithm.

[0074] The server analyzes the design request data received from the terminal.

[0075] Next, the AI ​​algorithm generates multiple design options based on your requirements, such as creating suitable design candidates based on inputs of screen size, color, and display type, and also generates 3D models and spec sheets during the process.

[0076] Step 4:

[0077] The server presents the generated design proposal to the user.

[0078] The server then sends the resulting design proposal back to the device, including a 3D view and specific specifications.

[0079] The device receives this data and displays a preview of the proposed design to the user.

[0080] Step 5:

[0081] The user selects a design idea.

[0082] The user reviews the presented design proposals and selects the one they like best. The selection is made through the device's UI by clicking a selection button.

[0083] Step 6:

[0084] The terminal notifies the server of the selection result.

[0085] After the user selects a design, the device sends the selection results back to the server, including the ID of the selected design.

[0086] Step 7:

[0087] The server stores and outputs the selected designs.

[0088] The server stores the data of the selected design proposal in a database.

[0089] Next, the design data is output in a format that can be used in the manufacturing process, for example, exported as a CAD file and fed to the manufacturing line.

[0090] Example 1

[0091] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0092] The conventional design process for mobile devices makes it difficult to reflect specific user requests, making it difficult to increase user satisfaction. Furthermore, the process from generating design proposals to manufacturing takes time and effort, making it impossible to quickly respond to user requests. Therefore, it is necessary to use an AI-based system to efficiently generate design proposals and reflect user requests.

[0093] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0094] In this invention, the server includes means for analyzing design requirements using a generative AI model, means for presenting the generated design proposals through a user interface, and means for saving the selected design proposal and outputting it in a format usable for the manufacturing process, thereby enabling the rapid generation of optimal design proposals based on the specific requirements of the user, and the display, selection, and output of the optimal design proposals.

[0095] "User" means an individual or legal entity that operates the system and inputs their design requests.

[0096] "Mobile device" refers to a portable electronic device such as a smartphone or tablet.

[0097] "Design requests" refer to requests regarding the specifications and features of the mobile device desired by the user.

[0098] "Server" means a central computer system that receives and analyzes design requests, and generates, provides, and stores design proposals.

[0099] A "generative AI model" is an algorithm or program that uses artificial intelligence technology to generate design ideas.

[0100] "Design proposal" refers to a proposal for the specific design and specifications of a mobile device created by a generative AI model.

[0101] "User interface" refers to the screen display and operating means that users use to operate the system.

[0102] A "3D model" is a digital model that shows the three-dimensional design of a mobile device.

[0103] "Manufacturing process" refers to the series of steps involved in producing an actual product based on the design proposal for a mobile device.

[0104] "Output" refers to providing the selected design proposal to an external party in a format that can be applied to manufacturing.

[0105] This system allows users to input their design requirements on a mobile device, and the server automatically generates multiple design proposals based on those requirements, and then presents, selects, saves, and outputs them. This system consists of three main components: the device, the server, and the user.

[0106] Users input their design requirements through dedicated applications or web pages. These applications and web pages are developed using frameworks such as React.js or Flutter (registered trademark) to provide a user interface. Users can input specific characteristics of their desired mobile device, such as screen size, color, display shape, and camera type. For example, a user might input requirements such as "6-inch screen size, red color, curved display, and dual-lens camera."

[0107] The device converts the input design requests into JSON format data and sends it to the server using an HTTP POST request. Specifically, it uses a JavaScript library to collect data and generate an HTTP request.

[0108] The server, built with Node.js or Python, receives the transmitted data. The received data is first stored in a database (e.g., PostgreSQL). The server then generates design proposals using a generative AI model (e.g., OpenAI's GPT-4 (registered trademark) is an example of a generative AI model). The server generates a prompt and sends it to the AI ​​model. An example prompt is, "Generate a 3D design proposal for a smartphone with a 6-inch red curved display." The generated design proposal includes a 3D model (e.g., using Blender or Three.js) and multiple specification details.

[0109] The server sends the generated design proposals to the terminal and presents them to the user. The terminal displays the received design proposals on the user interface, allowing the user to check and manipulate the 3D model. The user selects the desired design from the multiple presented proposals and performs the selection operation.

[0110] The terminal notifies the server of the selection results, which are then permanently stored in a database. The selected design proposal is output in a format that can be used in the manufacturing process (e.g., CAD data). This output process uses a Python script to generate a file in, for example, a SolidWorks format. This file is then sent directly to the manufacturing line and used to actually manufacture the product.

[0111] As a result, the system of the present invention can automatically generate and present optimal mobile device design proposals based on the user's detailed design requests, and quickly apply them to the manufacturing process.

[0112] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0113] Step 1:

[0114] The user inputs their design requirements

[0115] Users enter their design requirements using a dedicated application or web page. Specifically, they enter items such as "6-inch screen size," "red color," "curved display," and "dual-lens camera" on a UI built with React.js or Flutter, and then click the submit button. The entered data (design requirements) is processed as internal data in JSON format.

[0116] Step 2:

[0117] The device sends the request to the server

[0118] The terminal collects the design requests entered by the user and converts them into JSON format. This JSON data is sent to the server using an HTTP POST request. Specifically, a data collection library (e.g., Axios) is used to construct the request and send it to an endpoint (e.g., / design_request). The input is the user's design request, and the output is the request sent to the server.

[0119] Step 3:

[0120] The server analyzes the request and generates design proposals using a generative AI model.

[0121] The server is built using Node.js or Python and receives HTTP requests. The received JSON data is first stored in a database (such as PostgreSQL). The data is then analyzed to send a prompt to a generative AI model (e.g., OpenAI's GPT-4). An example prompt might be "Please design a mobile device with a 6-inch red curved display." This allows the AI ​​to generate multiple 3D designs and specifications. The input is the user's design request, and the output is the generated design proposal.

[0122] Step 4:

[0123] The server presents the generated design to the user.

[0124] The generated design proposal is sent from the server to the device. Specifically, a data packet containing the generated 3D model and specification information is sent to the device in JSON format. The device receives this data and displays it in a React.js or Flutter UI. The user can rotate the 3D model and check detailed specifications. The input is the generated design proposal, and the output is the proposed display on the UI.

[0125] Step 5:

[0126] The user selects a design option

[0127] The user selects the most desirable design from the multiple design proposals presented. The user makes the selection by clicking a button on the UI or by selecting an item from a list. This selection information is saved internally on the device. The input is the user's selection operation, and the output is the data of the selected design proposal.

[0128] Step 6:

[0129] The device notifies the server of the selection result

[0130] The device converts the data of the design proposal selected by the user back into JSON format and sends it to the server using an HTTP POST request. The server receives this request and permanently stores the selection information in a database. The input is the data of the selected design proposal, and the output is a notification to the server.

[0131] Step 7:

[0132] The server saves and outputs the selected design

[0133] The server stores the selected design in a database and converts it into a format that can be used for the manufacturing process (e.g., CAD data). This conversion is performed using a Python script, which generates a file in, for example, SolidWorks format. This file is then sent directly to the manufacturing line. The input is the data of the selected design, and the output is the CAD data for manufacturing.

[0134] (Application example 1)

[0135] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0136] Conventional mobile device design decision-making processes have difficulty reflecting specific user needs. Furthermore, because it is not easy for users to compare multiple design proposals, there are issues that prevent users from increasing their satisfaction. Furthermore, the lack of a way to actually visually check the generated design proposals often leaves users feeling uneasy about their final selection.

[0137] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0138] In this invention, the server includes: a means for a user to input design requests for a mobile device; a means for transmitting the design requests for the mobile device to the server; a means for the server to analyze the received design requests and generate multiple design proposals using a generation algorithm; a means for the server to present the generated design proposals to the user; a means for the user to confirm the generated design proposals using augmented reality technology; a means for the user to select from the multiple design proposals; and a means for the server to save the selected design proposal and output it in a format usable for the manufacturing process. This allows the user to easily generate, compare, and select design proposals for the mobile device based on specific design requests, and by visually confirming them using augmented reality technology, the user can feel confident about the final design selection.

[0139] "Mobile device design requirements" refer to specific requirements regarding the appearance and functionality of the mobile device desired by the user.

[0140] "Server" refers to a computer system that receives design requests from users, analyzes them, and generates, saves, and outputs design proposals.

[0141] "Generation algorithm" refers to a program or calculation method for automatically generating multiple design proposals based on the design requirements of a mobile device.

[0142] "Augmented reality technology" refers to technology that displays computer-generated information overlaid on real-world information.

[0143] "3D model" refers to a digital model of an object represented in three-dimensional space.

[0144] A "prompt sentence" refers to a specific instruction sentence that is input to a generative AI model.

[0145] "Design proposals" refer to multiple candidate mobile device designs generated based on user requests.

[0146] "Format usable for the manufacturing process" refers to a data format (e.g., CAD data) that is required to actually manufacture the selected design.

[0147] "Means for presenting to the user" refers to means for displaying the generated design proposal to the user.

[0148] This invention provides a system in which a user inputs design requirements on a mobile device, and a server automatically generates multiple design proposals based on those requirements, and presents, selects, saves, and outputs them. This system consists of three main components: a mobile device (a user device such as a smartphone or smart glasses), a server, and a user.

[0149] 1. Configuration and Operation Overview

[0150] User Device:

[0151] Users use a smartphone or smart glasses to input their design requests into the application.

[0152] The configured request is sent to the server via an HTTP POST request.

[0153] server:

[0154] The server analyzes the received design request using a generative algorithm.

[0155] The generative algorithm uses an artificial intelligence (AI) model to generate multiple design alternatives based on the customer's requirements.

[0156] The generated design proposal includes a 3D model of the mobile device and a prompt statement.

[0157] AR technology:

[0158] The computationally generated design proposals are displayed in augmented reality on the user's device using AR technology, allowing for real-world visual confirmation.

[0159] 2. Specific Examples

[0160] Suppose a user launches an application on their smartphone and inputs specific design requirements, such as "6-inch screen size, red color, curved display, dual cameras." These requirements are then sent to the server.

[0161] The server receives the request and uses a generative algorithm to generate multiple design alternatives, such as a 3D model of a 6-inch red curved mobile device, along with a corresponding prompt. An example prompt is as follows:

[0162] Generate a mobile design that meets the following requirements:

[0163] Screen size: 6 inches

[0164] Color: Red

[0165] Display Type: Curved Display

[0166] Camera: Dual camera

[0167] Generate multiple design proposals and return them in JSON format.

[0168] The generated design proposals are then sent back to the user's device, where they can be viewed using AR on a smartphone or smart glasses. After visually checking the design proposals using AR, the user can select the design they like best from the multiple proposals.

[0169] The selected design is then sent back to the server, where it is stored in a database in a format suitable for the manufacturing process and output as CAD data if necessary. This process allows users to easily specify, compare, and select the design of their desired mobile device. Augmented reality technology also allows users to visually confirm the design in the real world, giving them peace of mind in their selection.

[0170] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0171] Step 1:

[0172] Using a smartphone or smart glasses, a user launches the application and inputs their design requirements for a mobile device. The input requirements include specific information such as screen size, color, display type, and camera. For example, a user might input "6-inch screen size, red color, curved display, dual camera."

[0173] Step 2:

[0174] The terminal sends the design request entered by the user to the server using an HTTP POST request. At this time, the request data is sent in JSON format. The input is the user's design request data, and the output is a send request to the server.

[0175] Step 3:

[0176] The server receives the design request and analyzes it. Specifically, it parses the received JSON data and extracts each element (screen size, color, display type, camera, etc.). At this point, the input is the design request data, and the output is the analyzed request data.

[0177] Step 4:

[0178] The server uses a generative AI model to generate multiple design proposals based on the analyzed request data. The generative algorithm generates multiple 3D models and related prompts based on the input request elements. Specific data operations include 3D graphics generation and text generation. The input is the analyzed request data, and the output is multiple design proposals.

[0179] Step 5:

[0180] The server sends the generated design proposal back to the terminal. At this time, it sends the design proposal data to the terminal using an HTTP response. The input is the generated design proposal, and the output is the response data from the server to the terminal.

[0181] Step 6:

[0182] The device displays the received design proposal using augmented reality technology. Specifically, the generated 3D model is overlaid on the display of a smartphone or smart glasses. The user can visually check the design proposal in a real-world environment. The input is the design proposal data received from the server, and the output is the design proposal displayed in augmented reality.

[0183] Step 7:

[0184] The user selects their favorite design from multiple designs and sends that selection to the server via their device. Specifically, data including the ID of the selected design is sent to the server again via an HTTP POST request. The input is the design selected by the user, and the output is the data sent to the server.

[0185] Step 8:

[0186] The server stores the design proposal selected by the user in a database and outputs it in a format applicable to the manufacturing process (e.g., CAD data). At this time, the design proposal is converted into the required format and stored in the database. The input is the design proposal data selected by the user, and the output is data for the manufacturing process. Through this series of processes, a customized mobile device design based on the user's specific requests is efficiently generated and provided.

[0187] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0188] This invention is a system in which a user inputs design requirements on a mobile device, a server automatically generates multiple design proposals based on those requirements, and an emotion engine presents, selects, saves, and outputs the optimal design proposal based on the user's emotions. This system streamlines the process by which users select designs that reflect their intentions, including their emotions, and applies it to the manufacturing process. The system's main components are the device, server, emotion engine, and user.

[0189] System Overview

[0190] Input your design requirements

[0191] Users use a device application or web page to input their desired mobile device design requirements, such as screen size, color, display type, and camera specifications. This information is then organized within the device.

[0192] Sending and analyzing requests

[0193] The device sends the input design request to the server, which analyzes the request and uses an AI algorithm to generate multiple design proposals, including 3D models and various specifications.

[0194] Emotion recognition by emotion engine

[0195] The emotion engine analyzes the user's facial expressions and voice to recognize their emotions in real time. As the user browses through design proposals, the engine collects and analyzes their emotional data in real time, and prioritizes the presentation of designs that elicit a favorable response.

[0196] Presentation and selection of design proposals

[0197] The generated design proposals are sent from the server to the device and presented to the user. The emotion engine provides feedback on the emotion recognition results, and the most suitable design proposals for the user are displayed in sequence. The user can view these design proposals and select the one they like best.

[0198] Notification and saving of selection results

[0199] The selected design is then sent back to the server, which stores it in a database and outputs it in a format that can be used for the manufacturing process, often in CAD format.

[0200] Specific examples

[0201] 1. Enter your design requirements

[0202] The user opens the app and enters their preferences, such as "screen size 6 inches, color red, curved display, dual-lens camera."

[0203] 2. Sending and analyzing your request

[0204] Once the request is confirmed, the device sends it to the server, which receives the data.

[0205] 3. Generate design proposals

[0206] The server uses an AI algorithm to generate three design proposals, such as a 3D model of a 6-inch mobile device with a red curved display.

[0207] 4. Emotion Recognition by Emotion Engine

[0208] The emotion engine analyzes the user's facial expressions and voice in real time and prioritizes design ideas that will elicit a favorable response based on their emotions. For example, if a user smiles when looking at a particular design idea, that design idea will be placed at a high priority position.

[0209] 5. Presentation and selection of design proposals

[0210] Users compare the presented design proposals and select the one they like best, taking into account feedback from the emotion engine.

[0211] 6. Notification and saving of selection results

[0212] The terminal sends the selection results to the server, which stores the design proposal in a database and outputs it in a format that can be used in the manufacturing process, such as CAD data.

[0213] In this way, the system of the present invention can automatically generate and present optimal mobile device design proposals based on the user's detailed design requests and emotional feedback, and quickly apply them to the manufacturing process.

[0214] The processing flow will be explained below.

[0215] Step 1:

[0216] The user inputs the design requirements.

[0217] Users open an application or web page on their device and enter the desired mobile device features into a design request form, such as screen size, color, display type, and camera specifications.

[0218] Step 2:

[0219] The terminal transmits the request to the server.

[0220] When the user completes the input and presses the submit button, the device sends the request to the server. An HTTP POST request is used to send the design request data to the server in the appropriate format.

[0221] Step 3:

[0222] The server analyzes the request and generates design proposals using an AI algorithm.

[0223] The server analyzes the design request data received from the terminal.

[0224] The AI ​​algorithm then generates multiple design options based on the user's requirements, creating suitable design candidates based on the user's specified screen size, color, and display type, and generating 3D models and specifications for each design.

[0225] Step 4:

[0226] The server sends the generated design proposal to the terminal and presents it to the user.

[0227] The generated design proposal is sent from the server to the device, where it is displayed to the user, who can then rotate the 3D model and check its detailed specifications.

[0228] Step 5:

[0229] The emotion engine analyzes the user's facial expressions and voice to recognize emotions.

[0230] While the user is browsing the design proposals, the device's camera and microphone are used to collect the user's facial expressions and voice. This data is sent in real time to the emotion engine, which then analyzes the user's emotions. For example, if the user is smiling, it is determined that the design proposal is well-received.

[0231] Step 6:

[0232] The server changes the display order of design proposals based on the analysis results of the emotion engine.

[0233] The analysis results of the emotion engine are fed back to the server, which then adjusts the display to prioritize design proposals that have received the most positive user feedback. When the user next views design proposals, the highly favorable designs will be displayed at the top.

[0234] Step 7:

[0235] The user selects a design idea.

[0236] The user selects the design they like best from multiple options, taking into account feedback from the emotion engine. The selection is made through the device's UI by clicking a selection button.

[0237] Step 8:

[0238] The terminal notifies the server of the selection result.

[0239] After the user selects a design, the device sends the selection back to the server, including the ID of the selected design.

