System

Abstract

A system is provided.SOLUTION: A system comprising: means for receiving preferences and image data input by a user; means for analyzing the received preferences and image data and passing the input data to a generative model; and means for providing design elements (such as wallpaper, icons, sounds, etc.) generated by the generative model to the user.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] While there are many users who desire unique mobile phone designs or who are tired of existing designs, the problem is that many users lack the sense or skills to customize designs. Conventional technologies make it difficult for users to easily create designs that reflect their preferences and individuality. The objective of this invention is to provide a system that allows such users to easily enjoy unique and attractive designs. [Means for solving the problem]

[0005] To solve the above-mentioned problems, the present invention provides a system with the following features: a system including means for receiving preferences and image data input by a user, means for analyzing the received preferences and image data and passing the input data to a generative model, and means for providing the user with design elements (wallpaper, icons, sounds, etc.) generated by the generative model. Furthermore, by including means for generating input data for design generation based on keywords specified by the user and means for displaying a preview of the generated design elements to the user, the system allows even users without special skills to easily enjoy creating unique designs.

[0006] "User" means an individual or entity that utilizes the System to input preferences and image data to generate customized designs.

[0007] "Preference and image data" refers to specific keywords, images, colors, styles, and other information that a user provides to the system.

[0008] The "receiving means" is a part that has the function of receiving preferences and image data from a user through an input interface.

[0009] The "means for analysis" is the part that has the function of understanding the received preference and image data using natural language processing (NLP) and other technologies and extracting meaning and intent.

[0010] A "generative model" refers to an algorithm or machine learning model that automatically generates original design elements (wallpaper, icons, sounds, etc.) based on received input data.

[0011] The "means for providing" refers to the part that has the function of visually or audibly presenting the design elements generated by the generative model to the user and allowing them to select or download them.

[0012] "Input data" refers to the data that is processed by the generative model based on user-provided preferences and image data.

[0013] "Design elements" refer to the specific materials used to customize a mobile phone, such as wallpaper, icons, and sounds generated by a generative model.

[0014] "Preview" means a display or playback function that allows a user to visually and audibly confirm provided design elements before actually selecting or applying them. [Brief explanation of the drawings]

[0015] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0016] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0017] First, the terms used in the following description will be explained.

[0018] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), and an APU (Accelerated Processing Unit).

[0019] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0020] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0021] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0022] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0023] [First embodiment]

[0024] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0025] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0026] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0027] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0028] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0029] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0030] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0031] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0032] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0033] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0034] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0035] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0036] This invention relates to a system that allows users to enjoy designing and customizing mobile phones, and generates original design elements using a generative AI model based on preferences and image data entered by the user. The program processing required to implement this invention is described below.

[0037] 1. First, the user launches the customization application on their device. The application displays an interface, and the user inputs information about their preferred keywords and images (e.g., natural landscapes, blue and green tones). This input data is temporarily stored on the device.

[0038] 2. When the user presses the "Send" button, the device converts this input data into JSON format and sends it to the server, using an internet connection.

[0039] 3. The server analyzes the received JSON data to understand the user's preferences and impressions. Specifically, it uses a natural language processing (NLP) engine to analyze the meaning of the input and converts it into the appropriate data format to be passed to the generative model.

[0040] 4. The server passes the analyzed data to a generative AI model, which then generates original design elements (wallpapers, icons, ringtones, etc.) based on the data. For example, the keywords "natural scenery" and "blue and green" could generate a wallpaper depicting a blue sky and green forest, as well as a set of nature-themed leaf icons.

[0041] 5. The generated design elements are stored in a temporary database, and access URLs for each design element are generated. The server sends a response containing these URLs in JSON format to the terminal.

[0042] 6. The device parses the received JSON response and displays a preview of the generated design elements to the user, allowing the user to select the design elements they like and download or apply them.

[0043] For example, if a user inputs "nature scenery" and "blue and green," the server will analyze this and generate wallpaper with a blue sky and forest theme, as well as an icon set of leaves and flowers that evoke nature. This allows users to easily enjoy a mobile phone design that reflects their preferences and individuality.

[0044] The above is a detailed description of the embodiment of the present invention, which is a system that can easily reflect the user's preferences and ideas in the design of a mobile phone. This system makes it easy to enjoy unique and attractive designs.

[0045] The processing flow will be explained below.

[0046] Step 1:

[0047] A user launches a customization application on their device. The application displays a user interface (UI) containing input fields where the user can enter keywords that reflect their preferences or image (e.g., "nature landscapes" or "blue and green tones") and optionally upload reference images.

[0048] Step 2:

[0049] The terminal temporarily stores the data entered by the user and waits for the user to press the "Send" button after completing the input. When the user presses the "Send" button, the terminal converts the input data into JSON format and generates a send request to the server.

[0050] Step 3:

[0051] The device sends the generated request and sends JSON data including the user's input data to the server, using an internet connection.

[0052] Step 4:

[0053] The server parses the received JSON data. It extracts data from each field and uses a natural language processing (NLP) engine to understand the user's preferences and impressions. Specifically, it analyzes keywords such as "natural scenery" and "blue and green" and extracts their meanings.

[0054] Step 5:

[0055] The server converts the analysis results into an appropriate data format to be passed to the generative model. This data is then input to the generative model.

[0056] Step 6:

[0057] The server runs generative AI models to generate design elements: for example, an image generation model generates high-resolution wallpapers based on "nature landscapes" and "blue and green" data, an icon generation model generates a nature-themed icon set, and an audio generation model generates ringtones based on nature sounds.

[0058] Step 7:

[0059] The server stores the generated design elements in a temporary database and generates an access URL for each design element (wallpaper, icon set, ringtone), which allows the generated design element to be identified.

[0060] Step 8:

[0061] The server creates a JSON response containing the access URL for the generated design element and sends this response to the terminal.

[0062] Step 9:

[0063] The device parses the received JSON response and extracts the URL and data of each design element.

[0064] Step 10:

[0065] The device will update the UI to allow the user to preview the generated design elements, and the user can review the previews, select the design elements they like, and download or apply them to their mobile phone.

[0066] Example 1

[0067] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0068] Previously, when users customized the design of their mobile phones, they had no choice but to choose from existing templates or limited options, making it difficult to reflect the individual user's preferences and image. Furthermore, there was a lack of a way to easily generate different design elements (wallpapers, icons, sounds, etc.) under a unified theme. Another problem was the lack of a system that allowed users to preview the generated design elements before selecting them.

