system

Abstract

We provide the system. [Solution] A means of receiving preference information entered by the user via voice, A means of collecting external data and converting the content of the received audio, A means for analyzing user preferences based on the aforementioned collected data, A means for generating and visualizing design proposals based on the above analysis, A means of revising the design based on user feedback and presenting it again, A means of converting the modified design into a three-dimensional manufacturing format, A system that includes means for automatically generating products based on a three-dimensional manufacturing format.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] The manufacturing process of conventional personalized products has problems in that it is highly customizable but requires a lot of time and cost. Also, in order for consumers to materialize a design according to their preferences, specialized knowledge is often required, which is a high hurdle for general consumers. In addition, due to the lack of means for generating designs that can quickly respond to market changes, there is a problem that it is difficult to provide products optimized for consumers.

Means for Solving the Problems

[0005] This invention provides a system that efficiently collects preference information obtained from users and external data, and uses AI to analyze user preferences based on this information. Furthermore, it presents design proposals generated based on the analysis results, and by making modifications according to user feedback, more accurate personalization is possible. Finally, by rapidly manufacturing the finalized design using a 3D printing device, it overcomes conventional problems and realizes efficient and low-cost product delivery.

[0006] "User-inputted preference information" refers to information that users provide to the system regarding their design preferences and desires.

[0007] "External data" refers to data obtained from external sources, such as social media activity and past purchase history, that is used to analyze user preferences.

[0008] "Means of collection" refers to the function of efficiently collecting user preference information and external data and making it usable within the system.

[0009] "Methods for analyzing preferences" refer to processing processes that utilize AI and machine learning to identify user preferences based on collected information.

[0010] "A means of generating and presenting design proposals" refers to a function that creates designs based on the results of preference analysis and visualizes and presents those designs to users.

[0011] "A means of modifying the design based on feedback" refers to a function that allows existing designs to be changed and re-presented in response to user opinions and requests.

[0012] "Means of converting to a manufacturing format" refers to the process of converting a finalized design into a data format that can be manufactured using a three-dimensional printing device.

[0013] "Means for automatically generating products" refers to the function of creating physical products using three-dimensional printing technology based on a manufacturing format. [Brief explanation of the drawing]

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

Embodiments for Carrying Out the Invention

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

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

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

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

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

[0020] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0021] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0022] [First Embodiment]

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

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

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

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

[0027] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

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

[0029] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

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

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

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

[0035] The system of this invention is designed for use at home or in the office by installing a dedicated software application on a terminal. Users input information describing their preferences and requests through the terminal. The input interface is designed to be visually easy for users to understand, and offers choices regarding color, design style, application, and materials.

[0036] The server collects information provided by the user, as well as relevant external data such as social media activity and past purchase history, if permitted by the user, in real time. This allows for a precise understanding of user preferences and trends.

[0037] An AI agent on the server analyzes the collected data and uses machine learning algorithms to evaluate user preferences. Based on this, the AI ​​automatically generates optimal design proposals and sends them to the terminal. The terminal presents the user with visually visualized design proposals and has a feedback function to receive user feedback.

[0038] When a user provides feedback requesting revisions to a design proposal, the server receives the feedback, and an AI agent revises the design and generates a new proposal. This process is repeated until the user is satisfied with the design.

[0039] Finally, after the user reviews and approves the design, the server converts the design data into a manufacturing format and sends it to the 3D printer. The 3D printer automatically creates the product based on this manufacturing format. The finished product undergoes quality checks and is then delivered to the user.

[0040] As a concrete example, consider a scenario where a user wants to create a new lampshade to personalize their home decor. The user enters "a lampshade with a unique shape and warm colors" into their terminal. The server generates and visualizes suitable designs based on past purchase history and popular trends, presenting them to the user. If the user provides feedback that they would like the shape to be a little more angular, the AI ​​generates a revised version based on that information and presents it to the user again. Finally, the user confirms a design they are satisfied with, and a 3D printing machine brings it to life.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] Users log in to the system via their device and enter information about their design preferences and desires. This includes specific requirements such as desired colors, shapes, materials, and intended use.

[0044] Step 2:

[0045] The terminal sends the data entered by the user to the server.

[0046] Step 3:

[0047] The server receives user input information and, in addition, collects SNS data and past purchase history that the user has authorized.

[0048] Step 4:

[0049] The AI ​​agent on the server analyzes the collected data to identify user preferences and trends. Machine learning algorithms are used in this process.

[0050] Step 5:

[0051] The server generates design proposals based on the analysis results. The AI ​​agent creates multiple design proposals that reflect the user's preferences.

[0052] Step 6:

[0053] The server sends the generated design proposal to the terminal.

[0054] Step 7:

[0055] The device displays the received design proposals to the user. The user can visually review these designs and provide feedback.

[0056] Step 8:

[0057] Users provide feedback on specific revision requests for the presented design proposals. This feedback is sent from the device to the server.

[0058] Step 9:

[0059] The server receives user feedback, and an AI agent modifies the design. A new, revised version is generated.

[0060] Step 10:

[0061] The server sends the revised design proposal back to the terminal. This process is repeated until a design that satisfies the user is finalized.

[0062] Step 11:

[0063] Once the user confirms the final design, that information is sent to the server via the device.

[0064] Step 12:

[0065] The server converts the finalized design into a data format that can be manufactured using a 3D printing machine.

[0066] Step 13:

[0067] The server sends the manufacturing format to the 3D printing machine and instructs it to automatically produce the product.

[0068] Step 14:

[0069] A 3D printing machine creates products based on received data. The products undergo quality checks before being delivered to the user.

[0070] (Example 1)

[0071] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0072] Existing product design customization processes struggle to respond flexibly to user preferences and needs, making it difficult to quickly generate satisfactory designs. As a result, users are forced to repeatedly experiment and expend time and effort during the design process. Furthermore, the lack of technology to automatically generate products based on individual user needs makes it difficult to provide products that meet individual requests.

[0073] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0074] In this invention, the server includes means for the user to provide requests to the terminal through input information, means for the terminal to receive user input and collect external data, and means for analyzing the collected data with an AI agent and creating design proposals using a generative AI model. This enables the rapid generation of customized designs according to user preferences and the automated generation of products.

[0075] A "user" is someone who uses the system to customize product designs and provides input through a terminal.

[0076] A "terminal" is an electronic device that provides an interface for users to input information and review the generated design proposals.

[0077] A "server" is a central processing unit that receives input information from users, collects external data, and generates and modifies design proposals using AI agents.

[0078] "External data" refers to additional information used to analyze user preferences, such as social media information and past purchase history.

[0079] An "AI agent" is a program that uses machine learning algorithms to analyze collected data and evaluate user preferences.

[0080] A "generative AI model" is a mathematical model that uses artificial intelligence to generate design proposals that meet user needs based on input data.

[0081] A "design proposal" is a visual suggestion of a product generated by an AI agent based on user preferences and external data.

[0082] "Feedback" is the act of a user providing their opinions and requests regarding a design proposal that has been presented to them.

[0083] A "manufacturing format" is a set of technical instructions for generating a physical product from a final design using a 3D printing machine or similar device.

[0084] "Three-dimensional printing technology" is a manufacturing method that generates physical products in three dimensions based on a manufacturing format.

[0085] This invention is a system for effectively generating and manufacturing product designs tailored to the individual needs of users. It primarily consists of terminals, servers, AI agents, and 3D printing technology.

[0086] The terminal has a dedicated software application installed and provides a user-friendly interface for entering information. Users use this terminal to input their preferences and requests, including detailed selections of color, design style, intended use, and materials.

[0087] The server receives information sent from the terminal and, if necessary, collects external data authorized by the user in real time. An AI agent is deployed within the server, and this agent analyzes the data using a generative AI model. The AI ​​agent has the ability to accurately understand user preferences using machine learning algorithms and generate optimal design proposals.

[0088] The design proposal generated by the server is sent to the terminal and presented to the user visually. The user reviews this design proposal and provides feedback as needed. For example, they can provide detailed feedback such as "I want the shape to be more angular" or "I want the colors to be a little more vibrant."

[0089] Once the user finalizes the design, the server converts the design data into a manufacturing format. Based on this format, the product is automatically generated using 3D printing technology. For example, if a user wants to create a lampshade with a unique shape and warm colors for their home decor, it is possible to generate a specific design.

[0090] As a concrete example of a prompt, when a user enters "a lampshade with a unique shape and warm colors" into the terminal, the server generates and visualizes the best suggestions based on past history and trending designs.

[0091] In this way, the present invention is a system that enables the custom generation and rapid delivery of products tailored to the individual needs of each user.

[0092] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0093] Step 1:

[0094] The user opens a software application on their device and enters their design preferences and requests. This input includes detailed items such as color, design style, intended use, and materials. This input data is sent from the device to the server, initiating processing.

[0095] Step 2:

[0096] The server collects user input data received from the terminal. Furthermore, if the user grants permission, it also collects external data such as social media information and past purchase history. This data collection prepares the server to gain a more precise understanding of user preferences. The data is then passed on to the next AI agent for analysis.

[0097] Step 3:

[0098] The AI ​​agent installed on the server analyzes the collected data. It uses a generative AI model and machine learning algorithms to perform the data analysis. The output of this step is a design proposal based on user preferences. Based on the analysis results, it generates the optimal design proposal.

[0099] Step 4:

[0100] The server sends the generated design proposal to the terminal. The terminal visualizes this design proposal and presents it to the user on the screen. The user reviews the presented design proposal and provides feedback, including any necessary revisions or additional requests.

[0101] Step 5:

[0102] The server receives information from the user who entered feedback into the terminal. The server sends this feedback to the AI ​​agent, which then initiates the process of revising the design proposal. The AI ​​agent improves the design based on the feedback and generates a new design proposal.

[0103] Step 6:

[0104] The user reviews and confirms the final design on their device. The server converts this confirmed design into a manufacturing format. This format conversion prepares the design proposal for transmission to the 3D printing equipment as a concrete product specification.

[0105] Step 7:

[0106] The server transmits the manufacturing format to equipment compatible with 3D printing technology, initiating physical product generation. The 3D printing equipment automatically generates the product based on the format. The final product undergoes quality checks and is delivered to the user. In this step, all input and data processing are accomplished through physical results.

[0107] (Application Example 1)

[0108] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0109] In today's world, designing products that quickly and accurately reflect individual user preferences and needs is crucial for keeping pace with the rapid pace of life. However, current systems lack sufficient technology to receive voice commands, instantly visualize them, and flexibly modify designs while repeatedly incorporating user feedback. In addition, there is a lack of integration to easily utilize 3D manufacturing technology to realize custom designs.

[0110] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0111] In this invention, the server includes means for receiving preference information input from the user via voice, means for collecting external data and converting the content of the received voice, and means for analyzing the user's preferences based on the collected data. This enables immediate visualization of designs based on user voice instructions, rapid incorporation of user feedback, and three-dimensional manufacturing of custom designs.

[0112] A "user" is an individual or group that uses the system to input their preferences and requests.

[0113] "Receiving via voice" means collecting user instructions and preference information audibly using speech recognition technology.

[0114] "External data" refers to information outside the system, such as social media data and past purchase history, that is used to reinforce user preferences.

[0115] "Converting audio content" refers to the process of converting acquired audio instructions into a format that can be processed as text or data.

[0116] "Analyzing preferences" is the process of evaluating user preferences and trends from collected information and generating optimal design proposals.

[0117] "Generating and visualizing design proposals" means designing based on analysis results and presenting those results in an easy-to-understand manner for the user.

[0118] "Revising the design based on user feedback and re-presenting it" is the process of refreshing the design based on user evaluations and requests, and then displaying it again.

[0119] A "three-dimensional manufacturing format" is a data format used to physically manufacture a generated design.

[0120] "Automatically generating products" refers to the process of materializing digital designs into physical products using three-dimensional manufacturing equipment.

[0121] In a system implementing this invention, users can customize interior designs using voice commands via a home robot. Users can easily generate design proposals by communicating their desired design elements through a voice recognition system. The server uses the "SpeechRecognition" library to convert the user's voice instructions into text data. This converted data is sent to an AI agent, where machine learning frameworks such as "scikit-learn" or "TENSORFLOW®" are used to analyze the user's preferences.

[0122] The terminal generates design proposals based on the analysis results and visualizes them using tools such as "matplotlib" and "PyQt". The design proposals are presented to the user, and feedback can be received immediately. The server collects the user's feedback and modifies and updates the design as needed.

[0123] The finalized design is converted into a three-dimensional manufacturing format using the "py3DPrint" module and then materialized as a physical product using a 3D printer. This allows users to obtain custom-designed interior products.

[0124] As a concrete example, consider a scenario where a user speaks to a household robot and says, "I want an antique-style bookshelf." Upon receiving this instruction, the server generates a design for an antique-looking bookshelf and displays it on the terminal. If the user then provides feedback such as, "Make it look a bit more luxurious," the server incorporates this feedback, readjusts the design, and presents it to the user again.

[0125] For example, enter the following prompt:

[0126] User enters desired interior design: "Antique-style bookshelf"

[0127] Expected result from the AI: "Generate a bookshelf design that has an antique feel while incorporating modern functionality."

[0128] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0129] Step 1:

[0130] The user inputs voice commands to the home robot. An example of a voice command is to say to the robot, "I want an antique-style bookshelf."

[0131] Step 2:

[0132] The server uses speech recognition technology to convert the user's voice into text. The technology used is the "SpeechRecognition" library, which analyzes the user's voice data and converts it into text format. For example, the voice command "I want an antique-style bookshelf" will be output as text data.

