system

Abstract

We provide the system. [Solution] A means of receiving product requests from consumers via an input device, A means for generating product design proposals based on consumer requests using a generation program, A means of displaying the generated product design proposals to consumers and allowing consumers to make selections and provide instructions for improvement, A means of adjusting the product design in real time using a generation program based on consumer choices and improvement instructions, A means of communicating the final product design to craftsmen and obtaining technical feedback from them, A means of visualizing the product manufacturing process in real time and providing it to consumers, A system that includes this.

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, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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] There is a need to solve problems such as the difficulty of design proposals, insufficient smooth communication with craftsmen, and lack of transparency in the production process that consumers face when individually ordering custom-made Japanese traditional handicrafts. In the current process, individual preferences of consumers and language barriers between different languages hinder the production of high-quality custom products.

Means for Solving the Problems

[0005] This invention provides a system that receives product requests from consumers via an input device and generates product design proposals based on those requests using a generation program. It includes means for consumers to select and provide improvement instructions from the proposed product designs, and can adjust the product design in real time based on consumer feedback. This system enhances the reliability of production by sending the final product design to craftsmen and collecting their technical feedback. Furthermore, it effectively solves problems by providing consumers with real-time visibility into the production process and supporting communication between consumers and craftsmen with a multilingual translation function.

[0006] "Consumer" refers to a user who receives goods or services, and in the context of this invention, it means a user who orders custom-made traditional Japanese crafts.

[0007] A "product design proposal" refers to a specific product design proposal generated based on consumer requests, and is generated using AI.

[0008] A "generation program" refers to software that has an algorithm for generating design proposals based on consumer input information.

[0009] The term "craftsman" refers to a technician who possesses specific skills and is responsible for manufacturing products, and specifically refers to an individual skilled in the production of traditional Japanese crafts.

[0010] "Feedback" refers to information on improvements and evaluations provided based on consumer choices and craftsman's opinions, and is used to adjust product design.

[0011] "Real-time adjustment" refers to the process of updating product designs by immediately incorporating consumer feedback.

[0012] "Multilingual translation function" refers to software features that automatically translate text and audio to facilitate smooth communication between different languages.

[0013] "Visualization of the manufacturing process" refers to a function that shows consumers in real time how and at what stage a product is made. [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 numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

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

[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] This invention relates to a system that provides consumers with a process to customize and order traditional Japanese crafts, producing high-quality products through collaborative work with artisans. The entire system primarily functions using a server, user terminals, and artisan devices.

[0036] First, the user uses a device to input the product concept and requirements. For example, they might input a request such as, "I want to make ceramics with a cherry blossom pattern in a blue color scheme," on an online platform. This input is then sent to the server.

[0037] Next, the server uses a generation program to analyze the user's requests. The generation program utilizes a rich database and AI technology to generate multiple design options tailored to the user's preferences. The server then sends these design options to the user's device in real time.

[0038] Users can review the design proposals presented on their device and select the one they like. After making a selection, they can also specify areas they want to further customize. These instructions are sent back to the server, which uses a generation program to adjust the design proposal in real time.

[0039] The finalized design proposal is sent to the craftsman's device via the server. The craftsman then assesses the design's technical feasibility and sends any necessary feedback back to the server. The server receives this feedback and, if necessary, facilitates further adjustments between the user and the craftsman.

[0040] Once production begins, the server visualizes the production process in real time on the user's terminal. For example, it provides information on the current progress of the product, material selection, and techniques being used. This allows the user to always know how the product is being made.

[0041] Through this system, consumers and artisans can communicate smoothly and collaborate to create highly customized, original traditional crafts.

[0042] The following describes the processing flow.

[0043] Step 1:

[0044] The user uses a terminal to input information about their desired product requirements and design. This information includes the product type, design image, and desired colors and patterns. The entered data is sent to the server.

[0045] Step 2:

[0046] The server analyzes the user's request. Using a generation program, it generates design proposals for traditional Japanese crafts based on the request. This includes retrieving information from a database and generating creative designs using AI. The server then sends the generated design proposals to the user's device.

[0047] Step 3:

[0048] The generated design proposals are displayed on the user's device for review. The user selects their preferred design proposal and, if desired, provides specific instructions for further customization. These selections and instructions are then sent back to the server.

[0049] Step 4:

[0050] The server receives selections and instructions from the user and immediately adjusts the design proposal using a generation program. The design proposal, with its real-time changes, is then reflected back on the user's terminal, allowing the user to review the results.

[0051] Step 5:

[0052] Once the final design is decided, the server sends it to the craftsman's device. The craftsman reviews the design from a technical standpoint and provides feedback on feasibility, materials to be used, and techniques. This feedback is then sent back to the server.

[0053] Step 6:

[0054] The server analyzes feedback from craftsmen and provides information to users as needed. This allows users to gain a deeper understanding of the feasibility of the final product and the manufacturing process, and to smoothly move on to the next step if adjustments are necessary.

[0055] Step 7:

[0056] Once production begins, the server visualizes the progress of the production process in real time on the user's terminal. It provides the user with information such as material selection and the stage of production, allowing the user to track the process while waiting for the product to be completed.

[0057] (Example 1)

[0058] 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."

[0059] Modern consumers increasingly demand customized products tailored to their individual needs, but traditional manufacturing processes struggle to efficiently deliver bespoke products that reflect these detailed consumer requests. Furthermore, difficulties in effective communication between consumers and manufacturers, along with a lack of transparency in the production process, also pose problems.

[0060] 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.

[0061] In this invention, the server includes means for receiving product requests from consumers via an input interface, means for generating product design proposals based on consumer requests using a generation algorithm, and means for transmitting the design proposals to the consumer's device in real time and providing an interface for the consumer to make selections and improvement instructions. This enables the efficient provision of customized products that immediately reflect consumer requests and facilitates smooth communication between consumers and manufacturers.

[0062] "Consumers" refers to individuals or organizations that wish to use this system to order custom-made or customized products.

[0063] An "input interface" refers to a device or software that consumers use to input requests or instructions online.

[0064] A "generation algorithm" refers to a computational method that automatically generates product design proposals based on received requests.

[0065] "Device" refers to electronic devices, including computers and mobile devices, used by consumers and manufacturers.

[0066] "Manufacturer" refers to the engineers or craftsmen who actually manufacture the product based on the product design.

[0067] "Real-time" refers to the transmission, reception, and processing of information occurring almost instantaneously.

[0068] "Providing an interface" refers to providing users with a screen or means to interact with the system.

[0069] "Technical feedback" refers to the responses from manufacturers regarding the feasibility of a design proposal and suggestions for improvement.

[0070] "Visualizing information about the production process" refers to providing users with a visual representation of the progress of the product, the materials used, the techniques employed, and other relevant information.

[0071] The embodiments for carrying out this system invention are described below.

[0072] Users access an online platform using their device and enter details of their customized order for traditional crafts. This input includes specific information describing the user's requests; for example, they can submit a text message stating, "I would like a cherry blossom patterned ceramic piece made in blue tones." This information is then sent from the device to the server.

[0073] The server activates a generation algorithm based on the received request to generate product design proposals that meet consumer needs. A generation AI model is used to automatically generate a variety of design variations. At this time, existing product information and past order history recorded in the database are used to improve the accuracy and diversity of the design proposals.

[0074] Multiple design proposals are sent from the server to the user's terminal in real time, allowing the user to review and select a product design. The user can also send further instructions to the server via their terminal if they have additional requests.

[0075] The server adjusts the design proposal by reapplying the generation algorithm based on additional requests from the user. The final version of the design proposal is then sent to the creator's device. The creator reviews the design proposal on their device and provides technical feedback to the server. The server then uses this feedback to facilitate further adjustments between the user and the creator as needed.

[0076] Once production begins, the server monitors the product's progress, techniques used, materials, and other details, and visualizes this information in real time on the user's device, ensuring transparency in the production process. Users can check the production status at any time, allowing them to confidently oversee the custom-made process.

[0077] As a concrete example, a prompt message to be input into a generation AI model might be, "Please generate a ceramic design with a cherry blossom pattern and a blue base color." This prompt allows the AI ​​model to automatically propose design ideas that meet the user's preferences.

[0078] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0079] Step 1:

[0080] Users access an online platform via their device and enter customization requests for traditional Japanese crafts. The user's input includes details such as patterns, colors, and sizes. This information is sent as text data from the input device to the server.

[0081] Step 2:

[0082] The server analyzes the received user request data and generates a prompt. For example, it might create a prompt such as, "Please generate a ceramic design with a cherry blossom pattern and a blue base color." This prompt is then used as input to the AI ​​generation model.

[0083] Step 3:

[0084] The server uses a generative AI model to generate product design proposals based on prompt messages. Data processing utilizes an internal design database and AI technology to generate design variations that meet user requirements. The generated designs are output as image data and 3D models.

[0085] Step 4:

[0086] The server sends the generated design proposals to the user's terminal in real time. The user reviews and selects from the multiple design proposals provided on the terminal. An interface is provided that allows the user to input their selections and additional requests through checkboxes and text fields.

[0087] Step 5:

[0088] The user sends their selected design proposal and additional instructions to the server. The server analyzes these instructions, applies the generated AI model again, and adjusts the design. The output of this step is the final, adjusted design proposal.

[0089] Step 6:

[0090] The server sends the final design to the manufacturer's device. The manufacturer reviews the design on the device and evaluates its feasibility for manufacturing. Feedback from the manufacturer is sent to the server in text format.

[0091] Step 7:

[0092] The server relays the feedback received from the creator to the user and facilitates further adjustments between the user and creator as needed. If further adjustments are required, return to step 5.

[0093] Step 8:

[0094] Once production begins, the server monitors the progress of the production process and visualizes information based on the product's progress, material usage, and selected techniques on the user's terminal in real time. The user can then check the progress of the production through this information.

[0095] (Application Example 1)

[0096] 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."

[0097] When consumers order customized Japanese traditional crafts, the challenge lies in facilitating smooth communication with artisans, proposing optimal designs, and streamlining the entire purchase process. In particular, the ability to monitor the product's progress in real time is essential.

[0098] 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.

[0099] In this invention, the server includes means for receiving product requests from consumers via a user interface, means for generating product design proposals based on consumer requests using a generation algorithm, and means for processing electronic transactions. This makes it possible for consumers to easily customize and purchase bespoke traditional crafts and to check the progress of production in real time.

[0100] A "consumer" is the end user who orders and customizes a product.

[0101] A "user interface" is a component that provides a digital environment for consumers to input their product requirements.

[0102] A "generative algorithm" is a program that automatically creates product design proposals based on consumer input.

[0103] A "terminal" is an electronic device used by consumers to view and operate product design proposals.

[0104] The "creator" refers to the craftsmen or technicians responsible for the actual production of the product.

[0105] "Technical feedback" refers to information provided by the creator regarding the feasibility and potential improvements to the product design.

[0106] "Electronic transactions" refer to the buying and selling procedures for goods and services conducted via the internet.

[0107] The term "manufacturing process" refers to the series of work processes involved in completing a product.

[0108] "Visual display" means presenting manufacturing processes and product information in an easily understandable way on a digital screen.

[0109] In the system that implements this invention, the user first inputs product requirements using a terminal. The user interface receives this input and uses a generation algorithm to generate multiple product design proposals based on the requirements. The generated design proposals are displayed to the user on the terminal, and the user makes selections and requests for improvements. At this time, the terminal transmits the improved requests to the server in real time, and the generation algorithm makes further adjustments.

[0110] The server then sends the final selected product design to the creator and receives technical feedback. Based on this feedback, the design is further refined as needed, and this process is repeated. Once the manufacturing process begins, the server provides the user with a visual display of the manufacturing progress. This allows the user to understand how the product is being made in real time.

[0111] Electronic transactions are processed using a secure payment platform (e.g., Stripe) to complete payment for products ordered by the user. The server manages all transaction data and performs confirmation and cancellation as needed.

[0112] For example, if a user requests "pottery inspired by cherry blossoms in spring," the generation algorithm will suggest several cherry blossom motifs. The user can then select their favorite design and specify additional colors and patterns. An example of a prompt might be: "The user's requested design element is 'cherry blossoms in spring.' Please suggest designs that are feasible to produce while utilizing Japanese tradition and employing a modern approach."

[0113] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0114] Step 1:

[0115] The user enters product requirements using a terminal. These requirements include specific design, color, and pattern preferences, and the terminal prepares to send this information as digital data to the server.

[0116] Step 2:

[0117] The server receives user request data and uses a generative AI model to generate multiple product design proposals based on the requests. By passing the input request data as prompts to the generative model, it outputs a variety of design proposals. In doing so, it refers to similar cases from past databases to suggest the optimal design.

[0118] Step 3:

[0119] The terminal displays the generated product design proposal to the user. It analyzes the design proposal data received from the server and performs layout processing to make it visually easy to understand. The user reviews this output and inputs selections and improvement instructions.