[0069] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0070] In this invention, the server includes means for converting received preference and image data into JSON format and sending it to the server, means for the server to analyze the received JSON data and convert it into a data format suitable for the generative AI model, and means for storing design elements (wallpaper, icons, sounds, etc.) generated by the generative AI model in a database and generating an access URL. This makes it possible to easily generate original design elements that reflect the user's preferences and images with a unified theme, and to preview and select the generated design elements.

[0071] "User" refers to a person who uses the system to design and customize a mobile phone.

[0072] "Preference and image data" refers to information such as requests, keywords, and images regarding individual designs that users input into the system.

[0073] "JSON format" is an abbreviation for JavaScript Object Notation, and is a lightweight data exchange format for expressing data in text format.

[0074] "Server" refers to a computer system that receives, analyzes, and processes data sent from a user's terminal.

[0075] A "generative AI model" refers to an algorithm or program that uses artificial intelligence technology to generate design elements (wallpaper, icons, sounds, etc.) from data entered by the user.

[0076] "Data format" refers to the rules and conventions that define the structure and format in which data is represented.

[0077] "Design Elements" means individual design components used to customize a mobile phone, such as system-generated wallpaper, icons, sounds, etc.

[0078] "Database" refers to a management system for storing generated design elements, a data repository for easy access and retrieval.

[0079] "Access URL" refers to the unique resource locator required for a user to access a generated design element.

[0080] "Preview" refers to the function of displaying generated design elements so that the user can check them before selecting them.

[0081] This invention relates to a system that allows users to enjoy designing and customizing mobile phones. The purpose is to generate original design elements using a generative AI model based on user input preferences and image data. The program processing required to implement this invention is described in detail below.

[0082] The user first launches the customization application using the device. This application displays a user interface, allowing the user to input information about keywords and images they like. For example, the user may input keywords such as "nature landscape, blue and green." This input data is temporarily stored on the device.

[0083] Next, when the user presses the "Send" button, the device converts this input data into JSON format. The converted JSON data is sent over the internet to a server. The server parses the received JSON data and uses a natural language processing (NLP) engine to understand the meaning of the input data. This analysis converts it into the appropriate data format to be passed to the generative AI model.

[0084] Once the analysis is complete, the server passes the data to a generative AI model, which then generates original design elements (wallpaper, icons, audio, etc.) based on the received data. Examples of generative AI models used include OpenAI's GPT-3 and DALL-E. For example, the keywords "natural scenery" and "blue and green" generate a wallpaper depicting a blue sky and green forest, as well as a set of nature-themed leaf icons.

[0085] The generated design elements are stored in a temporary database, and an access URL for each design element is generated. The server sends a response containing these URLs in JSON format to the device. The device parses the received JSON response and displays a preview of the generated design elements to the user. The user can view the preview, select the design element they like, and download or apply it.

[0086] For example, if a user inputs "nature scenery" and "blue and green," the server analyzes this and passes it to a generative AI model. The generative AI model generates a wallpaper with a blue sky and forest theme, or an icon set of leaves and flowers that evokes nature. The device previews the generated designs, and the user can select the design they like and download or apply it.

[0087] An example prompt might be, "Use a generative AI model to create design elements with blue skies and forests, based on a natural landscape theme. Please use a blue and green color scheme."

[0088] This system allows users to easily enjoy designing their mobile phones to reflect their own tastes and personalities.

[0089] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0090] Step 1:

[0091] User Input Processing

[0092] The user launches the customization application using the device. The application displays a user interface, and the user inputs information about their preferred keywords and images. For example, they input "nature landscape, blue and green." This input data is temporarily stored on the device.

[0093] Input: Data entered into the user interface (e.g., nature scenes, blue and green)

[0094] Output: Input data temporarily saved on the device

[0095] Step 2:

[0096] Converting data to JSON and sending it

[0097] When the user presses the "Send" button, the device converts the saved input data into JSON format, for example, {"keywords": ["nature landscape", "blue and green"]}. The device then sends the converted JSON data to the server via the Internet.

[0098] Input: Temporarily saved input data

[0099] Output: JSON data sent to the server

[0100] Step 3:

[0101] Parsing JSON Data

[0102] The server parses the received JSON data. Using a natural language processing (NLP) engine, the server analyzes the meaning of the input data and converts it into an appropriate data format (e.g., a prompt) to be passed to the generative AI model. For example, the input data "Nature landscape, blue and green" is converted into the prompt "Design with a nature landscape theme. Color scheme: blue and green."

[0103] Input: Received JSON data

[0104] Output: Prompt sentence to be passed to the generative AI model

[0105] Step 4:

[0106] Passing data to generative AI models

[0107] The server passes the prompt to the generative AI model, which then generates original design elements (wallpaper, icons, audio, etc.) based on the prompt. For example, a prompt like "A design themed around natural landscapes. The color scheme is blue and green" generates a wallpaper depicting a blue sky and green forest, as well as a set of leafy icons.

[0108] Input: prompt statement

[0109] Output: Generated design elements

[0110] Step 5:

[0111] Save design elements and generate URLs

[0112] The server stores the generated design elements (e.g., wallpaper, icon set) in a temporary database. Access URLs for each design element are generated, and a response containing these URLs is returned in JSON format. For example, {"wallpaper": "https: / / example.com / wallpaper1", "icons": "https: / / example.com / icons1"} is generated.

[0113] Input: Generated design elements

[0114] Output: Design elements stored in the database and a generated access URL

[0115] Step 6:

[0116] Response analysis and preview display

[0117] The device analyzes the JSON response received from the server. Based on the access URL of the analyzed design element, a preview of the generated design element is displayed on the user interface. The user can view the preview, select the design element they like, and download or apply it. For example, the user can download wallpaper and set it as the home screen.

[0118] Input: JSON response received from the server

[0119] Output: A preview of the design elements displayed in the user interface

[0120] (Application example 1)

[0121] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0122] Conventional food delivery systems have difficulty providing food designs that reflect the user's taste and appearance preferences, making it difficult to increase user satisfaction. In addition, there are limited ways for users to customize the taste and appearance of food to their own preferences, creating a need for a more personalized experience.

[0123] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0124] In this invention, the server includes a means for receiving preferences and image data input by the user, a means for analyzing the received preferences and image data and passing the input data to the generative model, and a means for providing the user with design elements (wallpaper, icons, audio, presentation designs, etc.) generated by the generative model, thereby enabling the design of dishes that reflect the user's taste and appearance preferences.

[0125] "User-entered preference and image data" refers to information that a user inputs into an application to express their taste preferences and visual image.

[0126] The "means of receiving" is the mechanism by which the application receives preferences and image data input by the user.