[0240] Step 9:

[0241] The server stores and outputs the selected designs.

[0242] The server stores the data of the selected design in a database and outputs the design data in a format that can be used in the manufacturing process, for example, by exporting it as a CAD file and feeding it to the manufacturing line.

[0243] In this way, the system of the present invention can automatically generate and present optimal mobile device design proposals based on the user's detailed design requests and emotional feedback, and quickly apply them to the manufacturing process.

[0244] Example 2

[0245] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0246] Conventional design proposal generation systems for mobile information terminals have the problem of not being able to take the user's emotions into consideration during the process of inputting the user's design requirements and the selection process. This makes it difficult to present optimal design proposals that will increase user satisfaction. The present invention aims to solve this problem and provide a system that allows users to efficiently select designs that reflect their intentions, including their emotions.

[0247] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0248] In this invention, the server includes: a means for a user to input design requests to a mobile information device; a means for transmitting the design requests from the mobile information device to an information processing device; a means for the information processing device to analyze the received design requests and generate multiple design proposals using a generative AI model; a means for an emotion recognition engine to analyze the user's facial expressions and voice to generate emotion data; a means for collecting emotion data while the user is viewing the design proposals and preferentially presenting design proposals that show a favorable reaction; a means for the information processing device to present the generated design proposals to the user; a means for the user to select from the multiple design proposals; and a means for the information processing device to save the selected design proposal and output it in a format usable for the production process. This enables the presentation and selection of optimal design proposals that reflect the user's emotions.

[0249] A "user" is a person who operates this system and inputs design requests for a mobile information terminal.

[0250] A "personal digital assistant" is a portable electronic device such as a smartphone or tablet.

[0251] "Design requests" are specific requests regarding the appearance and functions of the mobile information terminal desired by the user.

[0252] An "information processing device" is a device such as a computer or server that analyzes and processes data.

[0253] A "generative AI model" is an algorithm or program that uses artificial intelligence technology to generate design ideas.

[0254] "Design proposals" are multiple design proposals generated based on the user's design requests.

[0255] An "emotion recognition engine" is a technology that recognizes a user's emotions through the analysis of facial expressions and voice.

[0256] "Emotion data" refers to data relating to a user's emotions that is collected and analyzed by an emotion recognition engine.

[0257] The "production process" is a series of steps for manufacturing an actual product based on the selected design proposal.

[0258] The "output format" is a data format generated by an information processing device for applying a design proposal to actual product manufacturing.

[0259] This invention is a system that generates and presents optimal design proposals for mobile information terminals based on the user's design requests and emotional feedback. This system consists of a terminal, a server, an emotion recognition engine, and a generative AI model.

[0260] First, a user inputs their desired design requirements for a mobile information device using an application on the device or a web page. These requirements include screen size, color, display type, camera specifications, etc. Specifically, a user might input a preference such as "6-inch screen size, red color, curved display, dual-lens camera."

[0261] The device formats the input design requests and sends them to the server. The device converts the data into JSON format and sends it to the server via an HTTP request. The hardware used for this is a smartphone or tablet.

[0262] The server analyzes the received design requests and generates multiple design proposals using a generative AI model (e.g., TENSORFLOW (registered trademark) or PyTorch). This allows the server to generate a 3D model of, for example, a 6-inch mobile information terminal with a red curved display. The generated design proposals include the 3D model and various specifications.

[0263] The emotion recognition engine collects the user's facial expressions and voice in real time and analyzes their emotions. Examples of emotion recognition engines that are used include Microsoft® Azure® Cognitive Services and Google® Cloud AI. While the user is browsing design proposals, facial expressions and voice are collected via the camera and microphone, and the system analyzes them in real time.

[0264] The emotion recognition engine uses the collected data to provide feedback on design proposals that elicit favorable reactions, and the server then uses this information to select which design proposals to prioritize. A specific example of this behavior is when a user smiles upon seeing a particular design proposal. In this case, the corresponding design proposal is placed at a high priority position.

[0265] The generated design proposals are sent from the server to the device and presented to the user. The user compares the multiple design proposals presented and selects the design proposal they like best based on feedback from the emotion recognition engine. At this time, the device sends the selection information back to the server.

[0266] The server stores the selected design in a database (e.g., MySQL® or MongoDB). The server then outputs the selected design in a format that can be used for the manufacturing process, such as CAD data. A specific example of this is when the selection information is sent to the server in JSON format, and the server stores the data in a database and exports it as a CAD file.

[0267] An example of a prompt sentence might be:

[0268] "Generate a design for a 6-inch, red, curved display, dual-lens camera phone."

[0269] "Please give priority to design ideas that make users smile."

[0270] This system makes it possible to quickly and accurately select and present optimal design proposals based on the user's design requests and emotional feedback, and then apply them to the manufacturing process.

[0271] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0272] Step 1: Enter your design requirements

[0273] The user launches an application or web page on the device and inputs the design requirements for the mobile information device they desire. Specifically, they input screen size, color, display type, camera specifications, etc. The input design requirements are temporarily saved on the device. In this case, the input data might be "6-inch screen size, red color, curved display, dual-lens camera," and the output is organized design requirements information.

[0274] Step 2: Submit your request

[0275] The terminal formats the input design request and sends it to the server. Specifically, it converts the design request into JSON format and sends it to the server using an HTTP request. The input data is the design request specified by the user, and the output is the formatted design request data sent to the server.

[0276] Step 3: Analyze design requirements

[0277] The server analyzes the received design requests. This analysis extracts important elements for generating the actual design. The input data is the design request data sent from the device, and the output is the analyzed element extraction results. Then, based on the analysis results, the data is input into the generative AI model.

[0278] Step 4: Generate design ideas

[0279] The server uses a generative AI model (e.g., TensorFlow or PyTorch) to generate multiple design proposals. In this process, the AI ​​algorithm creates multiple design proposals that meet the design requirements. The input data is the analyzed design requirement data, and the output is a set of design proposals that includes 3D models and specifications of multiple mobile devices.

[0280] Step 5: Collect emotion recognition data

[0281] The emotion recognition engine collects the user's facial expressions and voice in real time. It uses a camera and microphone to collect emotion data while the user is viewing the design proposal. The input data is the user's facial expressions and voice, and the output is the emotion data captured in real time.

[0282] Step 6: Analyze the sentiment data

[0283] The emotion recognition engine analyzes the collected emotion data and recognizes the user's emotional state, thereby determining which design proposals the user has a favorable reaction to. The input data is raw emotion data acquired in real time, and the output is the analyzed emotion recognition results.

[0284] Step 7: Present your design proposal

[0285] The server then sends the optimal design proposal to the device based on the multiple design proposals generated and feedback information from the emotion recognition engine.The design proposals are presented to the user on the device and displayed in the optimal order based on the emotion recognition results.The input data are the generated design proposals and the emotion analysis results, and the output is the optimal design proposal presented to the user.

[0286] Step 8: Select a design option

[0287] The user compares the presented design proposals and selects the one they like best. The input data are the design proposals presented to the user, and the output is the design proposal selected by the user.

[0288] Step 9: Communicate and save your selections

[0289] The terminal sends the design proposal selected by the user to the server. The server stores the selected design proposal in a database. The server then outputs the selected design proposal in a format that can be used for the manufacturing process (e.g., CAD data format). The input data is the user-selected design proposal, and the output is the stored design proposal data and data in the output format that is applied to the manufacturing process.

[0290] In this way, the system's processing flow is structured so that the user first inputs their design requirements, then evaluates the design proposals generated based on those requirements using emotional data, and then selects, saves, and outputs the most suitable proposal.

[0291] (Application example 2)

[0292] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0293] In the conventional design process for mobile devices and their accessories, it is difficult for users to quickly obtain designs that reflect their preferences and intentions. Furthermore, there is no way to reflect the user's emotions and reactions in the design selection, which means it takes time and effort to find the optimal design. Furthermore, the process is one-way, lacking a mechanism for providing feedback on the user's emotional responses. There is a need to build a system that can solve these problems and quickly provide optimal design proposals tailored to the user.

[0294] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: a means for a user to input design requests for a mobile device; a means for transmitting the design requests from the mobile device to the server; a means for the server to analyze the received design requests and generate multiple design proposals using an artificial intelligence algorithm; a means for the server to present the generated design proposals to the user; a means for recognizing the user's emotions using an emotion engine and presenting the optimal design proposal based on the emotion; a means for the user to select from the multiple design proposals; and a means for the server to save the selected design proposal and output it in a format usable for the manufacturing process. This makes it possible to quickly generate and present design proposals that reflect the user's individual requests and emotions, and to select and save the optimal design proposal.

[0295] A "mobile terminal" is an electronic device that a user holds in his or her hand and that is used for communication and information processing.

[0296] "Design requests" refers to information that indicates the detailed specifications and wishes of the design desired by the user.

[0297] A "server" is a computer system that receives, analyzes, stores, and transmits data over a network.

[0298] An "artificial intelligence algorithm" is a computational procedure that uses techniques such as machine learning and deep learning to analyze data and perform specific tasks.

[0299] "Design proposals" refer to multiple proposed models generated based on design requests.

[0300] The "emotion engine" is a technology that analyzes the user's facial expressions and voice and recognizes their emotions in real time.

[0301] A "three-dimensional model" is a computer-generated three-dimensional model that reproduces the shape and structure of a three-dimensional space.

[0302] A "manufacturing process" is a series of steps that actually create a product based on a designed product.

[0303] This invention is a system in which a user inputs design requests on a mobile device, and a server automatically generates multiple design proposals based on the requests, and presents, selects, saves, and outputs the most suitable design proposal based on the user's emotions through an emotion engine.

[0304] System Overview

[0305] 1. Enter your design requirements

[0306] Using the device's application, users can input their desired design for the mobile device, including details such as color, material, and print pattern.

[0307] 2. Sending and analyzing your request

[0308] The device sends the input design request to the server, which analyzes the received request and generates multiple design proposals using a generative AI model.

[0309] 3. Generate design proposals

[0310] The server uses the generative AI model to generate multiple design proposals based on the user's requests, including 3D models and various specifications.

[0311] 4. Emotion Recognition by Emotion Engine

[0312] The emotion engine analyzes the user's facial expressions and voice to recognize their emotions in real time. As the user browses through design proposals, it collects and analyzes their emotional data and prioritizes the presentation of designs that evoke a positive response.

[0313] 5. Presentation and selection of design proposals

[0314] The design proposals generated by the server are sent to the device, which then presents them to the user. Based on the results of the emotion engine, the most suitable design proposals are displayed in sequence. The user can view these design proposals and select the one they like best.

[0315] 6. Notification and saving of selection results

[0316] The selected design is sent back to the server, which stores it in a database. The final design is output in a format that can be used for the manufacturing process (e.g., CAD data format).

[0317] Program processing overview

[0318] The server uses a generative AI model to generate multiple design proposals based on the input design requirements. This process includes elements such as color, material, and shape, and generates a three-dimensional model based on each element. Next, an emotion engine analyzes the user's facial expressions and voice in real time to recognize their emotions. Design proposals that elicit a favorable response are presented to the user preferentially. The user selects the most suitable design proposal, and the result is sent to the server and stored in a database.

[0319] Specific examples

[0320] The user launches the application and inputs their request, such as "a blue, silicone, floral smartphone case." This request is sent to the server, which uses a generative AI model to generate three design proposals. The generated design proposals are presented to the user through the application, and an emotion engine analyzes the user's facial expressions, with the design proposal that elicits the most favorable reaction being displayed preferentially. The user selects the most suitable design proposal, and the result is saved on the server.

[0321] Example prompts to input to the generative AI model

[0322] Prompt: "Generate design ideas based on the design requirements for a blue, silicone, floral smartphone case."

[0323] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0324] Step 1:

[0325] The user inputs their design requirements using a mobile device. Specifically, they launch an application and input detailed specifications such as the desired color, material, and print pattern. This information is temporarily stored in the device.

[0326] input:

[0327] User design requests (e.g., blue, silicone, floral smartphone case)

[0328] output:

[0329] Organized design request data

[0330] Step 2:

[0331] The device sends the input design request to the server using a communication protocol such as an HTTP request.

[0332] input:

[0333] Organized design request data

[0334] output:

[0335] Design request data sent to the server

[0336] Step 3:

[0337] The server analyzes the design requests received and generates design proposals using a generative AI model. Specifically, it generates a 3D model and various specifications based on the user's requests.

[0338] input:

[0339] Design request data received by the server

[0340] output:

[0341] Multiple design proposals (3D models, specifications)

[0342] Step 4:

[0343] The server sends the generated design proposal to the terminal, which then presents it to the user, displaying it on the screen so that the user can view it.

[0344] input:

[0345] Multiple design ideas

[0346] output:

[0347] Design proposals displayed on the screen

[0348] Step 5:

[0349] The emotion engine analyzes the user's facial expressions and voice in real time to recognize the user's emotions. The camera and microphone capture the user's facial expressions and voice, and analyzes the data to determine the user's emotions.

[0350] input:

[0351] User's facial expression data, voice data

[0352] output:

[0353] User sentiment data (e.g., positive response, indifference, etc.)

[0354] Step 6:

[0355] The server receives feedback based on the emotion engine and presents to the user design proposals that the user responded favorably to with priority.

[0356] input:

[0357] User emotion data

[0358] Multiple design ideas

[0359] output:

[0360] The best design ideas are prioritized

[0361] Step 7:

[0362] The user selects the best design, the device sends the result back to the server, and the user taps the screen to confirm the selection.

[0363] input:

[0364] The best design ideas are prioritized

[0365] output:

[0366] Selection result data

[0367] Step 8:

[0368] The server stores the selected design proposals in a database and outputs the final design proposal in a format that can be used for the manufacturing process (e.g., CAD data format).

[0369] input:

[0370] Selection result data

[0371] output:

[0372] Saved design data, data for manufacturing processes (e.g. CAD data)

[0373] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0374] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0375] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0376] [Second embodiment]

[0377] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0378] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0379] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0380] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0381] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0382] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0383] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0384] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0385] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0386] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0387] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0388] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0389] This invention provides a system in which a user inputs design requirements on a mobile device, and a server automatically generates multiple design proposals based on those requirements, and presents, selects, saves, and outputs them. This system consists of three main components: the device, the server, and the user.

[0390] Processing flow

[0391] The user inputs their design requirements

[0392] Users input their desired mobile device features using a dedicated application on their device or a webpage, including specific design requirements such as a 6-inch screen size, red color, curved display, and dual-lens camera.

[0393] The device sends the request to the server

[0394] After the user has finished entering their design requirements, the device sends them to the server via a communication method such as an HTTP POST request.

[0395] The server analyzes the request and generates design proposals using an AI algorithm.

[0396] The server receives and analyzes the design requests sent from the device. Based on the analyzed requests, the server uses an AI algorithm to generate multiple design proposals. These proposals include a 3D model as a stereoscopic image and various specifications.

[0397] The server presents the generated design to the user.

[0398] The generated design proposals are sent from the server to the terminal and presented to the user, who can then freely browse these design proposals.

[0399] The user selects a design option

[0400] The user selects the design they like from the multiple designs presented, and the selected design is sent back to the server from the device.

[0401] The server saves and outputs the selected design

[0402] The selected design proposal is stored in a database by the server, and then output in a format (e.g., CAD data) suitable for the manufacturing process. This format can be used directly for manufacturing.

[0403] Specific examples

[0404] 1. The user enters their design requirements

[0405] Users launch the application and input their preferences, such as "screen size 6 inches, color red, curved display, dual lens camera."

[0406] 2. The device sends the request to the server

[0407] Once the request is finalized, the device sends it to the server, which receives the data.

[0408] 3. The server analyzes the request and generates a design proposal

[0409] The server analyzes the received design request and uses AI algorithms to generate multiple design proposals, such as a 3D model of a 6-inch mobile device with a red curved display.

[0410] 4. The server presents the generated design to the user.

[0411] The generated design proposals are sent from the server to the terminal and presented to the user, who can then check them in detail.

[0412] 5. User selects design options

[0413] The user selects the design that best suits their needs from the multiple design proposals presented.

[0414] 6. The device notifies the server of the selection result

[0415] The terminal notifies the server of the user's selection, which is then stored by the server.

[0416] 7. The server saves and outputs the selected design

[0417] The selected design is stored in a database by the server and output in a format that can be used in the manufacturing process, for example as a CAD file, and sent to the manufacturing line.

[0418] In this way, the system of the present invention automatically generates and presents optimal mobile device design proposals based on the user's detailed design requests, enabling them to be quickly applied to the manufacturing process.

[0419] The processing flow will be explained below.

[0420] Step 1:

[0421] The user inputs the design requirements.

[0422] Users open an application or web page on their device and fill out a design request form detailing the features of their desired mobile device, including screen size, color, display type, camera specifications, and other details.

[0423] Step 2:

[0424] The terminal transmits the request to the server.

[0425] When the user completes the input and presses the submit button, the device sends the request to the server. The HTTP POST request is used to format the design request data and send it to the server.

[0426] Step 3:

[0427] The server analyzes the request and generates design proposals using an AI algorithm.

[0428] The server analyzes the design request data received from the terminal.

[0429] Next, the AI ​​algorithm generates multiple design options based on your requirements, such as creating suitable design candidates based on inputs of screen size, color, and display type, and also generates 3D models and spec sheets during the process.

[0430] Step 4:

[0431] The server presents the generated design proposal to the user.

[0432] The server then sends the resulting design proposal back to the device, including a 3D view and specific specifications.

[0433] The device receives this data and displays a preview of the proposed design to the user.

[0434] Step 5:

[0435] The user selects a design idea.