[0120] Step 4:

[0121] The user sends the selected design proposal and improvement instructions back to the server via their device. The imported improvement instructions include adjustments to specific color tones and designs, and the server uses a generated AI model to readjust the design proposal in real time based on these instructions.

[0122] Step 5:

[0123] The final product design is sent from the server to the creator's device, and technical feedback is received from the creator. The creator evaluates the feasibility and areas for improvement of the design data sent as input and sends feedback back to the server. The server then makes any necessary adjustments with the user.

[0124] Step 6:

[0125] As the production process begins, the server visually displays the production progress on the terminal in real time. It receives progress information from the creator as input, processes the data to clearly show the user the product's completion level and current status, and then outputs it.

[0126] Step 7:

[0127] The server processes payments via electronic transactions and securely manages transaction data. It takes user payment information as input, performs authentication and authorization processes, and provides order completion and payment confirmation as output. Appropriate encryption technology is used throughout this process to ensure security.

[0128] 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.

[0129] This invention relates to a system that recognizes user emotions and uses that information to personalize and optimize the bespoke process for traditional Japanese crafts. The entire system consists of a server, a user terminal, a craftsman's device, and an emotion engine.

[0130] First, the user enters their product requirements via a terminal, providing information about their desired design and specifications. This information is sent to a server where the initial data is processed.

[0131] Here, the emotion engine analyzes the user's emotional state. This engine infers the user's emotions by inputting the user's voice and facial expression data into an emotion analysis model. The results of the emotion engine are sent to the server along with the user's request.

[0132] The server uses a generation program to create product design proposals that combine user requests with the results of emotional analysis. For example, if the user is relaxed, the generation program can suggest a design using calming colors and curves. Based on this information, the server creates the design proposal in real time and sends it to the user's terminal.

[0133] Users visualize design options on their devices and make their selections. Furthermore, an emotion engine is used to collect user reactions in real time, and if dissatisfaction or doubts are observed, the server can quickly adjust the design options. The system incorporates a flexible mechanism for refining the design based on user emotions.

[0134] The final design is transmitted to the craftsman via the server, who then evaluates its technical feasibility and provides feedback. The craftsman's information is organized on the server and presented to the user again as needed.

[0135] This invention enables personalized design proposals that take user emotions into consideration, facilitating smoother collaboration with craftsmen and facilitating the market deployment of high-quality, made-to-order products.

[0136] The following describes the processing flow.

[0137] Step 1:

[0138] Users enter detailed information about product requirements and design via their device. This includes desired materials, colors, and design elements, and the entered data is sent to the server.

[0139] Step 2:

[0140] The user's device uses its camera and microphone to collect data on the user's voice and facial expressions. This data is input into an emotion engine to analyze the user's emotional state. The analyzed emotion data is also sent to the server.

[0141] Step 3:

[0142] The server integrates user requests and emotional data, and uses a generation program to generate product design proposals that are suitable for the user's preferences and emotional state. The generated design proposals are sent to the user's terminal as multiple options.

[0143] Step 4:

[0144] Users review product design proposals presented on their devices and select their preferred design. The emotion engine then re-analyzes the user's emotions during the selection process, and the server makes any necessary design adjustments based on the results.

[0145] Step 5:

[0146] The final, adjusted design proposal is sent from the server to the craftsman's device. The craftsman reviews the design proposal and provides feedback from a technical perspective. This feedback is sent back to the server, and the user is notified if any technical improvements are needed.

[0147] Step 6:

[0148] The server manages the production process based on the final approved design and visualizes the progress in real time on the user's device. Users can track each stage of production and wait for the final product to be completed.

[0149] (Example 2)

[0150] 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".

[0151] Traditional product design processes struggle to incorporate individual consumer emotions and real-time feedback into design adjustments. Furthermore, differences in communication language and the inability to effectively utilize past consumer behavior data to optimize design proposals are hindered. As a result, truly personalized products that meet consumer expectations are limited.

[0152] 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.

[0153] In this invention, the server includes means for receiving product requests from consumers via a terminal, means for identifying the consumer's emotional state using emotion analysis technology, and means for generating product design proposals that fuse the consumer's requests with the identified emotional state using a generative AI model. This enables flexible and personalized design adjustments based on the consumer's emotions. Furthermore, by implementing multilingual translation technology, smooth communication with manufacturers can be achieved, and by using the consumer's past behavioral data, it is possible to provide more appropriate and optimized design proposals.

[0154] A "consumer" is an individual or group that receives a product or service.

[0155] "Product requirements" refer to information about the wishes and specifications that consumers express regarding a particular product.

[0156] A "terminal" refers to an electronic device used by a user to input or receive information.

[0157] A "server" is a computer system that provides data processing and storage functions over a network.

[0158] "Emotional analysis technology" is a technique that analyzes and infers an individual's emotional state from their voice, facial expressions, and other factors.

[0159] A "generative AI model" is an artificial intelligence model that generates new data or designs from specific input information based on an algorithm.

[0160] A "design proposal" refers to a proposal that includes drawings and specifications for realizing a specific product.

[0161] "Multilingual translation technology" is a technology that enables the exchange of information between different languages.

[0162] A "manufacturer" refers to a professional who undertakes the production of a product based on its design.

[0163] "Feedback" refers to opinions and information about the results or reactions in a particular process.

[0164] This invention is a product design system that enables advanced personalization based on consumer emotions. The system consists of a server, a consumer terminal, a manufacturer's device, and an emotion analysis engine.

[0165] Users input product requirements and specify desired designs and specifications via a terminal. The terminal allows input of text, images, audio, and other information through its interface. This information is filtered appropriately and sent to the server.

[0166] The server uses a generative AI model to process consumer emotional state data received from the emotion analysis engine. The server utilizes speech recognition and image analysis software to analyze the user's emotions from their voice and facial expression data. Based on this emotional information, the generative AI model generates product design proposals optimized for the consumer's needs. For example, if the server detects that the consumer is relaxed, it can propose a simple and warm design.

[0167] The generated design proposals are sent to the terminal in real time and presented to the user in a visual format. The user can review this and provide instructions for selection and adjustment as needed. The terminal interface is designed to allow consumers to easily adjust the details of their selected design.

[0168] The emotion analysis engine then analyzes consumer reactions, and if there are signs of consumer dissatisfaction, the server immediately generates and proposes a new design using an AI model. This process allows for flexible design adjustments based on consumer emotions.

[0169] The finalized design is transferred to the manufacturer's device. The manufacturer evaluates the technical feasibility and provides necessary feedback. This information is stored on a server and presented to consumers as feedback.

[0170] This invention enables design proposals that incorporate consumer emotions, thereby improving customer satisfaction with products. A concrete example of a prompt message would be the instruction, "Generate a teacup design with calming colors based on the user's emotional state."

[0171] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0172] Step 1:

[0173] Users use a terminal to input their product requests. Specifically, users input their desired design and specifications using text, images, and audio. Once this data is entered, the terminal sends the information to the server. The entered data is transferred to the server as structured data that accurately reflects the user's preferences.

[0174] Step 2:

[0175] The server receives input data from the user and performs initial data processing. Here, it extracts basic specifications based on the user's requests and prepares the necessary data for sentiment analysis. For example, the server converts audio data into a format that can be analyzed using digital signal processing, and processes it for subsequent sentiment analysis.

[0176] Step 3:

[0177] For emotion analysis, the server utilizes emotion analysis technology. The server analyzes the voice and facial expressions provided by the user and estimates their emotional state in real time. During this process, the server inputs data into an emotion analysis model, outputting an emotional state such as "relaxed." The analysis results are then used in the next processing step.

[0178] Step 4:

[0179] The server utilizes a generative AI model to generate product design proposals that combine user requests and emotional state data. For example, if the user is relaxed, the server will generate a design that uses calming colors and curves. The server forms prompt sentences using emotional state as keywords, inputs them into the generative AI model, and outputs the design proposals.

[0180] Step 5:

[0181] The server sends the generated design proposal to the user's terminal, allowing the user to select and adjust it. The user visualizes and reviews the design proposal through the terminal's interface. The server receives user feedback and determines whether further adjustments to the design proposal are necessary.

[0182] Step 6:

[0183] Based on user feedback, the server modifies the generated design proposal as needed. The server then uses sentiment analysis technology again to check the user's latest emotional state and refines the design proposal based on the feedback. This process provides a design that more closely matches the user's preferences.

[0184] Step 7:

[0185] The final design proposal is transferred from the server to the manufacturer's device, where the manufacturer evaluates the product's technical feasibility. Feedback from the manufacturer is sent to the server and shared with users as needed. This exchange of feedback helps the final product come closer to consumer expectations.

[0186] (Application Example 2)

[0187] 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".

[0188] Traditional custom-made product ordering systems have the problem of making it difficult to propose product designs that fully reflect consumers' personal feelings and preferences. Furthermore, communication between consumers and craftsmen in physical stores is often not smooth, leading to delays and increased effort in product design and manufacturing. A solution is needed to address these challenges and improve consumer satisfaction.

[0189] 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.

[0190] In this invention, the server includes means for receiving product requests from consumers via an input device, means for analyzing the consumer's emotional state using an emotion analysis device and reflecting that information in the generation of product design proposals, and means for displaying the optimal design proposal based on the consumer's emotional analysis results in a physical store to facilitate ordering. This makes it possible to generate product design proposals that reflect the individual emotions and preferences of consumers in real time and provide a smooth ordering experience.

[0191] A "consumer" is an individual or group that purchases products or services, and is the entity that inputs product requests and places orders through a system.

[0192] A "product design proposal" is a specific product design plan generated based on consumer requests and emotional data.

[0193] An "input device" is hardware used by consumers to input product order information, such as a smartphone or tablet.

[0194] A "generation program" is software used to generate product design proposals based on consumer requests and sentiment analysis results.

[0195] An "emotion analysis device" is a device that analyzes a consumer's facial expressions and voice to identify their emotional state.

[0196] An "engineer" is a professional who receives the final product design, evaluates its technical feasibility, and provides feedback.

[0197] A "physical store" refers to a physically existing sales location where consumers interact with products.

[0198] A "design proposal" is a product design plan displayed based on the results of consumer emotional analysis.

[0199] To implement this invention, a system is configured that includes a server, a consumer terminal, a technician's terminal, and an emotion analysis device. The operation of this system is as follows:

[0200] The server receives product requests from consumer devices. These consumer devices include input devices such as smartphones and tablets. In this process, consumers input the necessary design and specifications into their devices and send that data to the server.

[0201] The emotion analysis device collects consumer facial expression and voice data and inputs this data into an emotion analysis model to analyze the consumer's emotional state. The emotion analysis model can utilize Microsoft® Azure® Emotion API, among others. The analysis results are sent to a server and used to generate product design proposals.

[0202] The server uses a generation program to generate product design proposals that combine consumer requests with the results of sentiment analysis. The generation program, developed in Python, adjusts the design to match the consumer's emotional state. The generated design proposals are sent to the consumer's device in real time.

[0203] For example, when a consumer visits a store in a relaxed state, the terminal displays design proposals incorporating gentle colors and curves. Based on this information, the consumer can select a design and place a final order. The final design proposal is then sent to the technician's terminal, where they can provide feedback considering its technical feasibility.

[0204] An example of a prompt might be: "Based on this user's facial expressions and voice, we can see they are relaxed. Please propose a custom-made design with a relaxed atmosphere. The color scheme should be calm, and the design should make extensive use of curves." Such prompts will prompt the AI ​​model to propose an optimized design.

[0205] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0206] Step 1:

[0207] Users input their product requests using devices such as smartphones and tablets. They specify their desired design and specifications using text or by selecting options. The entered data is then sent directly to the server.

[0208] Step 2:

[0209] The server temporarily stores product request data received from the user and generates a prompt message based on that information. This prompt message is used as an instruction to the generation AI model. It is in the format of "Generate design proposals based on the user's request."

[0210] Step 3:

[0211] The user's device uses its camera and microphone to collect facial expressions and voice data in real time. This data is input into an emotion analysis device to identify the user's emotional state.

[0212] Step 4:

[0213] The emotion analysis device analyzes input facial expressions and voice data to estimate the user's current emotional state. Various statistical methods and AI models are used in this analysis. The analysis results are output in categories such as calm, relaxed, and excited, and sent to a server.

[0214] Step 5:

[0215] The server integrates user request data and sentiment analysis results, then runs a generation program to generate design proposals. Based on the input data, the design proposals are tailored to the user's current emotional state. The generated design proposals are then sent to the consumer's device.

[0216] Step 6:

[0217] The user reviews the design proposals sent from the server on their device, making selections and modifications. Based on the user's actions, the device resends the information to the server.

[0218] Step 7:

[0219] The server receives feedback from users and readjusts the design as needed. The final design is sent to engineers and used in the product manufacturing process.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] [Second Embodiment]

[0224] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0225] 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.