[0133] Step 3:

[0134] The server sends the converted text data to an AI agent for analysis. For example, it uses "scikit-learn" or "TensorFlow" to analyze user preferences and trends. The input is text data, and the output is a preference model as a result of the analysis.

[0135] Step 4:

[0136] The device generates design proposals based on a preference model transmitted from the AI ​​agent. During this process, a "generative AI model" is used to generate and visualize designs that match the user's requests. The output of this process is a design image to be presented to the user on the display.

[0137] Step 5:

[0138] The user reviews the presented design proposal and, if necessary, enters feedback into the device. For example, they might provide feedback such as, "Make it look a bit more premium."

[0139] Step 6:

[0140] The server modifies the design based on user feedback, updates the design proposal again via the AI ​​agent, and re-visualizes the modified design. The re-visualized design is then sent to the terminal and presented to the user.

[0141] Step 7:

[0142] Once the final design is decided, the terminal uses the "py3DPrint" module to convert the design into a 3D manufacturing format. For example, this conversion to a manufacturing format transforms the bookshelf design data into a format that can be 3D printed.

[0143] Step 8:

[0144] The server or terminal transmits the generated 3D manufacturing format to a 3D printing machine, where it is materialized as a physical product. As a result, the user's custom-made bookshelf is manufactured.

[0145] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0146] This invention is a system that provides more sophisticatedly personalized products by utilizing not only user preference information but also user emotional information. The system is equipped with an emotion engine that recognizes and analyzes the emotions of the user when they input information, allowing the user's temporary mood and reactions to be reflected in the design process.

[0147] Users input their design preferences and requests through their devices. The emotion engine senses the user's facial expressions and tone of voice during input, collecting emotional data. This process runs in parallel with the user's text input, enabling the system to capture the user's intuitive emotions.

[0148] The server analyzes user-provided preference information, historical data, and emotional data. The AI ​​agent combines this data and, through a machine learning model, automatically generates design proposals that take into account the user's long-term preferences and temporary emotions. This allows for proposals that resonate more emotionally than those based solely on preferences.

[0149] The device presents the generated design proposal to the user, recording their feedback along with their emotional state at that time. When the user provides feedback on desired changes, the server again utilizes the emotional data to adjust the design. Here, the server senses the user's emotions while providing feedback and uses this to further refine the design proposal.

[0150] Finally, once the user approves the design, the server converts it into a manufacturing format for the 3D printing machine. The manufacturing format is sent to the printing machine, which automatically generates the physical product. After the product is completed, a quality check is performed, and if there are no problems, it is delivered to the user.

[0151] As a concrete example, consider a user ordering a custom-made gift for a special event. When the user types "I want a sophisticated, high-end jewelry box" into their device, the emotion engine determines whether the user is tense or relaxed. If tense, the design options will be more formal and feature subdued colors. On the other hand, if relaxed, casual and playful designs will be presented. In either case, the emotions the user is feeling when providing feedback are taken into consideration, and further design modifications are made to ultimately create a highly personalized product.

[0152] The following describes the processing flow.

[0153] Step 1:

[0154] The user uses a device to log in to the system and input their design preferences and requirements. This input includes details such as the design's color, shape, materials, and intended use. With the user's permission, the device also records the user's emotional state via the microphone and camera.

[0155] Step 2:

[0156] The device transmits user preference information and emotional data to the server. Emotional data is acquired in real time from the user's facial expressions and voice while they are typing.

[0157] Step 3:

[0158] The server simultaneously collects the received preference information, sentiment data, and, if necessary, past purchase history and external data, then integrates and analyzes this data.

[0159] Step 4:

[0160] The AI ​​agent on the server uses all the collected data to analyze user preferences and emotions. Machine learning algorithms then generate design proposals that combine long-term preferences with temporary emotions.

[0161] Step 5:

[0162] The server sends the generated design proposal to the terminal. The design proposal also includes an explanation of how the user's emotional state is reflected.

[0163] Step 6:

[0164] The device visually presents design proposals to the user. The user reviews the design proposals and provides feedback if necessary.

[0165] Step 7:

[0166] The device sends user feedback and sentiment data to the server. This feedback includes specific design modifications and new requests.

[0167] Step 8:

[0168] The server considers user feedback and their emotions at the time, and an AI agent readjusts the design. A newly revised version is generated and sent back to the device.

[0169] Step 9:

[0170] Repeat the process from steps 6 to 8 until the user is satisfied.

[0171] Step 10:

[0172] Once the user confirms the final design, that information is sent to the server via the device.

[0173] Step 11:

[0174] The server converts the final design into a manufacturing format for the 3D printing machine. This format includes the data necessary for specific manufacturing.

[0175] Step 12:

[0176] The server sends the manufacturing format to the 3D printing machine and commands it to automatically produce the product.

[0177] Step 13:

[0178] The 3D printing machine manufactures products based on the submitted design data. After manufacturing, a quality check is performed, and if it passes, the product is shipped to the user.

[0179] (Example 2)

[0180] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0181] In providing personalized designs based on user preferences, conventional systems have faced the challenge of not adequately reflecting temporary emotional shifts or intuitive reactions. Furthermore, there is a growing need to consider the user's emotional state when generating design proposals to create product suggestions that resonate more emotionally.

[0182] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0183] In this invention, the server includes means for collecting user preference information and emotional information, means for analyzing the preference information and emotional information and identifying the user's emotional state using an emotional engine, and means for generating and presenting design proposals based on the analysis results using a generative AI model. This makes it possible to provide more precisely personalized design proposals that take into account the user's temporary emotional state, in addition to design proposals based solely on preferences.

[0184] "User preference information" refers to information that indicates a user's individual preferences and tastes, reflecting their interest in specific designs or products.

[0185] "Emotional information" refers to data that indicates a user's temporary emotional state or psychological response, and includes information obtained from facial expressions, tone of voice, and behavior.

[0186] An "emotion engine" is a system or technology that collects and analyzes user emotional information to identify their emotional state.

[0187] A "generative AI model" is a program that uses machine learning techniques to analyze data and implements an algorithm that provides optimal design suggestions based on user preferences and emotions.

[0188] A "manufacturing instruction format" is a format required when generating a product based on design data, and it is a format for transmitting instructions to manufacturing equipment.

[0189] "Article" is a general term referring to products or goods that are physically formed based on a generated design.

[0190] A "three-dimensional forming device" refers to a device that automatically forms three-dimensional objects based on digital data, and generally refers to a 3D printer.

[0191] This invention is a system that utilizes emotional information in addition to user preference information to provide more precisely personalized products. One embodiment of this invention provides a means for creating products that reflect the user's temporary emotional changes by analyzing the user's emotional state in real time and making design suggestions based on that analysis.

[0192] In implementing this system, users first input their design preferences and requests using a terminal. The terminal is equipped with a camera and microphone, which sense the user's facial expressions and voice tone, and analyze this using an emotion engine. This analysis is performed in real time, and the user's emotional information is identified.

[0193] The collected emotional and preference information is sent to a server. The server uses a generative AI model to comprehensively analyze this information and generate design proposals that take into account both the user's long-term preferences and temporary emotions. Machine learning techniques are used in this generation process.

[0194] The generated design proposals are presented to the user via their device. The user provides feedback on the presented designs, and their emotional state during this process is continuously analyzed. Based on the feedback and emotional data, the server refines the design proposals and makes a final proposal. This process makes it possible to create products that resonate with the user's emotions.

[0195] As a concrete example, consider a user who wants a custom-made gift for a specific event. When the user enters "I want a sophisticated, high-end jewelry box" into the device, the emotion engine determines the user's mood. If the user is nervous, more formal and subdued design options are suggested; if the user is relaxed, casual and playful design options are suggested.

[0196] One example of a prompt to input into a generative AI model is, "What design changes should be suggested when the user is feeling stressed?" Based on this prompt, the system can derive the optimal design changes that respond to the user's emotions.

[0197] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0198] Step 1:

[0199] The user inputs their design preferences and requests through the device. The device receives the text input and records it as preference information. It also uses the camera and microphone to sense the user's facial expressions and tone of voice, collecting emotional information in real time. As a result, the user's preference information and emotional information are aggregated as input data on the device.

[0200] Step 2:

[0201] The device sends collected preference and emotional information to the server. The server receives this data as input and performs data analysis using a machine learning model. The main purpose of the analysis is to identify the user's long-term preferences and temporary emotional states. This outputs basic data for generating design proposals.

[0202] Step 3:

[0203] The server uses a generative AI model to generate design proposals based on the analysis results. The generative AI model performs data calculations to create designs optimized for the user's preferences and emotions. As output, personalized design proposals are prepared.

[0204] Step 4:

[0205] The server sends the generated design proposal to the terminal. The terminal presents the design proposal to the user and requests feedback. The user reviews the design proposal and provides feedback by entering desired revisions and opinions. This feedback information is then obtained as new input data.

[0206] Step 5:

[0207] The device resends new emotional information collected simultaneously with user feedback to the server. The server uses this as input to analyze it again and adjust the design proposal. The adjusted design proposal is then output after further data processing based on the user's opinions and emotions.

[0208] Step 6:

[0209] Once the user is satisfied with the final design, the server converts the final design into a manufacturing instruction format. This conversion process involves data calculations using CAD software to complete the physical preparation for manufacturing the product. The output is the manufacturing instruction format.

[0210] Step 7:

[0211] The server transmits the manufacturing instruction format to the 3D forming machine. The 3D forming machine uses this as input and enters the process of automatically generating the product. Finally, the finished product is output and, after quality checks, is delivered to the user.

[0212] (Application Example 2)

[0213] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0214] There is a growing demand to provide a purchasing experience that is not only based on user preferences but also on emotions. Modern consumers expect not just products that match their hobbies and tastes, but also personalized product suggestions that reflect their emotions and moods at the time. However, conventional systems struggle to incorporate such emotionally conscious design suggestions, resulting in limited improvements to the user experience.

[0215] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0216] In this invention, the server includes means for collecting preference information and emotional data input by the user, means for analyzing the user's emotional information and preferences based on the collected data, and means for generating and presenting design proposals based on the analysis. This makes it possible to provide personalized product suggestions that take the user's emotions into consideration.

[0217] "Preference information" refers to data that shows the user's preferences and patterns regarding products and designs.

[0218] "Emotional data" refers to data that indicates the user's emotional state at a given time, obtained from things like their facial expressions and tone of voice.

[0219] "Analysis means" refers to a part of a system that includes a process for analyzing user intentions and emotions from collected preference information and emotional data, and for making relevant suggestions.

[0220] "Design proposals" refer to suggestions for product design and decoration generated based on user preference information and emotional data.

[0221] "Three-dimensional data" refers to data containing three-dimensional shape information constructed in digital space, and is used in the physical manufacturing of goods.

[0222] "Means for automatically creating articles" refers to a mechanism that forms a physical product by layering or processing materials based on input three-dimensional data.

[0223] A "three-dimensional modeling device" is a device that constructs a physical shape by layering materials three-dimensionally based on three-dimensional data.

[0224] As an embodiment of this invention, an emotion-customized shopping system is described. The server collects preference information and emotion data entered by the user from the terminal and comprehensively analyzes this data through analysis means. The data analysis mainly uses facial recognition technology and voice analysis technology. Specifically, the user's facial expressions acquired using a camera are analyzed using the TensorFlow API, and the tone of voice collected through a microphone is analyzed using the Amazon Polly API, a natural language processing technology. Based on this emotion data and preference information, the server generates an optimal design proposal and presents it to the terminal.

[0225] The device is equipped with an application that visualizes the generated design proposals, allowing users to review them. When users provide feedback, their emotions are analyzed, and the design is readjusted as needed. Throughout this feedback process, the user's emotions are constantly monitored by a server and used to make final adjustments to the design.

[0226] The final design is converted into 3D data, allowing the 3D modeling device to output it as a physical product. The design data used here is automatically formatted via the AutoCAD API and other means, facilitating the automated manufacturing of the items.

[0227] For example, if a user enters "I want a unique mug as a birthday present" into the device, and their emotional state is relaxed, colorful and playful design options will be suggested. Conversely, if they are stressed, more calming designs will be presented. An example of a prompt message would be, "Capture a photo of the user and analyze their voice using the Amazon Polly API. Based on the emotional information obtained, generate the optimal product design using the AutoCAD API."

[0228] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0229] Step 1:

[0230] The device captures the user's facial expressions with a camera and records their voice tone with a microphone. The resulting visual and audio data are sent to the server as emotion data. In this step, the input is the user's facial image and voice, and the output is emotion data.

[0231] Step 2:

[0232] The server processes the received emotion data, analyzes emotions from facial expressions using TensorFlow API with facial recognition technology, and analyzes emotions from speech using Amazon Polly API. As a result of this data processing, the user's specific emotional state is derived. The input is emotion data, and the output is the emotion analysis result.

[0233] Step 3:

[0234] The server integrates preference information obtained from the user with the sentiment analysis results obtained in step 2, and generates personalized design proposals using a generative AI model. This data processing proposes a design optimized for the user's emotions and preferences. The input is preference information and sentiment analysis results, and the output is the design proposal.

[0235] Step 4:

[0236] The device presents the generated design proposal to the user and accepts user feedback. This step collects satisfaction levels with the design and specific revision requests. The input is the design proposal, and the output is the feedback.

[0237] Step 5:

[0238] The server re-analyzes the emotional data obtained simultaneously with user feedback and adjusts the design proposal as needed. By performing another emotional analysis, the design is revised to reflect the emotional information. The input is the feedback content and emotional data, and the output is the revised design proposal.