[0127] The "means of analyzing and passing input data to the generative model" refers to a mechanism that analyzes the received data and sends the analysis results to the generative AI model as input data.

[0128] A "generative model" is an artificial intelligence system that generates original design elements (e.g., wallpaper, icons, sounds, presentation designs, etc.) based on analyzed data.

[0129] "Generated design elements" are specific elements of a design created by a generative AI model based on a user's preferences and imagination.

[0130] "Means for providing to users" refers to the mechanism by which the generated design elements are shown to users or provided in a usable form.

[0131] "Wallpaper, icons, sounds, presentation designs, etc." refers to visual and auditory design elements customized based on user preferences and used in smartphone and food delivery presentations.

[0132] The "means for displaying a preview to the user" is a mechanism for displaying the generated design elements in a form that allows the user to visually confirm them.

[0133] This invention is a system that uses a generative AI model to generate original design elements (wallpaper, icons, sounds, presentation designs, etc.) based on preferences and image data entered by the user. Specific examples of this system are described below.

[0134] 1. Overall system configuration

[0135] This system consists of a user device, a server, a generative AI model, and a database. The user device can be a smartphone, tablet, or computer, while the server is often installed on a cloud service. The generative AI model used is, for example, OpenAI's DALL-E.

[0136] 2. User Interface

[0137] The user launches the customization application using a terminal and enters information about their preferred flavors and visual image, for example, the following prompt:

[0138] "Prepare spicy dishes in a modern art style using red and yellow hues."

[0139] When the user presses the "Send" button, the device converts the input data into JSON format and sends it to the server, using an internet connection.

[0140] 3. Server Processing

[0141] The server parses the received JSON data to understand the user's preferences and impressions. Specifically, it uses a natural language processing (NLP) engine to analyze the meaning of the input and converts it into the appropriate data format to be passed to the generative AI model (for example, using the Google NLP API).

[0142] 4. Design generation using generative AI models

[0143] The server passes the analyzed data to a generative AI model, which then generates original design elements based on the received data. For example, using the keywords "spicy," "red and yellow," and "modern art," it generates a modern art-style presentation design for spicy food, based on red and yellow tones.

[0144] 5. Providing design elements

[0145] The generated design elements are stored in a temporary database, and access URLs for each design element are generated. The server sends a response containing these URLs in JSON format to the terminal. The user terminal parses the received JSON response and displays a preview of the generated design elements to the user.

[0146] 6. Specific Examples

[0147] For example, if a user inputs "spicy," "red and yellow," and "modern art," the server analyzes the input and generates a modern art-style presentation design for spicy food based on red and yellow tones. These generated design elements are stored in a database, and users can browse the design themes and select their favorite design.

[0148] This system makes it possible to provide personalized dish designs that reflect the user's preferences and ideas.

[0149] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0150] Step 1:

[0151] The user launches the customization application on the device and inputs information about the desired taste and visual image. The user inputs specific keywords and phrases. For example, the prompt "Please present a spicy dish in red and yellow tones in a modern art style" is used. The input here is text data.

[0152] Step 2:

[0153] When the user types something in and hits the submit button, the device converts the input data into JSON format, which makes it easier to send over the network. The converted JSON data looks like this:

[0154] {

[0155] "taste": "spicy",

[0156] "color_scheme": "red and yellow",

[0157] "presentation": "modern art"

[0158] }

[0159] This JSON data is sent to the server.

[0160] Step 3:

[0161] The server parses the received JSON data. Based on the received data, it uses a natural language processing (NLP) engine (e.g., Google NLP API) to understand the user's preferences and impressions. The result of the parsing is converted into an appropriate data format to be passed to the generative model. For example, the parsed data looks like this:

[0162] {

[0163] "parsed_taste": "spicy",

[0164] "parsed_color_scheme": ["red", "yellow"],

[0165] "parsed_presentation": "modern art"

[0166] }

[0167] This parsed data is passed to a generative AI model.

[0168] Step 4:

[0169] The server passes the analyzed data to a generative AI model. The generative AI model (e.g., OpenAI's DALL-E) generates original design elements based on this data. For example, using the keywords "spicy," "red and yellow," and "modern art," it generates a modern art-style presentation design for spicy food based on red and yellow tones. This generative process generates concrete design data.

[0170] Step 5:

[0171] The generated design elements are stored in a temporary database. A unique access URL is generated for each generated design element. The server sends a response containing these URLs in JSON format to the terminal. Example response from the server:

[0172] {

[0173] "design_url": "https: / / example.com / designs / 12345"

[0174] }

[0175] This allows the terminal to access the generated design elements.

[0176] Step 6:

[0177] The terminal parses the received JSON response and displays a preview of the generated design elements to the user. The user can view the preview, select the design element they like, and download or apply it. Specifically, the generated design elements are displayed on the application interface. The user can also rate the design elements.

[0178] Through the above processing steps, users can easily enjoy personalized food designs that reflect their own preferences and images.

[0179] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0180] This invention relates to a system that allows users to enjoy designing and customizing mobile phones, and generates original design elements by combining user preferences, image data, and an emotion engine that recognizes emotions. The program processing required to implement this invention is described below.

[0181] 1. First, the user launches the customization application on their device. The application displays a user interface (UI) that includes fields for inputting preferences and images. The user enters specific keywords (e.g., "nature landscape," "blue and green tones") and can upload reference images if desired.

[0182] 2. The device is equipped with an emotion engine that analyzes and recognizes user emotions (e.g., joy, sadness, surprise, etc.) in real time based on user input and interface usage. The recognized emotion data is stored together with preference and image data.

[0183] 3. When the user presses the "Send" button, the device converts the preferences, image data, and emotion data into JSON format and generates a send request to the server, using an internet connection.

[0184] 4. The server parses the received JSON data. It extracts data from each field and uses a natural language processing (NLP) engine to understand the user's preferences, impressions, and even emotions. For example, if the emotion "joy" is recognized along with the keyword "nature scene," the design will work to generate a bright and vibrant nature scene.

[0185] 5. The server converts the analysis results into an appropriate data format to be passed to the generative model. This data is input to the generative model.

[0186] 6. The server runs generative AI models to generate design elements. For example, the image generation model generates a high-resolution wallpaper based on "nature scenery," "blue and green," and the user's emotional data of "joy." The icon generation model generates a nature-themed icon set that evokes joy. The sound generation model generates a cheerful nature-inspired ringtone.

[0187] 7. The generated design elements are stored in a temporary database, and access URLs for each design element are generated. The server sends a response containing these URLs in JSON format to the terminal.

[0188] 8. The device parses the received JSON response and extracts the URL and data of each design element.