[0226] 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).

[0227] 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.

[0228] 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.

[0229] 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).

[0230] 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.

[0231] 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.

[0232] 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.

[0233] 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.

[0234] 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.

[0235] 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".

[0236] This invention relates to a system that provides consumers with a process to customize and order traditional Japanese crafts, producing high-quality products through collaborative work with artisans. The entire system primarily functions using a server, user terminals, and artisan devices.

[0237] First, the user uses a device to input the product concept and requirements. For example, they might input a request such as, "I want to make ceramics with a cherry blossom pattern in a blue color scheme," on an online platform. This input is then sent to the server.

[0238] Next, the server uses a generation program to analyze the user's requests. The generation program utilizes a rich database and AI technology to generate multiple design options tailored to the user's preferences. The server then sends these design options to the user's device in real time.

[0239] Users can review the design proposals presented on their device and select the one they like. After making a selection, they can also specify areas they want to further customize. These instructions are sent back to the server, which uses a generation program to adjust the design proposal in real time.

[0240] The finalized design proposal is sent to the craftsman's device via the server. The craftsman then assesses the design's technical feasibility and sends any necessary feedback back to the server. The server receives this feedback and, if necessary, facilitates further adjustments between the user and the craftsman.

[0241] Once production begins, the server visualizes the production process in real time on the user's terminal. For example, it provides information on the current progress of the product, material selection, and techniques being used. This allows the user to always know how the product is being made.

[0242] Through this system, consumers and artisans can communicate smoothly and collaborate to create highly customized, original traditional crafts.

[0243] The following describes the processing flow.

[0244] Step 1:

[0245] The user uses a terminal to input information about their desired product requirements and design. This information includes the product type, design image, and desired colors and patterns. The entered data is sent to the server.

[0246] Step 2:

[0247] The server analyzes the user's request. Using a generation program, it generates design proposals for traditional Japanese crafts based on the request. This includes retrieving information from a database and generating creative designs using AI. The server then sends the generated design proposals to the user's device.

[0248] Step 3:

[0249] The generated design proposals are displayed on the user's device for review. The user selects their preferred design proposal and, if desired, provides specific instructions for further customization. These selections and instructions are then sent back to the server.

[0250] Step 4:

[0251] The server receives selections and instructions from the user and immediately adjusts the design proposal using a generation program. The design proposal, with its real-time changes, is then reflected back on the user's terminal, allowing the user to review the results.

[0252] Step 5:

[0253] Once the final design is decided, the server sends it to the craftsman's device. The craftsman reviews the design from a technical standpoint and provides feedback on feasibility, materials to be used, and techniques. This feedback is then sent back to the server.

[0254] Step 6:

[0255] The server analyzes feedback from craftsmen and provides information to users as needed. This allows users to gain a deeper understanding of the feasibility of the final product and the manufacturing process, and to smoothly move on to the next step if adjustments are necessary.

[0256] Step 7:

[0257] Once production begins, the server visualizes the progress of the production process in real time on the user's terminal. It provides the user with information such as material selection and the stage of production, allowing the user to track the process while waiting for the product to be completed.

[0258] (Example 1)

[0259] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0260] Modern consumers increasingly demand customized products tailored to their individual needs, but traditional manufacturing processes struggle to efficiently deliver bespoke products that reflect these detailed consumer requests. Furthermore, difficulties in effective communication between consumers and manufacturers, along with a lack of transparency in the production process, also pose problems.

[0261] 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.

[0262] In this invention, the server includes means for receiving product requests from consumers via an input interface, means for generating product design proposals based on consumer requests using a generation algorithm, and means for transmitting the design proposals to the consumer's device in real time and providing an interface for the consumer to make selections and improvement instructions. This enables the efficient provision of customized products that immediately reflect consumer requests and facilitates smooth communication between consumers and manufacturers.

[0263] "Consumers" refers to individuals or organizations that wish to use this system to order custom-made or customized products.

[0264] An "input interface" refers to a device or software that consumers use to input requests or instructions online.

[0265] A "generation algorithm" refers to a computational method that automatically generates product design proposals based on received requests.

[0266] "Device" refers to electronic devices, including computers and mobile devices, used by consumers and manufacturers.

[0267] "Manufacturer" refers to the engineers or craftsmen who actually manufacture the product based on the product design.

[0268] "Real-time" refers to the transmission, reception, and processing of information occurring almost instantaneously.

[0269] "Providing an interface" refers to providing users with a screen or means to interact with the system.

[0270] "Technical feedback" refers to the responses from manufacturers regarding the feasibility of a design proposal and suggestions for improvement.

[0271] "Visualizing information about the production process" refers to providing users with a visual representation of the progress of the product, the materials used, the techniques employed, and other relevant information.

[0272] The embodiments for carrying out this system invention are described below.

[0273] Users access an online platform using their device and enter details of their customized order for traditional crafts. This input includes specific information describing the user's requests; for example, they can submit a text message stating, "I would like a cherry blossom patterned ceramic piece made in blue tones." This information is then sent from the device to the server.

[0274] The server activates a generation algorithm based on the received request to generate product design proposals that meet consumer needs. A generation AI model is used to automatically generate a variety of design variations. At this time, existing product information and past order history recorded in the database are used to improve the accuracy and diversity of the design proposals.

[0275] Multiple design proposals are sent from the server to the user's terminal in real time, allowing the user to review and select a product design. The user can also send further instructions to the server via their terminal if they have additional requests.

[0276] The server adjusts the design proposal by reapplying the generation algorithm based on additional requests from the user. The final version of the design proposal is then sent to the creator's device. The creator reviews the design proposal on their device and provides technical feedback to the server. The server then uses this feedback to facilitate further adjustments between the user and the creator as needed.

[0277] Once production begins, the server monitors the product's progress, techniques used, materials, and other details, and visualizes this information in real time on the user's device, ensuring transparency in the production process. Users can check the production status at any time, allowing them to confidently oversee the custom-made process.

[0278] As a concrete example, a prompt message to be input into a generation AI model might be, "Please generate a ceramic design with a cherry blossom pattern and a blue base color." This prompt allows the AI ​​model to automatically propose design ideas that meet the user's preferences.

[0279] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0280] Step 1:

[0281] The user accesses an online platform via a terminal and inputs customization requests for traditional Japanese handicrafts. The user's input includes details such as patterns, colors, and sizes. This information is transmitted from the input device to the server as text data.

[0282] Step 2:

[0283] The server analyzes the received user request data and generates a prompt sentence. For example, create a prompt sentence like "Please generate a pottery design with a cherry blossom pattern based on blue." This prompt is used as input to the generation AI model.

[0284] Step 3:

[0285] The server uses the generation AI model to generate product design proposals based on the prompt sentence. In data processing, the internal design database and AI technology are utilized to generate design variations according to the user's requests. The generated designs are output as image data or 3D models.

[0286] Step 4:

[0287] The server sends the generated design proposals to the user's terminal in real time. The user checks and selects from multiple design proposals provided on the terminal. An interface is provided through checkboxes and text fields for the user to input their selections and additional requests.

[0288] Step 5:

[0289] The user sends the selected design proposal and additional instructions to the server. The server analyzes these instructions, applies the generation AI model again, and adjusts the design. The output of this step is the final adjusted design proposal.

[0290] Step 6:

[0291] The server sends the final design to the manufacturer's device. The manufacturer reviews the design on the device and evaluates its feasibility for manufacturing. Feedback from the manufacturer is sent to the server in text format.

[0292] Step 7:

[0293] The server relays the feedback received from the creator to the user and facilitates further adjustments between the user and creator as needed. If further adjustments are required, return to step 5.

[0294] Step 8:

[0295] Once production begins, the server monitors the progress of the production process and visualizes information based on the product's progress, material usage, and selected techniques on the user's terminal in real time. The user can then check the progress of the production through this information.

[0296] (Application Example 1)

[0297] 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."

[0298] When consumers order customized Japanese traditional crafts, the challenge lies in facilitating smooth communication with artisans, proposing optimal designs, and streamlining the entire purchase process. In particular, the ability to monitor the product's progress in real time is essential.

[0299] 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.

[0300] In this invention, the server includes means for receiving a product request from a consumer via a user interface, means for generating a product design based on the consumer's request using a generation algorithm, and means for processing electronic transactions. As a result, consumers can easily customize and purchase made-to-order traditional handicrafts and check the progress of production in real time.

[0301] A "consumer" refers to the end user who places an order and customizes a product.

[0302] A "user interface" is a component that provides a digital environment for consumers to input product requests.

[0303] A "generation algorithm" is a program that automatically creates a product design based on consumer input.

[0304] A "terminal" is an electronic device for consumers to view and operate on a product design.

[0305] A "creator" refers to a craftsman or technician responsible for the specific production of a product.

[0306] "Technical feedback" is information provided by the creator regarding the feasibility and improvement points of product design.

[0307] "Electronic transaction" refers to the trading procedures of goods and services conducted via the Internet.

[0308] "Production process" refers to a series of work processes until a product is completed.

[0309] "Visually display" means to express the production process and product information clearly on a digital screen.

[0310] In the system that implements this invention, the user first inputs product requirements using a terminal. The user interface receives this input and uses a generation algorithm to generate multiple product design proposals based on the requirements. The generated design proposals are displayed to the user on the terminal, and the user makes selections and requests for improvements. At this time, the terminal transmits the improved requests to the server in real time, and the generation algorithm makes further adjustments.

[0311] The server then sends the final selected product design to the creator and receives technical feedback. Based on this feedback, the design is further refined as needed, and this process is repeated. Once the manufacturing process begins, the server provides the user with a visual display of the manufacturing progress. This allows the user to understand how the product is being made in real time.

[0312] Electronic transactions are processed using a secure payment platform (e.g., Stripe) to complete payment for products ordered by the user. The server manages all transaction data and performs confirmation and cancellation as needed.

[0313] For example, if a user requests "pottery inspired by cherry blossoms in spring," the generation algorithm will suggest several cherry blossom motifs. The user can then select their favorite design and specify additional colors and patterns. An example of a prompt might be: "The user's requested design element is 'cherry blossoms in spring.' Please suggest designs that are feasible to produce while utilizing Japanese tradition and employing a modern approach."

[0314] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0315] Step 1:

[0316] The user enters product requirements using a terminal. These requirements include specific design, color, and pattern preferences, and the terminal prepares to send this information as digital data to the server.

[0317] Step 2:

[0318] The server receives user request data and uses a generative AI model to generate multiple product design proposals based on the requests. By passing the input request data as prompts to the generative model, it outputs a variety of design proposals. In doing so, it refers to similar cases from past databases to suggest the optimal design.

[0319] Step 3:

[0320] The terminal displays the generated product design proposal to the user. It analyzes the design proposal data received from the server and performs layout processing to make it visually easy to understand. The user reviews this output and inputs selections and improvement instructions.

[0321] Step 4:

[0322] The user sends the selected design proposal and improvement instructions back to the server via their device. The imported improvement instructions include adjustments to specific color tones and designs, and the server uses a generated AI model to readjust the design proposal in real time based on these instructions.

[0323] Step 5:

[0324] The final product design is sent from the server to the creator's device, and technical feedback is received from the creator. The creator evaluates the feasibility and areas for improvement of the design data sent as input and sends feedback back to the server. The server then makes any necessary adjustments with the user.

[0325] Step 6:

[0326] As the production process begins, the server visually displays the production progress on the terminal in real time. It receives progress information from the creator as input, processes the data to clearly show the user the product's completion level and current status, and then outputs it.

[0327] Step 7:

[0328] The server processes payments via electronic transactions and securely manages transaction data. It takes user payment information as input, performs authentication and authorization processes, and provides order completion and payment confirmation as output. Appropriate encryption technology is used throughout this process to ensure security.

[0329] 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.

[0330] This invention relates to a system that recognizes user emotions and uses that information to personalize and optimize the bespoke process for traditional Japanese crafts. The entire system consists of a server, a user terminal, a craftsman's device, and an emotion engine.

[0331] First, the user enters their product requirements via a terminal, providing information about their desired design and specifications. This information is sent to a server where the initial data is processed.

[0332] Here, the emotion engine analyzes the user's emotional state. This engine infers the user's emotions by inputting the user's voice and facial expression data into an emotion analysis model. The results of the emotion engine are sent to the server along with the user's request.

[0333] The server uses a generation program to create product design proposals that combine user requests with the results of emotional analysis. For example, if the user is relaxed, the generation program can suggest a design using calming colors and curves. Based on this information, the server creates the design proposal in real time and sends it to the user's terminal.

[0334] Users visualize design options on their devices and make their selections. Furthermore, an emotion engine is used to collect user reactions in real time, and if dissatisfaction or doubts are observed, the server can quickly adjust the design options. The system incorporates a flexible mechanism for refining the design based on user emotions.