[0239] Step 6:

[0240] The final design proposal is converted into 3D data and sent from the server to the 3D printing machine. Here, the AutoCAD API is used to convert the data, allowing the manufacturing of the item to proceed automatically. The input is the revised design proposal, and the output is the manufacturing format.

[0241] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0242] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0243] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0244] [Second Embodiment]

[0245] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0246] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0247] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0248] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0249] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0250] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0251] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0252] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0253] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0254] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0255] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0256] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0257] The system of this invention is designed for use at home or in the office by installing a dedicated software application on a terminal. Users input information describing their preferences and requests through the terminal. The input interface is designed to be visually easy for users to understand, and offers choices regarding color, design style, application, and materials.

[0258] The server collects information provided by the user, as well as relevant external data such as social media activity and past purchase history, if permitted by the user, in real time. This allows for a precise understanding of user preferences and trends.

[0259] An AI agent on the server analyzes the collected data and uses machine learning algorithms to evaluate user preferences. Based on this, the AI ​​automatically generates optimal design proposals and sends them to the terminal. The terminal presents the user with visually visualized design proposals and has a feedback function to receive user feedback.

[0260] When a user provides feedback requesting revisions to a design proposal, the server receives the feedback, and an AI agent revises the design and generates a new proposal. This process is repeated until the user is satisfied with the design.

[0261] Finally, after the user reviews and approves the design, the server converts the design data into a manufacturing format and sends it to the 3D printer. The 3D printer automatically creates the product based on this manufacturing format. The finished product undergoes quality checks and is then delivered to the user.

[0262] As a concrete example, consider a scenario where a user wants to create a new lampshade to personalize their home decor. The user enters "a lampshade with a unique shape and warm colors" into their terminal. The server generates and visualizes suitable designs based on past purchase history and popular trends, presenting them to the user. If the user provides feedback that they would like the shape to be a little more angular, the AI ​​generates a revised version based on that information and presents it to the user again. Finally, the user confirms a design they are satisfied with, and a 3D printing machine brings it to life.

[0263] The following describes the processing flow.

[0264] Step 1:

[0265] Users log in to the system via their device and enter information about their design preferences and desires. This includes specific requirements such as desired colors, shapes, materials, and intended use.

[0266] Step 2:

[0267] The terminal sends the data entered by the user to the server.

[0268] Step 3:

[0269] The server receives user input information and, in addition, collects SNS data and past purchase history that the user has authorized.

[0270] Step 4:

[0271] The AI ​​agent on the server analyzes the collected data to identify user preferences and trends. Machine learning algorithms are used in this process.

[0272] Step 5:

[0273] The server generates design proposals based on the analysis results. The AI ​​agent creates multiple design proposals that reflect the user's preferences.

[0274] Step 6:

[0275] The server sends the generated design proposal to the terminal.

[0276] Step 7:

[0277] The device displays the received design proposals to the user. The user can visually review these designs and provide feedback.

[0278] Step 8:

[0279] The user provides specific feedback on the proposed design, and this feedback is sent from the terminal to the server.

[0280] Step 9:

[0281] The server receives the user's feedback, and the AI agent modifies the design, generating a new revised version.

[0282] Step 10:

[0283] The server sends the revised design back to the terminal again. This process is repeated until a design that satisfies the user is completed.

[0284] Step 11:

[0285] When the user finalizes the final design, the terminal sends this information to the server.

[0286] Step 12:

[0287] The server converts the finalized design into a data format that can be manufactured by a 3D printing device.

[0288] Step 13:

[0289] The server sends the manufacturing format to the 3D printing device and instructs it to automatically generate the product.

[0290] Step 14:

[0291] The 3D printing device creates the product based on the received data. The product undergoes quality inspection and is then delivered to the user.

[0292] (Example 1)

[0293] Next, Example 1 will be described. In the following description, the data processing device 12 is referred to as the "server", and the smart glasses 214 are referred to as the "terminal".

[0294] Existing product design customization processes struggle to respond flexibly to user preferences and needs, making it difficult to quickly generate satisfactory designs. As a result, users are forced to repeatedly experiment and expend time and effort during the design process. Furthermore, the lack of technology to automatically generate products based on individual user needs makes it difficult to provide products that meet individual requests.

[0295] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0296] In this invention, the server includes means for the user to provide requests to the terminal through input information, means for the terminal to receive user input and collect external data, and means for analyzing the collected data with an AI agent and creating design proposals using a generative AI model. This enables the rapid generation of customized designs according to user preferences and the automated generation of products.

[0297] A "user" is someone who uses the system to customize product designs and provides input through a terminal.

[0298] A "terminal" is an electronic device that provides an interface for users to input information and review the generated design proposals.

[0299] A "server" is a central processing unit that receives input information from users, collects external data, and generates and modifies design proposals using AI agents.

[0300] "External data" refers to additional information used to analyze user preferences, such as social media information and past purchase history.

[0301] An "AI agent" is a program that uses machine learning algorithms to analyze collected data and evaluate user preferences.

[0302] The "generative AI model" is a mathematical model using artificial intelligence for creating design proposals corresponding to user needs based on input data.

[0303] The "design proposal" is a visual proposal of a product generated by an AI agent based on user preferences and external data.

[0304] "Feedback" is the act of returning one's opinions and desires regarding a design proposal presented by a user.

[0305] The "manufacturing format" is a set of technical instructions for generating a final design proposal as a physical product using a three-dimensional printing device or the like.

[0306] The "three-dimensional printing technology" is a manufacturing method for three-dimensionally generating a physical product based on a manufacturing format.

[0307] The present invention is a system for effectively generating and manufacturing a product design according to individual user needs. It is mainly composed of a terminal, a server, an AI agent, and three-dimensional printing technology.

[0308] The terminal has a dedicated software application installed and provides an easy-to-use interface for the user to input information. The user uses this terminal to input their own preferences and desires. This includes detailed selections such as color, design style, usage, material, and the like.

[0309] The server receives the information transmitted from the terminal and has a function of collecting external data permitted by the user in real time as necessary. An AI agent is arranged in the server, and this agent analyzes data by utilizing a generative AI model. The AI agent has the ability to accurately grasp the user's preferences using machine learning algorithms and generate an optimal design proposal.

[0310] The design proposal generated by the server is sent to the terminal and presented to the user visually. The user reviews this design proposal and provides feedback as needed. For example, they can provide detailed feedback such as "I want the shape to be more angular" or "I want the colors to be a little more vibrant."

[0311] Once the user finalizes the design, the server converts the design data into a manufacturing format. Based on this format, the product is automatically generated using 3D printing technology. For example, if a user wants to create a lampshade with a unique shape and warm colors for their home decor, it is possible to generate a specific design.

[0312] As a concrete example of a prompt, when a user enters "a lampshade with a unique shape and warm colors" into the terminal, the server generates and visualizes the best suggestions based on past history and trending designs.

[0313] In this way, the present invention is a system that enables the custom generation and rapid delivery of products tailored to the individual needs of each user.

[0314] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0315] Step 1:

[0316] The user opens a software application on their device and enters their design preferences and requests. This input includes detailed items such as color, design style, intended use, and materials. This input data is sent from the device to the server, initiating processing.

[0317] Step 2:

[0318] The server collects user input data received from the terminal. Furthermore, if the user grants permission, it also collects external data such as social media information and past purchase history. This data collection prepares the server to gain a more precise understanding of user preferences. The data is then passed on to the next AI agent for analysis.

[0319] Step 3:

[0320] The AI ​​agent installed on the server analyzes the collected data. It uses a generative AI model and machine learning algorithms to perform the data analysis. The output of this step is a design proposal based on user preferences. Based on the analysis results, it generates the optimal design proposal.

[0321] Step 4:

[0322] The server sends the generated design proposal to the terminal. The terminal visualizes this design proposal and presents it to the user on the screen. The user reviews the presented design proposal and provides feedback, including any necessary revisions or additional requests.

[0323] Step 5:

[0324] The server receives information from the user who entered feedback into the terminal. The server sends this feedback to the AI ​​agent, which then initiates the process of revising the design proposal. The AI ​​agent improves the design based on the feedback and generates a new design proposal.

[0325] Step 6:

[0326] The user reviews and confirms the final design on their device. The server converts this confirmed design into a manufacturing format. This format conversion prepares the design proposal for transmission to the 3D printing equipment as a concrete product specification.

[0327] Step 7:

[0328] The server transmits the manufacturing format to equipment compatible with 3D printing technology, initiating physical product generation. The 3D printing equipment automatically generates the product based on the format. The final product undergoes quality checks and is delivered to the user. In this step, all input and data processing are accomplished through physical results.

[0329] (Application Example 1)

[0330] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0331] In today's world, designing products that quickly and accurately reflect individual user preferences and needs is crucial for keeping pace with the rapid pace of life. However, current systems lack sufficient technology to receive voice commands, instantly visualize them, and flexibly modify designs while repeatedly incorporating user feedback. In addition, there is a lack of integration to easily utilize 3D manufacturing technology to realize custom designs.

[0332] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0333] In this invention, the server includes means for receiving preference information input from the user via voice, means for collecting external data and converting the content of the received voice, and means for analyzing the user's preferences based on the collected data. This enables immediate visualization of designs based on user voice instructions, rapid incorporation of user feedback, and three-dimensional manufacturing of custom designs.

[0334] A "user" is an individual or group that uses the system to input their preferences and requests.

[0335] "Receiving via voice" means collecting user instructions and preference information audibly using speech recognition technology.

[0336] "External data" refers to information outside the system, such as social media data and past purchase history, that is used to reinforce user preferences.

[0337] "Converting audio content" refers to the process of converting acquired audio instructions into a format that can be processed as text or data.

[0338] "Analyzing preferences" is the process of evaluating user preferences and trends from collected information and generating optimal design proposals.

[0339] "Generating and visualizing design proposals" means designing based on analysis results and presenting those results in an easy-to-understand manner for the user.

[0340] "Revising the design based on user feedback and re-presenting it" is the process of refreshing the design based on user evaluations and requests, and then displaying it again.

[0341] A "three-dimensional manufacturing format" is a data format used to physically manufacture a generated design.

[0342] "Automatically generating products" refers to the process of materializing digital designs into physical products using three-dimensional manufacturing equipment.

[0343] In a system implementing this invention, users can customize interior designs using voice commands via a home robot. Users can easily generate design proposals by communicating their desired design elements through a voice recognition system. The server uses the "SpeechRecognition" library to convert the user's voice instructions into text data. This converted data is sent to an AI agent, where machine learning frameworks such as "scikit-learn" or "TensorFlow" are used to analyze the user's preferences.

[0344] The terminal generates design proposals based on the analysis results and visualizes them using tools such as "matplotlib" and "PyQt". The design proposals are presented to the user, and feedback can be received immediately. The server collects the user's feedback and modifies and updates the design as needed.

[0345] The finalized design is converted into a three-dimensional manufacturing format using the "py3DPrint" module and then materialized as a physical product using a 3D printer. This allows users to obtain custom-designed interior products.

[0346] As a concrete example, consider a scenario where a user speaks to a household robot and says, "I want an antique-style bookshelf." Upon receiving this instruction, the server generates a design for an antique-looking bookshelf and displays it on the terminal. If the user then provides feedback such as, "Make it look a bit more luxurious," the server incorporates this feedback, readjusts the design, and presents it to the user again.

[0347] For example, enter the following prompt:

[0348] User enters desired interior design: "Antique-style bookshelf"

[0349] Expected result from the AI: "Generate a bookshelf design that has an antique feel while incorporating modern functionality."

[0350] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0351] Step 1:

[0352] The user inputs voice commands to the home robot. An example of a voice command is to say to the robot, "I want an antique-style bookshelf."

[0353] Step 2:

[0354] The server uses speech recognition technology to convert the user's voice into text. The technology used is the "SpeechRecognition" library, which analyzes the user's voice data and converts it into text format. For example, the voice command "I want an antique-style bookshelf" will be output as text data.

[0355] Step 3:

[0356] The server sends the converted text data to an AI agent for analysis. For example, it uses "scikit-learn" or "TensorFlow" to analyze user preferences and trends. The input is text data, and the output is a preference model as a result of the analysis.

[0357] Step 4:

[0358] The device generates design proposals based on a preference model transmitted from the AI ​​agent. During this process, a "generative AI model" is used to generate and visualize designs that match the user's requests. The output of this process is a design image to be presented to the user on the display.

[0359] Step 5:

[0360] The user reviews the presented design proposal and, if necessary, enters feedback into the device. For example, they might provide feedback such as, "Make it look a bit more premium."

[0361] Step 6:

[0362] The server modifies the design based on user feedback, updates the design proposal again via the AI ​​agent, and re-visualizes the modified design. The re-visualized design is then sent to the terminal and presented to the user.

[0363] Step 7:

[0364] Once the final design is decided, the terminal uses the "py3DPrint" module to convert the design into a 3D manufacturing format. For example, this conversion to a manufacturing format transforms the bookshelf design data into a format that can be 3D printed.

[0365] Step 8:

[0366] The server or terminal transmits the generated 3D manufacturing format to a 3D printing machine, where it is materialized as a physical product. As a result, the user's custom-made bookshelf is manufactured.

[0367] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0368] This invention is a system that provides more sophisticatedly personalized products by utilizing not only user preference information but also user emotional information. The system is equipped with an emotion engine that recognizes and analyzes the emotions of the user when they input information, allowing the user's temporary mood and reactions to be reflected in the design process.