[0436] The user reviews the presented design proposals and selects the one they like best. The selection is made through the device's UI by clicking a selection button.

[0437] Step 6:

[0438] The terminal notifies the server of the selection result.

[0439] After the user selects a design, the device sends the selection results back to the server, including the ID of the selected design.

[0440] Step 7:

[0441] The server stores and outputs the selected designs.

[0442] The server stores the data of the selected design proposal in a database.

[0443] Next, the design data is output in a format that can be used in the manufacturing process, for example, exported as a CAD file and fed to the manufacturing line.

[0444] Example 1

[0445] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0446] The conventional design process for mobile devices makes it difficult to reflect specific user requests, making it difficult to increase user satisfaction. Furthermore, the process from generating design proposals to manufacturing takes time and effort, making it impossible to quickly respond to user requests. Therefore, it is necessary to use an AI-based system to efficiently generate design proposals and reflect user requests.

[0447] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0448] In this invention, the server includes means for analyzing design requirements using a generative AI model, means for presenting the generated design proposals through a user interface, and means for saving the selected design proposal and outputting it in a format usable for the manufacturing process, thereby enabling the rapid generation of optimal design proposals based on the specific requirements of the user, and the display, selection, and output of the optimal design proposals.

[0449] "User" means an individual or legal entity that operates the system and inputs their design requests.

[0450] "Mobile device" refers to a portable electronic device such as a smartphone or tablet.

[0451] "Design requests" refer to requests regarding the specifications and features of the mobile device desired by the user.

[0452] "Server" means a central computer system that receives and analyzes design requests, and generates, provides, and stores design proposals.

[0453] A "generative AI model" is an algorithm or program that uses artificial intelligence technology to generate design ideas.

[0454] "Design proposal" refers to a proposal for the specific design and specifications of a mobile device created by a generative AI model.

[0455] "User interface" refers to the screen display and operating means that users use to operate the system.

[0456] A "3D model" is a digital model that shows the three-dimensional design of a mobile device.

[0457] "Manufacturing process" refers to the series of steps involved in producing an actual product based on the design proposal for a mobile device.

[0458] "Output" refers to providing the selected design proposal to an external party in a format that can be applied to manufacturing.

[0459] This system allows users to input their design requirements on a mobile device, and the server automatically generates multiple design proposals based on those requirements, and then presents, selects, saves, and outputs them. This system consists of three main components: the device, the server, and the user.

[0460] Users input their design requirements through dedicated applications or web pages. These applications and web pages are developed using frameworks such as React.js or Flutter to provide the user interface. Users can input specific characteristics of their desired mobile device, such as screen size, color, display shape, and camera type. For example, a user might input a request such as "6-inch screen size, red color, curved display, dual-lens camera."

[0461] The device converts the input design requests into JSON format data and sends it to the server using an HTTP POST request. Specifically, it uses a JavaScript library to collect data and generate an HTTP request.

[0462] The server, built with Node.js or Python, receives the transmitted data. The received data is first stored in a database (e.g., PostgreSQL). The server then generates design proposals using a generative AI model (e.g., OpenAI's GPT-4 is an example of a generative AI model). The server generates a prompt and sends it to the AI ​​model. An example prompt is, "Generate a 3D design proposal for a smartphone with a 6-inch red curved display." The generated design proposal includes a 3D model (e.g., using Blender or Three.js) and multiple specification details.

[0463] The server sends the generated design proposals to the terminal and presents them to the user. The terminal displays the received design proposals on the user interface, allowing the user to check and manipulate the 3D model. The user selects the desired design from the multiple presented proposals and performs the selection operation.

[0464] The terminal notifies the server of the selection results, which are then permanently stored in a database. The selected design proposal is output in a format that can be used in the manufacturing process (e.g., CAD data). This output process uses a Python script to generate a file in, for example, a SolidWorks format. This file is then sent directly to the manufacturing line and used to actually manufacture the product.

[0465] As a result, the system of the present invention can automatically generate and present optimal mobile device design proposals based on the user's detailed design requests, and quickly apply them to the manufacturing process.

[0466] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0467] Step 1:

[0468] The user inputs their design requirements

[0469] Users enter their design requirements using a dedicated application or web page. Specifically, they enter items such as "6-inch screen size," "red color," "curved display," and "dual-lens camera" on a UI built with React.js or Flutter, and then click the submit button. The entered data (design requirements) is processed as internal data in JSON format.

[0470] Step 2:

[0471] The device sends the request to the server

[0472] The terminal collects the design requests entered by the user and converts them into JSON format. This JSON data is sent to the server using an HTTP POST request. Specifically, a data collection library (e.g., Axios) is used to construct the request and send it to an endpoint (e.g., / design_request). The input is the user's design request, and the output is the request sent to the server.

[0473] Step 3:

[0474] The server analyzes the request and generates design proposals using a generative AI model.

[0475] The server is built using Node.js or Python and receives HTTP requests. The received JSON data is first stored in a database (such as PostgreSQL). The data is then analyzed to send a prompt to a generative AI model (e.g., OpenAI's GPT-4). An example prompt might be "Please design a mobile device with a 6-inch red curved display." This allows the AI ​​to generate multiple 3D designs and specifications. The input is the user's design request, and the output is the generated design proposal.

[0476] Step 4:

[0477] The server presents the generated design to the user.

[0478] The generated design proposal is sent from the server to the device. Specifically, a data packet containing the generated 3D model and specification information is sent to the device in JSON format. The device receives this data and displays it in a React.js or Flutter UI. The user can rotate the 3D model and check detailed specifications. The input is the generated design proposal, and the output is the proposed display on the UI.

[0479] Step 5:

[0480] The user selects a design option

[0481] The user selects the most desirable design from the multiple design proposals presented. The user makes the selection by clicking a button on the UI or by selecting an item from a list. This selection information is saved internally on the device. The input is the user's selection operation, and the output is the data of the selected design proposal.

[0482] Step 6:

[0483] The device notifies the server of the selection result

[0484] The device converts the data of the design proposal selected by the user back into JSON format and sends it to the server using an HTTP POST request. The server receives this request and permanently stores the selection information in a database. The input is the data of the selected design proposal, and the output is a notification to the server.

[0485] Step 7:

[0486] The server saves and outputs the selected design

[0487] The server stores the selected design in a database and converts it into a format that can be used for the manufacturing process (e.g., CAD data). This conversion is performed using a Python script, which generates a file in, for example, SolidWorks format. This file is then sent directly to the manufacturing line. The input is the data of the selected design, and the output is the CAD data for manufacturing.

[0488] (Application example 1)

[0489] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0490] Conventional mobile device design decision-making processes have difficulty reflecting specific user needs. Furthermore, because it is not easy for users to compare multiple design proposals, there are issues that prevent users from increasing their satisfaction. Furthermore, the lack of a way to actually visually check the generated design proposals often leaves users feeling uneasy about their final selection.

[0491] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0492] In this invention, the server includes: a means for a user to input design requests for a mobile device; a means for transmitting the design requests for the mobile device to the server; a means for the server to analyze the received design requests and generate multiple design proposals using a generation algorithm; a means for the server to present the generated design proposals to the user; a means for the user to confirm the generated design proposals using augmented reality technology; a means for the user to select from the multiple design proposals; and a means for the server to save the selected design proposal and output it in a format usable for the manufacturing process. This allows the user to easily generate, compare, and select design proposals for the mobile device based on specific design requests, and by visually confirming them using augmented reality technology, the user can feel confident about the final design selection.

[0493] "Mobile device design requirements" refer to specific requirements regarding the appearance and functionality of the mobile device desired by the user.

[0494] "Server" refers to a computer system that receives design requests from users, analyzes them, and generates, saves, and outputs design proposals.

[0495] "Generation algorithm" refers to a program or calculation method for automatically generating multiple design proposals based on the design requirements of a mobile device.

[0496] "Augmented reality technology" refers to technology that displays computer-generated information overlaid on real-world information.

[0497] "3D model" refers to a digital model of an object represented in three-dimensional space.

[0498] A "prompt sentence" refers to a specific instruction sentence that is input to a generative AI model.

[0499] "Design proposals" refer to multiple candidate mobile device designs generated based on user requests.

[0500] "Format usable for the manufacturing process" refers to a data format (e.g., CAD data) that is required to actually manufacture the selected design.

[0501] "Means for presenting to the user" refers to means for displaying the generated design proposal to the user.

[0502] This invention provides a system in which a user inputs design requirements on a mobile device, and a server automatically generates multiple design proposals based on those requirements, and presents, selects, saves, and outputs them. This system consists of three main components: a mobile device (a user device such as a smartphone or smart glasses), a server, and a user.

[0503] 1. Configuration and Operation Overview

[0504] User Device:

[0505] Users use a smartphone or smart glasses to input their design requests into the application.

[0506] The configured request is sent to the server via an HTTP POST request.

[0507] server:

[0508] The server analyzes the received design request using a generative algorithm.

[0509] The generative algorithm uses an artificial intelligence (AI) model to generate multiple design alternatives based on the customer's requirements.

[0510] The generated design proposal includes a 3D model of the mobile device and a prompt statement.

[0511] AR technology:

[0512] The computationally generated design proposals are displayed in augmented reality on the user's device using AR technology, allowing for real-world visual confirmation.

[0513] 2. Specific Examples

[0514] Suppose a user launches an application on their smartphone and inputs specific design requirements, such as "6-inch screen size, red color, curved display, dual cameras." These requirements are then sent to the server.

[0515] The server receives the request and uses a generative algorithm to generate multiple design alternatives, such as a 3D model of a 6-inch red curved mobile device, along with a corresponding prompt. An example prompt is as follows:

[0516] Generate a mobile design that meets the following requirements:

[0517] Screen size: 6 inches

[0518] Color: Red

[0519] Display Type: Curved Display

[0520] Camera: Dual camera

[0521] Generate multiple design proposals and return them in JSON format.

[0522] The generated design proposals are then sent back to the user's device, where they can be viewed using AR on a smartphone or smart glasses. After visually checking the design proposals using AR, the user can select the design they like best from the multiple proposals.

[0523] The selected design is then sent back to the server, where it is stored in a database in a format suitable for the manufacturing process and output as CAD data if necessary. This process allows users to easily specify, compare, and select the design of their desired mobile device. Augmented reality technology also allows users to visually confirm the design in the real world, giving them peace of mind in their selection.

[0524] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0525] Step 1:

[0526] Using a smartphone or smart glasses, a user launches the application and inputs their design requirements for a mobile device. The input requirements include specific information such as screen size, color, display type, and camera. For example, a user might input "6-inch screen size, red color, curved display, dual camera."

[0527] Step 2:

[0528] The terminal sends the design request entered by the user to the server using an HTTP POST request. At this time, the request data is sent in JSON format. The input is the user's design request data, and the output is a send request to the server.

[0529] Step 3:

[0530] The server receives the design request and analyzes it. Specifically, it parses the received JSON data and extracts each element (screen size, color, display type, camera, etc.). At this point, the input is the design request data, and the output is the analyzed request data.

[0531] Step 4:

[0532] The server uses a generative AI model to generate multiple design proposals based on the analyzed request data. The generative algorithm generates multiple 3D models and related prompts based on the input request elements. Specific data operations include 3D graphics generation and text generation. The input is the analyzed request data, and the output is multiple design proposals.

[0533] Step 5:

[0534] The server sends the generated design proposal back to the terminal. At this time, it sends the design proposal data to the terminal using an HTTP response. The input is the generated design proposal, and the output is the response data from the server to the terminal.

[0535] Step 6:

[0536] The device displays the received design proposal using augmented reality technology. Specifically, the generated 3D model is overlaid on the display of a smartphone or smart glasses. The user can visually check the design proposal in a real-world environment. The input is the design proposal data received from the server, and the output is the design proposal displayed in augmented reality.

[0537] Step 7:

[0538] The user selects their favorite design from multiple designs and sends that selection to the server via their device. Specifically, data including the ID of the selected design is sent to the server again via an HTTP POST request. The input is the design selected by the user, and the output is the data sent to the server.

[0539] Step 8:

[0540] The server stores the design proposal selected by the user in a database and outputs it in a format applicable to the manufacturing process (e.g., CAD data). At this time, the design proposal is converted into the required format and stored in the database. The input is the design proposal data selected by the user, and the output is data for the manufacturing process. Through this series of processes, a customized mobile device design based on the user's specific requests is efficiently generated and provided.

[0541] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0542] This invention is a system in which a user inputs design requirements on a mobile device, a server automatically generates multiple design proposals based on those requirements, and an emotion engine presents, selects, saves, and outputs the optimal design proposal based on the user's emotions. This system streamlines the process by which users select designs that reflect their intentions, including their emotions, and applies it to the manufacturing process. The system's main components are the device, server, emotion engine, and user.

[0543] System Overview

[0544] Input your design requirements

[0545] Users use a device application or web page to input their desired mobile device design requirements, such as screen size, color, display type, and camera specifications. This information is then organized within the device.

[0546] Sending and analyzing requests

[0547] The device sends the input design request to the server, which analyzes the request and uses an AI algorithm to generate multiple design proposals, including 3D models and various specifications.

[0548] Emotion recognition by emotion engine

[0549] The emotion engine analyzes the user's facial expressions and voice to recognize their emotions in real time. As the user browses through design proposals, the engine collects and analyzes their emotional data in real time, and prioritizes the presentation of designs that elicit a favorable response.

[0550] Presentation and selection of design proposals

[0551] The generated design proposals are sent from the server to the device and presented to the user. The emotion engine provides feedback on the emotion recognition results, and the most suitable design proposals for the user are displayed in sequence. The user can view these design proposals and select the one they like best.

[0552] Notification and saving of selection results

[0553] The selected design is then sent back to the server, which stores it in a database and outputs it in a format that can be used for the manufacturing process, often in CAD format.

[0554] Specific examples

[0555] 1. Enter your design requirements

[0556] The user opens the app and enters their preferences, such as "screen size 6 inches, color red, curved display, dual-lens camera."

[0557] 2. Sending and analyzing your request

[0558] Once the request is confirmed, the device sends it to the server, which receives the data.

[0559] 3. Generate design proposals

[0560] The server uses an AI algorithm to generate three design proposals, such as a 3D model of a 6-inch mobile device with a red curved display.

[0561] 4. Emotion Recognition by Emotion Engine

[0562] The emotion engine analyzes the user's facial expressions and voice in real time and prioritizes design ideas that will elicit a favorable response based on their emotions. For example, if a user smiles when looking at a particular design idea, that design idea will be placed at a high priority position.

[0563] 5. Presentation and selection of design proposals

[0564] Users compare the presented design proposals and select the one they like best, taking into account feedback from the emotion engine.

[0565] 6. Notification and saving of selection results

[0566] The terminal sends the selection results to the server, which stores the design proposal in a database and outputs it in a format that can be used in the manufacturing process, such as CAD data.

[0567] In this way, the system of the present invention can automatically generate and present optimal mobile device design proposals based on the user's detailed design requests and emotional feedback, and quickly apply them to the manufacturing process.

[0568] The processing flow will be explained below.

[0569] Step 1:

[0570] The user inputs the design requirements.

[0571] Users open an application or web page on their device and enter the desired mobile device features into a design request form, such as screen size, color, display type, and camera specifications.

[0572] Step 2:

[0573] The terminal transmits the request to the server.

[0574] When the user completes the input and presses the submit button, the device sends the request to the server. An HTTP POST request is used to send the design request data to the server in the appropriate format.

[0575] Step 3:

[0576] The server analyzes the request and generates design proposals using an AI algorithm.

[0577] The server analyzes the design request data received from the terminal.

[0578] The AI ​​algorithm then generates multiple design options based on the user's requirements, creating suitable design candidates based on the user's specified screen size, color, and display type, and generating 3D models and specifications for each design.

[0579] Step 4:

[0580] The server sends the generated design proposal to the terminal and presents it to the user.

[0581] The generated design proposal is sent from the server to the device, where it is displayed to the user, who can then rotate the 3D model and check its detailed specifications.

[0582] Step 5:

[0583] The emotion engine analyzes the user's facial expressions and voice to recognize emotions.

[0584] While the user is browsing the design proposals, the device's camera and microphone are used to collect the user's facial expressions and voice. This data is sent in real time to the emotion engine, which then analyzes the user's emotions. For example, if the user is smiling, it is determined that the design proposal is well-received.

[0585] Step 6:

[0586] The server changes the display order of design proposals based on the analysis results of the emotion engine.

[0587] The analysis results of the emotion engine are fed back to the server, which then adjusts the display to prioritize design proposals that have received the most positive user feedback. When the user next views design proposals, the highly favorable designs will be displayed at the top.

[0588] Step 7:

[0589] The user selects a design idea.

[0590] The user selects the design they like best from multiple options, taking into account feedback from the emotion engine. The selection is made through the device's UI by clicking a selection button.

[0591] Step 8:

[0592] The terminal notifies the server of the selection result.

[0593] After the user selects a design, the device sends the selection back to the server, including the ID of the selected design.

[0594] Step 9:

[0595] The server stores and outputs the selected designs.

[0596] The server stores the data of the selected design in a database and outputs the design data in a format that can be used in the manufacturing process, for example, by exporting it as a CAD file and feeding it to the manufacturing line.

[0597] In this way, the system of the present invention can automatically generate and present optimal mobile device design proposals based on the user's detailed design requests and emotional feedback, and quickly apply them to the manufacturing process.