[0335] The final design is transmitted to the craftsman via the server, who then evaluates its technical feasibility and provides feedback. The craftsman's information is organized on the server and presented to the user again as needed.

[0336] This invention enables personalized design proposals that take user emotions into consideration, facilitating smoother collaboration with craftsmen and facilitating the market deployment of high-quality, made-to-order products.

[0337] The following describes the processing flow.

[0338] Step 1:

[0339] Users enter detailed information about product requirements and design via their device. This includes desired materials, colors, and design elements, and the entered data is sent to the server.

[0340] Step 2:

[0341] The user's device uses its camera and microphone to collect data on the user's voice and facial expressions. This data is input into an emotion engine to analyze the user's emotional state. The analyzed emotion data is also sent to the server.

[0342] Step 3:

[0343] The server integrates user requests and emotional data, and uses a generation program to generate product design proposals that are suitable for the user's preferences and emotional state. The generated design proposals are sent to the user's terminal as multiple options.

[0344] Step 4:

[0345] Users review product design proposals presented on their devices and select their preferred design. The emotion engine then re-analyzes the user's emotions during the selection process, and the server makes any necessary design adjustments based on the results.

[0346] Step 5:

[0347] The final, adjusted design proposal is sent from the server to the craftsman's device. The craftsman reviews the design proposal and provides feedback from a technical perspective. This feedback is sent back to the server, and the user is notified if any technical improvements are needed.

[0348] Step 6:

[0349] The server manages the production process based on the final approved design and visualizes the progress in real time on the user's device. Users can track each stage of production and wait for the final product to be completed.

[0350] (Example 2)

[0351] 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".

[0352] Traditional product design processes struggle to incorporate individual consumer emotions and real-time feedback into design adjustments. Furthermore, differences in communication language and the inability to effectively utilize past consumer behavior data to optimize design proposals are hindered. As a result, truly personalized products that meet consumer expectations are limited.

[0353] 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.

[0354] In this invention, the server includes means for receiving product requests from consumers via a terminal, means for identifying the consumer's emotional state using emotion analysis technology, and means for generating product design proposals that fuse the consumer's requests with the identified emotional state using a generative AI model. This enables flexible and personalized design adjustments based on the consumer's emotions. Furthermore, by implementing multilingual translation technology, smooth communication with manufacturers can be achieved, and by using the consumer's past behavioral data, it is possible to provide more appropriate and optimized design proposals.

[0355] A "consumer" is an individual or group that receives a product or service.

[0356] "Product requirements" refer to information about the wishes and specifications that consumers express regarding a particular product.

[0357] A "terminal" refers to an electronic device used by a user to input or receive information.

[0358] A "server" is a computer system that provides data processing and storage functions over a network.

[0359] "Emotional analysis technology" is a technique that analyzes and infers an individual's emotional state from their voice, facial expressions, and other factors.

[0360] A "generative AI model" is an artificial intelligence model that generates new data or designs from specific input information based on an algorithm.

[0361] A "design proposal" refers to a proposal that includes drawings and specifications for realizing a specific product.

[0362] "Multilingual translation technology" is a technology that enables the exchange of information between different languages.

[0363] A "manufacturer" refers to a professional who undertakes the production of a product based on its design.

[0364] "Feedback" refers to opinions and information about the results or reactions in a particular process.

[0365] This invention is a product design system that enables advanced personalization based on consumer emotions. The system consists of a server, a consumer terminal, a manufacturer's device, and an emotion analysis engine.

[0366] Users input product requirements and specify desired designs and specifications via a terminal. The terminal allows input of text, images, audio, and other information through its interface. This information is filtered appropriately and sent to the server.

[0367] The server uses a generative AI model to process consumer emotional state data received from the emotion analysis engine. The server utilizes speech recognition and image analysis software to analyze the user's emotions from their voice and facial expression data. Based on this emotional information, the generative AI model generates product design proposals optimized for the consumer's needs. For example, if the server detects that the consumer is relaxed, it can propose a simple and warm design.

[0368] The generated design proposals are sent to the terminal in real time and presented to the user in a visual format. The user can review this and provide instructions for selection and adjustment as needed. The terminal interface is designed to allow consumers to easily adjust the details of their selected design.

[0369] The emotion analysis engine then analyzes consumer reactions, and if there are signs of consumer dissatisfaction, the server immediately generates and proposes a new design using an AI model. This process allows for flexible design adjustments based on consumer emotions.

[0370] The finalized design is transferred to the manufacturer's device. The manufacturer evaluates the technical feasibility and provides necessary feedback. This information is stored on a server and presented to consumers as feedback.

[0371] This invention enables design proposals that incorporate consumer emotions, thereby improving customer satisfaction with products. A concrete example of a prompt message would be the instruction, "Generate a teacup design with calming colors based on the user's emotional state."

[0372] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0373] Step 1:

[0374] Users use a terminal to input their product requests. Specifically, users input their desired design and specifications using text, images, and audio. Once this data is entered, the terminal sends the information to the server. The entered data is transferred to the server as structured data that accurately reflects the user's preferences.

[0375] Step 2:

[0376] The server receives input data from the user and performs initial data processing. Here, it extracts basic specifications based on the user's requests and prepares the necessary data for sentiment analysis. For example, the server converts audio data into a format that can be analyzed using digital signal processing, and processes it for subsequent sentiment analysis.

[0377] Step 3:

[0378] For emotion analysis, the server utilizes emotion analysis technology. The server analyzes the voice and facial expressions provided by the user and estimates their emotional state in real time. During this process, the server inputs data into an emotion analysis model, outputting an emotional state such as "relaxed." The analysis results are then used in the next processing step.

[0379] Step 4:

[0380] The server utilizes a generative AI model to generate product design proposals that combine user requests and emotional state data. For example, if the user is relaxed, the server will generate a design that uses calming colors and curves. The server forms prompt sentences using emotional state as keywords, inputs them into the generative AI model, and outputs the design proposals.

[0381] Step 5:

[0382] The server sends the generated design proposal to the user's terminal, allowing the user to select and adjust it. The user visualizes and reviews the design proposal through the terminal's interface. The server receives user feedback and determines whether further adjustments to the design proposal are necessary.

[0383] Step 6:

[0384] Based on user feedback, the server modifies the generated design proposal as needed. The server then uses sentiment analysis technology again to check the user's latest emotional state and refines the design proposal based on the feedback. This process provides a design that more closely matches the user's preferences.

[0385] Step 7:

[0386] The final design proposal is transferred from the server to the manufacturer's device, where the manufacturer evaluates the product's technical feasibility. Feedback from the manufacturer is sent to the server and shared with users as needed. This exchange of feedback helps the final product come closer to consumer expectations.

[0387] (Application Example 2)

[0388] 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."

[0389] Traditional custom-made product ordering systems have the problem of making it difficult to propose product designs that fully reflect consumers' personal feelings and preferences. Furthermore, communication between consumers and craftsmen in physical stores is often not smooth, leading to delays and increased effort in product design and manufacturing. A solution is needed to address these challenges and improve consumer satisfaction.

[0390] 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.

[0391] In this invention, the server includes means for receiving product requests from consumers via an input device, means for analyzing the consumer's emotional state using an emotion analysis device and reflecting that information in the generation of product design proposals, and means for displaying the optimal design proposal based on the consumer's emotional analysis results in a physical store to facilitate ordering. This makes it possible to generate product design proposals that reflect the individual emotions and preferences of consumers in real time and provide a smooth ordering experience.

[0392] A "consumer" is an individual or group that purchases products or services, and is the entity that inputs product requests and places orders through a system.

[0393] A "product design proposal" is a specific product design plan generated based on consumer requests and emotional data.

[0394] An "input device" is hardware used by consumers to input product order information, such as a smartphone or tablet.

[0395] A "generation program" is software used to generate product design proposals based on consumer requests and sentiment analysis results.

[0396] An "emotion analysis device" is a device that analyzes a consumer's facial expressions and voice to identify their emotional state.

[0397] An "engineer" is a professional who receives the final product design, evaluates its technical feasibility, and provides feedback.

[0398] A "physical store" refers to a physically existing sales location where consumers interact with products.

[0399] A "design proposal" is a product design plan displayed based on the results of consumer emotional analysis.

[0400] To implement this invention, a system is configured that includes a server, a consumer terminal, a technician's terminal, and an emotion analysis device. The operation of this system is as follows:

[0401] The server receives product requests from consumer devices. These consumer devices include input devices such as smartphones and tablets. In this process, consumers input the necessary design and specifications into their devices and send that data to the server.

[0402] The emotion analysis device collects consumer facial expression and voice data and inputs this data into an emotion analysis model to analyze the consumer's emotional state. Microsoft Azure Emotion API, among others, can be used as the emotion analysis model. The analysis results are sent to a server and used to generate product design proposals.

[0403] The server uses a generation program to generate product design proposals that combine consumer requests with the results of sentiment analysis. The generation program, developed in Python, adjusts the design to match the consumer's emotional state. The generated design proposals are sent to the consumer's device in real time.

[0404] For example, when a consumer visits a store in a relaxed state, the terminal displays design proposals incorporating gentle colors and curves. Based on this information, the consumer can select a design and place a final order. The final design proposal is then sent to the technician's terminal, where they can provide feedback considering its technical feasibility.

[0405] An example of a prompt might be: "Based on this user's facial expressions and voice, we can see they are relaxed. Please propose a custom-made design with a relaxed atmosphere. The color scheme should be calm, and the design should make extensive use of curves." Such prompts will prompt the AI ​​model to propose an optimized design.

[0406] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0407] Step 1:

[0408] Users input their product requests using devices such as smartphones and tablets. They specify their desired design and specifications using text or by selecting options. The entered data is then sent directly to the server.

[0409] Step 2:

[0410] The server temporarily stores product request data received from the user and generates a prompt message based on that information. This prompt message is used as an instruction to the generation AI model. It is in the format of "Generate design proposals based on the user's request."

[0411] Step 3:

[0412] The user's device uses its camera and microphone to collect facial expressions and voice data in real time. This data is input into an emotion analysis device to identify the user's emotional state.

[0413] Step 4:

[0414] The emotion analysis device analyzes input facial expressions and voice data to estimate the user's current emotional state. Various statistical methods and AI models are used in this analysis. The analysis results are output in categories such as calm, relaxed, and excited, and sent to a server.

[0415] Step 5:

[0416] The server integrates user request data and sentiment analysis results, then runs a generation program to generate design proposals. Based on the input data, the design proposals are tailored to the user's current emotional state. The generated design proposals are then sent to the consumer's device.

[0417] Step 6:

[0418] The user reviews the design proposals sent from the server on their device, making selections and modifications. Based on the user's actions, the device resends the information to the server.

[0419] Step 7:

[0420] The server receives feedback from users and readjusts the design as needed. The final design is sent to engineers and used in the product manufacturing process.

[0421] 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.

[0422] 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.

[0423] 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.

[0424] [Third Embodiment]

[0425] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0426] 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.

[0427] 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).

[0428] 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.

[0429] 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.

[0430] 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).

[0431] 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.

[0432] 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.

[0433] 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.

[0434] 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.

[0435] 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.

[0436] 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".

[0437] This invention relates to a system that provides consumers with a process to customize and order traditional Japanese crafts, producing high-quality products through collaborative work with artisans. The entire system primarily functions using a server, user terminals, and artisan devices.

[0438] First, the user uses a device to input the product concept and requirements. For example, they might input a request such as, "I want to make ceramics with a cherry blossom pattern in a blue color scheme," on an online platform. This input is then sent to the server.

[0439] Next, the server uses a generation program to analyze the user's requests. The generation program utilizes a rich database and AI technology to generate multiple design options tailored to the user's preferences. The server then sends these design options to the user's device in real time.

[0440] Users can review the design proposals presented on their device and select the one they like. After making a selection, they can also specify areas they want to further customize. These instructions are sent back to the server, which uses a generation program to adjust the design proposal in real time.

[0441] The finalized design proposal is sent to the craftsman's device via the server. The craftsman then assesses the design's technical feasibility and sends any necessary feedback back to the server. The server receives this feedback and, if necessary, facilitates further adjustments between the user and the craftsman.

[0442] Once production begins, the server visualizes the production process in real time on the user's terminal. For example, it provides information on the current progress of the product, material selection, and techniques being used. This allows the user to always know how the product is being made.

[0443] Through this system, consumers and artisans can communicate smoothly and collaborate to create highly customized, original traditional crafts.

[0444] The following describes the processing flow.

[0445] Step 1:

[0446] The user uses a terminal to input information about their desired product requirements and design. This information includes the product type, design image, and desired colors and patterns. The entered data is sent to the server.

[0447] Step 2:

[0448] The server analyzes the user's request. Using a generation program, it generates design proposals for traditional Japanese crafts based on the request. This includes retrieving information from a database and generating creative designs using AI. The server then sends the generated design proposals to the user's device.