[0189] 9. The device will update the UI to allow the user to preview the generated design elements. The user can review the previews, select the design elements they like, and download or apply them to their phone.

[0190] For example, if a user inputs "nature scenery" and "blue and green" and the emotion engine recognizes "joy," the server will analyze this and generate a bright and attractive blue sky and forest themed wallpaper, as well as a set of leaves and flowers icons that evoke a natural feeling and express joy. In this way, users can easily enjoy mobile phone designs that reflect their preferences and emotions.

[0191] The above is a detailed description of the embodiment of the present invention, which is a system that allows users to customize the design of their mobile phone to reflect their preferences, images, and even emotions. This system allows users to easily enjoy unique and attractive designs.

[0192] The processing flow will be explained below.

[0193] Step 1:

[0194] The user launches the customization application on their device. The application displays a user interface (UI) that includes fields for inputting preferences and images. The user enters specific keywords (e.g., "nature landscapes," "blue and green tones") and can optionally upload reference images.

[0195] Step 2:

[0196] The device temporarily stores the user's input data and waits for the user to press the "Send" button after completing the input. When the user presses the "Send" button, the device passes the preference and image data to the emotion engine.

[0197] Step 3:

[0198] The device's built-in emotion engine analyzes user emotions in real time based on user input and interface usage. For example, it recognizes emotions by analyzing keywords, phrases, touch gestures, and even the speed and strength of input. The recognized emotion data (e.g., "happiness" or "surprise") is stored together with preference and image data.

[0199] Step 4:

[0200] The device converts the preferences, image data, and emotion data into JSON format and generates a request to send it to the server, using an internet connection.

[0201] Step 5:

[0202] The server parses the received JSON data, extracts data from each field, and uses a natural language processing (NLP) engine to understand the user's preferences, imagery, and even emotions. For example, it analyzes the keywords "nature scenery" and "blue and green," and the emotion "joy."

[0203] Step 6:

[0204] The server converts the analysis results into a data format appropriate for the generative model, and this data is then input into the generative model.

[0205] Step 7:

[0206] The server runs generative AI models to generate design elements. For example, the image generation model generates high-resolution wallpapers based on "natural scenery," "blue and green," and the emotion "joy." The icon generation model creates an icon set that evokes feelings of nature and joy, and the audio generation model creates ringtones based on upbeat natural sounds.

[0207] Step 8:

[0208] The generated design elements are stored in a temporary database. The server generates a URL for each design element and sends a response containing the URL in JSON format to the device.

[0209] Step 9:

[0210] The device parses the received JSON response and extracts the URL and data of each design element.

[0211] Step 10:

[0212] The device will update the UI to allow the user to preview the generated design elements, and the user can review the previews, select the design elements they like, and download or apply them to their mobile phone.

[0213] For example, if a user inputs "nature scenery" and "blue and green" and the emotion engine recognizes "joy," the server will analyze this and generate a bright and attractive blue sky and forest themed wallpaper, an icon set of leaves and flowers that evokes a sense of nature and expresses joy, and a ringtone based on bright natural sounds. In this way, users can easily enjoy a mobile phone design that reflects their preferences and emotions.

[0214] Example 2

[0215] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0216] Conventional customization systems mainly generate designs based on direct user input, making it difficult to reflect the user's emotions or temporary moods. Furthermore, the diversity of generated design elements and the provision of unique designs are limited. This makes it difficult for users to obtain design elements that perfectly match their actual requirements, resulting in low satisfaction.

[0217] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for receiving preferences, image data, and emotion data input by the user, means for analyzing the received preferences, image data, and emotion data and passing the input data to the generative model, and means for providing the user with design elements (wallpaper, icons, sounds, etc.) generated by the generative model. This makes it possible to generate diverse and unique design elements that reflect the user's emotions and preferences in real time.

[0218] "User" refers to an individual who uses the system to customize designs.

[0219] "Preferences and image data" refers to information including keywords specified by the user and reference images uploaded by the user.

[0220] "Emotional data" refers to data that indicates a user's emotional state obtained by analyzing user input and interface usage.

[0221] "Analysis" refers to the process of understanding and interpreting the meaning of received preference, image, and emotional data.

[0222] A "generative model" refers to an algorithm or AI model that automatically generates design elements based on input data.

[0223] "Design elements" refer to decorative items such as wallpaper, icons, and sounds created by generative models.

[0224] "Serving" refers to the process of making the generated design elements available to users.

[0225] "Means" refers to a method or apparatus for performing a particular function or process.

[0226] A "system" refers to a set of hardware and software that generates and provides design elements that reflect the user's preferences and emotions.

[0227] "Preview" means a display feature that allows a user to visually preview generated design elements before downloading or applying them.

[0228] This invention relates to a system that allows users to enjoy designing and customizing mobile phones, and generates original design elements by combining the user's preferences, image data, and an emotion engine that recognizes emotions. The processing of the program required to implement this invention will be described.

[0229] Program Overview

[0230] A user launches the customization application using their mobile device. The application displays a user interface (UI) and provides fields for the user to input preferences and images. In this section, the user can enter specific keywords (e.g., "nature landscape," "blue and green tones") and upload reference images, if desired.

[0231] The device's built-in emotion engine analyzes and recognizes the user's emotions (e.g., joy, sadness, surprise, etc.) in real time based on the user's input and interface usage, and the recognized emotion data is stored together with preference and image data.

[0232] When the user presses the "Send" button, the device converts the preferences, image data, and emotion data into JSON format and generates a send request to the server. This transmission is performed over an internet connection, and the server parses the received JSON data. On the server side, a natural language processing (NLP) engine is used to extract the data from each field and understand the user's preferences, images, and emotions. For example, if the keyword "nature scene" and the emotion "joy" are recognized, the design will work to generate a bright and attractive nature scene.

[0233] The server converts the analysis results into the appropriate data format to be passed to the generative model. This data is input into the generative AI model. The server runs the generative AI model to generate design elements. For example, the image generation model generates a high-resolution wallpaper based on "nature scenery," "blue and green," and "joy," the icon generation model generates a joyful icon set with a nature theme, and the sound generation model generates an upbeat nature-inspired ringtone.

[0234] The generated design elements are stored in a temporary database, and an access URL for each design element is generated. The server sends a response containing these URLs in JSON format to the device. The device parses the received JSON response and extracts the URL and data for each design element. The device then updates the UI so that the user can preview the generated design elements. The user can check the displayed previews, select the design element they like, and download or apply it to their mobile phone.