[0598] Example 2

[0599] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0600] Conventional design proposal generation systems for mobile information terminals have the problem of not being able to take the user's emotions into consideration during the process of inputting the user's design requirements and the selection process. This makes it difficult to present optimal design proposals that will increase user satisfaction. The present invention aims to solve this problem and provide a system that allows users to efficiently select designs that reflect their intentions, including their emotions.

[0601] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0602] In this invention, the server includes: a means for a user to input design requests to a mobile information device; a means for transmitting the design requests from the mobile information device to an information processing device; a means for the information processing device to analyze the received design requests and generate multiple design proposals using a generative AI model; a means for an emotion recognition engine to analyze the user's facial expressions and voice to generate emotion data; a means for collecting emotion data while the user is viewing the design proposals and preferentially presenting design proposals that show a favorable reaction; a means for the information processing device to present the generated design proposals to the user; a means for the user to select from the multiple design proposals; and a means for the information processing device to save the selected design proposal and output it in a format usable for the production process. This enables the presentation and selection of optimal design proposals that reflect the user's emotions.

[0603] A "user" is a person who operates this system and inputs design requests for a mobile information terminal.

[0604] A "personal digital assistant" is a portable electronic device such as a smartphone or tablet.

[0605] "Design requests" are specific requests regarding the appearance and functions of the mobile information terminal desired by the user.

[0606] An "information processing device" is a device such as a computer or server that analyzes and processes data.

[0607] A "generative AI model" is an algorithm or program that uses artificial intelligence technology to generate design ideas.

[0608] "Design proposals" are multiple design proposals generated based on the user's design requests.

[0609] An "emotion recognition engine" is a technology that recognizes a user's emotions through the analysis of facial expressions and voice.

[0610] "Emotion data" refers to data relating to a user's emotions that is collected and analyzed by an emotion recognition engine.

[0611] The "production process" is a series of steps for manufacturing an actual product based on the selected design proposal.

[0612] The "output format" is a data format generated by an information processing device for applying a design proposal to actual product manufacturing.

[0613] This invention is a system that generates and presents optimal design proposals for mobile information terminals based on the user's design requests and emotional feedback. This system consists of a terminal, a server, an emotion recognition engine, and a generative AI model.

[0614] First, a user inputs their desired design requirements for a mobile information device using an application on the device or a web page. These requirements include screen size, color, display type, camera specifications, etc. Specifically, a user might input a preference such as "6-inch screen size, red color, curved display, dual-lens camera."

[0615] The device formats the input design requests and sends them to the server. The device converts the data into JSON format and sends it to the server via an HTTP request. The hardware used for this is a smartphone or tablet.

[0616] The server analyzes the received design requests and generates multiple design proposals using a generative AI model (e.g., TensorFlow or PyTorch). This allows the server to generate a 3D model of, for example, a 6-inch mobile information terminal with a red curved display. The generated design proposals include the 3D model and various specifications.

[0617] The emotion recognition engine collects the user's facial expressions and voice in real time and analyzes their emotions. Examples of emotion recognition engines that are used include Microsoft Azure Cognitive Services and Google Cloud AI. While the user is browsing design proposals, facial expressions and voice are collected via the camera and microphone, and the system analyzes them in real time.

[0618] The emotion recognition engine uses the collected data to provide feedback on design proposals that elicit favorable reactions, and the server then uses this information to select which design proposals to prioritize. A specific example of this behavior is when a user smiles upon seeing a particular design proposal. In this case, the corresponding design proposal is placed at a high priority position.

[0619] The generated design proposals are sent from the server to the device and presented to the user. The user compares the multiple design proposals presented and selects the design proposal they like best based on feedback from the emotion recognition engine. At this time, the device sends the selection information back to the server.

[0620] The server stores the selected design in a database (e.g., MySQL or MongoDB). The server then outputs the selected design in a format that can be used for the manufacturing process, such as CAD data. A specific example of this is when the selection information is sent to the server in JSON format, and the server stores the data in a database and exports it as a CAD file.

[0621] An example of a prompt sentence might be:

[0622] "Generate a design for a 6-inch, red, curved display, dual-lens camera phone."

[0623] "Please give priority to design ideas that make users smile."

[0624] This system makes it possible to quickly and accurately select and present optimal design proposals based on the user's design requests and emotional feedback, and then apply them to the manufacturing process.

[0625] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0626] Step 1: Enter your design requirements

[0627] The user launches an application or web page on the device and inputs the design requirements for the mobile information device they desire. Specifically, they input screen size, color, display type, camera specifications, etc. The input design requirements are temporarily saved on the device. In this case, the input data might be "6-inch screen size, red color, curved display, dual-lens camera," and the output is organized design requirements information.

[0628] Step 2: Submit your request

[0629] The terminal formats the input design request and sends it to the server. Specifically, it converts the design request into JSON format and sends it to the server using an HTTP request. The input data is the design request specified by the user, and the output is the formatted design request data sent to the server.

[0630] Step 3: Analyze design requirements

[0631] The server analyzes the received design requests. This analysis extracts important elements for generating the actual design. The input data is the design request data sent from the device, and the output is the analyzed element extraction results. Then, based on the analysis results, the data is input into the generative AI model.

[0632] Step 4: Generate design ideas

[0633] The server uses a generative AI model (e.g., TensorFlow or PyTorch) to generate multiple design proposals. In this process, the AI ​​algorithm creates multiple design proposals that meet the design requirements. The input data is the analyzed design requirement data, and the output is a set of design proposals that includes 3D models and specifications of multiple mobile devices.

[0634] Step 5: Collect emotion recognition data

[0635] The emotion recognition engine collects the user's facial expressions and voice in real time. It uses a camera and microphone to collect emotion data while the user is viewing the design proposal. The input data is the user's facial expressions and voice, and the output is the emotion data captured in real time.

[0636] Step 6: Analyze the sentiment data

[0637] The emotion recognition engine analyzes the collected emotion data and recognizes the user's emotional state, thereby determining which design proposals the user has a favorable reaction to. The input data is raw emotion data acquired in real time, and the output is the analyzed emotion recognition results.

[0638] Step 7: Present your design proposal

[0639] The server then sends the optimal design proposal to the device based on the multiple design proposals generated and feedback information from the emotion recognition engine.The design proposals are presented to the user on the device and displayed in the optimal order based on the emotion recognition results.The input data are the generated design proposals and the emotion analysis results, and the output is the optimal design proposal presented to the user.

[0640] Step 8: Select a design option

[0641] The user compares the presented design proposals and selects the one they like best. The input data are the design proposals presented to the user, and the output is the design proposal selected by the user.

[0642] Step 9: Communicate and save your selections

[0643] The terminal sends the design proposal selected by the user to the server. The server stores the selected design proposal in a database. The server then outputs the selected design proposal in a format that can be used for the manufacturing process (e.g., CAD data format). The input data is the user-selected design proposal, and the output is the stored design proposal data and data in the output format that is applied to the manufacturing process.

[0644] In this way, the system's processing flow is structured so that the user first inputs their design requirements, then evaluates the design proposals generated based on those requirements using emotional data, and then selects, saves, and outputs the most suitable proposal.

[0645] (Application example 2)

[0646] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0647] In the conventional design process for mobile devices and their accessories, it is difficult for users to quickly obtain designs that reflect their preferences and intentions. Furthermore, there is no way to reflect the user's emotions and reactions in the design selection, which means it takes time and effort to find the optimal design. Furthermore, the process is one-way, lacking a mechanism for providing feedback on the user's emotional responses. There is a need to build a system that can solve these problems and quickly provide optimal design proposals tailored to the user.

[0648] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: a means for a user to input design requests for a mobile device; a means for transmitting the design requests from the mobile device to the server; a means for the server to analyze the received design requests and generate multiple design proposals using an artificial intelligence algorithm; a means for the server to present the generated design proposals to the user; a means for recognizing the user's emotions using an emotion engine and presenting the optimal design proposal based on the emotion; a means for the user to select from the multiple design proposals; and a means for the server to save the selected design proposal and output it in a format usable for the manufacturing process. This makes it possible to quickly generate and present design proposals that reflect the user's individual requests and emotions, and to select and save the optimal design proposal.

[0649] A "mobile terminal" is an electronic device that a user holds in his or her hand and that is used for communication and information processing.

[0650] "Design requests" refers to information that indicates the detailed specifications and wishes of the design desired by the user.

[0651] A "server" is a computer system that receives, analyzes, stores, and transmits data over a network.

[0652] An "artificial intelligence algorithm" is a computational procedure that uses techniques such as machine learning and deep learning to analyze data and perform specific tasks.

[0653] "Design proposals" refer to multiple proposed models generated based on design requests.

[0654] The "emotion engine" is a technology that analyzes the user's facial expressions and voice and recognizes their emotions in real time.

[0655] A "three-dimensional model" is a computer-generated three-dimensional model that reproduces the shape and structure of a three-dimensional space.

[0656] A "manufacturing process" is a series of steps that actually create a product based on a designed product.

[0657] This invention is a system in which a user inputs design requests on a mobile device, and a server automatically generates multiple design proposals based on the requests, and presents, selects, saves, and outputs the most suitable design proposal based on the user's emotions through an emotion engine.

[0658] System Overview

[0659] 1. Enter your design requirements

[0660] Using the device's application, users can input their desired design for the mobile device, including details such as color, material, and print pattern.

[0661] 2. Sending and analyzing your request

[0662] The device sends the input design request to the server, which analyzes the received request and generates multiple design proposals using a generative AI model.

[0663] 3. Generate design proposals

[0664] The server uses the generative AI model to generate multiple design proposals based on the user's requests, including 3D models and various specifications.

[0665] 4. Emotion Recognition by Emotion Engine

[0666] The emotion engine analyzes the user's facial expressions and voice to recognize their emotions in real time. As the user browses through design proposals, it collects and analyzes their emotional data and prioritizes the presentation of designs that evoke a positive response.

[0667] 5. Presentation and selection of design proposals

[0668] The design proposals generated by the server are sent to the device, which then presents them to the user. Based on the results of the emotion engine, the most suitable design proposals are displayed in sequence. The user can view these design proposals and select the one they like best.

[0669] 6. Notification and saving of selection results

[0670] The selected design is sent back to the server, which stores it in a database. The final design is output in a format that can be used for the manufacturing process (e.g., CAD data format).

[0671] Program processing overview

[0672] The server uses a generative AI model to generate multiple design proposals based on the input design requirements. This process includes elements such as color, material, and shape, and generates a three-dimensional model based on each element. Next, an emotion engine analyzes the user's facial expressions and voice in real time to recognize their emotions. Design proposals that elicit a favorable response are presented to the user preferentially. The user selects the most suitable design proposal, and the result is sent to the server and stored in a database.

[0673] Specific examples

[0674] The user launches the application and inputs their request, such as "a blue, silicone, floral smartphone case." This request is sent to the server, which uses a generative AI model to generate three design proposals. The generated design proposals are presented to the user through the application, and an emotion engine analyzes the user's facial expressions, with the design proposal that elicits the most favorable reaction being displayed preferentially. The user selects the most suitable design proposal, and the result is saved on the server.

[0675] Example prompts to input to the generative AI model

[0676] Prompt: "Generate design ideas based on the design requirements for a blue, silicone, floral smartphone case."

[0677] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0678] Step 1:

[0679] The user inputs their design requirements using a mobile device. Specifically, they launch an application and input detailed specifications such as the desired color, material, and print pattern. This information is temporarily stored in the device.

[0680] input:

[0681] User design requests (e.g., blue, silicone, floral smartphone case)

[0682] output:

[0683] Organized design request data

[0684] Step 2:

[0685] The device sends the input design request to the server using a communication protocol such as an HTTP request.

[0686] input:

[0687] Organized design request data

[0688] output:

[0689] Design request data sent to the server

[0690] Step 3:

[0691] The server analyzes the design requests received and generates design proposals using a generative AI model. Specifically, it generates a 3D model and various specifications based on the user's requests.

[0692] input:

[0693] Design request data received by the server

[0694] output:

[0695] Multiple design proposals (3D models, specifications)

[0696] Step 4:

[0697] The server sends the generated design proposal to the terminal, which then presents it to the user, displaying it on the screen so that the user can view it.

[0698] input:

[0699] Multiple design ideas

[0700] output:

[0701] Design proposals displayed on the screen

[0702] Step 5:

[0703] The emotion engine analyzes the user's facial expressions and voice in real time to recognize the user's emotions. The camera and microphone capture the user's facial expressions and voice, and analyzes the data to determine the user's emotions.

[0704] input:

[0705] User's facial expression data, voice data

[0706] output:

[0707] User sentiment data (e.g., positive response, indifference, etc.)

[0708] Step 6:

[0709] The server receives feedback based on the emotion engine and presents to the user design proposals that the user responded favorably to with priority.

[0710] input:

[0711] User emotion data

[0712] Multiple design ideas

[0713] output:

[0714] The best design ideas are prioritized

[0715] Step 7:

[0716] The user selects the best design, the device sends the result back to the server, and the user taps the screen to confirm the selection.

[0717] input:

[0718] The best design ideas are prioritized

[0719] output:

[0720] Selection result data

[0721] Step 8:

[0722] The server stores the selected design proposals in a database and outputs the final design proposal in a format that can be used for the manufacturing process (e.g., CAD data format).

[0723] input:

[0724] Selection result data

[0725] output:

[0726] Saved design data, data for manufacturing processes (e.g. CAD data)

[0727] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0728] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0729] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0730] [Third embodiment]

[0731] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0732] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0733] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0734] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0735] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0736] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0737] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0738] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0739] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0740] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0741] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0742] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0743] This invention provides a system in which a user inputs design requirements on a mobile device, and a server automatically generates multiple design proposals based on those requirements, and presents, selects, saves, and outputs them. This system consists of three main components: the device, the server, and the user.

[0744] Processing flow

[0745] The user inputs their design requirements

[0746] Users input their desired mobile device features using a dedicated application on their device or a webpage, including specific design requirements such as a 6-inch screen size, red color, curved display, and dual-lens camera.

[0747] The device sends the request to the server

[0748] After the user has finished entering their design requirements, the device sends them to the server via a communication method such as an HTTP POST request.

[0749] The server analyzes the request and generates design proposals using an AI algorithm.

[0750] The server receives and analyzes the design requests sent from the device. Based on the analyzed requests, the server uses an AI algorithm to generate multiple design proposals. These proposals include a 3D model as a stereoscopic image and various specifications.

[0751] The server presents the generated design to the user.

[0752] The generated design proposals are sent from the server to the terminal and presented to the user, who can then freely browse these design proposals.

[0753] The user selects a design option

[0754] The user selects the design they like from the multiple designs presented, and the selected design is sent back to the server from the device.

[0755] The server saves and outputs the selected design

[0756] The selected design proposal is stored in a database by the server, and then output in a format (e.g., CAD data) suitable for the manufacturing process. This format can be used directly for manufacturing.

[0757] Specific examples

[0758] 1. The user enters their design requirements

[0759] Users launch the application and input their preferences, such as "screen size 6 inches, color red, curved display, dual lens camera."

[0760] 2. The device sends the request to the server

[0761] Once the request is finalized, the device sends it to the server, which receives the data.

[0762] 3. The server analyzes the request and generates a design proposal

[0763] The server analyzes the received design request and uses AI algorithms to generate multiple design proposals, such as a 3D model of a 6-inch mobile device with a red curved display.

[0764] 4. The server presents the generated design to the user.

[0765] The generated design proposals are sent from the server to the terminal and presented to the user, who can then check them in detail.

[0766] 5. User selects design options

[0767] The user selects the design that best suits their needs from the multiple design proposals presented.

[0768] 6. The device notifies the server of the selection result

[0769] The terminal notifies the server of the user's selection, which is then stored by the server.

[0770] 7. The server saves and outputs the selected design

[0771] The selected design is stored in a database by the server and output in a format that can be used in the manufacturing process, for example as a CAD file, and sent to the manufacturing line.

[0772] In this way, the system of the present invention automatically generates and presents optimal mobile device design proposals based on the user's detailed design requests, enabling them to be quickly applied to the manufacturing process.

[0773] The processing flow will be explained below.

[0774] Step 1:

[0775] The user inputs the design requirements.

[0776] Users open an application or web page on their device and fill out a design request form detailing the features of their desired mobile device, including screen size, color, display type, camera specifications, and other details.

[0777] Step 2:

[0778] The terminal transmits the request to the server.

[0779] When the user completes the input and presses the submit button, the device sends the request to the server. The HTTP POST request is used to format the design request data and send it to the server.

[0780] Step 3:

[0781] The server analyzes the request and generates design proposals using an AI algorithm.

[0782] The server analyzes the design request data received from the terminal.

[0783] Next, the AI ​​algorithm generates multiple design options based on your requirements, such as creating suitable design candidates based on inputs of screen size, color, and display type, and also generates 3D models and spec sheets during the process.

[0784] Step 4:

[0785] The server presents the generated design proposal to the user.

[0786] The server then sends the resulting design proposal back to the device, including a 3D view and specific specifications.

[0787] The device receives this data and displays a preview of the proposed design to the user.

[0788] Step 5:

[0789] The user selects a design idea.

[0790] The user reviews the presented design proposals and selects the one they like best. The selection is made through the device's UI by clicking a selection button.

[0791] Step 6:

[0792] The terminal notifies the server of the selection result.

[0793] After the user selects a design, the device sends the selection results back to the server, including the ID of the selected design.

[0794] Step 7:

[0795] The server stores and outputs the selected designs.

[0796] The server stores the data of the selected design proposal in a database.

[0797] Next, the design data is output in a format that can be used in the manufacturing process, for example, exported as a CAD file and fed to the manufacturing line.