[0369] Users input their design preferences and requests through their devices. The emotion engine senses the user's facial expressions and tone of voice during input, collecting emotional data. This process runs in parallel with the user's text input, enabling the system to capture the user's intuitive emotions.

[0370] The server analyzes user-provided preference information, historical data, and emotional data. The AI ​​agent combines this data and, through a machine learning model, automatically generates design proposals that take into account the user's long-term preferences and temporary emotions. This allows for proposals that resonate more emotionally than those based solely on preferences.

[0371] The device presents the generated design proposal to the user, recording their feedback along with their emotional state at that time. When the user provides feedback on desired changes, the server again utilizes the emotional data to adjust the design. Here, the server senses the user's emotions while providing feedback and uses this to further refine the design proposal.

[0372] Finally, once the user approves the design, the server converts it into a manufacturing format for the 3D printing machine. The manufacturing format is sent to the printing machine, which automatically generates the physical product. After the product is completed, a quality check is performed, and if there are no problems, it is delivered to the user.

[0373] As a concrete example, consider a user ordering a custom-made gift for a special event. When the user types "I want a sophisticated, high-end jewelry box" into their device, the emotion engine determines whether the user is tense or relaxed. If tense, the design options will be more formal and feature subdued colors. On the other hand, if relaxed, casual and playful designs will be presented. In either case, the emotions the user is feeling when providing feedback are taken into consideration, and further design modifications are made to ultimately create a highly personalized product.

[0374] The following describes the processing flow.

[0375] Step 1:

[0376] The user uses a device to log in to the system and input their design preferences and requirements. This input includes details such as the design's color, shape, materials, and intended use. With the user's permission, the device also records the user's emotional state via the microphone and camera.

[0377] Step 2:

[0378] The device transmits user preference information and emotional data to the server. Emotional data is acquired in real time from the user's facial expressions and voice while they are typing.

[0379] Step 3:

[0380] The server simultaneously collects the received preference information, sentiment data, and, if necessary, past purchase history and external data, then integrates and analyzes this data.

[0381] Step 4:

[0382] The AI ​​agent on the server uses all the collected data to analyze user preferences and emotions. Machine learning algorithms then generate design proposals that combine long-term preferences with temporary emotions.

[0383] Step 5:

[0384] The server sends the generated design proposal to the terminal. The design proposal also includes an explanation of how the user's emotional state is reflected.

[0385] Step 6:

[0386] The device visually presents design proposals to the user. The user reviews the design proposals and provides feedback if necessary.

[0387] Step 7:

[0388] The device sends user feedback and sentiment data to the server. This feedback includes specific design modifications and new requests.

[0389] Step 8:

[0390] The server considers user feedback and their emotions at the time, and an AI agent readjusts the design. A newly revised version is generated and sent back to the device.

[0391] Step 9:

[0392] Repeat the process from steps 6 to 8 until the user is satisfied.

[0393] Step 10:

[0394] Once the user confirms the final design, that information is sent to the server via the device.

[0395] Step 11:

[0396] The server converts the final design into a manufacturing format for the 3D printing machine. This format includes the data necessary for specific manufacturing.

[0397] Step 12:

[0398] The server sends the manufacturing format to the 3D printing machine and commands it to automatically produce the product.

[0399] Step 13:

[0400] The 3D printing machine manufactures products based on the submitted design data. After manufacturing, a quality check is performed, and if it passes, the product is shipped to the user.

[0401] (Example 2)

[0402] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0403] In providing personalized designs based on user preferences, conventional systems have faced the challenge of not adequately reflecting temporary emotional shifts or intuitive reactions. Furthermore, there is a growing need to consider the user's emotional state when generating design proposals to create product suggestions that resonate more emotionally.

[0404] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0405] In this invention, the server includes means for collecting user preference information and emotional information, means for analyzing the preference information and emotional information and identifying the user's emotional state using an emotional engine, and means for generating and presenting design proposals based on the analysis results using a generative AI model. This makes it possible to provide more precisely personalized design proposals that take into account the user's temporary emotional state, in addition to design proposals based solely on preferences.

[0406] "User preference information" refers to information that indicates a user's individual preferences and tastes, reflecting their interest in specific designs or products.

[0407] "Emotional information" refers to data that indicates a user's temporary emotional state or psychological response, and includes information obtained from facial expressions, tone of voice, and behavior.

[0408] An "emotion engine" is a system or technology that collects and analyzes user emotional information to identify their emotional state.

[0409] A "generative AI model" is a program that uses machine learning techniques to analyze data and implements an algorithm that provides optimal design suggestions based on user preferences and emotions.

[0410] A "manufacturing instruction format" is a format required when generating a product based on design data, and it is a format for transmitting instructions to manufacturing equipment.

[0411] "Article" is a general term referring to products or goods that are physically formed based on a generated design.

[0412] A "three-dimensional forming device" refers to a device that automatically forms three-dimensional objects based on digital data, and generally refers to a 3D printer.

[0413] This invention is a system that utilizes emotional information in addition to user preference information to provide more precisely personalized products. One embodiment of this invention provides a means for creating products that reflect the user's temporary emotional changes by analyzing the user's emotional state in real time and making design suggestions based on that analysis.

[0414] In implementing this system, users first input their design preferences and requests using a terminal. The terminal is equipped with a camera and microphone, which sense the user's facial expressions and voice tone, and analyze this using an emotion engine. This analysis is performed in real time, and the user's emotional information is identified.

[0415] The collected emotional and preference information is sent to a server. The server uses a generative AI model to comprehensively analyze this information and generate design proposals that take into account both the user's long-term preferences and temporary emotions. Machine learning techniques are used in this generation process.

[0416] The generated design proposals are presented to the user via their device. The user provides feedback on the presented designs, and their emotional state during this process is continuously analyzed. Based on the feedback and emotional data, the server refines the design proposals and makes a final proposal. This process makes it possible to create products that resonate with the user's emotions.

[0417] As a concrete example, consider a user who wants a custom-made gift for a specific event. When the user enters "I want a sophisticated, high-end jewelry box" into the device, the emotion engine determines the user's mood. If the user is nervous, more formal and subdued design options are suggested; if the user is relaxed, casual and playful design options are suggested.

[0418] One example of a prompt to input into a generative AI model is, "What design changes should be suggested when the user is feeling stressed?" Based on this prompt, the system can derive the optimal design changes that respond to the user's emotions.

[0419] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0420] Step 1:

[0421] The user inputs their design preferences and requests through the device. The device receives the text input and records it as preference information. It also uses the camera and microphone to sense the user's facial expressions and tone of voice, collecting emotional information in real time. As a result, the user's preference information and emotional information are aggregated as input data on the device.

[0422] Step 2:

[0423] The device sends collected preference and emotional information to the server. The server receives this data as input and performs data analysis using a machine learning model. The main purpose of the analysis is to identify the user's long-term preferences and temporary emotional states. This outputs basic data for generating design proposals.

[0424] Step 3:

[0425] The server uses a generative AI model to generate design proposals based on the analysis results. The generative AI model performs data calculations to create designs optimized for the user's preferences and emotions. As output, personalized design proposals are prepared.

[0426] Step 4:

[0427] The server sends the generated design proposal to the terminal. The terminal presents the design proposal to the user and requests feedback. The user reviews the design proposal and provides feedback by entering desired revisions and opinions. This feedback information is then obtained as new input data.

[0428] Step 5:

[0429] The device resends new emotional information collected simultaneously with user feedback to the server. The server uses this as input to analyze it again and adjust the design proposal. The adjusted design proposal is then output after further data processing based on the user's opinions and emotions.

[0430] Step 6:

[0431] Once the user is satisfied with the final design, the server converts the final design into a manufacturing instruction format. This conversion process involves data calculations using CAD software to complete the physical preparation for manufacturing the product. The output is the manufacturing instruction format.

[0432] Step 7:

[0433] The server transmits the manufacturing instruction format to the 3D forming machine. The 3D forming machine uses this as input and enters the process of automatically generating the product. Finally, the finished product is output and, after quality checks, is delivered to the user.

[0434] (Application Example 2)

[0435] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0436] There is a growing demand to provide a purchasing experience that is not only based on user preferences but also on emotions. Modern consumers expect not just products that match their hobbies and tastes, but also personalized product suggestions that reflect their emotions and moods at the time. However, conventional systems struggle to incorporate such emotionally conscious design suggestions, resulting in limited improvements to the user experience.

[0437] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0438] In this invention, the server includes means for collecting preference information and emotional data input by the user, means for analyzing the user's emotional information and preferences based on the collected data, and means for generating and presenting design proposals based on the analysis. This makes it possible to provide personalized product suggestions that take the user's emotions into consideration.

[0439] "Preference information" refers to data that shows the user's preferences and patterns regarding products and designs.

[0440] "Emotional data" refers to data that indicates the user's emotional state at a given time, obtained from things like their facial expressions and tone of voice.

[0441] "Analysis means" refers to a part of a system that includes a process for analyzing user intentions and emotions from collected preference information and emotional data, and for making relevant suggestions.

[0442] "Design proposals" refer to suggestions for product design and decoration generated based on user preference information and emotional data.

[0443] "Three-dimensional data" refers to data containing three-dimensional shape information constructed in digital space, and is used in the physical manufacturing of goods.

[0444] "Means for automatically creating articles" refers to a mechanism that forms a physical product by layering or processing materials based on input three-dimensional data.

[0445] A "three-dimensional modeling device" is a device that constructs a physical shape by layering materials three-dimensionally based on three-dimensional data.

[0446] As an embodiment of this invention, an emotion-customized shopping system is described. The server collects preference information and emotion data entered by the user from the terminal and comprehensively analyzes this data through analysis means. The data analysis mainly uses facial recognition technology and voice analysis technology. Specifically, the user's facial expressions acquired using a camera are analyzed using the TensorFlow API, and the tone of voice collected through a microphone is analyzed using the Amazon Polly API, a natural language processing technology. Based on this emotion data and preference information, the server generates an optimal design proposal and presents it to the terminal.

[0447] The device is equipped with an application that visualizes the generated design proposals, allowing users to review them. When users provide feedback, their emotions are analyzed, and the design is readjusted as needed. Throughout this feedback process, the user's emotions are constantly monitored by a server and used to make final adjustments to the design.

[0448] The final design is converted into 3D data, allowing the 3D modeling device to output it as a physical product. The design data used here is automatically formatted via the AutoCAD API and other means, facilitating the automated manufacturing of the items.

[0449] For example, if a user enters "I want a unique mug as a birthday present" into the device, and their emotional state is relaxed, colorful and playful design options will be suggested. Conversely, if they are stressed, more calming designs will be presented. An example of a prompt message would be, "Capture a photo of the user and analyze their voice using the Amazon Polly API. Based on the emotional information obtained, generate the optimal product design using the AutoCAD API."

[0450] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0451] Step 1:

[0452] The device captures the user's facial expressions with a camera and records their voice tone with a microphone. The resulting visual and audio data are sent to the server as emotion data. In this step, the input is the user's facial image and voice, and the output is emotion data.

[0453] Step 2:

[0454] The server processes the received emotion data, analyzes emotions from facial expressions using TensorFlow API with facial recognition technology, and analyzes emotions from speech using Amazon Polly API. As a result of this data processing, the user's specific emotional state is derived. The input is emotion data, and the output is the emotion analysis result.

[0455] Step 3:

[0456] The server integrates preference information obtained from the user with the sentiment analysis results obtained in step 2, and generates personalized design proposals using a generative AI model. This data processing proposes a design optimized for the user's emotions and preferences. The input is preference information and sentiment analysis results, and the output is the design proposal.

[0457] Step 4:

[0458] The device presents the generated design proposal to the user and accepts user feedback. This step collects satisfaction levels with the design and specific revision requests. The input is the design proposal, and the output is the feedback.

[0459] Step 5:

[0460] The server re-analyzes the emotional data obtained simultaneously with user feedback and adjusts the design proposal as needed. By performing another emotional analysis, the design is revised to reflect the emotional information. The input is the feedback content and emotional data, and the output is the revised design proposal.

[0461] Step 6:

[0462] The final design proposal is converted into 3D data and sent from the server to the 3D printing machine. Here, the AutoCAD API is used to convert the data, allowing the manufacturing of the item to proceed automatically. The input is the revised design proposal, and the output is the manufacturing format.

[0463] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0464] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0465] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0466] [Third Embodiment]

[0467] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0468] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0469] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0470] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0471] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0472] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0473] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0474] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0475] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0476] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0477] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0478] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0479] The system of this invention is designed for use at home or in the office by installing a dedicated software application on a terminal. Users input information describing their preferences and requests through the terminal. The input interface is designed to be visually easy for users to understand, and offers choices regarding color, design style, application, and materials.

[0480] The server collects information provided by the user, as well as relevant external data such as social media activity and past purchase history, if permitted by the user, in real time. This allows for a precise understanding of user preferences and trends.

[0481] An AI agent on the server analyzes the collected data and uses machine learning algorithms to evaluate user preferences. Based on this, the AI ​​automatically generates optimal design proposals and sends them to the terminal. The terminal presents the user with visually visualized design proposals and has a feedback function to receive user feedback.

[0482] When a user provides feedback requesting revisions to a design proposal, the server receives the feedback, and an AI agent revises the design and generates a new proposal. This process is repeated until the user is satisfied with the design.

[0483] Finally, after the user reviews and approves the design, the server converts the design data into a manufacturing format and sends it to the 3D printer. The 3D printer automatically creates the product based on this manufacturing format. The finished product undergoes quality checks and is then delivered to the user.