[0235] Specific examples

[0236] For example, consider a case where a user inputs "nature scenery" and "blue and green," and the emotion engine recognizes "joy." This data is converted into JSON format and sent to the server. The server analyzes it and provides data for the image generation model and icon generation model to operate. The server can then generate a bright and attractive wallpaper with a blue sky and forest theme, or an icon set of leaves and flowers that evokes a natural feeling and expresses joy. This allows users to easily enjoy mobile phone designs that reflect their preferences and emotions.

[0237] Prompt Sentence Examples

[0238] "Natural Landscapes, Blue and Green, Joy"

[0239] This invention makes it possible to generate personalized designs that take into account the user's emotions, something that was not possible with conventional customization systems.

[0240] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0241] Program processing flow

[0242] Step 1:

[0243] A user launches a customization application using their device. The application displays a user interface (UI) and provides fields for the user to input preferences and images. This can be presented as text fields or image upload fields, where the user can enter specific keywords (e.g., "nature landscapes," "blue and green tones") or upload a reference image.

[0244] Input: Keywords entered by users and uploaded image data.

[0245] Output: Input preferences and image data.

[0246] Step 2:

[0247] The device's built-in emotion engine monitors user input and interface usage in real time to analyze and recognize emotions, often using facial recognition cameras and sensors to recognize emotions such as happiness, sadness, and surprise.

[0248] Input: User facial expressions and operation logs.

[0249] Output: Parsed emotion data.

[0250] Step 3:

[0251] When the user presses the "Send" button, the device converts the entered preferences, image data, and emotion data into JSON format, which is then generated as a send request to the server and sent over the internet connection.

[0252] Input: User preferences, image data, and emotional data.

[0253] Output: JSON formatted data.

[0254] Step 4:

[0255] The server parses the received JSON data. A natural language processing (NLP) engine is used to process and understand the data in each field. For example, if the keyword "nature scene" and the emotion "joy" are recognized, the design will be steered to generate a bright and attractive nature scene.

[0256] Input: JSON data.

[0257] Output: Parsed keywords and sentiment data.

[0258] Step 5:

[0259] The server converts the analysis results into an appropriate data format to be passed to the generative model, where a prompt sentence is formed to be input to the generative AI model.

[0260] Input: Analysis result data.

[0261] Output: A prompt sentence for the generative AI model.

[0262] Step 6:

[0263] The server runs generative AI models to generate design elements: an image generation model might produce a high-resolution wallpaper, an icon generation model might produce a joyful icon set with a nature theme, and a sound generation model might generate a cheerful ringtone.

[0264] Input: A prompt sentence for the generative AI model.

[0265] Output: Generated design elements (wallpapers, icons, sounds, etc.).

[0266] Step 7:

[0267] The generated design elements are stored in a temporary database, and access URLs for each design element are generated. The server sends a response containing these URLs in JSON format to the terminal.

[0268] Input: Generated design elements.

[0269] Output: A JSON response containing the access URL.

[0270] Step 8:

[0271] The device parses the received JSON response and extracts the URL and data of each design element, allowing access to the generated design elements.

[0272] Input: JSON response.

[0273] Output: URLs and data of design elements.

[0274] Step 9:

[0275] The device updates the user interface to allow the user to preview the generated design elements, and the user reviews the previews, selects the design element that suits their preference, and downloads or applies it to their mobile phone.

[0276] Input: URL or data of the design element.

[0277] Output: Updated UI and preview screen.

[0278] Through these processing steps, personalized design elements that reflect the user's emotions and preferences are generated and presented.

[0279] (Application example 2)

[0280] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0281] Conventional methods for customizing store displays and decorations have difficulty in instantly generating specific design proposals that reflect a user's preferences, images, and even emotions. In particular, it has been difficult for users to instantly obtain appropriate display proposals based on emotion recognition using specific keywords and reference images. Therefore, there is a need for a method to efficiently and effectively customize in-store decorations.

[0282] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0283] In this invention, the server includes means for receiving preferences and image data input by a user, means for analyzing the received preferences and image data and passing the input data to a generative model, means for providing the user with design elements (wallpaper, icons, sounds, etc.) generated by the generative model, means for collecting and analyzing user emotion data using emotion recognition means, means for reflecting the analyzed emotion data in design generation, and means for previewing the generated design elements for customizing store decorations and displays. This makes it possible to easily customize store decorations and displays in accordance with the user's preferences and emotions.

[0284] "User" means any individual or entity that uses the System to create or customize Design Elements.

[0285] "Preferences and image data" refers to design-related keywords, themes, reference images, etc. specified by the user.

[0286] A "generative model" is a machine learning model that generates design elements based on user preferences, image data, and emotional data.

[0287] "Design elements" are visual and auditory elements such as wallpaper, icons, and sounds generated by generative models.

[0288] "Emotion recognition means" is a technology for collecting and analyzing emotional data from user input and reference images.

[0289] "Preview" is a function that displays the generated design elements in advance, allowing the user to check their contents.

[0290] A "display plan" is a specific design plan used for store decoration and display.

[0291] This invention relates to a system for customizing store decorations and displays based on user preferences, images, and emotions. The system involves the cooperation of a smartphone, an emotion recognition engine, a generative AI model, and a server.

[0292] First, a store employee launches the application on their smartphone. The application displays a user interface with fields for inputting the store's theme and decorative image. For example, they can enter keywords such as "spring flowers" or "warm atmosphere" and upload images they would like to use as inspiration.

[0293] The smartphone is equipped with an emotion recognition engine (e.g., Microsoft Azure's Face API) that analyzes the staff member's emotions (e.g., joy, excitement, relaxation, etc.) in real time from the input content and reference images, thereby collecting emotional data along with preference and image data.

[0294] When a staff member presses the "send" button, the device converts preferences, image data, and emotion data into JSON format and sends it over the internet to a server. The server then analyzes the received JSON data and uses Google's Natural Language API to understand the keywords and emotions entered. It then uses a generative AI model such as OpenAI's DALL-E to generate display ideas that fit the store's theme.

[0295] The generated display proposals are temporarily stored on the server, and an access URL for each proposal is generated. A response containing these URLs is sent in JSON format to the smartphone. The smartphone analyzes the received JSON response and displays a preview of the generated display proposals. Staff can then check these previews, select the display proposal that best suits their needs, and apply it to the actual exhibition.

[0296] As a concrete example, the following prompt sentences can be fed into a generative AI model:

[0297] Example prompts: "Spring flowers," "Warm atmosphere," "Relaxing," "Image: sakura_field.jpg"

[0298] Result: "A display concept themed around a spring flower field. Bright pinks and greens dominate, with relaxing, soft lighting."