[0798] Example 1

[0799] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0800] The conventional design process for mobile devices makes it difficult to reflect specific user requests, making it difficult to increase user satisfaction. Furthermore, the process from generating design proposals to manufacturing takes time and effort, making it impossible to quickly respond to user requests. Therefore, it is necessary to use an AI-based system to efficiently generate design proposals and reflect user requests.

[0801] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0802] In this invention, the server includes means for analyzing design requirements using a generative AI model, means for presenting the generated design proposals through a user interface, and means for saving the selected design proposal and outputting it in a format usable for the manufacturing process, thereby enabling the rapid generation of optimal design proposals based on the specific requirements of the user, and the display, selection, and output of the optimal design proposals.

[0803] "User" means an individual or legal entity that operates the system and inputs their design requests.

[0804] "Mobile device" refers to a portable electronic device such as a smartphone or tablet.

[0805] "Design requests" refer to requests regarding the specifications and features of the mobile device desired by the user.

[0806] "Server" means a central computer system that receives and analyzes design requests, and generates, provides, and stores design proposals.

[0807] A "generative AI model" is an algorithm or program that uses artificial intelligence technology to generate design ideas.

[0808] "Design proposal" refers to a proposal for the specific design and specifications of a mobile device created by a generative AI model.

[0809] "User interface" refers to the screen display and operating means that users use to operate the system.

[0810] A "3D model" is a digital model that shows the three-dimensional design of a mobile device.

[0811] "Manufacturing process" refers to the series of steps involved in producing an actual product based on the design proposal for a mobile device.

[0812] "Output" refers to providing the selected design proposal to an external party in a format that can be applied to manufacturing.

[0813] This system allows users to input their design requirements on a mobile device, and the server automatically generates multiple design proposals based on those requirements, and then presents, selects, saves, and outputs them. This system consists of three main components: the device, the server, and the user.

[0814] Users input their design requirements through dedicated applications or web pages. These applications and web pages are developed using frameworks such as React.js or Flutter to provide the user interface. Users can input specific characteristics of their desired mobile device, such as screen size, color, display shape, and camera type. For example, a user might input a request such as "6-inch screen size, red color, curved display, dual-lens camera."

[0815] The device converts the input design requests into JSON format data and sends it to the server using an HTTP POST request. Specifically, it uses a JavaScript library to collect data and generate an HTTP request.

[0816] The server, built with Node.js or Python, receives the transmitted data. The received data is first stored in a database (e.g., PostgreSQL). The server then generates design proposals using a generative AI model (e.g., OpenAI's GPT-4 is an example of a generative AI model). The server generates a prompt and sends it to the AI ​​model. An example prompt is, "Generate a 3D design proposal for a smartphone with a 6-inch red curved display." The generated design proposal includes a 3D model (e.g., using Blender or Three.js) and multiple specification details.

[0817] The server sends the generated design proposals to the terminal and presents them to the user. The terminal displays the received design proposals on the user interface, allowing the user to check and manipulate the 3D model. The user selects the desired design from the multiple presented proposals and performs the selection operation.

[0818] The terminal notifies the server of the selection results, which are then permanently stored in a database. The selected design proposal is output in a format that can be used in the manufacturing process (e.g., CAD data). This output process uses a Python script to generate a file in, for example, a SolidWorks format. This file is then sent directly to the manufacturing line and used to actually manufacture the product.

[0819] As a result, the system of the present invention can automatically generate and present optimal mobile device design proposals based on the user's detailed design requests, and quickly apply them to the manufacturing process.

[0820] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0821] Step 1:

[0822] The user inputs their design requirements

[0823] Users enter their design requirements using a dedicated application or web page. Specifically, they enter items such as "6-inch screen size," "red color," "curved display," and "dual-lens camera" on a UI built with React.js or Flutter, and then click the submit button. The entered data (design requirements) is processed as internal data in JSON format.

[0824] Step 2:

[0825] The device sends the request to the server

[0826] The terminal collects the design requests entered by the user and converts them into JSON format. This JSON data is sent to the server using an HTTP POST request. Specifically, a data collection library (e.g., Axios) is used to construct the request and send it to an endpoint (e.g., / design_request). The input is the user's design request, and the output is the request sent to the server.

[0827] Step 3:

[0828] The server analyzes the request and generates design proposals using a generative AI model.

[0829] The server is built using Node.js or Python and receives HTTP requests. The received JSON data is first stored in a database (such as PostgreSQL). The data is then analyzed to send a prompt to a generative AI model (e.g., OpenAI's GPT-4). An example prompt might be "Please design a mobile device with a 6-inch red curved display." This allows the AI ​​to generate multiple 3D designs and specifications. The input is the user's design request, and the output is the generated design proposal.

[0830] Step 4:

[0831] The server presents the generated design to the user.

[0832] The generated design proposal is sent from the server to the device. Specifically, a data packet containing the generated 3D model and specification information is sent to the device in JSON format. The device receives this data and displays it in a React.js or Flutter UI. The user can rotate the 3D model and check detailed specifications. The input is the generated design proposal, and the output is the proposed display on the UI.

[0833] Step 5:

[0834] The user selects a design option

[0835] The user selects the most desirable design from the multiple design proposals presented. The user makes the selection by clicking a button on the UI or by selecting an item from a list. This selection information is saved internally on the device. The input is the user's selection operation, and the output is the data of the selected design proposal.

[0836] Step 6:

[0837] The device notifies the server of the selection result

[0838] The device converts the data of the design proposal selected by the user back into JSON format and sends it to the server using an HTTP POST request. The server receives this request and permanently stores the selection information in a database. The input is the data of the selected design proposal, and the output is a notification to the server.

[0839] Step 7:

[0840] The server saves and outputs the selected design

[0841] The server stores the selected design in a database and converts it into a format that can be used for the manufacturing process (e.g., CAD data). This conversion is performed using a Python script, which generates a file in, for example, SolidWorks format. This file is then sent directly to the manufacturing line. The input is the data of the selected design, and the output is the CAD data for manufacturing.

[0842] (Application example 1)

[0843] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0844] Conventional mobile device design decision-making processes have difficulty reflecting specific user needs. Furthermore, because it is not easy for users to compare multiple design proposals, there are issues that prevent users from increasing their satisfaction. Furthermore, the lack of a way to actually visually check the generated design proposals often leaves users feeling uneasy about their final selection.

[0845] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0846] In this invention, the server includes: a means for a user to input design requests for a mobile device; a means for transmitting the design requests for the mobile device to the server; a means for the server to analyze the received design requests and generate multiple design proposals using a generation algorithm; a means for the server to present the generated design proposals to the user; a means for the user to confirm the generated design proposals using augmented reality technology; a means for the user to select from the multiple design proposals; and a means for the server to save the selected design proposal and output it in a format usable for the manufacturing process. This allows the user to easily generate, compare, and select design proposals for the mobile device based on specific design requests, and by visually confirming them using augmented reality technology, the user can feel confident about the final design selection.

[0847] "Mobile device design requirements" refer to specific requirements regarding the appearance and functionality of the mobile device desired by the user.

[0848] "Server" refers to a computer system that receives design requests from users, analyzes them, and generates, saves, and outputs design proposals.

[0849] "Generation algorithm" refers to a program or calculation method for automatically generating multiple design proposals based on the design requirements of a mobile device.

[0850] "Augmented reality technology" refers to technology that displays computer-generated information overlaid on real-world information.

[0851] "3D model" refers to a digital model of an object represented in three-dimensional space.

[0852] A "prompt sentence" refers to a specific instruction sentence that is input to a generative AI model.

[0853] "Design proposals" refer to multiple candidate mobile device designs generated based on user requests.

[0854] "Format usable for the manufacturing process" refers to a data format (e.g., CAD data) that is required to actually manufacture the selected design.

[0855] "Means for presenting to the user" refers to means for displaying the generated design proposal to the user.

[0856] This invention provides a system in which a user inputs design requirements on a mobile device, and a server automatically generates multiple design proposals based on those requirements, and presents, selects, saves, and outputs them. This system consists of three main components: a mobile device (a user device such as a smartphone or smart glasses), a server, and a user.

[0857] 1. Configuration and Operation Overview

[0858] User Device:

[0859] Users use a smartphone or smart glasses to input their design requests into the application.

[0860] The configured request is sent to the server via an HTTP POST request.

[0861] server:

[0862] The server analyzes the received design request using a generative algorithm.

[0863] The generative algorithm uses an artificial intelligence (AI) model to generate multiple design alternatives based on the customer's requirements.

[0864] The generated design proposal includes a 3D model of the mobile device and a prompt statement.

[0865] AR technology:

[0866] The computationally generated design proposals are displayed in augmented reality on the user's device using AR technology, allowing for real-world visual confirmation.

[0867] 2. Specific Examples

[0868] Suppose a user launches an application on their smartphone and inputs specific design requirements, such as "6-inch screen size, red color, curved display, dual cameras." These requirements are then sent to the server.

[0869] The server receives the request and uses a generative algorithm to generate multiple design alternatives, such as a 3D model of a 6-inch red curved mobile device, along with a corresponding prompt. An example prompt is as follows:

[0870] Generate a mobile design that meets the following requirements:

[0871] Screen size: 6 inches

[0872] Color: Red

[0873] Display Type: Curved Display

[0874] Camera: Dual camera

[0875] Generate multiple design proposals and return them in JSON format.

[0876] The generated design proposals are then sent back to the user's device, where they can be viewed using AR on a smartphone or smart glasses. After visually checking the design proposals using AR, the user can select the design they like best from the multiple proposals.

[0877] The selected design is then sent back to the server, where it is stored in a database in a format suitable for the manufacturing process and output as CAD data if necessary. This process allows users to easily specify, compare, and select the design of their desired mobile device. Augmented reality technology also allows users to visually confirm the design in the real world, giving them peace of mind in their selection.

[0878] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0879] Step 1:

[0880] Using a smartphone or smart glasses, a user launches the application and inputs their design requirements for a mobile device. The input requirements include specific information such as screen size, color, display type, and camera. For example, a user might input "6-inch screen size, red color, curved display, dual camera."

[0881] Step 2:

[0882] The terminal sends the design request entered by the user to the server using an HTTP POST request. At this time, the request data is sent in JSON format. The input is the user's design request data, and the output is a send request to the server.

[0883] Step 3:

[0884] The server receives the design request and analyzes it. Specifically, it parses the received JSON data and extracts each element (screen size, color, display type, camera, etc.). At this point, the input is the design request data, and the output is the analyzed request data.

[0885] Step 4:

[0886] The server uses a generative AI model to generate multiple design proposals based on the analyzed request data. The generative algorithm generates multiple 3D models and related prompts based on the input request elements. Specific data operations include 3D graphics generation and text generation. The input is the analyzed request data, and the output is multiple design proposals.

[0887] Step 5:

[0888] The server sends the generated design proposal back to the terminal. At this time, it sends the design proposal data to the terminal using an HTTP response. The input is the generated design proposal, and the output is the response data from the server to the terminal.

[0889] Step 6:

[0890] The device displays the received design proposal using augmented reality technology. Specifically, the generated 3D model is overlaid on the display of a smartphone or smart glasses. The user can visually check the design proposal in a real-world environment. The input is the design proposal data received from the server, and the output is the design proposal displayed in augmented reality.

[0891] Step 7:

[0892] The user selects their favorite design from multiple designs and sends that selection to the server via their device. Specifically, data including the ID of the selected design is sent to the server again via an HTTP POST request. The input is the design selected by the user, and the output is the data sent to the server.

[0893] Step 8:

[0894] The server stores the design proposal selected by the user in a database and outputs it in a format applicable to the manufacturing process (e.g., CAD data). At this time, the design proposal is converted into the required format and stored in the database. The input is the design proposal data selected by the user, and the output is data for the manufacturing process. Through this series of processes, a customized mobile device design based on the user's specific requests is efficiently generated and provided.

[0895] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0896] This invention is a system in which a user inputs design requirements on a mobile device, a server automatically generates multiple design proposals based on those requirements, and an emotion engine presents, selects, saves, and outputs the optimal design proposal based on the user's emotions. This system streamlines the process by which users select designs that reflect their intentions, including their emotions, and applies it to the manufacturing process. The system's main components are the device, server, emotion engine, and user.

[0897] System Overview

[0898] Input your design requirements

[0899] Users use a device application or web page to input their desired mobile device design requirements, such as screen size, color, display type, and camera specifications. This information is then organized within the device.

[0900] Sending and analyzing requests

[0901] The device sends the input design request to the server, which analyzes the request and uses an AI algorithm to generate multiple design proposals, including 3D models and various specifications.

[0902] Emotion recognition by emotion engine

[0903] The emotion engine analyzes the user's facial expressions and voice to recognize their emotions in real time. As the user browses through design proposals, the engine collects and analyzes their emotional data in real time, and prioritizes the presentation of designs that elicit a favorable response.

[0904] Presentation and selection of design proposals

[0905] The generated design proposals are sent from the server to the device and presented to the user. The emotion engine provides feedback on the emotion recognition results, and the most suitable design proposals for the user are displayed in sequence. The user can view these design proposals and select the one they like best.

[0906] Notification and saving of selection results

[0907] The selected design is then sent back to the server, which stores it in a database and outputs it in a format that can be used for the manufacturing process, often in CAD format.

[0908] Specific examples

[0909] 1. Enter your design requirements

[0910] The user opens the app and enters their preferences, such as "screen size 6 inches, color red, curved display, dual-lens camera."

[0911] 2. Sending and analyzing your request

[0912] Once the request is confirmed, the device sends it to the server, which receives the data.

[0913] 3. Generate design proposals

[0914] The server uses an AI algorithm to generate three design proposals, such as a 3D model of a 6-inch mobile device with a red curved display.

[0915] 4. Emotion Recognition by Emotion Engine

[0916] The emotion engine analyzes the user's facial expressions and voice in real time and prioritizes design ideas that will elicit a favorable response based on their emotions. For example, if a user smiles when looking at a particular design idea, that design idea will be placed at a high priority position.

[0917] 5. Presentation and selection of design proposals

[0918] Users compare the presented design proposals and select the one they like best, taking into account feedback from the emotion engine.

[0919] 6. Notification and saving of selection results

[0920] The terminal sends the selection results to the server, which stores the design proposal in a database and outputs it in a format that can be used in the manufacturing process, such as CAD data.

[0921] In this way, the system of the present invention can automatically generate and present optimal mobile device design proposals based on the user's detailed design requests and emotional feedback, and quickly apply them to the manufacturing process.

[0922] The processing flow will be explained below.

[0923] Step 1:

[0924] The user inputs the design requirements.

[0925] Users open an application or web page on their device and enter the desired mobile device features into a design request form, such as screen size, color, display type, and camera specifications.

[0926] Step 2:

[0927] The terminal transmits the request to the server.

[0928] When the user completes the input and presses the submit button, the device sends the request to the server. An HTTP POST request is used to send the design request data to the server in the appropriate format.

[0929] Step 3:

[0930] The server analyzes the request and generates design proposals using an AI algorithm.

[0931] The server analyzes the design request data received from the terminal.

[0932] The AI ​​algorithm then generates multiple design options based on the user's requirements, creating suitable design candidates based on the user's specified screen size, color, and display type, and generating 3D models and specifications for each design.

[0933] Step 4:

[0934] The server sends the generated design proposal to the terminal and presents it to the user.

[0935] The generated design proposal is sent from the server to the device, where it is displayed to the user, who can then rotate the 3D model and check its detailed specifications.

[0936] Step 5:

[0937] The emotion engine analyzes the user's facial expressions and voice to recognize emotions.

[0938] While the user is browsing the design proposals, the device's camera and microphone are used to collect the user's facial expressions and voice. This data is sent in real time to the emotion engine, which then analyzes the user's emotions. For example, if the user is smiling, it is determined that the design proposal is well-received.

[0939] Step 6:

[0940] The server changes the display order of design proposals based on the analysis results of the emotion engine.

[0941] The analysis results of the emotion engine are fed back to the server, which then adjusts the display to prioritize design proposals that have received the most positive user feedback. When the user next views design proposals, the highly favorable designs will be displayed at the top.

[0942] Step 7:

[0943] The user selects a design idea.

[0944] The user selects the design they like best from multiple options, taking into account feedback from the emotion engine. The selection is made through the device's UI by clicking a selection button.

[0945] Step 8:

[0946] The terminal notifies the server of the selection result.

[0947] After the user selects a design, the device sends the selection back to the server, including the ID of the selected design.

[0948] Step 9:

[0949] The server stores and outputs the selected designs.

[0950] The server stores the data of the selected design in a database and outputs the design data in a format that can be used in the manufacturing process, for example, by exporting it as a CAD file and feeding it to the manufacturing line.

[0951] In this way, the system of the present invention can automatically generate and present optimal mobile device design proposals based on the user's detailed design requests and emotional feedback, and quickly apply them to the manufacturing process.

[0952] Example 2

[0953] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0954] Conventional design proposal generation systems for mobile information terminals have the problem of not being able to take the user's emotions into consideration during the process of inputting the user's design requirements and the selection process. This makes it difficult to present optimal design proposals that will increase user satisfaction. The present invention aims to solve this problem and provide a system that allows users to efficiently select designs that reflect their intentions, including their emotions.

[0955] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0956] In this invention, the server includes: a means for a user to input design requests to a mobile information device; a means for transmitting the design requests from the mobile information device to an information processing device; a means for the information processing device to analyze the received design requests and generate multiple design proposals using a generative AI model; a means for an emotion recognition engine to analyze the user's facial expressions and voice to generate emotion data; a means for collecting emotion data while the user is viewing the design proposals and preferentially presenting design proposals that show a favorable reaction; a means for the information processing device to present the generated design proposals to the user; a means for the user to select from the multiple design proposals; and a means for the information processing device to save the selected design proposal and output it in a format usable for the production process. This enables the presentation and selection of optimal design proposals that reflect the user's emotions.