[0484] As a concrete example, consider a scenario where a user wants to create a new lampshade to personalize their home decor. The user enters "a lampshade with a unique shape and warm colors" into their terminal. The server generates and visualizes suitable designs based on past purchase history and popular trends, presenting them to the user. If the user provides feedback that they would like the shape to be a little more angular, the AI ​​generates a revised version based on that information and presents it to the user again. Finally, the user confirms a design they are satisfied with, and a 3D printing machine brings it to life.

[0485] The following describes the processing flow.

[0486] Step 1:

[0487] Users log in to the system via their device and enter information about their design preferences and desires. This includes specific requirements such as desired colors, shapes, materials, and intended use.

[0488] Step 2:

[0489] The terminal sends the data entered by the user to the server.

[0490] Step 3:

[0491] The server receives user input information and, in addition, collects SNS data and past purchase history that the user has authorized.

[0492] Step 4:

[0493] The AI ​​agent on the server analyzes the collected data to identify user preferences and trends. Machine learning algorithms are used in this process.

[0494] Step 5:

[0495] The server generates design proposals based on the analysis results. The AI ​​agent creates multiple design proposals that reflect the user's preferences.

[0496] Step 6:

[0497] The server sends the generated design proposal to the terminal.

[0498] Step 7:

[0499] The device displays the received design proposals to the user. The user can visually review these designs and provide feedback.

[0500] Step 8:

[0501] Users provide feedback on specific revision requests for the presented design proposals. This feedback is sent from the device to the server.

[0502] Step 9:

[0503] The server receives user feedback, and an AI agent modifies the design. A new, revised version is generated.

[0504] Step 10:

[0505] The server sends the revised design proposal back to the terminal. This process is repeated until a design that satisfies the user is finalized.

[0506] Step 11:

[0507] Once the user confirms the final design, that information is sent to the server via the device.

[0508] Step 12:

[0509] The server converts the finalized design into a data format that can be manufactured using a 3D printing machine.

[0510] Step 13:

[0511] The server sends the manufacturing format to the 3D printing machine and instructs it to automatically produce the product.

[0512] Step 14:

[0513] A 3D printing machine creates products based on received data. The products undergo quality checks before being delivered to the user.

[0514] (Example 1)

[0515] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0516] Existing product design customization processes struggle to respond flexibly to user preferences and needs, making it difficult to quickly generate satisfactory designs. As a result, users are forced to repeatedly experiment and expend time and effort during the design process. Furthermore, the lack of technology to automatically generate products based on individual user needs makes it difficult to provide products that meet individual requests.

[0517] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0518] In this invention, the server includes means for the user to provide requests to the terminal through input information, means for the terminal to receive user input and collect external data, and means for analyzing the collected data with an AI agent and creating design proposals using a generative AI model. This enables the rapid generation of customized designs according to user preferences and the automated generation of products.

[0519] A "user" is someone who uses the system to customize product designs and provides input through a terminal.

[0520] A "terminal" is an electronic device that provides an interface for users to input information and review the generated design proposals.

[0521] A "server" is a central processing unit that receives input information from users, collects external data, and generates and modifies design proposals using AI agents.

[0522] "External data" refers to additional information used to analyze user preferences, such as social media information and past purchase history.

[0523] An "AI agent" is a program that uses machine learning algorithms to analyze collected data and evaluate user preferences.

[0524] A "generative AI model" is a mathematical model that uses artificial intelligence to generate design proposals that meet user needs based on input data.

[0525] A "design proposal" is a visual suggestion of a product generated by an AI agent based on user preferences and external data.

[0526] "Feedback" is the act of a user providing their opinions and requests regarding a design proposal that has been presented to them.

[0527] A "manufacturing format" is a set of technical instructions for generating a physical product from a final design using a 3D printing machine or similar device.

[0528] "Three-dimensional printing technology" is a manufacturing method that generates physical products in three dimensions based on a manufacturing format.

[0529] This invention is a system for effectively generating and manufacturing product designs tailored to the individual needs of users. It primarily consists of terminals, servers, AI agents, and 3D printing technology.

[0530] The terminal has a dedicated software application installed and provides a user-friendly interface for entering information. Users use this terminal to input their preferences and requests, including detailed selections of color, design style, intended use, and materials.

[0531] The server receives information sent from the terminal and, if necessary, collects external data authorized by the user in real time. An AI agent is deployed within the server, and this agent analyzes the data using a generative AI model. The AI ​​agent has the ability to accurately understand user preferences using machine learning algorithms and generate optimal design proposals.

[0532] The design proposal generated by the server is sent to the terminal and presented to the user visually. The user reviews this design proposal and provides feedback as needed. For example, they can provide detailed feedback such as "I want the shape to be more angular" or "I want the colors to be a little more vibrant."

[0533] Once the user finalizes the design, the server converts the design data into a manufacturing format. Based on this format, the product is automatically generated using 3D printing technology. For example, if a user wants to create a lampshade with a unique shape and warm colors for their home decor, it is possible to generate a specific design.

[0534] As a concrete example of a prompt, when a user enters "a lampshade with a unique shape and warm colors" into the terminal, the server generates and visualizes the best suggestions based on past history and trending designs.

[0535] In this way, the present invention is a system that enables the custom generation and rapid delivery of products tailored to the individual needs of each user.

[0536] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0537] Step 1:

[0538] The user opens a software application on their device and enters their design preferences and requests. This input includes detailed items such as color, design style, intended use, and materials. This input data is sent from the device to the server, initiating processing.

[0539] Step 2:

[0540] The server collects user input data received from the terminal. Furthermore, if the user grants permission, it also collects external data such as social media information and past purchase history. This data collection prepares the server to gain a more precise understanding of user preferences. The data is then passed on to the next AI agent for analysis.

[0541] Step 3:

[0542] The AI ​​agent installed on the server analyzes the collected data. It uses a generative AI model and machine learning algorithms to perform the data analysis. The output of this step is a design proposal based on user preferences. Based on the analysis results, it generates the optimal design proposal.

[0543] Step 4:

[0544] The server sends the generated design proposal to the terminal. The terminal visualizes this design proposal and presents it to the user on the screen. The user reviews the presented design proposal and provides feedback, including any necessary revisions or additional requests.

[0545] Step 5:

[0546] The server receives information from the user who entered feedback into the terminal. The server sends this feedback to the AI ​​agent, which then initiates the process of revising the design proposal. The AI ​​agent improves the design based on the feedback and generates a new design proposal.

[0547] Step 6:

[0548] The user reviews and confirms the final design on their device. The server converts this confirmed design into a manufacturing format. This format conversion prepares the design proposal for transmission to the 3D printing equipment as a concrete product specification.

[0549] Step 7:

[0550] The server transmits the manufacturing format to equipment compatible with 3D printing technology, initiating physical product generation. The 3D printing equipment automatically generates the product based on the format. The final product undergoes quality checks and is delivered to the user. In this step, all input and data processing are accomplished through physical results.

[0551] (Application Example 1)

[0552] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0553] In today's world, designing products that quickly and accurately reflect individual user preferences and needs is crucial for keeping pace with the rapid pace of life. However, current systems lack sufficient technology to receive voice commands, instantly visualize them, and flexibly modify designs while repeatedly incorporating user feedback. In addition, there is a lack of integration to easily utilize 3D manufacturing technology to realize custom designs.

[0554] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0555] In this invention, the server includes means for receiving preference information input from the user via voice, means for collecting external data and converting the content of the received voice, and means for analyzing the user's preferences based on the collected data. This enables immediate visualization of designs based on user voice instructions, rapid incorporation of user feedback, and three-dimensional manufacturing of custom designs.

[0556] A "user" is an individual or group that uses the system to input their preferences and requests.

[0557] "Receiving via voice" means collecting user instructions and preference information audibly using speech recognition technology.

[0558] "External data" refers to information outside the system, such as social media data and past purchase history, that is used to reinforce user preferences.

[0559] "Converting audio content" refers to the process of converting acquired audio instructions into a format that can be processed as text or data.

[0560] "Analyzing preferences" is the process of evaluating user preferences and trends from collected information and generating optimal design proposals.

[0561] "Generating and visualizing design proposals" means designing based on analysis results and presenting those results in an easy-to-understand manner for the user.

[0562] "Revising the design based on user feedback and re-presenting it" is the process of refreshing the design based on user evaluations and requests, and then displaying it again.

[0563] A "three-dimensional manufacturing format" is a data format used to physically manufacture a generated design.

[0564] "Automatically generating products" refers to the process of materializing digital designs into physical products using three-dimensional manufacturing equipment.

[0565] In a system implementing this invention, users can customize interior designs using voice commands via a home robot. Users can easily generate design proposals by communicating their desired design elements through a voice recognition system. The server uses the "SpeechRecognition" library to convert the user's voice instructions into text data. This converted data is sent to an AI agent, where machine learning frameworks such as "scikit-learn" or "TensorFlow" are used to analyze the user's preferences.

[0566] The terminal generates design proposals based on the analysis results and visualizes them using tools such as "matplotlib" and "PyQt". The design proposals are presented to the user, and feedback can be received immediately. The server collects the user's feedback and modifies and updates the design as needed.

[0567] The finalized design is converted into a three-dimensional manufacturing format using the "py3DPrint" module and then materialized as a physical product using a 3D printer. This allows users to obtain custom-designed interior products.

[0568] As a concrete example, consider a scenario where a user speaks to a household robot and says, "I want an antique-style bookshelf." Upon receiving this instruction, the server generates a design for an antique-looking bookshelf and displays it on the terminal. If the user then provides feedback such as, "Make it look a bit more luxurious," the server incorporates this feedback, readjusts the design, and presents it to the user again.

[0569] For example, enter the following prompt:

[0570] User enters desired interior design: "Antique-style bookshelf"

[0571] Expected result from the AI: "Generate a bookshelf design that has an antique feel while incorporating modern functionality."

[0572] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0573] Step 1:

[0574] The user inputs voice commands to the home robot. An example of a voice command is to say to the robot, "I want an antique-style bookshelf."

[0575] Step 2:

[0576] The server uses speech recognition technology to convert the user's voice into text. The technology used is the "SpeechRecognition" library, which analyzes the user's voice data and converts it into text format. For example, the voice command "I want an antique-style bookshelf" will be output as text data.

[0577] Step 3:

[0578] The server sends the converted text data to an AI agent for analysis. For example, it uses "scikit-learn" or "TensorFlow" to analyze user preferences and trends. The input is text data, and the output is a preference model as a result of the analysis.

[0579] Step 4:

[0580] The device generates design proposals based on a preference model transmitted from the AI ​​agent. During this process, a "generative AI model" is used to generate and visualize designs that match the user's requests. The output of this process is a design image to be presented to the user on the display.

[0581] Step 5:

[0582] The user reviews the presented design proposal and, if necessary, enters feedback into the device. For example, they might provide feedback such as, "Make it look a bit more premium."

[0583] Step 6:

[0584] The server modifies the design based on user feedback, updates the design proposal again via the AI ​​agent, and re-visualizes the modified design. The re-visualized design is then sent to the terminal and presented to the user.

[0585] Step 7:

[0586] Once the final design is decided, the terminal uses the "py3DPrint" module to convert the design into a 3D manufacturing format. For example, this conversion to a manufacturing format transforms the bookshelf design data into a format that can be 3D printed.

[0587] Step 8:

[0588] The server or terminal transmits the generated 3D manufacturing format to a 3D printing machine, where it is materialized as a physical product. As a result, the user's custom-made bookshelf is manufactured.

[0589] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0590] This invention is a system that provides more sophisticatedly personalized products by utilizing not only user preference information but also user emotional information. The system is equipped with an emotion engine that recognizes and analyzes the emotions of the user when they input information, allowing the user's temporary mood and reactions to be reflected in the design process.

[0591] Users input their design preferences and requests through their devices. The emotion engine senses the user's facial expressions and tone of voice during input, collecting emotional data. This process runs in parallel with the user's text input, enabling the system to capture the user's intuitive emotions.

[0592] The server analyzes user-provided preference information, historical data, and emotional data. The AI ​​agent combines this data and, through a machine learning model, automatically generates design proposals that take into account the user's long-term preferences and temporary emotions. This allows for proposals that resonate more emotionally than those based solely on preferences.

[0593] The device presents the generated design proposal to the user, recording their feedback along with their emotional state at that time. When the user provides feedback on desired changes, the server again utilizes the emotional data to adjust the design. Here, the server senses the user's emotions while providing feedback and uses this to further refine the design proposal.

[0594] Finally, once the user approves the design, the server converts it into a manufacturing format for the 3D printing machine. The manufacturing format is sent to the printing machine, which automatically generates the physical product. After the product is completed, a quality check is performed, and if there are no problems, it is delivered to the user.

[0595] As a concrete example, consider a user ordering a custom-made gift for a special event. When the user types "I want a sophisticated, high-end jewelry box" into their device, the emotion engine determines whether the user is tense or relaxed. If tense, the design options will be more formal and feature subdued colors. On the other hand, if relaxed, casual and playful designs will be presented. In either case, the emotions the user is feeling when providing feedback are taken into consideration, and further design modifications are made to ultimately create a highly personalized product.

[0596] The following describes the processing flow.

[0597] Step 1:

[0598] The user uses a device to log in to the system and input their design preferences and requirements. This input includes details such as the design's color, shape, materials, and intended use. With the user's permission, the device also records the user's emotional state via the microphone and camera.