[0299] As described above, by using this system, it is possible to easily customize store decorations and displays to suit the user's preferences and emotions.

[0300] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0301] Step 1:

[0302] Users enter the store's theme and decorative image and upload reference images.

[0303] Input: Keywords (e.g., "spring flowers" or "warm atmosphere") and reference images

[0304] Output: Keywords and image data

[0305] Specific operations: Launch the smartphone application, enter keywords in the fields displayed in the user interface, and upload reference images.

[0306] Step 2:

[0307] An emotion recognition means analyzes emotions from the user's input and the reference image.

[0308] Input: Keywords, reference images

[0309] Output: Emotion data (e.g., joy, excitement, relaxation)

[0310] How it works: The smartphone's built-in emotion recognition engine (for example, Microsoft Azure's Face API) analyzes keywords and reference images to recognize emotions in real time.

[0311] Step 3:

[0312] The device converts preference, image data, and emotion data into JSON format and sends it to the server.

[0313] Input: Keywords, reference images, emotion data

[0314] Output: JSON format data

[0315] Specific operation: The smartphone compiles the input keywords, reference images, and analyzed emotion data into a single JSON data set and sends it to a server via the Internet.

[0316] Step 4:

[0317] The server analyzes the received JSON data and understands keywords and sentiment data.

[0318] Input: JSON format data

[0319] Output: Parsed keywords and sentiment data

[0320] Specific operation: The server uses Google's Natural Language API to extract and analyze keywords and sentiment data from the JSON data.

[0321] Step 5:

[0322] The server inputs the analysis data into the generated AI model and generates a display proposal.

[0323] Input: Analyzed keywords, sentiment data

[0324] Output: Generated display plan and its access URL

[0325] How it works: The server inputs the analyzed keywords and emotion data into a generative AI model such as OpenAI's DALL-E, and generates display ideas that fit the store's theme. The generated display ideas are temporarily saved and an access URL is generated.

[0326] Step 6:

[0327] The server sends the URL of the generated display plan in JSON format to the terminal.

[0328] Input: Generated display plan and its access URL

[0329] Output: Response data in JSON format

[0330] Specific operation: The server creates a JSON format response including the access URL for the generated display proposal and sends it to the smartphone.

[0331] Step 7:

[0332] The device analyzes the received JSON response and displays a preview of the proposed display.

[0333] Input: Response data in JSON format

[0334] Output: Preview of the display

[0335] Specific operation: The smartphone parses the JSON response and displays the generated display proposals on the preview screen. The user checks the preview and selects a display proposal.

[0336] As a concrete example, the following prompt sentence is input into the generative AI model, and a display proposal is generated:

[0337] Example prompts: "Spring flowers," "Warm atmosphere," "Relaxing," "Image: sakura_field.jpg"

[0338] As a result, the generated display proposal is "A display concept based on a spring flower field theme, with bright pink and green as the main colors and soft, relaxing lighting."

[0339] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0340] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0341] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0342] [Second embodiment]

[0343] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0344] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0345] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0346] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0347] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0348] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0349] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0350] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0351] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0352] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0353] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0354] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0355] This invention relates to a system that allows users to enjoy designing and customizing mobile phones, and generates original design elements using a generative AI model based on preferences and image data entered by the user. The program processing required to implement this invention is described below.

[0356] 1. First, the user launches the customization application on their device. The application displays an interface, and the user inputs information about their preferred keywords and images (e.g., natural landscapes, blue and green tones). This input data is temporarily stored on the device.

[0357] 2. When the user presses the "Send" button, the device converts this input data into JSON format and sends it to the server, using an internet connection.

[0358] 3. The server analyzes the received JSON data to understand the user's preferences and impressions. Specifically, it uses a natural language processing (NLP) engine to analyze the meaning of the input and converts it into the appropriate data format to be passed to the generative model.

[0359] 4. The server passes the analyzed data to a generative AI model, which then generates original design elements (wallpapers, icons, ringtones, etc.) based on the data. For example, the keywords "natural scenery" and "blue and green" could generate a wallpaper depicting a blue sky and green forest, as well as a set of nature-themed leaf icons.

[0360] 5. The generated design elements are stored in a temporary database, and access URLs for each design element are generated. The server sends a response containing these URLs in JSON format to the terminal.

[0361] 6. The device parses the received JSON response and displays a preview of the generated design elements to the user, allowing the user to select the design elements they like and download or apply them.

[0362] For example, if a user inputs "nature scenery" and "blue and green," the server will analyze this and generate wallpaper with a blue sky and forest theme, as well as an icon set of leaves and flowers that evoke nature. This allows users to easily enjoy a mobile phone design that reflects their preferences and individuality.

[0363] The above is a detailed description of the embodiment of the present invention, which is a system that can easily reflect the user's preferences and ideas in the design of a mobile phone. This system makes it easy to enjoy unique and attractive designs.

[0364] The processing flow will be explained below.

[0365] Step 1:

[0366] A user launches a customization application on their device. The application displays a user interface (UI) containing input fields where the user can enter keywords that reflect their preferences or image (e.g., "nature landscapes" or "blue and green tones") and optionally upload reference images.

[0367] Step 2:

[0368] The terminal temporarily stores the data entered by the user and waits for the user to press the "Send" button after completing the input. When the user presses the "Send" button, the terminal converts the input data into JSON format and generates a send request to the server.

[0369] Step 3:

[0370] The device sends the generated request and sends JSON data including the user's input data to the server, using an internet connection.

[0371] Step 4:

[0372] The server parses the received JSON data. It extracts data from each field and uses a natural language processing (NLP) engine to understand the user's preferences and impressions. Specifically, it analyzes keywords such as "natural scenery" and "blue and green" and extracts their meanings.

[0373] Step 5:

[0374] The server converts the analysis results into an appropriate data format to be passed to the generative model. This data is then input to the generative model.

[0375] Step 6:

[0376] The server runs generative AI models to generate design elements: for example, an image generation model generates high-resolution wallpapers based on "nature landscapes" and "blue and green" data, an icon generation model generates a nature-themed icon set, and an audio generation model generates ringtones based on nature sounds.

[0377] Step 7:

[0378] The server stores the generated design elements in a temporary database and generates an access URL for each design element (wallpaper, icon set, ringtone), which allows the generated design element to be identified.

[0379] Step 8:

[0380] The server creates a JSON response containing the access URL for the generated design element and sends this response to the terminal.

[0381] Step 9:

[0382] The device parses the received JSON response and extracts the URL and data of each design element.

[0383] Step 10:

[0384] The device will update the UI to allow the user to preview the generated design elements, and the user can review the previews, select the design elements they like, and download or apply them to their mobile phone.