[0449] Step 3:

[0450] The generated design proposals are displayed on the user's device for review. The user selects their preferred design proposal and, if desired, provides specific instructions for further customization. These selections and instructions are then sent back to the server.

[0451] Step 4:

[0452] The server receives selections and instructions from the user and immediately adjusts the design proposal using a generation program. The design proposal, with its real-time changes, is then reflected back on the user's terminal, allowing the user to review the results.

[0453] Step 5:

[0454] Once the final design is decided, the server sends it to the craftsman's device. The craftsman reviews the design from a technical standpoint and provides feedback on feasibility, materials to be used, and techniques. This feedback is then sent back to the server.

[0455] Step 6:

[0456] The server analyzes feedback from craftsmen and provides information to users as needed. This allows users to gain a deeper understanding of the feasibility of the final product and the manufacturing process, and to smoothly move on to the next step if adjustments are necessary.

[0457] Step 7:

[0458] Once production begins, the server visualizes the progress of the production process in real time on the user's terminal. It provides the user with information such as material selection and the stage of production, allowing the user to track the process while waiting for the product to be completed.

[0459] (Example 1)

[0460] 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."

[0461] Modern consumers increasingly demand customized products tailored to their individual needs, but traditional manufacturing processes struggle to efficiently deliver bespoke products that reflect these detailed consumer requests. Furthermore, difficulties in effective communication between consumers and manufacturers, along with a lack of transparency in the production process, also pose problems.

[0462] 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.

[0463] In this invention, the server includes means for receiving product requests from consumers via an input interface, means for generating product design proposals based on consumer requests using a generation algorithm, and means for transmitting the design proposals to the consumer's device in real time and providing an interface for the consumer to make selections and improvement instructions. This enables the efficient provision of customized products that immediately reflect consumer requests and facilitates smooth communication between consumers and manufacturers.

[0464] "Consumers" refers to individuals or organizations that wish to use this system to order custom-made or customized products.

[0465] An "input interface" refers to a device or software that consumers use to input requests or instructions online.

[0466] A "generation algorithm" refers to a computational method that automatically generates product design proposals based on received requests.

[0467] "Device" refers to electronic devices, including computers and mobile devices, used by consumers and manufacturers.

[0468] "Manufacturer" refers to the engineers or craftsmen who actually manufacture the product based on the product design.

[0469] "Real-time" refers to the transmission, reception, and processing of information occurring almost instantaneously.

[0470] "Providing an interface" refers to providing users with a screen or means to interact with the system.

[0471] "Technical feedback" refers to the responses from manufacturers regarding the feasibility of a design proposal and suggestions for improvement.

[0472] "Visualizing information about the production process" refers to providing users with a visual representation of the progress of the product, the materials used, the techniques employed, and other relevant information.

[0473] The embodiments for carrying out this system invention are described below.

[0474] Users access an online platform using their device and enter details of their customized order for traditional crafts. This input includes specific information describing the user's requests; for example, they can submit a text message stating, "I would like a cherry blossom patterned ceramic piece made in blue tones." This information is then sent from the device to the server.

[0475] The server activates a generation algorithm based on the received request to generate product design proposals that meet consumer needs. A generation AI model is used to automatically generate a variety of design variations. At this time, existing product information and past order history recorded in the database are used to improve the accuracy and diversity of the design proposals.

[0476] Multiple design proposals are sent from the server to the user's terminal in real time, allowing the user to review and select a product design. The user can also send further instructions to the server via their terminal if they have additional requests.

[0477] The server adjusts the design proposal by reapplying the generation algorithm based on additional requests from the user. The final version of the design proposal is then sent to the creator's device. The creator reviews the design proposal on their device and provides technical feedback to the server. The server then uses this feedback to facilitate further adjustments between the user and the creator as needed.

[0478] Once production begins, the server monitors the product's progress, techniques used, materials, and other details, and visualizes this information in real time on the user's device, ensuring transparency in the production process. Users can check the production status at any time, allowing them to confidently oversee the custom-made process.

[0479] As a concrete example, a prompt message to be input into a generation AI model might be, "Please generate a ceramic design with a cherry blossom pattern and a blue base color." This prompt allows the AI ​​model to automatically propose design ideas that meet the user's preferences.

[0480] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0481] Step 1:

[0482] Users access an online platform via their device and enter customization requests for traditional Japanese crafts. The user's input includes details such as patterns, colors, and sizes. This information is sent as text data from the input device to the server.

[0483] Step 2:

[0484] The server analyzes the received user request data and generates a prompt. For example, it might create a prompt such as, "Please generate a ceramic design with a cherry blossom pattern and a blue base color." This prompt is then used as input to the AI ​​generation model.

[0485] Step 3:

[0486] The server uses a generative AI model to generate product design proposals based on prompt messages. Data processing utilizes an internal design database and AI technology to generate design variations that meet user requirements. The generated designs are output as image data and 3D models.

[0487] Step 4:

[0488] The server sends the generated design proposals to the user's terminal in real time. The user reviews and selects from the multiple design proposals provided on the terminal. An interface is provided that allows the user to input their selections and additional requests through checkboxes and text fields.

[0489] Step 5:

[0490] The user sends their selected design proposal and additional instructions to the server. The server analyzes these instructions, applies the generated AI model again, and adjusts the design. The output of this step is the final, adjusted design proposal.

[0491] Step 6:

[0492] The server sends the final design to the manufacturer's device. The manufacturer reviews the design on the device and evaluates its feasibility for manufacturing. Feedback from the manufacturer is sent to the server in text format.

[0493] Step 7:

[0494] The server relays the feedback received from the creator to the user and facilitates further adjustments between the user and creator as needed. If further adjustments are required, return to step 5.

[0495] Step 8:

[0496] Once production begins, the server monitors the progress of the production process and visualizes information based on the product's progress, material usage, and selected techniques on the user's terminal in real time. The user can then check the progress of the production through this information.

[0497] (Application Example 1)

[0498] 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."

[0499] When consumers order customized Japanese traditional crafts, the challenge lies in facilitating smooth communication with artisans, proposing optimal designs, and streamlining the entire purchase process. In particular, the ability to monitor the product's progress in real time is essential.

[0500] 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.

[0501] In this invention, the server includes means for receiving product requests from consumers via a user interface, means for generating product design proposals based on consumer requests using a generation algorithm, and means for processing electronic transactions. This makes it possible for consumers to easily customize and purchase bespoke traditional crafts and to check the progress of production in real time.

[0502] A "consumer" is the end user who orders and customizes a product.

[0503] A "user interface" is a component that provides a digital environment for consumers to input their product requirements.

[0504] A "generative algorithm" is a program that automatically creates product design proposals based on consumer input.

[0505] A "terminal" is an electronic device used by consumers to view and operate product design proposals.

[0506] The "creator" refers to the craftsmen or technicians responsible for the actual production of the product.

[0507] "Technical feedback" refers to information provided by the creator regarding the feasibility and potential improvements to the product design.

[0508] "Electronic transactions" refer to the buying and selling procedures for goods and services conducted via the internet.

[0509] The term "manufacturing process" refers to the series of work processes involved in completing a product.

[0510] "Visual display" means presenting manufacturing processes and product information in an easily understandable way on a digital screen.

[0511] In the system that implements this invention, the user first inputs product requirements using a terminal. The user interface receives this input and uses a generation algorithm to generate multiple product design proposals based on the requirements. The generated design proposals are displayed to the user on the terminal, and the user makes selections and requests for improvements. At this time, the terminal transmits the improved requests to the server in real time, and the generation algorithm makes further adjustments.

[0512] The server then sends the final selected product design to the creator and receives technical feedback. Based on this feedback, the design is further refined as needed, and this process is repeated. Once the manufacturing process begins, the server provides the user with a visual display of the manufacturing progress. This allows the user to understand how the product is being made in real time.

[0513] Electronic transactions are processed using a secure payment platform (e.g., Stripe) to complete payment for products ordered by the user. The server manages all transaction data and performs confirmation and cancellation as needed.

[0514] For example, if a user requests "pottery inspired by cherry blossoms in spring," the generation algorithm will suggest several cherry blossom motifs. The user can then select their favorite design and specify additional colors and patterns. An example of a prompt might be: "The user's requested design element is 'cherry blossoms in spring.' Please suggest designs that are feasible to produce while utilizing Japanese tradition and employing a modern approach."

[0515] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0516] Step 1:

[0517] The user enters product requirements using a terminal. These requirements include specific design, color, and pattern preferences, and the terminal prepares to send this information as digital data to the server.

[0518] Step 2:

[0519] The server receives user request data and uses a generative AI model to generate multiple product design proposals based on the requests. By passing the input request data as prompts to the generative model, it outputs a variety of design proposals. In doing so, it refers to similar cases from past databases to suggest the optimal design.

[0520] Step 3:

[0521] The terminal displays the generated product design proposal to the user. It analyzes the design proposal data received from the server and performs layout processing to make it visually easy to understand. The user reviews this output and inputs selections and improvement instructions.

[0522] Step 4:

[0523] The user sends the selected design proposal and improvement instructions back to the server via their device. The imported improvement instructions include adjustments to specific color tones and designs, and the server uses a generated AI model to readjust the design proposal in real time based on these instructions.

[0524] Step 5:

[0525] The final product design is sent from the server to the creator's device, and technical feedback is received from the creator. The creator evaluates the feasibility and areas for improvement of the design data sent as input and sends feedback back to the server. The server then makes any necessary adjustments with the user.

[0526] Step 6:

[0527] As the production process begins, the server visually displays the production progress on the terminal in real time. It receives progress information from the creator as input, processes the data to clearly show the user the product's completion level and current status, and then outputs it.

[0528] Step 7:

[0529] The server processes payments via electronic transactions and securely manages transaction data. It takes user payment information as input, performs authentication and authorization processes, and provides order completion and payment confirmation as output. Appropriate encryption technology is used throughout this process to ensure security.

[0530] 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.

[0531] This invention relates to a system that recognizes user emotions and uses that information to personalize and optimize the bespoke process for traditional Japanese crafts. The entire system consists of a server, a user terminal, a craftsman's device, and an emotion engine.

[0532] First, the user enters their product requirements via a terminal, providing information about their desired design and specifications. This information is sent to a server where the initial data is processed.

[0533] Here, the emotion engine analyzes the user's emotional state. This engine infers the user's emotions by inputting the user's voice and facial expression data into an emotion analysis model. The results of the emotion engine are sent to the server along with the user's request.

[0534] The server uses a generation program to create product design proposals that combine user requests with the results of emotional analysis. For example, if the user is relaxed, the generation program can suggest a design using calming colors and curves. Based on this information, the server creates the design proposal in real time and sends it to the user's terminal.

[0535] Users visualize design options on their devices and make their selections. Furthermore, an emotion engine is used to collect user reactions in real time, and if dissatisfaction or doubts are observed, the server can quickly adjust the design options. The system incorporates a flexible mechanism for refining the design based on user emotions.

[0536] The final design is transmitted to the craftsman via the server, who then evaluates its technical feasibility and provides feedback. The craftsman's information is organized on the server and presented to the user again as needed.

[0537] This invention enables personalized design proposals that take user emotions into consideration, facilitating smoother collaboration with craftsmen and facilitating the market deployment of high-quality, made-to-order products.

[0538] The following describes the processing flow.

[0539] Step 1:

[0540] Users enter detailed information about product requirements and design via their device. This includes desired materials, colors, and design elements, and the entered data is sent to the server.

[0541] Step 2:

[0542] The user's device uses its camera and microphone to collect data on the user's voice and facial expressions. This data is input into an emotion engine to analyze the user's emotional state. The analyzed emotion data is also sent to the server.

[0543] Step 3:

[0544] The server integrates user requests and emotional data, and uses a generation program to generate product design proposals that are suitable for the user's preferences and emotional state. The generated design proposals are sent to the user's terminal as multiple options.

[0545] Step 4:

[0546] Users review product design proposals presented on their devices and select their preferred design. The emotion engine then re-analyzes the user's emotions during the selection process, and the server makes any necessary design adjustments based on the results.

[0547] Step 5:

[0548] The final, adjusted design proposal is sent from the server to the craftsman's device. The craftsman reviews the design proposal and provides feedback from a technical perspective. This feedback is sent back to the server, and the user is notified if any technical improvements are needed.

[0549] Step 6:

[0550] The server manages the production process based on the final approved design and visualizes the progress in real time on the user's device. Users can track each stage of production and wait for the final product to be completed.

[0551] (Example 2)

[0552] 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."

[0553] Traditional product design processes struggle to incorporate individual consumer emotions and real-time feedback into design adjustments. Furthermore, differences in communication language and the inability to effectively utilize past consumer behavior data to optimize design proposals are hindered. As a result, truly personalized products that meet consumer expectations are limited.

[0554] 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.