[0599] Step 2:

[0600] The device transmits user preference information and emotional data to the server. Emotional data is acquired in real time from the user's facial expressions and voice while they are typing.

[0601] Step 3:

[0602] The server simultaneously collects the received preference information, sentiment data, and, if necessary, past purchase history and external data, then integrates and analyzes this data.

[0603] Step 4:

[0604] The AI ​​agent on the server uses all the collected data to analyze user preferences and emotions. Machine learning algorithms then generate design proposals that combine long-term preferences with temporary emotions.

[0605] Step 5:

[0606] The server sends the generated design proposal to the terminal. The design proposal also includes an explanation of how the user's emotional state is reflected.

[0607] Step 6:

[0608] The device visually presents design proposals to the user. The user reviews the design proposals and provides feedback if necessary.

[0609] Step 7:

[0610] The device sends user feedback and sentiment data to the server. This feedback includes specific design modifications and new requests.

[0611] Step 8:

[0612] The server considers user feedback and their emotions at the time, and an AI agent readjusts the design. A newly revised version is generated and sent back to the device.

[0613] Step 9:

[0614] Repeat the process from steps 6 to 8 until the user is satisfied.

[0615] Step 10:

[0616] Once the user confirms the final design, that information is sent to the server via the device.

[0617] Step 11:

[0618] The server converts the final design into a manufacturing format for the 3D printing machine. This format includes the data necessary for specific manufacturing.

[0619] Step 12:

[0620] The server sends the manufacturing format to the 3D printing machine and commands it to automatically produce the product.

[0621] Step 13:

[0622] The 3D printing machine manufactures products based on the submitted design data. After manufacturing, a quality check is performed, and if it passes, the product is shipped to the user.

[0623] (Example 2)

[0624] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0625] In providing personalized designs based on user preferences, conventional systems have faced the challenge of not adequately reflecting temporary emotional shifts or intuitive reactions. Furthermore, there is a growing need to consider the user's emotional state when generating design proposals to create product suggestions that resonate more emotionally.

[0626] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0627] In this invention, the server includes means for collecting user preference information and emotional information, means for analyzing the preference information and emotional information and identifying the user's emotional state using an emotional engine, and means for generating and presenting design proposals based on the analysis results using a generative AI model. This makes it possible to provide more precisely personalized design proposals that take into account the user's temporary emotional state, in addition to design proposals based solely on preferences.

[0628] "User preference information" refers to information that indicates a user's individual preferences and tastes, reflecting their interest in specific designs or products.

[0629] "Emotional information" refers to data that indicates a user's temporary emotional state or psychological response, and includes information obtained from facial expressions, tone of voice, and behavior.

[0630] An "emotion engine" is a system or technology that collects and analyzes user emotional information to identify their emotional state.

[0631] A "generative AI model" is a program that uses machine learning techniques to analyze data and implements an algorithm that provides optimal design suggestions based on user preferences and emotions.

[0632] A "manufacturing instruction format" is a format required when generating a product based on design data, and it is a format for transmitting instructions to manufacturing equipment.

[0633] "Article" is a general term referring to products or goods that are physically formed based on a generated design.

[0634] A "three-dimensional forming device" refers to a device that automatically forms three-dimensional objects based on digital data, and generally refers to a 3D printer.

[0635] This invention is a system that utilizes emotional information in addition to user preference information to provide more precisely personalized products. One embodiment of this invention provides a means for creating products that reflect the user's temporary emotional changes by analyzing the user's emotional state in real time and making design suggestions based on that analysis.

[0636] In implementing this system, users first input their design preferences and requests using a terminal. The terminal is equipped with a camera and microphone, which sense the user's facial expressions and voice tone, and analyze this using an emotion engine. This analysis is performed in real time, and the user's emotional information is identified.

[0637] The collected emotional and preference information is sent to a server. The server uses a generative AI model to comprehensively analyze this information and generate design proposals that take into account both the user's long-term preferences and temporary emotions. Machine learning techniques are used in this generation process.

[0638] The generated design proposals are presented to the user via their device. The user provides feedback on the presented designs, and their emotional state during this process is continuously analyzed. Based on the feedback and emotional data, the server refines the design proposals and makes a final proposal. This process makes it possible to create products that resonate with the user's emotions.

[0639] As a concrete example, consider a user who wants a custom-made gift for a specific event. When the user enters "I want a sophisticated, high-end jewelry box" into the device, the emotion engine determines the user's mood. If the user is nervous, more formal and subdued design options are suggested; if the user is relaxed, casual and playful design options are suggested.

[0640] One example of a prompt to input into a generative AI model is, "What design changes should be suggested when the user is feeling stressed?" Based on this prompt, the system can derive the optimal design changes that respond to the user's emotions.

[0641] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0642] Step 1:

[0643] The user inputs their design preferences and requests through the device. The device receives the text input and records it as preference information. It also uses the camera and microphone to sense the user's facial expressions and tone of voice, collecting emotional information in real time. As a result, the user's preference information and emotional information are aggregated as input data on the device.

[0644] Step 2:

[0645] The device sends collected preference and emotional information to the server. The server receives this data as input and performs data analysis using a machine learning model. The main purpose of the analysis is to identify the user's long-term preferences and temporary emotional states. This outputs basic data for generating design proposals.

[0646] Step 3:

[0647] The server uses a generative AI model to generate design proposals based on the analysis results. The generative AI model performs data calculations to create designs optimized for the user's preferences and emotions. As output, personalized design proposals are prepared.

[0648] Step 4:

[0649] The server sends the generated design proposal to the terminal. The terminal presents the design proposal to the user and requests feedback. The user reviews the design proposal and provides feedback by entering desired revisions and opinions. This feedback information is then obtained as new input data.

[0650] Step 5:

[0651] The device resends new emotional information collected simultaneously with user feedback to the server. The server uses this as input to analyze it again and adjust the design proposal. The adjusted design proposal is then output after further data processing based on the user's opinions and emotions.

[0652] Step 6:

[0653] Once the user is satisfied with the final design, the server converts the final design into a manufacturing instruction format. This conversion process involves data calculations using CAD software to complete the physical preparation for manufacturing the product. The output is the manufacturing instruction format.

[0654] Step 7:

[0655] The server transmits the manufacturing instruction format to the 3D forming machine. The 3D forming machine uses this as input and enters the process of automatically generating the product. Finally, the finished product is output and, after quality checks, is delivered to the user.

[0656] (Application Example 2)

[0657] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0658] There is a growing demand to provide a purchasing experience that is not only based on user preferences but also on emotions. Modern consumers expect not just products that match their hobbies and tastes, but also personalized product suggestions that reflect their emotions and moods at the time. However, conventional systems struggle to incorporate such emotionally conscious design suggestions, resulting in limited improvements to the user experience.

[0659] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0660] In this invention, the server includes means for collecting preference information and emotional data input by the user, means for analyzing the user's emotional information and preferences based on the collected data, and means for generating and presenting design proposals based on the analysis. This makes it possible to provide personalized product suggestions that take the user's emotions into consideration.

[0661] "Preference information" refers to data that shows the user's preferences and patterns regarding products and designs.

[0662] "Emotional data" refers to data that indicates the user's emotional state at a given time, obtained from things like their facial expressions and tone of voice.

[0663] "Analysis means" refers to a part of a system that includes a process for analyzing user intentions and emotions from collected preference information and emotional data, and for making relevant suggestions.

[0664] "Design proposals" refer to suggestions for product design and decoration generated based on user preference information and emotional data.

[0665] "Three-dimensional data" refers to data containing three-dimensional shape information constructed in digital space, and is used in the physical manufacturing of goods.

[0666] "Means for automatically creating articles" refers to a mechanism that forms a physical product by layering or processing materials based on input three-dimensional data.

[0667] A "three-dimensional modeling device" is a device that constructs a physical shape by layering materials three-dimensionally based on three-dimensional data.

[0668] As an embodiment of this invention, an emotion-customized shopping system is described. The server collects preference information and emotion data entered by the user from the terminal and comprehensively analyzes this data through analysis means. The data analysis mainly uses facial recognition technology and voice analysis technology. Specifically, the user's facial expressions acquired using a camera are analyzed using the TensorFlow API, and the tone of voice collected through a microphone is analyzed using the Amazon Polly API, a natural language processing technology. Based on this emotion data and preference information, the server generates an optimal design proposal and presents it to the terminal.

[0669] The device is equipped with an application that visualizes the generated design proposals, allowing users to review them. When users provide feedback, their emotions are analyzed, and the design is readjusted as needed. Throughout this feedback process, the user's emotions are constantly monitored by a server and used to make final adjustments to the design.

[0670] The final design is converted into 3D data, allowing the 3D modeling device to output it as a physical product. The design data used here is automatically formatted via the AutoCAD API and other means, facilitating the automated manufacturing of the items.

[0671] For example, if a user enters "I want a unique mug as a birthday present" into the device, and their emotional state is relaxed, colorful and playful design options will be suggested. Conversely, if they are stressed, more calming designs will be presented. An example of a prompt message would be, "Capture a photo of the user and analyze their voice using the Amazon Polly API. Based on the emotional information obtained, generate the optimal product design using the AutoCAD API."

[0672] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0673] Step 1:

[0674] The device captures the user's facial expressions with a camera and records their voice tone with a microphone. The resulting visual and audio data are sent to the server as emotion data. In this step, the input is the user's facial image and voice, and the output is emotion data.

[0675] Step 2:

[0676] The server processes the received emotion data, analyzes emotions from facial expressions using TensorFlow API with facial recognition technology, and analyzes emotions from speech using Amazon Polly API. As a result of this data processing, the user's specific emotional state is derived. The input is emotion data, and the output is the emotion analysis result.

[0677] Step 3:

[0678] The server integrates preference information obtained from the user with the sentiment analysis results obtained in step 2, and generates personalized design proposals using a generative AI model. This data processing proposes a design optimized for the user's emotions and preferences. The input is preference information and sentiment analysis results, and the output is the design proposal.

[0679] Step 4:

[0680] The device presents the generated design proposal to the user and accepts user feedback. This step collects satisfaction levels with the design and specific revision requests. The input is the design proposal, and the output is the feedback.

[0681] Step 5:

[0682] The server re-analyzes the emotional data obtained simultaneously with user feedback and adjusts the design proposal as needed. By performing another emotional analysis, the design is revised to reflect the emotional information. The input is the feedback content and emotional data, and the output is the revised design proposal.

[0683] Step 6:

[0684] The final design proposal is converted into 3D data and sent from the server to the 3D printing machine. Here, the AutoCAD API is used to convert the data, allowing the manufacturing of the item to proceed automatically. The input is the revised design proposal, and the output is the manufacturing format.

[0685] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0686] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0687] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0688] [Fourth Embodiment]

[0689] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0690] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0691] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0692] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0693] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0694] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0695] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0696] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0697] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0698] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0699] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0700] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0701] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0702] The system of this invention is designed for use at home or in the office by installing a dedicated software application on a terminal. Users input information describing their preferences and requests through the terminal. The input interface is designed to be visually easy for users to understand, and offers choices regarding color, design style, application, and materials.

[0703] The server collects information provided by the user, as well as relevant external data such as social media activity and past purchase history, if permitted by the user, in real time. This allows for a precise understanding of user preferences and trends.

[0704] An AI agent on the server analyzes the collected data and uses machine learning algorithms to evaluate user preferences. Based on this, the AI ​​automatically generates optimal design proposals and sends them to the terminal. The terminal presents the user with visually visualized design proposals and has a feedback function to receive user feedback.

[0705] When a user provides feedback requesting revisions to a design proposal, the server receives the feedback, and an AI agent revises the design and generates a new proposal. This process is repeated until the user is satisfied with the design.

[0706] Finally, after the user reviews and approves the design, the server converts the design data into a manufacturing format and sends it to the 3D printer. The 3D printer automatically creates the product based on this manufacturing format. The finished product undergoes quality checks and is then delivered to the user.

[0707] As a concrete example, consider a scenario where a user wants to create a new lampshade to personalize their home decor. The user enters "a lampshade with a unique shape and warm colors" into their terminal. The server generates and visualizes suitable designs based on past purchase history and popular trends, presenting them to the user. If the user provides feedback that they would like the shape to be a little more angular, the AI ​​generates a revised version based on that information and presents it to the user again. Finally, the user confirms a design they are satisfied with, and a 3D printing machine brings it to life.

[0708] The following describes the processing flow.

[0709] Step 1:

[0710] Users log in to the system via their device and enter information about their design preferences and desires. This includes specific requirements such as desired colors, shapes, materials, and intended use.

[0711] Step 2:

[0712] The terminal sends the data entered by the user to the server.

[0713] Step 3:

[0714] The server receives user input information and, in addition, collects SNS data and past purchase history that the user has authorized.

[0715] Step 4:

[0716] The AI ​​agent on the server analyzes the collected data to identify user preferences and trends. Machine learning algorithms are used in this process.

[0717] Step 5:

[0718] The server generates design proposals based on the analysis results. The AI ​​agent creates multiple design proposals that reflect the user's preferences.

[0719] Step 6:

[0720] The server sends the generated design proposal to the terminal.

[0721] Step 7:

[0722] The device displays the received design proposals to the user. The user can visually review these designs and provide feedback.

[0723] Step 8:

[0724] Users provide feedback on specific revision requests for the presented design proposals. This feedback is sent from the device to the server.

[0725] Step 9:

[0726] The server receives user feedback, and an AI agent modifies the design. A new, revised version is generated.

[0727] Step 10:

[0728] The server sends the revised design proposal back to the terminal. This process is repeated until a design that satisfies the user is finalized.