[0385] Example 1

[0386] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0387] Previously, when users customized the design of their mobile phones, they had no choice but to choose from existing templates or limited options, making it difficult to reflect the individual user's preferences and image. Furthermore, there was a lack of a way to easily generate different design elements (wallpapers, icons, sounds, etc.) under a unified theme. Another problem was the lack of a system that allowed users to preview the generated design elements before selecting them.

[0388] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0389] In this invention, the server includes means for converting received preference and image data into JSON format and sending it to the server, means for the server to analyze the received JSON data and convert it into a data format suitable for the generative AI model, and means for storing design elements (wallpaper, icons, sounds, etc.) generated by the generative AI model in a database and generating an access URL. This makes it possible to easily generate original design elements that reflect the user's preferences and images with a unified theme, and to preview and select the generated design elements.

[0390] "User" refers to a person who uses the system to design and customize a mobile phone.

[0391] "Preference and image data" refers to information such as requests, keywords, and images regarding individual designs that users input into the system.

[0392] "JSON format" is an abbreviation for JavaScript Object Notation, and is a lightweight data exchange format for expressing data in text format.

[0393] "Server" refers to a computer system that receives, analyzes, and processes data sent from a user's terminal.

[0394] A "generative AI model" refers to an algorithm or program that uses artificial intelligence technology to generate design elements (wallpaper, icons, sounds, etc.) from data entered by the user.

[0395] "Data format" refers to the rules and conventions that define the structure and format in which data is represented.

[0396] "Design Elements" means individual design components used to customize a mobile phone, such as system-generated wallpaper, icons, sounds, etc.

[0397] "Database" refers to a management system for storing generated design elements, a data repository for easy access and retrieval.

[0398] "Access URL" refers to the unique resource locator required for a user to access a generated design element.

[0399] "Preview" refers to the function of displaying generated design elements so that the user can check them before selecting them.

[0400] This invention relates to a system that allows users to enjoy designing and customizing mobile phones. The purpose is to generate original design elements using a generative AI model based on user input preferences and image data. The program processing required to implement this invention is described in detail below.

[0401] The user first launches the customization application using the device. This application displays a user interface, allowing the user to input information about keywords and images they like. For example, the user may input keywords such as "nature landscape, blue and green." This input data is temporarily stored on the device.

[0402] Next, when the user presses the "Send" button, the device converts this input data into JSON format. The converted JSON data is sent over the internet to a server. The server parses the received JSON data and uses a natural language processing (NLP) engine to understand the meaning of the input data. This analysis converts it into the appropriate data format to be passed to the generative AI model.

[0403] Once the analysis is complete, the server passes the data to a generative AI model, which then generates original design elements (wallpaper, icons, audio, etc.) based on the received data. Examples of generative AI models used include OpenAI's GPT-3 and DALL-E. For example, the keywords "natural scenery" and "blue and green" generate a wallpaper depicting a blue sky and green forest, as well as a set of nature-themed leaf icons.

[0404] The generated design elements are stored in a temporary database, and an access URL for each design element is generated. The server sends a response containing these URLs in JSON format to the device. The device parses the received JSON response and displays a preview of the generated design elements to the user. The user can view the preview, select the design element they like, and download or apply it.

[0405] For example, if a user inputs "nature scenery" and "blue and green," the server analyzes this and passes it to a generative AI model. The generative AI model generates a wallpaper with a blue sky and forest theme, or an icon set of leaves and flowers that evokes nature. The device previews the generated designs, and the user can select the design they like and download or apply it.

[0406] An example prompt might be, "Use a generative AI model to create design elements with blue skies and forests, based on a natural landscape theme. Please use a blue and green color scheme."

[0407] This system allows users to easily enjoy designing their mobile phones to reflect their own tastes and personalities.

[0408] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0409] Step 1:

[0410] User Input Processing

[0411] The user launches the customization application using the device. The application displays a user interface, and the user inputs information about their preferred keywords and images. For example, they input "nature landscape, blue and green." This input data is temporarily stored on the device.

[0412] Input: Data entered into the user interface (e.g., nature scenes, blue and green)

[0413] Output: Input data temporarily saved on the device

[0414] Step 2:

[0415] Converting data to JSON and sending it

[0416] When the user presses the "Send" button, the device converts the saved input data into JSON format, for example, {"keywords": ["nature landscape", "blue and green"]}. The device then sends the converted JSON data to the server via the Internet.

[0417] Input: Temporarily saved input data

[0418] Output: JSON data sent to the server

[0419] Step 3:

[0420] Parsing JSON Data

[0421] The server parses the received JSON data. Using a natural language processing (NLP) engine, the server analyzes the meaning of the input data and converts it into an appropriate data format (e.g., a prompt) to be passed to the generative AI model. For example, the input data "Nature landscape, blue and green" is converted into the prompt "Design with a nature landscape theme. Color scheme: blue and green."

[0422] Input: Received JSON data

[0423] Output: Prompt sentence to be passed to the generative AI model

[0424] Step 4:

[0425] Passing data to generative AI models

[0426] The server passes the prompt to the generative AI model, which then generates original design elements (wallpaper, icons, audio, etc.) based on the prompt. For example, a prompt like "A design themed around natural landscapes. The color scheme is blue and green" generates a wallpaper depicting a blue sky and green forest, as well as a set of leafy icons.

[0427] Input: prompt statement

[0428] Output: Generated design elements

[0429] Step 5:

[0430] Save design elements and generate URLs

[0431] The server stores the generated design elements (e.g., wallpaper, icon set) in a temporary database. Access URLs for each design element are generated, and a response containing these URLs is returned in JSON format. For example, {"wallpaper": "https: / / example.com / wallpaper1", "icons": "https: / / example.com / icons1"} is generated.

[0432] Input: Generated design elements

[0433] Output: Design elements stored in the database and a generated access URL

[0434] Step 6:

[0435] Response analysis and preview display

[0436] The device analyzes the JSON response received from the server. Based on the access URL of the analyzed design element, a preview of the generated design element is displayed on the user interface. The user can view the preview, select the design element they like, and download or apply it. For example, the user can download wallpaper and set it as the home screen.

[0437] Input: JSON response received from the server

[0438] Output: A preview of the design elements displayed in the user interface

[0439] (Application example 1)

[0440] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0441] Conventional food delivery systems have difficulty providing food designs that reflect the user's taste and appearance preferences, making it difficult to increase user satisfaction. In addition, there are limited ways for users to customize the taste and appearance of food to their own preferences, creating a need for a more personalized experience.