[0555] In this invention, the server includes means for receiving product requests from consumers via a terminal, means for identifying the consumer's emotional state using emotion analysis technology, and means for generating product design proposals that fuse the consumer's requests with the identified emotional state using a generative AI model. This enables flexible and personalized design adjustments based on the consumer's emotions. Furthermore, by implementing multilingual translation technology, smooth communication with manufacturers can be achieved, and by using the consumer's past behavioral data, it is possible to provide more appropriate and optimized design proposals.

[0556] A "consumer" is an individual or group that receives a product or service.

[0557] "Product requirements" refer to information about the wishes and specifications that consumers express regarding a particular product.

[0558] A "terminal" refers to an electronic device used by a user to input or receive information.

[0559] A "server" is a computer system that provides data processing and storage functions over a network.

[0560] "Emotional analysis technology" is a technique that analyzes and infers an individual's emotional state from their voice, facial expressions, and other factors.

[0561] A "generative AI model" is an artificial intelligence model that generates new data or designs from specific input information based on an algorithm.

[0562] A "design proposal" refers to a proposal that includes drawings and specifications for realizing a specific product.

[0563] "Multilingual translation technology" is a technology that enables the exchange of information between different languages.

[0564] A "manufacturer" refers to a professional who undertakes the production of a product based on its design.

[0565] "Feedback" refers to opinions and information about the results or reactions in a particular process.

[0566] This invention is a product design system that enables advanced personalization based on consumer emotions. The system consists of a server, a consumer terminal, a manufacturer's device, and an emotion analysis engine.

[0567] Users input product requirements and specify desired designs and specifications via a terminal. The terminal allows input of text, images, audio, and other information through its interface. This information is filtered appropriately and sent to the server.

[0568] The server uses a generative AI model to process consumer emotional state data received from the emotion analysis engine. The server utilizes speech recognition and image analysis software to analyze the user's emotions from their voice and facial expression data. Based on this emotional information, the generative AI model generates product design proposals optimized for the consumer's needs. For example, if the server detects that the consumer is relaxed, it can propose a simple and warm design.

[0569] The generated design proposals are sent to the terminal in real time and presented to the user in a visual format. The user can review this and provide instructions for selection and adjustment as needed. The terminal interface is designed to allow consumers to easily adjust the details of their selected design.

[0570] The emotion analysis engine then analyzes consumer reactions, and if there are signs of consumer dissatisfaction, the server immediately generates and proposes a new design using an AI model. This process allows for flexible design adjustments based on consumer emotions.

[0571] The finalized design is transferred to the manufacturer's device. The manufacturer evaluates the technical feasibility and provides necessary feedback. This information is stored on a server and presented to consumers as feedback.

[0572] This invention enables design proposals that incorporate consumer emotions, thereby improving customer satisfaction with products. A concrete example of a prompt message would be the instruction, "Generate a teacup design with calming colors based on the user's emotional state."

[0573] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0574] Step 1:

[0575] Users use a terminal to input their product requests. Specifically, users input their desired design and specifications using text, images, and audio. Once this data is entered, the terminal sends the information to the server. The entered data is transferred to the server as structured data that accurately reflects the user's preferences.

[0576] Step 2:

[0577] The server receives input data from the user and performs initial data processing. Here, it extracts basic specifications based on the user's requests and prepares the necessary data for sentiment analysis. For example, the server converts audio data into a format that can be analyzed using digital signal processing, and processes it for subsequent sentiment analysis.

[0578] Step 3:

[0579] For emotion analysis, the server utilizes emotion analysis technology. The server analyzes the voice and facial expressions provided by the user and estimates their emotional state in real time. During this process, the server inputs data into an emotion analysis model, outputting an emotional state such as "relaxed." The analysis results are then used in the next processing step.

[0580] Step 4:

[0581] The server utilizes a generative AI model to generate product design proposals that combine user requests and emotional state data. For example, if the user is relaxed, the server will generate a design that uses calming colors and curves. The server forms prompt sentences using emotional state as keywords, inputs them into the generative AI model, and outputs the design proposals.

[0582] Step 5:

[0583] The server sends the generated design proposal to the user's terminal, allowing the user to select and adjust it. The user visualizes and reviews the design proposal through the terminal's interface. The server receives user feedback and determines whether further adjustments to the design proposal are necessary.

[0584] Step 6:

[0585] Based on user feedback, the server modifies the generated design proposal as needed. The server then uses sentiment analysis technology again to check the user's latest emotional state and refines the design proposal based on the feedback. This process provides a design that more closely matches the user's preferences.

[0586] Step 7:

[0587] The final design proposal is transferred from the server to the manufacturer's device, where the manufacturer evaluates the product's technical feasibility. Feedback from the manufacturer is sent to the server and shared with users as needed. This exchange of feedback helps the final product come closer to consumer expectations.

[0588] (Application Example 2)

[0589] 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."

[0590] Traditional custom-made product ordering systems have the problem of making it difficult to propose product designs that fully reflect consumers' personal feelings and preferences. Furthermore, communication between consumers and craftsmen in physical stores is often not smooth, leading to delays and increased effort in product design and manufacturing. A solution is needed to address these challenges and improve consumer satisfaction.

[0591] 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.

[0592] In this invention, the server includes means for receiving product requests from consumers via an input device, means for analyzing the consumer's emotional state using an emotion analysis device and reflecting that information in the generation of product design proposals, and means for displaying the optimal design proposal based on the consumer's emotional analysis results in a physical store to facilitate ordering. This makes it possible to generate product design proposals that reflect the individual emotions and preferences of consumers in real time and provide a smooth ordering experience.

[0593] A "consumer" is an individual or group that purchases products or services, and is the entity that inputs product requests and places orders through a system.

[0594] A "product design proposal" is a specific product design plan generated based on consumer requests and emotional data.

[0595] An "input device" is hardware used by consumers to input product order information, such as a smartphone or tablet.

[0596] A "generation program" is software used to generate product design proposals based on consumer requests and sentiment analysis results.

[0597] An "emotion analysis device" is a device that analyzes a consumer's facial expressions and voice to identify their emotional state.

[0598] An "engineer" is a professional who receives the final product design, evaluates its technical feasibility, and provides feedback.

[0599] A "physical store" refers to a physically existing sales location where consumers interact with products.

[0600] A "design proposal" is a product design plan displayed based on the results of consumer emotional analysis.

[0601] To implement this invention, a system is configured that includes a server, a consumer terminal, a technician's terminal, and an emotion analysis device. The operation of this system is as follows:

[0602] The server receives product requests from consumer devices. These consumer devices include input devices such as smartphones and tablets. In this process, consumers input the necessary design and specifications into their devices and send that data to the server.

[0603] The emotion analysis device collects consumer facial expression and voice data and inputs this data into an emotion analysis model to analyze the consumer's emotional state. Microsoft Azure Emotion API, among others, can be used as the emotion analysis model. The analysis results are sent to a server and used to generate product design proposals.

[0604] The server uses a generation program to generate product design proposals that combine consumer requests with the results of sentiment analysis. The generation program, developed in Python, adjusts the design to match the consumer's emotional state. The generated design proposals are sent to the consumer's device in real time.

[0605] For example, when a consumer visits a store in a relaxed state, the terminal displays design proposals incorporating gentle colors and curves. Based on this information, the consumer can select a design and place a final order. The final design proposal is then sent to the technician's terminal, where they can provide feedback considering its technical feasibility.

[0606] An example of a prompt might be: "Based on this user's facial expressions and voice, we can see they are relaxed. Please propose a custom-made design with a relaxed atmosphere. The color scheme should be calm, and the design should make extensive use of curves." Such prompts will prompt the AI ​​model to propose an optimized design.

[0607] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0608] Step 1:

[0609] Users input their product requests using devices such as smartphones and tablets. They specify their desired design and specifications using text or by selecting options. The entered data is then sent directly to the server.

[0610] Step 2:

[0611] The server temporarily stores product request data received from the user and generates a prompt message based on that information. This prompt message is used as an instruction to the generation AI model. It is in the format of "Generate design proposals based on the user's request."

[0612] Step 3:

[0613] The user's device uses its camera and microphone to collect facial expressions and voice data in real time. This data is input into an emotion analysis device to identify the user's emotional state.

[0614] Step 4:

[0615] The emotion analysis device analyzes input facial expressions and voice data to estimate the user's current emotional state. Various statistical methods and AI models are used in this analysis. The analysis results are output in categories such as calm, relaxed, and excited, and sent to a server.

[0616] Step 5:

[0617] The server integrates user request data and sentiment analysis results, then runs a generation program to generate design proposals. Based on the input data, the design proposals are tailored to the user's current emotional state. The generated design proposals are then sent to the consumer's device.

[0618] Step 6:

[0619] The user reviews the design proposals sent from the server on their device, making selections and modifications. Based on the user's actions, the device resends the information to the server.

[0620] Step 7:

[0621] The server receives feedback from users and readjusts the design as needed. The final design is sent to engineers and used in the product manufacturing process.

[0622] 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.

[0623] 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.

[0624] 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.

[0625] [Fourth Embodiment]

[0626] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0627] 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.

[0628] 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).

[0629] 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.

[0630] 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.

[0631] 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).

[0632] 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.

[0633] 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.

[0634] 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.

[0635] 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.

[0636] 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.

[0637] 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.

[0638] 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".

[0639] This invention relates to a system that provides consumers with a process to customize and order traditional Japanese crafts, producing high-quality products through collaborative work with artisans. The entire system primarily functions using a server, user terminals, and artisan devices.

[0640] First, the user uses a device to input the product concept and requirements. For example, they might input a request such as, "I want to make ceramics with a cherry blossom pattern in a blue color scheme," on an online platform. This input is then sent to the server.

[0641] Next, the server uses a generation program to analyze the user's requests. The generation program utilizes a rich database and AI technology to generate multiple design options tailored to the user's preferences. The server then sends these design options to the user's device in real time.

[0642] Users can review the design proposals presented on their device and select the one they like. After making a selection, they can also specify areas they want to further customize. These instructions are sent back to the server, which uses a generation program to adjust the design proposal in real time.

[0643] The finalized design proposal is sent to the craftsman's device via the server. The craftsman then assesses the design's technical feasibility and sends any necessary feedback back to the server. The server receives this feedback and, if necessary, facilitates further adjustments between the user and the craftsman.

[0644] Once production begins, the server visualizes the production process in real time on the user's terminal. For example, it provides information on the current progress of the product, material selection, and techniques being used. This allows the user to always know how the product is being made.

[0645] Through this system, consumers and artisans can communicate smoothly and collaborate to create highly customized, original traditional crafts.

[0646] The following describes the processing flow.

[0647] Step 1:

[0648] The user uses a terminal to input information about their desired product requirements and design. This information includes the product type, design image, and desired colors and patterns. The entered data is sent to the server.

[0649] Step 2:

[0650] The server analyzes the user's request. Using a generation program, it generates design proposals for traditional Japanese crafts based on the request. This includes retrieving information from a database and generating creative designs using AI. The server then sends the generated design proposals to the user's device.

[0651] Step 3:

[0652] The generated design proposals are displayed on the user's device for review. The user selects their preferred design proposal and, if desired, provides specific instructions for further customization. These selections and instructions are then sent back to the server.

[0653] Step 4:

[0654] The server receives selections and instructions from the user and immediately adjusts the design proposal using a generation program. The design proposal, with its real-time changes, is then reflected back on the user's terminal, allowing the user to review the results.

[0655] Step 5:

[0656] Once the final design is decided, the server sends it to the craftsman's device. The craftsman reviews the design from a technical standpoint and provides feedback on feasibility, materials to be used, and techniques. This feedback is then sent back to the server.

[0657] Step 6:

[0658] The server analyzes feedback from craftsmen and provides information to users as needed. This allows users to gain a deeper understanding of the feasibility of the final product and the manufacturing process, and to smoothly move on to the next step if adjustments are necessary.

[0659] Step 7:

[0660] Once production begins, the server visualizes the progress of the production process in real time on the user's terminal. It provides the user with information such as material selection and the stage of production, allowing the user to track the process while waiting for the product to be completed.

[0661] (Example 1)

[0662] 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".

[0663] Modern consumers increasingly demand customized products tailored to their individual needs, but traditional manufacturing processes struggle to efficiently deliver bespoke products that reflect these detailed consumer requests. Furthermore, difficulties in effective communication between consumers and manufacturers, along with a lack of transparency in the production process, also pose problems.

[0664] 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.

[0665] In this invention, the server includes means for receiving product requests from consumers via an input interface, means for generating product design proposals based on consumer requests using a generation algorithm, and means for transmitting the design proposals to the consumer's device in real time and providing an interface for the consumer to make selections and improvement instructions. This enables the efficient provision of customized products that immediately reflect consumer requests and facilitates smooth communication between consumers and manufacturers.