[0729] Step 11:

[0730] Once the user confirms the final design, that information is sent to the server via the device.

[0731] Step 12:

[0732] The server converts the finalized design into a data format that can be manufactured using a 3D printing machine.

[0733] Step 13:

[0734] The server sends the manufacturing format to the 3D printing machine and instructs it to automatically produce the product.

[0735] Step 14:

[0736] A 3D printing machine creates products based on received data. The products undergo quality checks before being delivered to the user.

[0737] (Example 1)

[0738] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0739] Existing product design customization processes struggle to respond flexibly to user preferences and needs, making it difficult to quickly generate satisfactory designs. As a result, users are forced to repeatedly experiment and expend time and effort during the design process. Furthermore, the lack of technology to automatically generate products based on individual user needs makes it difficult to provide products that meet individual requests.

[0740] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0741] In this invention, the server includes means for the user to provide requests to the terminal through input information, means for the terminal to receive user input and collect external data, and means for analyzing the collected data with an AI agent and creating design proposals using a generative AI model. This enables the rapid generation of customized designs according to user preferences and the automated generation of products.

[0742] A "user" is someone who uses the system to customize product designs and provides input through a terminal.

[0743] A "terminal" is an electronic device that provides an interface for users to input information and review the generated design proposals.

[0744] A "server" is a central processing unit that receives input information from users, collects external data, and generates and modifies design proposals using AI agents.

[0745] "External data" refers to additional information used to analyze user preferences, such as social media information and past purchase history.

[0746] An "AI agent" is a program that uses machine learning algorithms to analyze collected data and evaluate user preferences.

[0747] A "generative AI model" is a mathematical model that uses artificial intelligence to generate design proposals that meet user needs based on input data.

[0748] A "design proposal" is a visual suggestion of a product generated by an AI agent based on user preferences and external data.

[0749] "Feedback" is the act of a user providing their opinions and requests regarding a design proposal that has been presented to them.

[0750] A "manufacturing format" is a set of technical instructions for generating a physical product from a final design using a 3D printing machine or similar device.

[0751] "Three-dimensional printing technology" is a manufacturing method that generates physical products in three dimensions based on a manufacturing format.

[0752] This invention is a system for effectively generating and manufacturing product designs tailored to the individual needs of users. It primarily consists of terminals, servers, AI agents, and 3D printing technology.

[0753] The terminal has a dedicated software application installed and provides a user-friendly interface for entering information. Users use this terminal to input their preferences and requests, including detailed selections of color, design style, intended use, and materials.

[0754] The server receives information sent from the terminal and, if necessary, collects external data authorized by the user in real time. An AI agent is deployed within the server, and this agent analyzes the data using a generative AI model. The AI ​​agent has the ability to accurately understand user preferences using machine learning algorithms and generate optimal design proposals.

[0755] The design proposal generated by the server is sent to the terminal and presented to the user visually. The user reviews this design proposal and provides feedback as needed. For example, they can provide detailed feedback such as "I want the shape to be more angular" or "I want the colors to be a little more vibrant."

[0756] Once the user finalizes the design, the server converts the design data into a manufacturing format. Based on this format, the product is automatically generated using 3D printing technology. For example, if a user wants to create a lampshade with a unique shape and warm colors for their home decor, it is possible to generate a specific design.

[0757] As a concrete example of a prompt, when a user enters "a lampshade with a unique shape and warm colors" into the terminal, the server generates and visualizes the best suggestions based on past history and trending designs.

[0758] In this way, the present invention is a system that enables the custom generation and rapid delivery of products tailored to the individual needs of each user.

[0759] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0760] Step 1:

[0761] The user opens a software application on their device and enters their design preferences and requests. This input includes detailed items such as color, design style, intended use, and materials. This input data is sent from the device to the server, initiating processing.

[0762] Step 2:

[0763] The server collects user input data received from the terminal. Furthermore, if the user grants permission, it also collects external data such as social media information and past purchase history. This data collection prepares the server to gain a more precise understanding of user preferences. The data is then passed on to the next AI agent for analysis.

[0764] Step 3:

[0765] The AI ​​agent installed on the server analyzes the collected data. It uses a generative AI model and machine learning algorithms to perform the data analysis. The output of this step is a design proposal based on user preferences. Based on the analysis results, it generates the optimal design proposal.

[0766] Step 4:

[0767] The server sends the generated design proposal to the terminal. The terminal visualizes this design proposal and presents it to the user on the screen. The user reviews the presented design proposal and provides feedback, including any necessary revisions or additional requests.

[0768] Step 5:

[0769] The server receives information from the user who entered feedback into the terminal. The server sends this feedback to the AI ​​agent, which then initiates the process of revising the design proposal. The AI ​​agent improves the design based on the feedback and generates a new design proposal.

[0770] Step 6:

[0771] The user reviews and confirms the final design on their device. The server converts this confirmed design into a manufacturing format. This format conversion prepares the design proposal for transmission to the 3D printing equipment as a concrete product specification.

[0772] Step 7:

[0773] The server transmits the manufacturing format to equipment compatible with 3D printing technology, initiating physical product generation. The 3D printing equipment automatically generates the product based on the format. The final product undergoes quality checks and is delivered to the user. In this step, all input and data processing are accomplished through physical results.

[0774] (Application Example 1)

[0775] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0776] In today's world, designing products that quickly and accurately reflect individual user preferences and needs is crucial for keeping pace with the rapid pace of life. However, current systems lack sufficient technology to receive voice commands, instantly visualize them, and flexibly modify designs while repeatedly incorporating user feedback. In addition, there is a lack of integration to easily utilize 3D manufacturing technology to realize custom designs.

[0777] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0778] In this invention, the server includes means for receiving preference information input from the user via voice, means for collecting external data and converting the content of the received voice, and means for analyzing the user's preferences based on the collected data. This enables immediate visualization of designs based on user voice instructions, rapid incorporation of user feedback, and three-dimensional manufacturing of custom designs.

[0779] A "user" is an individual or group that uses the system to input their preferences and requests.

[0780] "Receiving via voice" means collecting user instructions and preference information audibly using speech recognition technology.

[0781] "External data" refers to information outside the system, such as social media data and past purchase history, that is used to reinforce user preferences.

[0782] "Converting audio content" refers to the process of converting acquired audio instructions into a format that can be processed as text or data.

[0783] "Analyzing preferences" is the process of evaluating user preferences and trends from collected information and generating optimal design proposals.

[0784] "Generating and visualizing design proposals" means designing based on analysis results and presenting those results in an easy-to-understand manner for the user.

[0785] "Revising the design based on user feedback and re-presenting it" is the process of refreshing the design based on user evaluations and requests, and then displaying it again.

[0786] A "three-dimensional manufacturing format" is a data format used to physically manufacture a generated design.

[0787] "Automatically generating products" refers to the process of materializing digital designs into physical products using three-dimensional manufacturing equipment.

[0788] In a system implementing this invention, users can customize interior designs using voice commands via a home robot. Users can easily generate design proposals by communicating their desired design elements through a voice recognition system. The server uses the "SpeechRecognition" library to convert the user's voice instructions into text data. This converted data is sent to an AI agent, where machine learning frameworks such as "scikit-learn" or "TensorFlow" are used to analyze the user's preferences.

[0789] The terminal generates design proposals based on the analysis results and visualizes them using tools such as "matplotlib" and "PyQt". The design proposals are presented to the user, and feedback can be received immediately. The server collects the user's feedback and modifies and updates the design as needed.

[0790] The finalized design is converted into a three-dimensional manufacturing format using the "py3DPrint" module and then materialized as a physical product using a 3D printer. This allows users to obtain custom-designed interior products.

[0791] As a concrete example, consider a scenario where a user speaks to a household robot and says, "I want an antique-style bookshelf." Upon receiving this instruction, the server generates a design for an antique-looking bookshelf and displays it on the terminal. If the user then provides feedback such as, "Make it look a bit more luxurious," the server incorporates this feedback, readjusts the design, and presents it to the user again.

[0792] For example, enter the following prompt:

[0793] User enters desired interior design: "Antique-style bookshelf"

[0794] Expected result from the AI: "Generate a bookshelf design that has an antique feel while incorporating modern functionality."

[0795] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0796] Step 1:

[0797] The user inputs voice commands to the home robot. An example of a voice command is to say to the robot, "I want an antique-style bookshelf."

[0798] Step 2:

[0799] The server uses speech recognition technology to convert the user's voice into text. The technology used is the "SpeechRecognition" library, which analyzes the user's voice data and converts it into text format. For example, the voice command "I want an antique-style bookshelf" will be output as text data.

[0800] Step 3:

[0801] The server sends the converted text data to an AI agent for analysis. For example, it uses "scikit-learn" or "TensorFlow" to analyze user preferences and trends. The input is text data, and the output is a preference model as a result of the analysis.

[0802] Step 4:

[0803] The device generates design proposals based on a preference model transmitted from the AI ​​agent. During this process, a "generative AI model" is used to generate and visualize designs that match the user's requests. The output of this process is a design image to be presented to the user on the display.

[0804] Step 5:

[0805] The user reviews the presented design proposal and, if necessary, enters feedback into the device. For example, they might provide feedback such as, "Make it look a bit more premium."

[0806] Step 6:

[0807] The server modifies the design based on user feedback, updates the design proposal again via the AI ​​agent, and re-visualizes the modified design. The re-visualized design is then sent to the terminal and presented to the user.

[0808] Step 7:

[0809] Once the final design is decided, the terminal uses the "py3DPrint" module to convert the design into a 3D manufacturing format. For example, this conversion to a manufacturing format transforms the bookshelf design data into a format that can be 3D printed.

[0810] Step 8:

[0811] The server or terminal transmits the generated 3D manufacturing format to a 3D printing machine, where it is materialized as a physical product. As a result, the user's custom-made bookshelf is manufactured.

[0812] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0813] This invention is a system that provides more sophisticatedly personalized products by utilizing not only user preference information but also user emotional information. The system is equipped with an emotion engine that recognizes and analyzes the emotions of the user when they input information, allowing the user's temporary mood and reactions to be reflected in the design process.

[0814] Users input their design preferences and requests through their devices. The emotion engine senses the user's facial expressions and tone of voice during input, collecting emotional data. This process runs in parallel with the user's text input, enabling the system to capture the user's intuitive emotions.

[0815] The server analyzes user-provided preference information, historical data, and emotional data. The AI ​​agent combines this data and, through a machine learning model, automatically generates design proposals that take into account the user's long-term preferences and temporary emotions. This allows for proposals that resonate more emotionally than those based solely on preferences.

[0816] The device presents the generated design proposal to the user, recording their feedback along with their emotional state at that time. When the user provides feedback on desired changes, the server again utilizes the emotional data to adjust the design. Here, the server senses the user's emotions while providing feedback and uses this to further refine the design proposal.

[0817] Finally, once the user approves the design, the server converts it into a manufacturing format for the 3D printing machine. The manufacturing format is sent to the printing machine, which automatically generates the physical product. After the product is completed, a quality check is performed, and if there are no problems, it is delivered to the user.

[0818] As a concrete example, consider a user ordering a custom-made gift for a special event. When the user types "I want a sophisticated, high-end jewelry box" into their device, the emotion engine determines whether the user is tense or relaxed. If tense, the design options will be more formal and feature subdued colors. On the other hand, if relaxed, casual and playful designs will be presented. In either case, the emotions the user is feeling when providing feedback are taken into consideration, and further design modifications are made to ultimately create a highly personalized product.

[0819] The following describes the processing flow.

[0820] Step 1:

[0821] The user uses a device to log in to the system and input their design preferences and requirements. This input includes details such as the design's color, shape, materials, and intended use. With the user's permission, the device also records the user's emotional state via the microphone and camera.

[0822] Step 2:

[0823] The device transmits user preference information and emotional data to the server. Emotional data is acquired in real time from the user's facial expressions and voice while they are typing.

[0824] Step 3:

[0825] The server simultaneously collects the received preference information, sentiment data, and, if necessary, past purchase history and external data, then integrates and analyzes this data.

[0826] Step 4:

[0827] The AI ​​agent on the server uses all the collected data to analyze user preferences and emotions. Machine learning algorithms then generate design proposals that combine long-term preferences with temporary emotions.

[0828] Step 5:

[0829] The server sends the generated design proposal to the terminal. The design proposal also includes an explanation of how the user's emotional state is reflected.

[0830] Step 6:

[0831] The device visually presents design proposals to the user. The user reviews the design proposals and provides feedback if necessary.

[0832] Step 7:

[0833] The device sends user feedback and sentiment data to the server. This feedback includes specific design modifications and new requests.

[0834] Step 8:

[0835] The server considers user feedback and their emotions at the time, and an AI agent readjusts the design. A newly revised version is generated and sent back to the device.

[0836] Step 9:

[0837] Repeat the process from steps 6 to 8 until the user is satisfied.

[0838] Step 10:

[0839] Once the user confirms the final design, that information is sent to the server via the device.

[0840] Step 11:

[0841] The server converts the final design into a manufacturing format for the 3D printing machine. This format includes the data necessary for specific manufacturing.

[0842] Step 12:

[0843] The server sends the manufacturing format to the 3D printing machine and commands it to automatically produce the product.

[0844] Step 13:

[0845] The 3D printing machine manufactures products based on the submitted design data. After manufacturing, a quality check is performed, and if it passes, the product is shipped to the user.