[0957] A "user" is a person who operates this system and inputs design requests for a mobile information terminal.

[0958] A "personal digital assistant" is a portable electronic device such as a smartphone or tablet.

[0959] "Design requests" are specific requests regarding the appearance and functions of the mobile information terminal desired by the user.

[0960] An "information processing device" is a device such as a computer or server that analyzes and processes data.

[0961] A "generative AI model" is an algorithm or program that uses artificial intelligence technology to generate design ideas.

[0962] "Design proposals" are multiple design proposals generated based on the user's design requests.

[0963] An "emotion recognition engine" is a technology that recognizes a user's emotions through the analysis of facial expressions and voice.

[0964] "Emotion data" refers to data relating to a user's emotions that is collected and analyzed by an emotion recognition engine.

[0965] The "production process" is a series of steps for manufacturing an actual product based on the selected design proposal.

[0966] The "output format" is a data format generated by an information processing device for applying a design proposal to actual product manufacturing.

[0967] This invention is a system that generates and presents optimal design proposals for mobile information terminals based on the user's design requests and emotional feedback. This system consists of a terminal, a server, an emotion recognition engine, and a generative AI model.

[0968] First, a user inputs their desired design requirements for a mobile information device using an application on the device or a web page. These requirements include screen size, color, display type, camera specifications, etc. Specifically, a user might input a preference such as "6-inch screen size, red color, curved display, dual-lens camera."

[0969] The device formats the input design requests and sends them to the server. The device converts the data into JSON format and sends it to the server via an HTTP request. The hardware used for this is a smartphone or tablet.

[0970] The server analyzes the received design requests and generates multiple design proposals using a generative AI model (e.g., TensorFlow or PyTorch). This allows the server to generate a 3D model of, for example, a 6-inch mobile information terminal with a red curved display. The generated design proposals include the 3D model and various specifications.

[0971] The emotion recognition engine collects the user's facial expressions and voice in real time and analyzes their emotions. Examples of emotion recognition engines that are used include Microsoft Azure Cognitive Services and Google Cloud AI. While the user is browsing design proposals, facial expressions and voice are collected via the camera and microphone, and the system analyzes them in real time.

[0972] The emotion recognition engine uses the collected data to provide feedback on design proposals that elicit favorable reactions, and the server then uses this information to select which design proposals to prioritize. A specific example of this behavior is when a user smiles upon seeing a particular design proposal. In this case, the corresponding design proposal is placed at a high priority position.

[0973] The generated design proposals are sent from the server to the device and presented to the user. The user compares the multiple design proposals presented and selects the design proposal they like best based on feedback from the emotion recognition engine. At this time, the device sends the selection information back to the server.

[0974] The server stores the selected design in a database (e.g., MySQL or MongoDB). The server then outputs the selected design in a format that can be used for the manufacturing process, such as CAD data. A specific example of this is when the selection information is sent to the server in JSON format, and the server stores the data in a database and exports it as a CAD file.

[0975] An example of a prompt sentence might be:

[0976] "Generate a design for a 6-inch, red, curved display, dual-lens camera phone."

[0977] "Please give priority to design ideas that make users smile."

[0978] This system makes it possible to quickly and accurately select and present optimal design proposals based on the user's design requests and emotional feedback, and then apply them to the manufacturing process.

[0979] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0980] Step 1: Enter your design requirements

[0981] The user launches an application or web page on the device and inputs the design requirements for the mobile information device they desire. Specifically, they input screen size, color, display type, camera specifications, etc. The input design requirements are temporarily saved on the device. In this case, the input data might be "6-inch screen size, red color, curved display, dual-lens camera," and the output is organized design requirements information.

[0982] Step 2: Submit your request

[0983] The terminal formats the input design request and sends it to the server. Specifically, it converts the design request into JSON format and sends it to the server using an HTTP request. The input data is the design request specified by the user, and the output is the formatted design request data sent to the server.

[0984] Step 3: Analyze design requirements

[0985] The server analyzes the received design requests. This analysis extracts important elements for generating the actual design. The input data is the design request data sent from the device, and the output is the analyzed element extraction results. Then, based on the analysis results, the data is input into the generative AI model.

[0986] Step 4: Generate design ideas

[0987] The server uses a generative AI model (e.g., TensorFlow or PyTorch) to generate multiple design proposals. In this process, the AI ​​algorithm creates multiple design proposals that meet the design requirements. The input data is the analyzed design requirement data, and the output is a set of design proposals that includes 3D models and specifications of multiple mobile devices.

[0988] Step 5: Collect emotion recognition data

[0989] The emotion recognition engine collects the user's facial expressions and voice in real time. It uses a camera and microphone to collect emotion data while the user is viewing the design proposal. The input data is the user's facial expressions and voice, and the output is the emotion data captured in real time.

[0990] Step 6: Analyze the sentiment data

[0991] The emotion recognition engine analyzes the collected emotion data and recognizes the user's emotional state, thereby determining which design proposals the user has a favorable reaction to. The input data is raw emotion data acquired in real time, and the output is the analyzed emotion recognition results.

[0992] Step 7: Present your design proposal

[0993] The server then sends the optimal design proposal to the device based on the multiple design proposals generated and feedback information from the emotion recognition engine.The design proposals are presented to the user on the device and displayed in the optimal order based on the emotion recognition results.The input data are the generated design proposals and the emotion analysis results, and the output is the optimal design proposal presented to the user.

[0994] Step 8: Select a design option

[0995] The user compares the presented design proposals and selects the one they like best. The input data are the design proposals presented to the user, and the output is the design proposal selected by the user.

[0996] Step 9: Communicate and save your selections

[0997] The terminal sends the design proposal selected by the user to the server. The server stores the selected design proposal in a database. The server then outputs the selected design proposal in a format that can be used for the manufacturing process (e.g., CAD data format). The input data is the user-selected design proposal, and the output is the stored design proposal data and data in the output format that is applied to the manufacturing process.

[0998] In this way, the system's processing flow is structured so that the user first inputs their design requirements, then evaluates the design proposals generated based on those requirements using emotional data, and then selects, saves, and outputs the most suitable proposal.

[0999] (Application example 2)

[1000] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1001] In the conventional design process for mobile devices and their accessories, it is difficult for users to quickly obtain designs that reflect their preferences and intentions. Furthermore, there is no way to reflect the user's emotions and reactions in the design selection, which means it takes time and effort to find the optimal design. Furthermore, the process is one-way, lacking a mechanism for providing feedback on the user's emotional responses. There is a need to build a system that can solve these problems and quickly provide optimal design proposals tailored to the user.

[1002] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: a means for a user to input design requests for a mobile device; a means for transmitting the design requests from the mobile device to the server; a means for the server to analyze the received design requests and generate multiple design proposals using an artificial intelligence algorithm; a means for the server to present the generated design proposals to the user; a means for recognizing the user's emotions using an emotion engine and presenting the optimal design proposal based on the emotion; a means for the user to select from the multiple design proposals; and a means for the server to save the selected design proposal and output it in a format usable for the manufacturing process. This makes it possible to quickly generate and present design proposals that reflect the user's individual requests and emotions, and to select and save the optimal design proposal.

[1003] A "mobile terminal" is an electronic device that a user holds in his or her hand and that is used for communication and information processing.

[1004] "Design requests" refers to information that indicates the detailed specifications and wishes of the design desired by the user.

[1005] A "server" is a computer system that receives, analyzes, stores, and transmits data over a network.

[1006] An "artificial intelligence algorithm" is a computational procedure that uses techniques such as machine learning and deep learning to analyze data and perform specific tasks.

[1007] "Design proposals" refer to multiple proposed models generated based on design requests.

[1008] The "emotion engine" is a technology that analyzes the user's facial expressions and voice and recognizes their emotions in real time.

[1009] A "three-dimensional model" is a computer-generated three-dimensional model that reproduces the shape and structure of a three-dimensional space.

[1010] A "manufacturing process" is a series of steps that actually create a product based on a designed product.

[1011] This invention is a system in which a user inputs design requests on a mobile device, and a server automatically generates multiple design proposals based on the requests, and presents, selects, saves, and outputs the most suitable design proposal based on the user's emotions through an emotion engine.

[1012] System Overview

[1013] 1. Enter your design requirements

[1014] Using the device's application, users can input their desired design for the mobile device, including details such as color, material, and print pattern.

[1015] 2. Sending and analyzing your request

[1016] The device sends the input design request to the server, which analyzes the received request and generates multiple design proposals using a generative AI model.

[1017] 3. Generate design proposals

[1018] The server uses the generative AI model to generate multiple design proposals based on the user's requests, including 3D models and various specifications.

[1019] 4. Emotion Recognition by Emotion Engine

[1020] The emotion engine analyzes the user's facial expressions and voice to recognize their emotions in real time. As the user browses through design proposals, it collects and analyzes their emotional data and prioritizes the presentation of designs that evoke a positive response.

[1021] 5. Presentation and selection of design proposals

[1022] The design proposals generated by the server are sent to the device, which then presents them to the user. Based on the results of the emotion engine, the most suitable design proposals are displayed in sequence. The user can view these design proposals and select the one they like best.

[1023] 6. Notification and saving of selection results

[1024] The selected design is sent back to the server, which stores it in a database. The final design is output in a format that can be used for the manufacturing process (e.g., CAD data format).

[1025] Program processing overview

[1026] The server uses a generative AI model to generate multiple design proposals based on the input design requirements. This process includes elements such as color, material, and shape, and generates a three-dimensional model based on each element. Next, an emotion engine analyzes the user's facial expressions and voice in real time to recognize their emotions. Design proposals that elicit a favorable response are presented to the user preferentially. The user selects the most suitable design proposal, and the result is sent to the server and stored in a database.

[1027] Specific examples

[1028] The user launches the application and inputs their request, such as "a blue, silicone, floral smartphone case." This request is sent to the server, which uses a generative AI model to generate three design proposals. The generated design proposals are presented to the user through the application, and an emotion engine analyzes the user's facial expressions, with the design proposal that elicits the most favorable reaction being displayed preferentially. The user selects the most suitable design proposal, and the result is saved on the server.

[1029] Example prompts to input to the generative AI model

[1030] Prompt: "Generate design ideas based on the design requirements for a blue, silicone, floral smartphone case."

[1031] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1032] Step 1:

[1033] The user inputs their design requirements using a mobile device. Specifically, they launch an application and input detailed specifications such as the desired color, material, and print pattern. This information is temporarily stored in the device.

[1034] input:

[1035] User design requests (e.g., blue, silicone, floral smartphone case)

[1036] output:

[1037] Organized design request data

[1038] Step 2:

[1039] The device sends the input design request to the server using a communication protocol such as an HTTP request.

[1040] input:

[1041] Organized design request data

[1042] output:

[1043] Design request data sent to the server

[1044] Step 3:

[1045] The server analyzes the design requests received and generates design proposals using a generative AI model. Specifically, it generates a 3D model and various specifications based on the user's requests.

[1046] input:

[1047] Design request data received by the server

[1048] output:

[1049] Multiple design proposals (3D models, specifications)

[1050] Step 4:

[1051] The server sends the generated design proposal to the terminal, which then presents it to the user, displaying it on the screen so that the user can view it.

[1052] input:

[1053] Multiple design ideas

[1054] output:

[1055] Design proposals displayed on the screen

[1056] Step 5:

[1057] The emotion engine analyzes the user's facial expressions and voice in real time to recognize the user's emotions. The camera and microphone capture the user's facial expressions and voice, and analyzes the data to determine the user's emotions.

[1058] input:

[1059] User's facial expression data, voice data

[1060] output:

[1061] User sentiment data (e.g., positive response, indifference, etc.)

[1062] Step 6:

[1063] The server receives feedback based on the emotion engine and presents to the user design proposals that the user responded favorably to with priority.

[1064] input:

[1065] User emotion data

[1066] Multiple design ideas

[1067] output:

[1068] The best design ideas are prioritized

[1069] Step 7:

[1070] The user selects the best design, the device sends the result back to the server, and the user taps the screen to confirm the selection.

[1071] input:

[1072] The best design ideas are prioritized

[1073] output:

[1074] Selection result data

[1075] Step 8:

[1076] The server stores the selected design proposals in a database and outputs the final design proposal in a format that can be used for the manufacturing process (e.g., CAD data format).

[1077] input:

[1078] Selection result data

[1079] output:

[1080] Saved design data, data for manufacturing processes (e.g. CAD data)

[1081] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[1082] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1083] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1084] [Fourth embodiment]

[1085] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1086] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1087] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[1088] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[1089] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1090] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1091] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[1092] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[1093] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[1094] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[1095] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[1096] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1097] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1098] This invention provides a system in which a user inputs design requirements on a mobile device, and a server automatically generates multiple design proposals based on those requirements, and presents, selects, saves, and outputs them. This system consists of three main components: the device, the server, and the user.

[1099] Processing flow

[1100] The user inputs their design requirements

[1101] Users input their desired mobile device features using a dedicated application on their device or a webpage, including specific design requirements such as a 6-inch screen size, red color, curved display, and dual-lens camera.

[1102] The device sends the request to the server

[1103] After the user has finished entering their design requirements, the device sends them to the server via a communication method such as an HTTP POST request.

[1104] The server analyzes the request and generates design proposals using an AI algorithm.

[1105] The server receives and analyzes the design requests sent from the device. Based on the analyzed requests, the server uses an AI algorithm to generate multiple design proposals. These proposals include a 3D model as a stereoscopic image and various specifications.

[1106] The server presents the generated design to the user.

[1107] The generated design proposals are sent from the server to the terminal and presented to the user, who can then freely browse these design proposals.

[1108] The user selects a design option

[1109] The user selects the design they like from the multiple designs presented, and the selected design is sent back to the server from the device.

[1110] The server saves and outputs the selected design

[1111] The selected design proposal is stored in a database by the server, and then output in a format (e.g., CAD data) suitable for the manufacturing process. This format can be used directly for manufacturing.

[1112] Specific examples

[1113] 1. The user enters their design requirements

[1114] Users launch the application and input their preferences, such as "screen size 6 inches, color red, curved display, dual lens camera."

[1115] 2. The device sends the request to the server

[1116] Once the request is finalized, the device sends it to the server, which receives the data.

[1117] 3. The server analyzes the request and generates a design proposal

[1118] The server analyzes the received design request and uses AI algorithms to generate multiple design proposals, such as a 3D model of a 6-inch mobile device with a red curved display.

[1119] 4. The server presents the generated design to the user.

[1120] The generated design proposals are sent from the server to the terminal and presented to the user, who can then check them in detail.

[1121] 5. User selects design options

[1122] The user selects the design that best suits their needs from the multiple design proposals presented.

[1123] 6. The device notifies the server of the selection result

[1124] The terminal notifies the server of the user's selection, which is then stored by the server.

[1125] 7. The server saves and outputs the selected design

[1126] The selected design is stored in a database by the server and output in a format that can be used in the manufacturing process, for example as a CAD file, and sent to the manufacturing line.

[1127] In this way, the system of the present invention automatically generates and presents optimal mobile device design proposals based on the user's detailed design requests, enabling them to be quickly applied to the manufacturing process.

[1128] The processing flow will be explained below.

[1129] Step 1:

[1130] The user inputs the design requirements.

[1131] Users open an application or web page on their device and fill out a design request form detailing the features of their desired mobile device, including screen size, color, display type, camera specifications, and other details.

[1132] Step 2:

[1133] The terminal transmits the request to the server.

[1134] When the user completes the input and presses the submit button, the device sends the request to the server. The HTTP POST request is used to format the design request data and send it to the server.

[1135] Step 3:

[1136] The server analyzes the request and generates design proposals using an AI algorithm.

[1137] The server analyzes the design request data received from the terminal.

[1138] Next, the AI ​​algorithm generates multiple design options based on your requirements, such as creating suitable design candidates based on inputs of screen size, color, and display type, and also generates 3D models and spec sheets during the process.

[1139] Step 4:

[1140] The server presents the generated design proposal to the user.

[1141] The server then sends the resulting design proposal back to the device, including a 3D view and specific specifications.

[1142] The device receives this data and displays a preview of the proposed design to the user.

[1143] Step 5:

[1144] The user selects a design idea.

[1145] The user reviews the presented design proposals and selects the one they like best. The selection is made through the device's UI by clicking a selection button.

[1146] Step 6:

[1147] The terminal notifies the server of the selection result.

[1148] After the user selects a design, the device sends the selection results back to the server, including the ID of the selected design.

[1149] Step 7:

[1150] The server stores and outputs the selected designs.

[1151] The server stores the data of the selected design proposal in a database.

[1152] Next, the design data is output in a format that can be used in the manufacturing process, for example, exported as a CAD file and fed to the manufacturing line.

[1153] Example 1

[1154] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1155] The conventional design process for mobile devices makes it difficult to reflect specific user requests, making it difficult to increase user satisfaction. Furthermore, the process from generating design proposals to manufacturing takes time and effort, making it impossible to quickly respond to user requests. Therefore, it is necessary to use an AI-based system to efficiently generate design proposals and reflect user requests.

[1156] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1157] In this invention, the server includes means for analyzing design requirements using a generative AI model, means for presenting the generated design proposals through a user interface, and means for saving the selected design proposal and outputting it in a format usable for the manufacturing process, thereby enabling the rapid generation of optimal design proposals based on the specific requirements of the user, and the display, selection, and output of the optimal design proposals.