[0666] "Consumers" refers to individuals or organizations that wish to use this system to order custom-made or customized products.

[0667] An "input interface" refers to a device or software that consumers use to input requests or instructions online.

[0668] A "generation algorithm" refers to a computational method that automatically generates product design proposals based on received requests.

[0669] "Device" refers to electronic devices, including computers and mobile devices, used by consumers and manufacturers.

[0670] "Manufacturer" refers to the engineers or craftsmen who actually manufacture the product based on the product design.

[0671] "Real-time" refers to the transmission, reception, and processing of information occurring almost instantaneously.

[0672] "Providing an interface" refers to providing users with a screen or means to interact with the system.

[0673] "Technical feedback" refers to the responses from manufacturers regarding the feasibility of a design proposal and suggestions for improvement.

[0674] "Visualizing information about the production process" refers to providing users with a visual representation of the progress of the product, the materials used, the techniques employed, and other relevant information.

[0675] The embodiments for carrying out this system invention are described below.

[0676] Users access an online platform using their device and enter details of their customized order for traditional crafts. This input includes specific information describing the user's requests; for example, they can submit a text message stating, "I would like a cherry blossom patterned ceramic piece made in blue tones." This information is then sent from the device to the server.

[0677] The server activates a generation algorithm based on the received request to generate product design proposals that meet consumer needs. A generation AI model is used to automatically generate a variety of design variations. At this time, existing product information and past order history recorded in the database are used to improve the accuracy and diversity of the design proposals.

[0678] Multiple design proposals are sent from the server to the user's terminal in real time, allowing the user to review and select a product design. The user can also send further instructions to the server via their terminal if they have additional requests.

[0679] The server adjusts the design proposal by reapplying the generation algorithm based on additional requests from the user. The final version of the design proposal is then sent to the creator's device. The creator reviews the design proposal on their device and provides technical feedback to the server. The server then uses this feedback to facilitate further adjustments between the user and the creator as needed.

[0680] Once production begins, the server monitors the product's progress, techniques used, materials, and other details, and visualizes this information in real time on the user's device, ensuring transparency in the production process. Users can check the production status at any time, allowing them to confidently oversee the custom-made process.

[0681] As a concrete example, a prompt message to be input into a generation AI model might be, "Please generate a ceramic design with a cherry blossom pattern and a blue base color." This prompt allows the AI ​​model to automatically propose design ideas that meet the user's preferences.

[0682] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0683] Step 1:

[0684] Users access an online platform via their device and enter customization requests for traditional Japanese crafts. The user's input includes details such as patterns, colors, and sizes. This information is sent as text data from the input device to the server.

[0685] Step 2:

[0686] The server analyzes the received user request data and generates a prompt. For example, it might create a prompt such as, "Please generate a ceramic design with a cherry blossom pattern and a blue base color." This prompt is then used as input to the AI ​​generation model.

[0687] Step 3:

[0688] The server uses a generative AI model to generate product design proposals based on prompt messages. Data processing utilizes an internal design database and AI technology to generate design variations that meet user requirements. The generated designs are output as image data and 3D models.

[0689] Step 4:

[0690] The server sends the generated design proposals to the user's terminal in real time. The user reviews and selects from the multiple design proposals provided on the terminal. An interface is provided that allows the user to input their selections and additional requests through checkboxes and text fields.

[0691] Step 5:

[0692] The user sends their selected design proposal and additional instructions to the server. The server analyzes these instructions, applies the generated AI model again, and adjusts the design. The output of this step is the final, adjusted design proposal.

[0693] Step 6:

[0694] The server sends the final design to the manufacturer's device. The manufacturer reviews the design on the device and evaluates its feasibility for manufacturing. Feedback from the manufacturer is sent to the server in text format.

[0695] Step 7:

[0696] The server relays the feedback received from the creator to the user and facilitates further adjustments between the user and creator as needed. If further adjustments are required, return to step 5.

[0697] Step 8:

[0698] Once production begins, the server monitors the progress of the production process and visualizes information based on the product's progress, material usage, and selected techniques on the user's terminal in real time. The user can then check the progress of the production through this information.

[0699] (Application Example 1)

[0700] 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".

[0701] When consumers order customized Japanese traditional crafts, the challenge lies in facilitating smooth communication with artisans, proposing optimal designs, and streamlining the entire purchase process. In particular, the ability to monitor the product's progress in real time is essential.

[0702] 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.

[0703] In this invention, the server includes means for receiving product requests from consumers via a user interface, means for generating product design proposals based on consumer requests using a generation algorithm, and means for processing electronic transactions. This makes it possible for consumers to easily customize and purchase bespoke traditional crafts and to check the progress of production in real time.

[0704] A "consumer" is the end user who orders and customizes a product.

[0705] A "user interface" is a component that provides a digital environment for consumers to input their product requirements.

[0706] A "generative algorithm" is a program that automatically creates product design proposals based on consumer input.

[0707] A "terminal" is an electronic device used by consumers to view and operate product design proposals.

[0708] The "creator" refers to the craftsmen or technicians responsible for the actual production of the product.

[0709] "Technical feedback" refers to information provided by the creator regarding the feasibility and potential improvements to the product design.

[0710] "Electronic transactions" refer to the buying and selling procedures for goods and services conducted via the internet.

[0711] The term "manufacturing process" refers to the series of work processes involved in completing a product.

[0712] "Visual display" means presenting manufacturing processes and product information in an easily understandable way on a digital screen.

[0713] In the system that implements this invention, the user first inputs product requirements using a terminal. The user interface receives this input and uses a generation algorithm to generate multiple product design proposals based on the requirements. The generated design proposals are displayed to the user on the terminal, and the user makes selections and requests for improvements. At this time, the terminal transmits the improved requests to the server in real time, and the generation algorithm makes further adjustments.

[0714] The server then sends the final selected product design to the creator and receives technical feedback. Based on this feedback, the design is further refined as needed, and this process is repeated. Once the manufacturing process begins, the server provides the user with a visual display of the manufacturing progress. This allows the user to understand how the product is being made in real time.

[0715] Electronic transactions are processed using a secure payment platform (e.g., Stripe) to complete payment for products ordered by the user. The server manages all transaction data and performs confirmation and cancellation as needed.

[0716] For example, if a user requests "pottery inspired by cherry blossoms in spring," the generation algorithm will suggest several cherry blossom motifs. The user can then select their favorite design and specify additional colors and patterns. An example of a prompt might be: "The user's requested design element is 'cherry blossoms in spring.' Please suggest designs that are feasible to produce while utilizing Japanese tradition and employing a modern approach."

[0717] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0718] Step 1:

[0719] The user enters product requirements using a terminal. These requirements include specific design, color, and pattern preferences, and the terminal prepares to send this information as digital data to the server.

[0720] Step 2:

[0721] The server receives user request data and uses a generative AI model to generate multiple product design proposals based on the requests. By passing the input request data as prompts to the generative model, it outputs a variety of design proposals. In doing so, it refers to similar cases from past databases to suggest the optimal design.

[0722] Step 3:

[0723] The terminal displays the generated product design proposal to the user. It analyzes the design proposal data received from the server and performs layout processing to make it visually easy to understand. The user reviews this output and inputs selections and improvement instructions.

[0724] Step 4:

[0725] The user sends the selected design proposal and improvement instructions back to the server via their device. The imported improvement instructions include adjustments to specific color tones and designs, and the server uses a generated AI model to readjust the design proposal in real time based on these instructions.

[0726] Step 5:

[0727] The final product design is sent from the server to the creator's device, and technical feedback is received from the creator. The creator evaluates the feasibility and areas for improvement of the design data sent as input and sends feedback back to the server. The server then makes any necessary adjustments with the user.

[0728] Step 6:

[0729] As the production process begins, the server visually displays the production progress on the terminal in real time. It receives progress information from the creator as input, processes the data to clearly show the user the product's completion level and current status, and then outputs it.

[0730] Step 7:

[0731] The server processes payments via electronic transactions and securely manages transaction data. It takes user payment information as input, performs authentication and authorization processes, and provides order completion and payment confirmation as output. Appropriate encryption technology is used throughout this process to ensure security.

[0732] 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.

[0733] This invention relates to a system that recognizes user emotions and uses that information to personalize and optimize the bespoke process for traditional Japanese crafts. The entire system consists of a server, a user terminal, a craftsman's device, and an emotion engine.

[0734] First, the user enters their product requirements via a terminal, providing information about their desired design and specifications. This information is sent to a server where the initial data is processed.

[0735] Here, the emotion engine analyzes the user's emotional state. This engine infers the user's emotions by inputting the user's voice and facial expression data into an emotion analysis model. The results of the emotion engine are sent to the server along with the user's request.

[0736] The server uses a generation program to create product design proposals that combine user requests with the results of emotional analysis. For example, if the user is relaxed, the generation program can suggest a design using calming colors and curves. Based on this information, the server creates the design proposal in real time and sends it to the user's terminal.

[0737] Users visualize design options on their devices and make their selections. Furthermore, an emotion engine is used to collect user reactions in real time, and if dissatisfaction or doubts are observed, the server can quickly adjust the design options. The system incorporates a flexible mechanism for refining the design based on user emotions.

[0738] The final design is transmitted to the craftsman via the server, who then evaluates its technical feasibility and provides feedback. The craftsman's information is organized on the server and presented to the user again as needed.

[0739] This invention enables personalized design proposals that take user emotions into consideration, facilitating smoother collaboration with craftsmen and facilitating the market deployment of high-quality, made-to-order products.

[0740] The following describes the processing flow.

[0741] Step 1:

[0742] Users enter detailed information about product requirements and design via their device. This includes desired materials, colors, and design elements, and the entered data is sent to the server.

[0743] Step 2:

[0744] The user's device uses its camera and microphone to collect data on the user's voice and facial expressions. This data is input into an emotion engine to analyze the user's emotional state. The analyzed emotion data is also sent to the server.

[0745] Step 3:

[0746] The server integrates user requests and emotional data, and uses a generation program to generate product design proposals that are suitable for the user's preferences and emotional state. The generated design proposals are sent to the user's terminal as multiple options.

[0747] Step 4:

[0748] Users review product design proposals presented on their devices and select their preferred design. The emotion engine then re-analyzes the user's emotions during the selection process, and the server makes any necessary design adjustments based on the results.

[0749] Step 5:

[0750] The final, adjusted design proposal is sent from the server to the craftsman's device. The craftsman reviews the design proposal and provides feedback from a technical perspective. This feedback is sent back to the server, and the user is notified if any technical improvements are needed.

[0751] Step 6:

[0752] The server manages the production process based on the final approved design and visualizes the progress in real time on the user's device. Users can track each stage of production and wait for the final product to be completed.

[0753] (Example 2)

[0754] 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".

[0755] Traditional product design processes struggle to incorporate individual consumer emotions and real-time feedback into design adjustments. Furthermore, differences in communication language and the inability to effectively utilize past consumer behavior data to optimize design proposals are hindered. As a result, truly personalized products that meet consumer expectations are limited.

[0756] 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.

[0757] In this invention, the server includes means for receiving product requests from consumers via a terminal, means for identifying the consumer's emotional state using emotion analysis technology, and means for generating product design proposals that fuse the consumer's requests with the identified emotional state using a generative AI model. This enables flexible and personalized design adjustments based on the consumer's emotions. Furthermore, by implementing multilingual translation technology, smooth communication with manufacturers can be achieved, and by using the consumer's past behavioral data, it is possible to provide more appropriate and optimized design proposals.

[0758] A "consumer" is an individual or group that receives a product or service.

[0759] "Product requirements" refer to information about the wishes and specifications that consumers express regarding a particular product.

[0760] A "terminal" refers to an electronic device used by a user to input or receive information.

[0761] A "server" is a computer system that provides data processing and storage functions over a network.

[0762] "Emotional analysis technology" is a technique that analyzes and infers an individual's emotional state from their voice, facial expressions, and other factors.

[0763] A "generative AI model" is an artificial intelligence model that generates new data or designs from specific input information based on an algorithm.

[0764] A "design proposal" refers to a proposal that includes drawings and specifications for realizing a specific product.

[0765] "Multilingual translation technology" is a technology that enables the exchange of information between different languages.

[0766] A "manufacturer" refers to a professional who undertakes the production of a product based on its design.

[0767] "Feedback" refers to opinions and information about the results or reactions in a particular process.

[0768] This invention is a product design system that enables advanced personalization based on consumer emotions. The system consists of a server, a consumer terminal, a manufacturer's device, and an emotion analysis engine.

[0769] Users input product requirements and specify desired designs and specifications via a terminal. The terminal allows input of text, images, audio, and other information through its interface. This information is filtered appropriately and sent to the server.

[0770] The server uses a generative AI model to process consumer emotional state data received from the emotion analysis engine. The server utilizes speech recognition and image analysis software to analyze the user's emotions from their voice and facial expression data. Based on this emotional information, the generative AI model generates product design proposals optimized for the consumer's needs. For example, if the server detects that the consumer is relaxed, it can propose a simple and warm design.