[0846] (Example 2)

[0847] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0848] In providing personalized designs based on user preferences, conventional systems have faced the challenge of not adequately reflecting temporary emotional shifts or intuitive reactions. Furthermore, there is a growing need to consider the user's emotional state when generating design proposals to create product suggestions that resonate more emotionally.

[0849] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0850] In this invention, the server includes means for collecting user preference information and emotional information, means for analyzing the preference information and emotional information and identifying the user's emotional state using an emotional engine, and means for generating and presenting design proposals based on the analysis results using a generative AI model. This makes it possible to provide more precisely personalized design proposals that take into account the user's temporary emotional state, in addition to design proposals based solely on preferences.

[0851] "User preference information" refers to information that indicates a user's individual preferences and tastes, reflecting their interest in specific designs or products.

[0852] "Emotional information" refers to data that indicates a user's temporary emotional state or psychological response, and includes information obtained from facial expressions, tone of voice, and behavior.

[0853] An "emotion engine" is a system or technology that collects and analyzes user emotional information to identify their emotional state.

[0854] A "generative AI model" is a program that uses machine learning techniques to analyze data and implements an algorithm that provides optimal design suggestions based on user preferences and emotions.

[0855] A "manufacturing instruction format" is a format required when generating a product based on design data, and it is a format for transmitting instructions to manufacturing equipment.

[0856] "Article" is a general term referring to products or goods that are physically formed based on a generated design.

[0857] A "three-dimensional forming device" refers to a device that automatically forms three-dimensional objects based on digital data, and generally refers to a 3D printer.

[0858] This invention is a system that utilizes emotional information in addition to user preference information to provide more precisely personalized products. One embodiment of this invention provides a means for creating products that reflect the user's temporary emotional changes by analyzing the user's emotional state in real time and making design suggestions based on that analysis.

[0859] In implementing this system, users first input their design preferences and requests using a terminal. The terminal is equipped with a camera and microphone, which sense the user's facial expressions and voice tone, and analyze this using an emotion engine. This analysis is performed in real time, and the user's emotional information is identified.

[0860] The collected emotional and preference information is sent to a server. The server uses a generative AI model to comprehensively analyze this information and generate design proposals that take into account both the user's long-term preferences and temporary emotions. Machine learning techniques are used in this generation process.

[0861] The generated design proposals are presented to the user via their device. The user provides feedback on the presented designs, and their emotional state during this process is continuously analyzed. Based on the feedback and emotional data, the server refines the design proposals and makes a final proposal. This process makes it possible to create products that resonate with the user's emotions.

[0862] As a concrete example, consider a user who wants a custom-made gift for a specific event. When the user enters "I want a sophisticated, high-end jewelry box" into the device, the emotion engine determines the user's mood. If the user is nervous, more formal and subdued design options are suggested; if the user is relaxed, casual and playful design options are suggested.

[0863] One example of a prompt to input into a generative AI model is, "What design changes should be suggested when the user is feeling stressed?" Based on this prompt, the system can derive the optimal design changes that respond to the user's emotions.

[0864] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0865] Step 1:

[0866] The user inputs their design preferences and requests through the device. The device receives the text input and records it as preference information. It also uses the camera and microphone to sense the user's facial expressions and tone of voice, collecting emotional information in real time. As a result, the user's preference information and emotional information are aggregated as input data on the device.

[0867] Step 2:

[0868] The device sends collected preference and emotional information to the server. The server receives this data as input and performs data analysis using a machine learning model. The main purpose of the analysis is to identify the user's long-term preferences and temporary emotional states. This outputs basic data for generating design proposals.

[0869] Step 3:

[0870] The server uses a generative AI model to generate design proposals based on the analysis results. The generative AI model performs data calculations to create designs optimized for the user's preferences and emotions. As output, personalized design proposals are prepared.

[0871] Step 4:

[0872] The server sends the generated design proposal to the terminal. The terminal presents the design proposal to the user and requests feedback. The user reviews the design proposal and provides feedback by entering desired revisions and opinions. This feedback information is then obtained as new input data.

[0873] Step 5:

[0874] The device resends new emotional information collected simultaneously with user feedback to the server. The server uses this as input to analyze it again and adjust the design proposal. The adjusted design proposal is then output after further data processing based on the user's opinions and emotions.

[0875] Step 6:

[0876] Once the user is satisfied with the final design, the server converts the final design into a manufacturing instruction format. This conversion process involves data calculations using CAD software to complete the physical preparation for manufacturing the product. The output is the manufacturing instruction format.

[0877] Step 7:

[0878] The server transmits the manufacturing instruction format to the 3D forming machine. The 3D forming machine uses this as input and enters the process of automatically generating the product. Finally, the finished product is output and, after quality checks, is delivered to the user.

[0879] (Application Example 2)

[0880] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0881] There is a growing demand to provide a purchasing experience that is not only based on user preferences but also on emotions. Modern consumers expect not just products that match their hobbies and tastes, but also personalized product suggestions that reflect their emotions and moods at the time. However, conventional systems struggle to incorporate such emotionally conscious design suggestions, resulting in limited improvements to the user experience.

[0882] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0883] In this invention, the server includes means for collecting preference information and emotional data input by the user, means for analyzing the user's emotional information and preferences based on the collected data, and means for generating and presenting design proposals based on the analysis. This makes it possible to provide personalized product suggestions that take the user's emotions into consideration.

[0884] "Preference information" refers to data that shows the user's preferences and patterns regarding products and designs.

[0885] "Emotional data" refers to data that indicates the user's emotional state at a given time, obtained from things like their facial expressions and tone of voice.

[0886] "Analysis means" refers to a part of a system that includes a process for analyzing user intentions and emotions from collected preference information and emotional data, and for making relevant suggestions.

[0887] "Design proposals" refer to suggestions for product design and decoration generated based on user preference information and emotional data.

[0888] "Three-dimensional data" refers to data containing three-dimensional shape information constructed in digital space, and is used in the physical manufacturing of goods.

[0889] "Means for automatically creating articles" refers to a mechanism that forms a physical product by layering or processing materials based on input three-dimensional data.

[0890] A "three-dimensional modeling device" is a device that constructs a physical shape by layering materials three-dimensionally based on three-dimensional data.

[0891] As an embodiment of this invention, an emotion-customized shopping system is described. The server collects preference information and emotion data entered by the user from the terminal and comprehensively analyzes this data through analysis means. The data analysis mainly uses facial recognition technology and voice analysis technology. Specifically, the user's facial expressions acquired using a camera are analyzed using the TensorFlow API, and the tone of voice collected through a microphone is analyzed using the Amazon Polly API, a natural language processing technology. Based on this emotion data and preference information, the server generates an optimal design proposal and presents it to the terminal.

[0892] The device is equipped with an application that visualizes the generated design proposals, allowing users to review them. When users provide feedback, their emotions are analyzed, and the design is readjusted as needed. Throughout this feedback process, the user's emotions are constantly monitored by a server and used to make final adjustments to the design.

[0893] The final design is converted into 3D data, allowing the 3D modeling device to output it as a physical product. The design data used here is automatically formatted via the AutoCAD API and other means, facilitating the automated manufacturing of the items.

[0894] For example, if a user enters "I want a unique mug as a birthday present" into the device, and their emotional state is relaxed, colorful and playful design options will be suggested. Conversely, if they are stressed, more calming designs will be presented. An example of a prompt message would be, "Capture a photo of the user and analyze their voice using the Amazon Polly API. Based on the emotional information obtained, generate the optimal product design using the AutoCAD API."

[0895] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0896] Step 1:

[0897] The device captures the user's facial expressions with a camera and records their voice tone with a microphone. The resulting visual and audio data are sent to the server as emotion data. In this step, the input is the user's facial image and voice, and the output is emotion data.

[0898] Step 2:

[0899] The server processes the received emotion data, analyzes emotions from facial expressions using TensorFlow API with facial recognition technology, and analyzes emotions from speech using Amazon Polly API. As a result of this data processing, the user's specific emotional state is derived. The input is emotion data, and the output is the emotion analysis result.

[0900] Step 3:

[0901] The server integrates preference information obtained from the user with the sentiment analysis results obtained in step 2, and generates personalized design proposals using a generative AI model. This data processing proposes a design optimized for the user's emotions and preferences. The input is preference information and sentiment analysis results, and the output is the design proposal.

[0902] Step 4:

[0903] The device presents the generated design proposal to the user and accepts user feedback. This step collects satisfaction levels with the design and specific revision requests. The input is the design proposal, and the output is the feedback.

[0904] Step 5:

[0905] The server re-analyzes the emotional data obtained simultaneously with user feedback and adjusts the design proposal as needed. By performing another emotional analysis, the design is revised to reflect the emotional information. The input is the feedback content and emotional data, and the output is the revised design proposal.

[0906] Step 6:

[0907] The final design proposal is converted into 3D data and sent from the server to the 3D printing machine. Here, the AutoCAD API is used to convert the data, allowing the manufacturing of the item to proceed automatically. The input is the revised design proposal, and the output is the manufacturing format.

[0908] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0909] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0910] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0911] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0912] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0913] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0914] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0915] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0916] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0917] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0918] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0919] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0920] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0921] 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.

[0922] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0923] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0924] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0925] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0926] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0927] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0928] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0929] The following is further disclosed regarding the embodiments described above.

[0930] (Claim 1)

[0931] A means of collecting user-submitted preference information and external data,

[0932] A means for analyzing user preferences based on the aforementioned collected data,

[0933] A means for generating and presenting design proposals based on the above analysis,

[0934] A means of modifying the design based on user feedback,

[0935] A means of converting the modified design into a manufacturing format,

[0936] A system that includes means for automatically generating products based on a manufacturing format.

[0937] (Claim 2)

[0938] The system according to claim 1, wherein the analysis means analyzes user preferences using machine learning.

[0939] (Claim 3)

[0940] The system according to claim 1, wherein the automatic generation means manufactures a product using a three-dimensional printing apparatus.

[0941] "Example 1"

[0942] (Claim 1)

[0943] A means by which the user provides a request to the terminal through input information,

[0944] The aforementioned terminal receives user input, and the server collects external data.

[0945] A method for generating design proposals using an AI agent to analyze data collected by a server, and a generative AI model.

[0946] A device displays the generated design proposals and provides a means to receive feedback from the user.

[0947] A means for the server to modify the design proposal based on user feedback,

[0948] A system that includes means for converting a finalized design into a manufacturing format and generating products using automated equipment.

[0949] (Claim 2)

[0950] The system according to claim 1, wherein the analysis means performs data analysis using a machine learning algorithm.

[0951] (Claim 3)

[0952] The system according to claim 1, wherein the automated device creates a product using three-dimensional printing technology.

[0953] "Application Example 1"

[0954] (Claim 1)

[0955] A means of receiving preference information entered by the user via voice,

[0956] A means of collecting external data and converting the content of the received audio,

[0957] A means for analyzing user preferences based on the aforementioned collected data,

[0958] A means for generating and visualizing design proposals based on the above analysis,

[0959] A means of revising the design based on user feedback and presenting it again,

[0960] A means of converting the modified design into a three-dimensional manufacturing format,

[0961] A system that includes means for automatically generating products based on a three-dimensional manufacturing format.

[0962] (Claim 2)

[0963] The system according to claim 1, wherein the voice information conversion means uses voice recognition technology.

[0964] (Claim 3)

[0965] The system according to claim 1, wherein the automatic product generation means uses a three-dimensional manufacturing apparatus.

[0966] "Example 2 of combining an emotion engine"

[0967] (Claim 1)

[0968] Means for collecting user preference information and emotional information,

[0969] A means for analyzing the aforementioned preference information and emotional information, and using an emotional engine to identify the user's emotional state,

[0970] A means of generating and presenting design proposals based on analysis results using a generative AI model,

[0971] A means of adjusting the design based on user feedback and sentiment data,

[0972] A means of converting the adjusted design into a manufacturing instruction format,

[0973] A system including means for automatically generating goods based on a manufacturing instruction format.

[0974] (Claim 2)

[0975] The system according to claim 1, wherein the analysis means uses machine learning to analyze the user's preferences and emotional state.

[0976] (Claim 3)

[0977] The system according to claim 1, wherein the automatic generation means manufactures an article using a three-dimensional forming apparatus.

[0978] "Application example 2 when combining with an emotional engine"

[0979] (Claim 1)

[0980] A means for collecting preference information and emotional data entered by users,

[0981] A means for analyzing user emotional information and preferences based on the aforementioned collected data,

[0982] A means for generating and presenting design proposals based on the aforementioned analysis,

[0983] A means of modifying the design based on user feedback and emotional state,

[0984] A means of converting the modified design into three-dimensional data,

[0985] A system that includes means for automatically creating objects based on three-dimensional data.

[0986] (Claim 2)

[0987] The system according to claim 1, wherein the analysis means uses artificial intelligence technology to analyze the user's preferences and emotional information.

[0988] (Claim 3)

[0989] The system according to claim 1, wherein the means for automatically creating the article uses a three-dimensional modeling device to create the product. [Explanation of symbols]

[0990] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of receiving preference information entered by the user via voice, A means of collecting external data and converting the content of the received audio, A means for analyzing user preferences based on the aforementioned collected data, A means for generating and visualizing design proposals based on the above analysis, A means of revising the design based on user feedback and presenting it again, A means of converting the modified design into a three-dimensional manufacturing format, A system that includes means for automatically generating products based on a three-dimensional manufacturing format.

2. The system according to claim 1, wherein the voice information conversion means uses voice recognition technology.

3. The system according to claim 1, wherein the automatic product generation means uses a three-dimensional manufacturing apparatus.

Images