[1158] "User" means an individual or legal entity that operates the system and inputs their design requests.

[1159] "Mobile device" refers to a portable electronic device such as a smartphone or tablet.

[1160] "Design requests" refer to requests regarding the specifications and features of the mobile device desired by the user.

[1161] "Server" means a central computer system that receives and analyzes design requests, and generates, provides, and stores design proposals.

[1162] A "generative AI model" is an algorithm or program that uses artificial intelligence technology to generate design ideas.

[1163] "Design proposal" refers to a proposal for the specific design and specifications of a mobile device created by a generative AI model.

[1164] "User interface" refers to the screen display and operating means that users use to operate the system.

[1165] A "3D model" is a digital model that shows the three-dimensional design of a mobile device.

[1166] "Manufacturing process" refers to the series of steps involved in producing an actual product based on the design proposal for a mobile device.

[1167] "Output" refers to providing the selected design proposal to an external party in a format that can be applied to manufacturing.

[1168] This system allows users to input their design requirements on a mobile device, and the server automatically generates multiple design proposals based on those requirements, and then presents, selects, saves, and outputs them. This system consists of three main components: the device, the server, and the user.

[1169] Users input their design requirements through dedicated applications or web pages. These applications and web pages are developed using frameworks such as React.js or Flutter to provide the user interface. Users can input specific characteristics of their desired mobile device, such as screen size, color, display shape, and camera type. For example, a user might input a request such as "6-inch screen size, red color, curved display, dual-lens camera."

[1170] The device converts the input design requests into JSON format data and sends it to the server using an HTTP POST request. Specifically, it uses a JavaScript library to collect data and generate an HTTP request.

[1171] The server, built with Node.js or Python, receives the transmitted data. The received data is first stored in a database (e.g., PostgreSQL). The server then generates design proposals using a generative AI model (e.g., OpenAI's GPT-4 is an example of a generative AI model). The server generates a prompt and sends it to the AI ​​model. An example prompt is, "Generate a 3D design proposal for a smartphone with a 6-inch red curved display." The generated design proposal includes a 3D model (e.g., using Blender or Three.js) and multiple specification details.

[1172] The server sends the generated design proposals to the terminal and presents them to the user. The terminal displays the received design proposals on the user interface, allowing the user to check and manipulate the 3D model. The user selects the desired design from the multiple presented proposals and performs the selection operation.

[1173] The terminal notifies the server of the selection results, which are then permanently stored in a database. The selected design proposal is output in a format that can be used in the manufacturing process (e.g., CAD data). This output process uses a Python script to generate a file in, for example, a SolidWorks format. This file is then sent directly to the manufacturing line and used to actually manufacture the product.

[1174] As a result, the system of the present invention can automatically generate and present optimal mobile device design proposals based on the user's detailed design requests, and quickly apply them to the manufacturing process.

[1175] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1176] Step 1:

[1177] The user inputs their design requirements

[1178] Users enter their design requirements using a dedicated application or web page. Specifically, they enter items such as "6-inch screen size," "red color," "curved display," and "dual-lens camera" on a UI built with React.js or Flutter, and then click the submit button. The entered data (design requirements) is processed as internal data in JSON format.

[1179] Step 2:

[1180] The device sends the request to the server

[1181] The terminal collects the design requests entered by the user and converts them into JSON format. This JSON data is sent to the server using an HTTP POST request. Specifically, a data collection library (e.g., Axios) is used to construct the request and send it to an endpoint (e.g., / design_request). The input is the user's design request, and the output is the request sent to the server.

[1182] Step 3:

[1183] The server analyzes the request and generates design proposals using a generative AI model.

[1184] The server is built using Node.js or Python and receives HTTP requests. The received JSON data is first stored in a database (such as PostgreSQL). The data is then analyzed to send a prompt to a generative AI model (e.g., OpenAI's GPT-4). An example prompt might be "Please design a mobile device with a 6-inch red curved display." This allows the AI ​​to generate multiple 3D designs and specifications. The input is the user's design request, and the output is the generated design proposal.

[1185] Step 4:

[1186] The server presents the generated design to the user.

[1187] The generated design proposal is sent from the server to the device. Specifically, a data packet containing the generated 3D model and specification information is sent to the device in JSON format. The device receives this data and displays it in a React.js or Flutter UI. The user can rotate the 3D model and check detailed specifications. The input is the generated design proposal, and the output is the proposed display on the UI.

[1188] Step 5:

[1189] The user selects a design option

[1190] The user selects the most desirable design from the multiple design proposals presented. The user makes the selection by clicking a button on the UI or by selecting an item from a list. This selection information is saved internally on the device. The input is the user's selection operation, and the output is the data of the selected design proposal.

[1191] Step 6:

[1192] The device notifies the server of the selection result

[1193] The device converts the data of the design proposal selected by the user back into JSON format and sends it to the server using an HTTP POST request. The server receives this request and permanently stores the selection information in a database. The input is the data of the selected design proposal, and the output is a notification to the server.

[1194] Step 7:

[1195] The server saves and outputs the selected design

[1196] The server stores the selected design in a database and converts it into a format that can be used for the manufacturing process (e.g., CAD data). This conversion is performed using a Python script, which generates a file in, for example, SolidWorks format. This file is then sent directly to the manufacturing line. The input is the data of the selected design, and the output is the CAD data for manufacturing.

[1197] (Application example 1)

[1198] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1199] Conventional mobile device design decision-making processes have difficulty reflecting specific user needs. Furthermore, because it is not easy for users to compare multiple design proposals, there are issues that prevent users from increasing their satisfaction. Furthermore, the lack of a way to actually visually check the generated design proposals often leaves users feeling uneasy about their final selection.

[1200] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1201] In this invention, the server includes: a means for a user to input design requests for a mobile device; a means for transmitting the design requests for the mobile device to the server; a means for the server to analyze the received design requests and generate multiple design proposals using a generation algorithm; a means for the server to present the generated design proposals to the user; a means for the user to confirm the generated design proposals using augmented reality technology; a means for the user to select from the multiple design proposals; and a means for the server to save the selected design proposal and output it in a format usable for the manufacturing process. This allows the user to easily generate, compare, and select design proposals for the mobile device based on specific design requests, and by visually confirming them using augmented reality technology, the user can feel confident about the final design selection.

[1202] "Mobile device design requirements" refer to specific requirements regarding the appearance and functionality of the mobile device desired by the user.

[1203] "Server" refers to a computer system that receives design requests from users, analyzes them, and generates, saves, and outputs design proposals.

[1204] "Generation algorithm" refers to a program or calculation method for automatically generating multiple design proposals based on the design requirements of a mobile device.

[1205] "Augmented reality technology" refers to technology that displays computer-generated information overlaid on real-world information.

[1206] "3D model" refers to a digital model of an object represented in three-dimensional space.

[1207] A "prompt sentence" refers to a specific instruction sentence that is input to a generative AI model.

[1208] "Design proposals" refer to multiple candidate mobile device designs generated based on user requests.

[1209] "Format usable for the manufacturing process" refers to a data format (e.g., CAD data) that is required to actually manufacture the selected design.

[1210] "Means for presenting to the user" refers to means for displaying the generated design proposal to the user.

[1211] This invention provides a system in which a user inputs design requirements on a mobile device, and a server automatically generates multiple design proposals based on those requirements, and presents, selects, saves, and outputs them. This system consists of three main components: a mobile device (a user device such as a smartphone or smart glasses), a server, and a user.

[1212] 1. Configuration and Operation Overview

[1213] User Device:

[1214] Users use a smartphone or smart glasses to input their design requests into the application.

[1215] The configured request is sent to the server via an HTTP POST request.

[1216] server:

[1217] The server analyzes the received design request using a generative algorithm.

[1218] The generative algorithm uses an artificial intelligence (AI) model to generate multiple design alternatives based on the customer's requirements.

[1219] The generated design proposal includes a 3D model of the mobile device and a prompt statement.

[1220] AR technology:

[1221] The computationally generated design proposals are displayed in augmented reality on the user's device using AR technology, allowing for real-world visual confirmation.

[1222] 2. Specific Examples

[1223] Suppose a user launches an application on their smartphone and inputs specific design requirements, such as "6-inch screen size, red color, curved display, dual cameras." These requirements are then sent to the server.

[1224] The server receives the request and uses a generative algorithm to generate multiple design alternatives, such as a 3D model of a 6-inch red curved mobile device, along with a corresponding prompt. An example prompt is as follows:

[1225] Generate a mobile design that meets the following requirements:

[1226] Screen size: 6 inches

[1227] Color: Red

[1228] Display Type: Curved Display

[1229] Camera: Dual camera

[1230] Generate multiple design proposals and return them in JSON format.

[1231] The generated design proposals are then sent back to the user's device, where they can be viewed using AR on a smartphone or smart glasses. After visually checking the design proposals using AR, the user can select the design they like best from the multiple proposals.

[1232] The selected design is then sent back to the server, where it is stored in a database in a format suitable for the manufacturing process and output as CAD data if necessary. This process allows users to easily specify, compare, and select the design of their desired mobile device. Augmented reality technology also allows users to visually confirm the design in the real world, giving them peace of mind in their selection.

[1233] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1234] Step 1:

[1235] Using a smartphone or smart glasses, a user launches the application and inputs their design requirements for a mobile device. The input requirements include specific information such as screen size, color, display type, and camera. For example, a user might input "6-inch screen size, red color, curved display, dual camera."

[1236] Step 2:

[1237] The terminal sends the design request entered by the user to the server using an HTTP POST request. At this time, the request data is sent in JSON format. The input is the user's design request data, and the output is a send request to the server.

[1238] Step 3:

[1239] The server receives the design request and analyzes it. Specifically, it parses the received JSON data and extracts each element (screen size, color, display type, camera, etc.). At this point, the input is the design request data, and the output is the analyzed request data.

[1240] Step 4:

[1241] The server uses a generative AI model to generate multiple design proposals based on the analyzed request data. The generative algorithm generates multiple 3D models and related prompts based on the input request elements. Specific data operations include 3D graphics generation and text generation. The input is the analyzed request data, and the output is multiple design proposals.

[1242] Step 5:

[1243] The server sends the generated design proposal back to the terminal. At this time, it sends the design proposal data to the terminal using an HTTP response. The input is the generated design proposal, and the output is the response data from the server to the terminal.

[1244] Step 6:

[1245] The device displays the received design proposal using augmented reality technology. Specifically, the generated 3D model is overlaid on the display of a smartphone or smart glasses. The user can visually check the design proposal in a real-world environment. The input is the design proposal data received from the server, and the output is the design proposal displayed in augmented reality.

[1246] Step 7:

[1247] The user selects their favorite design from multiple designs and sends that selection to the server via their device. Specifically, data including the ID of the selected design is sent to the server again via an HTTP POST request. The input is the design selected by the user, and the output is the data sent to the server.

[1248] Step 8:

[1249] The server stores the design proposal selected by the user in a database and outputs it in a format applicable to the manufacturing process (e.g., CAD data). At this time, the design proposal is converted into the required format and stored in the database. The input is the design proposal data selected by the user, and the output is data for the manufacturing process. Through this series of processes, a customized mobile device design based on the user's specific requests is efficiently generated and provided.

[1250] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1251] This invention is a system in which a user inputs design requirements on a mobile device, a server automatically generates multiple design proposals based on those requirements, and an emotion engine presents, selects, saves, and outputs the optimal design proposal based on the user's emotions. This system streamlines the process by which users select designs that reflect their intentions, including their emotions, and applies it to the manufacturing process. The system's main components are the device, server, emotion engine, and user.

[1252] System Overview

[1253] Input your design requirements

[1254] Users use a device application or web page to input their desired mobile device design requirements, such as screen size, color, display type, and camera specifications. This information is then organized within the device.

[1255] Sending and analyzing requests

[1256] The device sends the input design request to the server, which analyzes the request and uses an AI algorithm to generate multiple design proposals, including 3D models and various specifications.

[1257] Emotion recognition by emotion engine

[1258] The emotion engine analyzes the user's facial expressions and voice to recognize their emotions in real time. As the user browses through design proposals, the engine collects and analyzes their emotional data in real time, and prioritizes the presentation of designs that elicit a favorable response.

[1259] Presentation and selection of design proposals

[1260] The generated design proposals are sent from the server to the device and presented to the user. The emotion engine provides feedback on the emotion recognition results, and the most suitable design proposals for the user are displayed in sequence. The user can view these design proposals and select the one they like best.

[1261] Notification and saving of selection results

[1262] The selected design is then sent back to the server, which stores it in a database and outputs it in a format that can be used for the manufacturing process, often in CAD format.

[1263] Specific examples

[1264] 1. Enter your design requirements

[1265] The user opens the app and enters their preferences, such as "screen size 6 inches, color red, curved display, dual-lens camera."

[1266] 2. Sending and analyzing your request

[1267] Once the request is confirmed, the device sends it to the server, which receives the data.

[1268] 3. Generate design proposals

[1269] The server uses an AI algorithm to generate three design proposals, such as a 3D model of a 6-inch mobile device with a red curved display.

[1270] 4. Emotion Recognition by Emotion Engine

[1271] The emotion engine analyzes the user's facial expressions and voice in real time and prioritizes design ideas that will elicit a favorable response based on their emotions. For example, if a user smiles when looking at a particular design idea, that design idea will be placed at a high priority position.

[1272] 5. Presentation and selection of design proposals

[1273] Users compare the presented design proposals and select the one they like best, taking into account feedback from the emotion engine.

[1274] 6. Notification and saving of selection results

[1275] The terminal sends the selection results to the server, which stores the design proposal in a database and outputs it in a format that can be used in the manufacturing process, such as CAD data.

[1276] In this way, the system of the present invention can automatically generate and present optimal mobile device design proposals based on the user's detailed design requests and emotional feedback, and quickly apply them to the manufacturing process.

[1277] The processing flow will be explained below.

[1278] Step 1:

[1279] The user inputs the design requirements.

[1280] Users open an application or web page on their device and enter the desired mobile device features into a design request form, such as screen size, color, display type, and camera specifications.

[1281] Step 2:

[1282] The terminal transmits the request to the server.

[1283] When the user completes the input and presses the submit button, the device sends the request to the server. An HTTP POST request is used to send the design request data to the server in the appropriate format.

[1284] Step 3:

[1285] The server analyzes the request and generates design proposals using an AI algorithm.

[1286] The server analyzes the design request data received from the terminal.

[1287] The AI ​​algorithm then generates multiple design options based on the user's requirements, creating suitable design candidates based on the user's specified screen size, color, and display type, and generating 3D models and specifications for each design.

[1288] Step 4:

[1289] The server sends the generated design proposal to the terminal and presents it to the user.

[1290] The generated design proposal is sent from the server to the device, where it is displayed to the user, who can then rotate the 3D model and check its detailed specifications.

[1291] Step 5:

[1292] The emotion engine analyzes the user's facial expressions and voice to recognize emotions.

[1293] While the user is browsing the design proposals, the device's camera and microphone are used to collect the user's facial expressions and voice. This data is sent in real time to the emotion engine, which then analyzes the user's emotions. For example, if the user is smiling, it is determined that the design proposal is well-received.

[1294] Step 6:

[1295] The server changes the display order of design proposals based on the analysis results of the emotion engine.

[1296] The analysis results of the emotion engine are fed back to the server, which then adjusts the display to prioritize design proposals that have received the most positive user feedback. When the user next views design proposals, the highly favorable designs will be displayed at the top.

[1297] Step 7:

[1298] The user selects a design idea.

[1299] The user selects the design they like best from multiple options, taking into account feedback from the emotion engine. The selection is made through the device's UI by clicking a selection button.

[1300] Step 8:

[1301] The terminal notifies the server of the selection result.

[1302] After the user selects a design, the device sends the selection back to the server, including the ID of the selected design.

[1303] Step 9:

[1304] The server stores and outputs the selected designs.

[1305] The server stores the data of the selected design in a database and outputs the design data in a format that can be used in the manufacturing process, for example, by exporting it as a CAD file and feeding it to the manufacturing line.

[1306] In this way, the system of the present invention can automatically generate and present optimal mobile device design proposals based on the user's detailed design requests and emotional feedback, and quickly apply them to the manufacturing process.

[1307] Example 2

[1308] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1309] Conventional design proposal generation systems for mobile information terminals have the problem of not being able to take the user's emotions into consideration during the process of inputting the user's design requirements and the selection process. This makes it difficult to present optimal design proposals that will increase user satisfaction. The present invention aims to solve this problem and provide a system that allows users to efficiently select designs that reflect their intentions, including their emotions.

[1310] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1311] In this invention, the server includes: a means for a user to input design requests to a mobile information device; a means for transmitting the design requests from the mobile information device to an information processing device; a means for the information processing device to analyze the received design requests and generate multiple design proposals using a generative AI model; a means for an emotion recognition engine to analyze the user's facial expressions and voice to generate emotion data; a means for collecting emotion data while the user is viewing the design proposals and preferentially presenting design proposals that show a favorable reaction; a means for the information processing device to present the generated design proposals to the user; a means for the user to select from the multiple design proposals; and a means for the information processing device to save the selected design proposal and output it in a format usable for the production process. This enables the presentation and selection of optimal design proposals that reflect the user's emotions.

[1312] A "user" is a person who operates this system and inputs design requests for a mobile information terminal.

[1313] A "personal digital assistant" is a portable electronic device such as a smartphone or tablet.

[1314] "Design requests" are specific requests regarding the appearance and functions of the mobile information terminal desired by the user.