[0771] The generated design proposals are sent to the terminal in real time and presented to the user in a visual format. The user can review this and provide instructions for selection and adjustment as needed. The terminal interface is designed to allow consumers to easily adjust the details of their selected design.

[0772] The emotion analysis engine then analyzes consumer reactions, and if there are signs of consumer dissatisfaction, the server immediately generates and proposes a new design using an AI model. This process allows for flexible design adjustments based on consumer emotions.

[0773] The finalized design is transferred to the manufacturer's device. The manufacturer evaluates the technical feasibility and provides necessary feedback. This information is stored on a server and presented to consumers as feedback.

[0774] This invention enables design proposals that incorporate consumer emotions, thereby improving customer satisfaction with products. A concrete example of a prompt message would be the instruction, "Generate a teacup design with calming colors based on the user's emotional state."

[0775] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0776] Step 1:

[0777] Users use a terminal to input their product requests. Specifically, users input their desired design and specifications using text, images, and audio. Once this data is entered, the terminal sends the information to the server. The entered data is transferred to the server as structured data that accurately reflects the user's preferences.

[0778] Step 2:

[0779] The server receives input data from the user and performs initial data processing. Here, it extracts basic specifications based on the user's requests and prepares the necessary data for sentiment analysis. For example, the server converts audio data into a format that can be analyzed using digital signal processing, and processes it for subsequent sentiment analysis.

[0780] Step 3:

[0781] For emotion analysis, the server utilizes emotion analysis technology. The server analyzes the voice and facial expressions provided by the user and estimates their emotional state in real time. During this process, the server inputs data into an emotion analysis model, outputting an emotional state such as "relaxed." The analysis results are then used in the next processing step.

[0782] Step 4:

[0783] The server utilizes a generative AI model to generate product design proposals that combine user requests and emotional state data. For example, if the user is relaxed, the server will generate a design that uses calming colors and curves. The server forms prompt sentences using emotional state as keywords, inputs them into the generative AI model, and outputs the design proposals.

[0784] Step 5:

[0785] The server sends the generated design proposal to the user's terminal, allowing the user to select and adjust it. The user visualizes and reviews the design proposal through the terminal's interface. The server receives user feedback and determines whether further adjustments to the design proposal are necessary.

[0786] Step 6:

[0787] Based on user feedback, the server modifies the generated design proposal as needed. The server then uses sentiment analysis technology again to check the user's latest emotional state and refines the design proposal based on the feedback. This process provides a design that more closely matches the user's preferences.

[0788] Step 7:

[0789] The final design proposal is transferred from the server to the manufacturer's device, where the manufacturer evaluates the product's technical feasibility. Feedback from the manufacturer is sent to the server and shared with users as needed. This exchange of feedback helps the final product come closer to consumer expectations.

[0790] (Application Example 2)

[0791] 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".

[0792] Traditional custom-made product ordering systems have the problem of making it difficult to propose product designs that fully reflect consumers' personal feelings and preferences. Furthermore, communication between consumers and craftsmen in physical stores is often not smooth, leading to delays and increased effort in product design and manufacturing. A solution is needed to address these challenges and improve consumer satisfaction.

[0793] 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.

[0794] In this invention, the server includes means for receiving product requests from consumers via an input device, means for analyzing the consumer's emotional state using an emotion analysis device and reflecting that information in the generation of product design proposals, and means for displaying the optimal design proposal based on the consumer's emotional analysis results in a physical store to facilitate ordering. This makes it possible to generate product design proposals that reflect the individual emotions and preferences of consumers in real time and provide a smooth ordering experience.

[0795] A "consumer" is an individual or group that purchases products or services, and is the entity that inputs product requests and places orders through a system.

[0796] A "product design proposal" is a specific product design plan generated based on consumer requests and emotional data.

[0797] An "input device" is hardware used by consumers to input product order information, such as a smartphone or tablet.

[0798] A "generation program" is software used to generate product design proposals based on consumer requests and sentiment analysis results.

[0799] An "emotion analysis device" is a device that analyzes a consumer's facial expressions and voice to identify their emotional state.

[0800] An "engineer" is a professional who receives the final product design, evaluates its technical feasibility, and provides feedback.

[0801] A "physical store" refers to a physically existing sales location where consumers interact with products.

[0802] A "design proposal" is a product design plan displayed based on the results of consumer emotional analysis.

[0803] To implement this invention, a system is configured that includes a server, a consumer terminal, a technician's terminal, and an emotion analysis device. The operation of this system is as follows:

[0804] The server receives product requests from consumer devices. These consumer devices include input devices such as smartphones and tablets. In this process, consumers input the necessary design and specifications into their devices and send that data to the server.

[0805] The emotion analysis device collects consumer facial expression and voice data and inputs this data into an emotion analysis model to analyze the consumer's emotional state. Microsoft Azure Emotion API, among others, can be used as the emotion analysis model. The analysis results are sent to a server and used to generate product design proposals.

[0806] The server uses a generation program to generate product design proposals that combine consumer requests with the results of sentiment analysis. The generation program, developed in Python, adjusts the design to match the consumer's emotional state. The generated design proposals are sent to the consumer's device in real time.

[0807] For example, when a consumer visits a store in a relaxed state, the terminal displays design proposals incorporating gentle colors and curves. Based on this information, the consumer can select a design and place a final order. The final design proposal is then sent to the technician's terminal, where they can provide feedback considering its technical feasibility.

[0808] An example of a prompt might be: "Based on this user's facial expressions and voice, we can see they are relaxed. Please propose a custom-made design with a relaxed atmosphere. The color scheme should be calm, and the design should make extensive use of curves." Such prompts will prompt the AI ​​model to propose an optimized design.

[0809] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0810] Step 1:

[0811] Users input their product requests using devices such as smartphones and tablets. They specify their desired design and specifications using text or by selecting options. The entered data is then sent directly to the server.

[0812] Step 2:

[0813] The server temporarily stores product request data received from the user and generates a prompt message based on that information. This prompt message is used as an instruction to the generation AI model. It is in the format of "Generate design proposals based on the user's request."

[0814] Step 3:

[0815] The user's device uses its camera and microphone to collect facial expressions and voice data in real time. This data is input into an emotion analysis device to identify the user's emotional state.

[0816] Step 4:

[0817] The emotion analysis device analyzes input facial expressions and voice data to estimate the user's current emotional state. Various statistical methods and AI models are used in this analysis. The analysis results are output in categories such as calm, relaxed, and excited, and sent to a server.

[0818] Step 5:

[0819] The server integrates user request data and sentiment analysis results, then runs a generation program to generate design proposals. Based on the input data, the design proposals are tailored to the user's current emotional state. The generated design proposals are then sent to the consumer's device.

[0820] Step 6:

[0821] The user reviews the design proposals sent from the server on their device, making selections and modifications. Based on the user's actions, the device resends the information to the server.

[0822] Step 7:

[0823] The server receives feedback from users and readjusts the design as needed. The final design is sent to engineers and used in the product manufacturing process.

[0824] 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.

[0825] 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.

[0826] 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.

[0827] 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.

[0828] 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.

[0829] 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.

[0830] 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.

[0831] 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.

[0832] 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."

[0833] 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.

[0834] 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.

[0835] 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.

[0836] 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.

[0837] 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.

[0838] 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.

[0839] 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.

[0840] 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.

[0841] 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.

[0842] 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.

[0843] 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.

[0844] 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.

[0845] The following is further disclosed regarding the embodiments described above.

[0846] (Claim 1)

[0847] A means of receiving product requests from consumers via an input device,

[0848] A means for generating product design proposals based on consumer requests using a generation program,

[0849] A means of displaying the generated product design proposals to consumers and allowing consumers to make selections and provide instructions for improvement,

[0850] A means of adjusting the product design in real time using a generation program based on consumer choices and improvement instructions,

[0851] A means of communicating the final product design to craftsmen and obtaining technical feedback from them,

[0852] A means of visualizing the product manufacturing process in real time and providing it to consumers,

[0853] A system that includes this.

[0854] (Claim 2)

[0855] The system according to claim 1, comprising a multilingual translation function to support communication between consumers and artisans.

[0856] (Claim 3)

[0857] The system according to claim 1, comprising means for analyzing consumer preferences and past purchase history, and for a generation program to propose an optimal product design based thereon.

[0858] "Example 1"

[0859] (Claim 1)

[0860] A means of receiving product requests from consumers via an input interface,

[0861] A means for generating product design proposals based on consumer requests using a generation algorithm,

[0862] A means for transmitting design proposals to consumer devices in real time and providing an interface for consumers to make selections and provide instructions for improvement,

[0863] A means of adjusting design proposals in real time using a generation algorithm based on consumer selections and additional instructions,

[0864] A means of communicating the final design proposal to the manufacturer's device and obtaining technical feedback from the manufacturer,

[0865] A means of monitoring the progress of production and making information about the production process visible to consumers in real time,

[0866] A system that includes this.

[0867] (Claim 2)

[0868] The system according to claim 1, comprising an automatic translation function to support communication between consumers and manufacturers.

[0869] (Claim 3)

[0870] The system according to claim 1, comprising means for analyzing consumer preferences and past order history, and for a generation algorithm to propose an optimal product design based thereon.

[0871] "Application Example 1"

[0872] (Claim 1)

[0873] A means of receiving product requests from consumers through a user interface,

[0874] A means for generating product design proposals based on consumer requests using a generation algorithm,

[0875] A means for consumers to display the generated product design proposals on a terminal and make selections and improvement instructions,

[0876] A means of adjusting the design proposal each time using a generation algorithm based on consumer choices and improvement instructions,

[0877] A means of communicating the final product design to the creator and obtaining technical feedback from the creator,

[0878] A means of visually displaying and providing the manufacturing process to consumers,

[0879] Means for processing electronic transactions,

[0880] A system that includes this.

[0881] (Claim 2)

[0882] The system according to claim 1, comprising the ability for consumers to customize products and purchase them through electronic transactions.

[0883] (Claim 3)

[0884] The system according to claim 1, including a function to notify the consumer of the production progress in real time according to their customizations.

[0885] "Example 2 of combining an emotion engine"

[0886] (Claim 1)

[0887] A means of receiving product requests from consumers via a terminal,

[0888] A means of identifying a consumer's emotional state using emotion analysis technology,

[0889] A means of generating product design proposals that fuse consumer demands and identified emotional states using a generative AI model,

[0890] A means of visually presenting the generated design proposals on a terminal for consumers to make selections and adjustments,

[0891] A means of analyzing consumer reactions in real time and modifying design proposals based on those reactions,

[0892] A means of communicating the final design to the manufacturer and collecting technical feedback from the manufacturer,

[0893] A system that includes this.

[0894] (Claim 2)

[0895] The system according to claim 1, characterized by implementing multilingual translation technology to facilitate communication between consumers and manufacturers.

[0896] (Claim 3)

[0897] The system according to claim 1, comprising means for a generative AI model to propose an optimized design proposal using past consumer behavior data and preferences.

[0898] "Application example 2 when combining with an emotional engine"

[0899] (Claim 1)

[0900] A means of receiving product requests from consumers via an input device,

[0901] A means for generating product design proposals based on consumer requests using a generation program,

[0902] A means of analyzing consumers' emotional states using an emotion analysis device and reflecting that information in the generation of product design proposals,

[0903] A means of displaying the generated product design proposals to consumers and allowing consumers to make selections and provide instructions for improvement,

[0904] A means of adjusting product design in real time using a generation program based on consumer choices and improvement instructions,

[0905] A means of communicating the final product design to engineers and obtaining technical feedback from them,

[0906] In physical stores, a means of displaying optimal design suggestions based on consumer emotion analysis results to encourage orders,

[0907] A system that includes this.

[0908] (Claim 2)

[0909] The system according to claim 1, further comprising a multilingual translation function to facilitate communication between consumers and engineers.

[0910] (Claim 3)

[0911] The system according to claim 1, comprising means for analyzing consumer preferences and past purchase history, and for a generation program to propose an optimal product design based thereon. [Explanation of Symbols]

[0912] 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 product requests from consumers via an input device, A means for generating product design proposals based on consumer requests using a generation program, A means of displaying the generated product design proposals to consumers and allowing consumers to make selections and provide instructions for improvement, A means of adjusting the product design in real time using a generation program based on consumer choices and improvement instructions, A means of communicating the final product design to craftsmen and obtaining technical feedback from them, A means of visualizing the product manufacturing process in real time and providing it to consumers, A system that includes this.

2. The system according to claim 1, further comprising a multilingual translation function to support communication between consumers and artisans.

3. The system according to claim 1, comprising means for analyzing consumer preferences and past purchase history, and for a generation program to propose an optimal product design based thereon.

Images