[1315] An "information processing device" is a device such as a computer or server that analyzes and processes data.

[1316] A "generative AI model" is an algorithm or program that uses artificial intelligence technology to generate design ideas.

[1317] "Design proposals" are multiple design proposals generated based on the user's design requests.

[1318] An "emotion recognition engine" is a technology that recognizes a user's emotions through the analysis of facial expressions and voice.

[1319] "Emotion data" refers to data relating to a user's emotions that is collected and analyzed by an emotion recognition engine.

[1320] The "production process" is a series of steps for manufacturing an actual product based on the selected design proposal.

[1321] The "output format" is a data format generated by an information processing device for applying a design proposal to actual product manufacturing.

[1322] This invention is a system that generates and presents optimal design proposals for mobile information terminals based on the user's design requests and emotional feedback. This system consists of a terminal, a server, an emotion recognition engine, and a generative AI model.

[1323] First, a user inputs their desired design requirements for a mobile information device using an application on the device or a web page. These requirements include screen size, color, display type, camera specifications, etc. Specifically, a user might input a preference such as "6-inch screen size, red color, curved display, dual-lens camera."

[1324] The device formats the input design requests and sends them to the server. The device converts the data into JSON format and sends it to the server via an HTTP request. The hardware used for this is a smartphone or tablet.

[1325] The server analyzes the received design requests and generates multiple design proposals using a generative AI model (e.g., TensorFlow or PyTorch). This allows the server to generate a 3D model of, for example, a 6-inch mobile information terminal with a red curved display. The generated design proposals include the 3D model and various specifications.

[1326] The emotion recognition engine collects the user's facial expressions and voice in real time and analyzes their emotions. Examples of emotion recognition engines that are used include Microsoft Azure Cognitive Services and Google Cloud AI. While the user is browsing design proposals, facial expressions and voice are collected via the camera and microphone, and the system analyzes them in real time.

[1327] The emotion recognition engine uses the collected data to provide feedback on design proposals that elicit favorable reactions, and the server then uses this information to select which design proposals to prioritize. A specific example of this behavior is when a user smiles upon seeing a particular design proposal. In this case, the corresponding design proposal is placed at a high priority position.

[1328] The generated design proposals are sent from the server to the device and presented to the user. The user compares the multiple design proposals presented and selects the design proposal they like best based on feedback from the emotion recognition engine. At this time, the device sends the selection information back to the server.

[1329] The server stores the selected design in a database (e.g., MySQL or MongoDB). The server then outputs the selected design in a format that can be used for the manufacturing process, such as CAD data. A specific example of this is when the selection information is sent to the server in JSON format, and the server stores the data in a database and exports it as a CAD file.

[1330] An example of a prompt sentence might be:

[1331] "Generate a design for a 6-inch, red, curved display, dual-lens camera phone."

[1332] "Please give priority to design ideas that make users smile."

[1333] This system makes it possible to quickly and accurately select and present optimal design proposals based on the user's design requests and emotional feedback, and then apply them to the manufacturing process.

[1334] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1335] Step 1: Enter your design requirements

[1336] The user launches an application or web page on the device and inputs the design requirements for the mobile information device they desire. Specifically, they input screen size, color, display type, camera specifications, etc. The input design requirements are temporarily saved on the device. In this case, the input data might be "6-inch screen size, red color, curved display, dual-lens camera," and the output is organized design requirements information.

[1337] Step 2: Submit your request

[1338] The terminal formats the input design request and sends it to the server. Specifically, it converts the design request into JSON format and sends it to the server using an HTTP request. The input data is the design request specified by the user, and the output is the formatted design request data sent to the server.

[1339] Step 3: Analyze design requirements

[1340] The server analyzes the received design requests. This analysis extracts important elements for generating the actual design. The input data is the design request data sent from the device, and the output is the analyzed element extraction results. Then, based on the analysis results, the data is input into the generative AI model.

[1341] Step 4: Generate design ideas

[1342] The server uses a generative AI model (e.g., TensorFlow or PyTorch) to generate multiple design proposals. In this process, the AI ​​algorithm creates multiple design proposals that meet the design requirements. The input data is the analyzed design requirement data, and the output is a set of design proposals that includes 3D models and specifications of multiple mobile devices.

[1343] Step 5: Collect emotion recognition data

[1344] The emotion recognition engine collects the user's facial expressions and voice in real time. It uses a camera and microphone to collect emotion data while the user is viewing the design proposal. The input data is the user's facial expressions and voice, and the output is the emotion data captured in real time.

[1345] Step 6: Analyze the sentiment data

[1346] The emotion recognition engine analyzes the collected emotion data and recognizes the user's emotional state, thereby determining which design proposals the user has a favorable reaction to. The input data is raw emotion data acquired in real time, and the output is the analyzed emotion recognition results.

[1347] Step 7: Present your design proposal

[1348] The server then sends the optimal design proposal to the device based on the multiple design proposals generated and feedback information from the emotion recognition engine.The design proposals are presented to the user on the device and displayed in the optimal order based on the emotion recognition results.The input data are the generated design proposals and the emotion analysis results, and the output is the optimal design proposal presented to the user.

[1349] Step 8: Select a design option

[1350] The user compares the presented design proposals and selects the one they like best. The input data are the design proposals presented to the user, and the output is the design proposal selected by the user.

[1351] Step 9: Communicate and save your selections

[1352] The terminal sends the design proposal selected by the user to the server. The server stores the selected design proposal in a database. The server then outputs the selected design proposal in a format that can be used for the manufacturing process (e.g., CAD data format). The input data is the user-selected design proposal, and the output is the stored design proposal data and data in the output format that is applied to the manufacturing process.

[1353] In this way, the system's processing flow is structured so that the user first inputs their design requirements, then evaluates the design proposals generated based on those requirements using emotional data, and then selects, saves, and outputs the most suitable proposal.

[1354] (Application example 2)

[1355] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1356] In the conventional design process for mobile devices and their accessories, it is difficult for users to quickly obtain designs that reflect their preferences and intentions. Furthermore, there is no way to reflect the user's emotions and reactions in the design selection, which means it takes time and effort to find the optimal design. Furthermore, the process is one-way, lacking a mechanism for providing feedback on the user's emotional responses. There is a need to build a system that can solve these problems and quickly provide optimal design proposals tailored to the user.

[1357] The specific processing by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: a means for a user to input design requests for a mobile device; a means for transmitting the design requests from the mobile device to the server; a means for the server to analyze the received design requests and generate multiple design proposals using an artificial intelligence algorithm; a means for the server to present the generated design proposals to the user; a means for recognizing the user's emotions using an emotion engine and presenting the optimal design proposal based on the emotion; a means for the user to select from the multiple design proposals; and a means for the server to save the selected design proposal and output it in a format usable for the manufacturing process. This makes it possible to quickly generate and present design proposals that reflect the user's individual requests and emotions, and to select and save the optimal design proposal.

[1358] A "mobile terminal" is an electronic device that a user holds in his or her hand and that is used for communication and information processing.

[1359] "Design requests" refers to information that indicates the detailed specifications and wishes of the design desired by the user.

[1360] A "server" is a computer system that receives, analyzes, stores, and transmits data over a network.

[1361] An "artificial intelligence algorithm" is a computational procedure that uses techniques such as machine learning and deep learning to analyze data and perform specific tasks.

[1362] "Design proposals" refer to multiple proposed models generated based on design requests.

[1363] The "emotion engine" is a technology that analyzes the user's facial expressions and voice and recognizes their emotions in real time.

[1364] A "three-dimensional model" is a computer-generated three-dimensional model that reproduces the shape and structure of a three-dimensional space.

[1365] A "manufacturing process" is a series of steps that actually create a product based on a designed product.

[1366] This invention is a system in which a user inputs design requests on a mobile device, and a server automatically generates multiple design proposals based on the requests, and presents, selects, saves, and outputs the most suitable design proposal based on the user's emotions through an emotion engine.

[1367] System Overview

[1368] 1. Enter your design requirements

[1369] Using the device's application, users can input their desired design for the mobile device, including details such as color, material, and print pattern.

[1370] 2. Sending and analyzing your request

[1371] The device sends the input design request to the server, which analyzes the received request and generates multiple design proposals using a generative AI model.

[1372] 3. Generate design proposals

[1373] The server uses the generative AI model to generate multiple design proposals based on the user's requests, including 3D models and various specifications.

[1374] 4. Emotion Recognition by Emotion Engine

[1375] The emotion engine analyzes the user's facial expressions and voice to recognize their emotions in real time. As the user browses through design proposals, it collects and analyzes their emotional data and prioritizes the presentation of designs that evoke a positive response.

[1376] 5. Presentation and selection of design proposals

[1377] The design proposals generated by the server are sent to the device, which then presents them to the user. Based on the results of the emotion engine, the most suitable design proposals are displayed in sequence. The user can view these design proposals and select the one they like best.

[1378] 6. Notification and saving of selection results

[1379] The selected design is sent back to the server, which stores it in a database. The final design is output in a format that can be used for the manufacturing process (e.g., CAD data format).

[1380] Program processing overview

[1381] The server uses a generative AI model to generate multiple design proposals based on the input design requirements. This process includes elements such as color, material, and shape, and generates a three-dimensional model based on each element. Next, an emotion engine analyzes the user's facial expressions and voice in real time to recognize their emotions. Design proposals that elicit a favorable response are presented to the user preferentially. The user selects the most suitable design proposal, and the result is sent to the server and stored in a database.

[1382] Specific examples

[1383] The user launches the application and inputs their request, such as "a blue, silicone, floral smartphone case." This request is sent to the server, which uses a generative AI model to generate three design proposals. The generated design proposals are presented to the user through the application, and an emotion engine analyzes the user's facial expressions, with the design proposal that elicits the most favorable reaction being displayed preferentially. The user selects the most suitable design proposal, and the result is saved on the server.

[1384] Example prompts to input to the generative AI model

[1385] Prompt: "Generate design ideas based on the design requirements for a blue, silicone, floral smartphone case."

[1386] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1387] Step 1:

[1388] The user inputs their design requirements using a mobile device. Specifically, they launch an application and input detailed specifications such as the desired color, material, and print pattern. This information is temporarily stored in the device.

[1389] input:

[1390] User design requests (e.g., blue, silicone, floral smartphone case)

[1391] output:

[1392] Organized design request data

[1393] Step 2:

[1394] The device sends the input design request to the server using a communication protocol such as an HTTP request.

[1395] input:

[1396] Organized design request data

[1397] output:

[1398] Design request data sent to the server

[1399] Step 3:

[1400] The server analyzes the design requests received and generates design proposals using a generative AI model. Specifically, it generates a 3D model and various specifications based on the user's requests.

[1401] input:

[1402] Design request data received by the server

[1403] output:

[1404] Multiple design proposals (3D models, specifications)

[1405] Step 4:

[1406] The server sends the generated design proposal to the terminal, which then presents it to the user, displaying it on the screen so that the user can view it.

[1407] input:

[1408] Multiple design ideas

[1409] output:

[1410] Design proposals displayed on the screen

[1411] Step 5:

[1412] The emotion engine analyzes the user's facial expressions and voice in real time to recognize the user's emotions. The camera and microphone capture the user's facial expressions and voice, and analyzes the data to determine the user's emotions.

[1413] input:

[1414] User's facial expression data, voice data

[1415] output:

[1416] User sentiment data (e.g., positive response, indifference, etc.)

[1417] Step 6:

[1418] The server receives feedback based on the emotion engine and presents to the user design proposals that the user responded favorably to with priority.

[1419] input:

[1420] User emotion data

[1421] Multiple design ideas

[1422] output:

[1423] The best design ideas are prioritized

[1424] Step 7:

[1425] The user selects the best design, the device sends the result back to the server, and the user taps the screen to confirm the selection.

[1426] input:

[1427] The best design ideas are prioritized

[1428] output:

[1429] Selection result data

[1430] Step 8:

[1431] The server stores the selected design proposals in a database and outputs the final design proposal in a format that can be used for the manufacturing process (e.g., CAD data format).

[1432] input:

[1433] Selection result data

[1434] output:

[1435] Saved design data, data for manufacturing processes (e.g. CAD data)

[1436] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1437] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1438] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1439] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1440] FIG. 9 is a diagram illustrating an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1441] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1442] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1443] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1444] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1445] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1446] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1447] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1448] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1449] 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.

[1450] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1451] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1452] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1453] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1454] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1455] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1456] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1457] The following is further disclosed regarding the above embodiment.

[1458] (Claim 1)

[1459] A means for a user to input design requests for a mobile device;

[1460] means for transmitting design requests for the mobile device to a server;

[1461] A means for analyzing the design request received by the server and generating multiple design proposals using an algorithm;

[1462] A means for the server to present the generated design proposal to the user;

[1463] a means for users to select from multiple design options;

[1464] a means for storing the selected design proposal in a server and outputting it in a format that can be used in a manufacturing process;

[1465] A system including:

[1466] (Claim 2)

[1467] 10. The system according to claim 1, further comprising means for displaying the design proposal generated by the server to the user via a screen of the mobile terminal.

[1468] (Claim 3)

[1469] 10. The system of claim 1, wherein the algorithm uses artificial intelligence and the generated design proposals include a 3D model of the mobile device.

[1470] "Example 1"

[1471] (Claim 1)

[1472] A means for a user to input design requests for a mobile device;

[1473] means for transmitting design requests for the mobile device to a server;

[1474] A means for analyzing the design requests received by the server and generating multiple design proposals using a generative AI model;

[1475] A means for the server to present the generated design proposal to the user;

[1476] a means for users to select from multiple design options;

[1477] a means for storing the selected design proposal in a server and outputting it in a format that can be used in a manufacturing process;

[1478] A system including:

[1479] (Claim 2)

[1480] 10. The system of claim 1, further comprising means for displaying the server-generated design proposals to a user through a user interface of a mobile device.

[1481] (Claim 3)

[1482] 10. The system of claim 1, wherein the generative AI model uses artificial intelligence and the generated design proposals include a three-dimensional model of a mobile device.

[1483] "Application Example 1"

[1484] (Claim 1)

[1485] A means for a user to input design requests for a mobile device;

[1486] means for transmitting design requests for the mobile device to a server;

[1487] A means for analyzing the design request received by the server and generating multiple design proposals using an algorithm;

[1488] A means for the server to present the generated design proposal to the user;

[1489] a means for users to select from multiple design options;

[1490] a means for storing the selected design proposal in a server and outputting it in a format that can be used in a manufacturing process;

[1491] A means for a user to check the generated design proposal using augmented reality technology;

[1492] A system including:

[1493] (Claim 2)

[1494] 10. The system of claim 1, further comprising means for displaying the server-generated design proposals to the user via a mobile device screen or an augmented reality device.

[1495] (Claim 3)

[1496] 10. The system of claim 1, wherein the algorithm uses a generative algorithm and the generated design proposal includes a 3D model of the mobile device and a prompt statement.

[1497] "Example 2: Combining Emotion Engines"

[1498] (Claim 1)

[1499] A means for a user to input design requests for a mobile information terminal;

[1500] means for transmitting design requests for a portable information terminal to an information processing device;

[1501] A means for analyzing the design request received by the information processing device and generating a plurality of design proposals using a generative AI model;

[1502] A means for generating emotion data by an emotion recognition engine analyzing a user's facial expression and voice;

[1503] A means for collecting emotional data while a user is viewing design proposals and preferentially presenting design proposals that elicit a favorable response;

[1504] a means for the information processing device to present the generated design proposal to a user;

[1505] a means for a user to select from multiple design alternatives;

[1506] a means for storing the selected design proposal in an information processing device and outputting it in a format that can be used in the production process;

[1507] A system including:

[1508] (Claim 2)

[1509] 2. The system according to claim 1, further comprising means for displaying the design proposal generated by the information processing device to a user through a screen of an information terminal.

[1510] (Claim 3)

[1511] 10. The system of claim 1, wherein the generative AI model uses artificial intelligence and the generated design proposals include a three-dimensional model of a mobile information device.

[1512] "Application example 2 when combining emotion engines"

[1513] (Claim 1)

[1514] A means for a user to input design requests for a mobile device;

[1515] means for transmitting design requests for the mobile device to a server;

[1516] A means for analyzing the design request received by the server and generating multiple design proposals using an artificial intelligence algorithm;

[1517] A means for the server to present the generated design proposal to the user;

[1518] A means for recognizing the user's emotions using an emotion engine and presenting optimal design proposals based on those emotions;

[1519] a means for users to select from multiple design options;

[1520] a means for storing the selected design proposal in a server and outputting it in a format that can be used in a manufacturing process;

[1521] A system including:

[1522] (Claim 2)

[1523] 2. The system according to claim 1, further comprising means for displaying the design proposals generated by the server to the user through the screen of the mobile terminal and through voice analysis.

[1524] (Claim 3)

[1525] 10. The system of claim 1, wherein the generated design proposal comprises a three-dimensional model of the mobile device. [Explanation of symbols]

[1526] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means for a user to input design requests for a mobile device; means for transmitting design requests for the mobile device to a server; A means for analyzing the design request received by the server and generating multiple design proposals using an algorithm; A means for the server to present the generated design proposal to the user; a means for users to select from multiple design options; a means for storing the selected design proposal in a server and outputting it in a format that can be used in a manufacturing process; A system including:

2. 2. The system according to claim 1, further comprising means for displaying the design proposal generated by the server to the user on a screen of the mobile terminal.

3. 10. The system of claim 1, wherein the algorithm uses artificial intelligence and the generated design proposals include a 3D model of the mobile device.

Images