[0442] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0443] In this invention, the server includes a means for receiving preferences and image data input by the user, a means for analyzing the received preferences and image data and passing the input data to the generative model, and a means for providing the user with design elements (wallpaper, icons, audio, presentation designs, etc.) generated by the generative model, thereby enabling the design of dishes that reflect the user's taste and appearance preferences.

[0444] "User-entered preference and image data" refers to information that a user inputs into an application to express their taste preferences and visual image.

[0445] The "means of receiving" is the mechanism by which the application receives preferences and image data input by the user.

[0446] The "means of analyzing and passing input data to the generative model" refers to a mechanism that analyzes the received data and sends the analysis results to the generative AI model as input data.

[0447] A "generative model" is an artificial intelligence system that generates original design elements (e.g., wallpaper, icons, sounds, presentation designs, etc.) based on analyzed data.

[0448] "Generated design elements" are specific elements of a design created by a generative AI model based on a user's preferences and imagination.

[0449] "Means for providing to users" refers to the mechanism by which the generated design elements are shown to users or provided in a usable form.

[0450] "Wallpaper, icons, sounds, presentation designs, etc." refers to visual and auditory design elements customized based on user preferences and used in smartphone and food delivery presentations.

[0451] The "means for displaying a preview to the user" is a mechanism for displaying the generated design elements in a form that allows the user to visually confirm them.

[0452] This invention is a system that uses a generative AI model to generate original design elements (wallpaper, icons, sounds, presentation designs, etc.) based on preferences and image data entered by the user. Specific examples of this system are described below.

[0453] 1. Overall system configuration

[0454] This system consists of a user device, a server, a generative AI model, and a database. The user device can be a smartphone, tablet, or computer, while the server is often installed on a cloud service. The generative AI model used is, for example, OpenAI's DALL-E.

[0455] 2. User Interface

[0456] The user launches the customization application using a terminal and enters information about their preferred flavors and visual image, for example, the following prompt:

[0457] "Prepare spicy dishes in a modern art style using red and yellow hues."

[0458] When the user presses the "Send" button, the device converts the input data into JSON format and sends it to the server, using an internet connection.

[0459] 3. Server Processing

[0460] The server parses the received JSON data to understand the user's preferences and impressions. Specifically, it uses a natural language processing (NLP) engine to analyze the meaning of the input and converts it into the appropriate data format to be passed to the generative AI model (for example, using the Google NLP API).

[0461] 4. Design generation using generative AI models

[0462] The server passes the analyzed data to a generative AI model, which then generates original design elements based on the received data. For example, using the keywords "spicy," "red and yellow," and "modern art," it generates a modern art-style presentation design for spicy food, based on red and yellow tones.

[0463] 5. Providing design elements

[0464] The generated design elements are stored in a temporary database, and access URLs for each design element are generated. The server sends a response containing these URLs in JSON format to the terminal. The user terminal parses the received JSON response and displays a preview of the generated design elements to the user.

[0465] 6. Specific Examples

[0466] For example, if a user inputs "spicy," "red and yellow," and "modern art," the server analyzes the input and generates a modern art-style presentation design for spicy food based on red and yellow tones. These generated design elements are stored in a database, and users can browse the design themes and select their favorite design.

[0467] This system makes it possible to provide personalized dish designs that reflect the user's preferences and ideas.

[0468] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0469] Step 1:

[0470] The user launches the customization application on the device and inputs information about the desired taste and visual image. The user inputs specific keywords and phrases. For example, the prompt "Please present a spicy dish in red and yellow tones in a modern art style" is used. The input here is text data.

[0471] Step 2:

[0472] When the user types something in and hits the submit button, the device converts the input data into JSON format, which makes it easier to send over the network. The converted JSON data looks like this:

[0473] {

[0474] "taste": "spicy",

[0475] "color_scheme": "red and yellow",

[0476] "presentation": "modern art"

[0477] }

[0478] This JSON data is sent to the server.

[0479] Step 3:

[0480] The server parses the received JSON data. Based on the received data, it uses a natural language processing (NLP) engine (e.g., Google NLP API) to understand the user's preferences and impressions. The result of the parsing is converted into an appropriate data format to be passed to the generative model. For example, the parsed data looks like this:

[0481] {

[0482] "parsed_taste": "spicy",

[0483] "parsed_color_scheme": ["red", "yellow"],

[0484] "parsed_presentation": "modern art"

[0485] }

[0486] This parsed data is passed to a generative AI model.

[0487] Step 4:

[0488] The server passes the analyzed data to a generative AI model. The generative AI model (e.g., OpenAI's DALL-E) generates original design elements based on this data. For example, using the keywords "spicy," "red and yellow," and "modern art," it generates a modern art-style presentation design for spicy food based on red and yellow tones. This generative process generates concrete design data.

[0489] Step 5:

[0490] The generated design elements are stored in a temporary database. A unique access URL is generated for each generated design element. The server sends a response containing these URLs in JSON format to the terminal. Example response from the server:

[0491] {

[0492] "design_url": "https: / / example.com / designs / 12345"

[0493] }

[0494] This allows the terminal to access the generated design elements.

[0495] Step 6:

[0496] The terminal parses the received JSON response and displays a preview of the generated design elements to the user. The user can view the preview, select the design element they like, and download or apply it. Specifically, the generated design elements are displayed on the application interface. The user can also rate the design elements.

[0497] Through the above processing steps, users can easily enjoy personalized food designs that reflect their own preferences and images.

[0498] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0499] This invention relates to a system that allows users to enjoy designing and customizing mobile phones, and generates original design elements by combining user preferences, image data, and an emotion engine that recognizes emotions. The program processing required to implement this invention is described below.

[0500] 1. First, the user launches the customization application on their device. The application displays a user interface (UI) that includes fields for inputting preferences and images. The user enters specific keywords (e.g., "nature landscape," "blue and green tones") and can upload reference images if desired.

[0501] 2. The device is equipped with an emotion engine that analyzes and recognizes user emotions (e.g., joy, sadness, surprise, etc.) in real time based on user input and interface usage. The recognized emotion data is stored together with preference and image data.

[0502] 3. When the user presses the "Send" button, the device converts the preferences, image data, and emotion data into JSON format and generates a send request to the server, using an internet connection.

[0503] 4. The server parses the received JSON data. It extracts data from each field and uses a natural language processing (NLP) engine to understand the user's preferences, impressions, and even emotions. For example, if the emotion "joy" is recognized along with the keyword "nature scene," the design will work to generate a bright and vibrant nature scene.

[0504] 5. The server converts the analysis results into an appropriate data format to be passed to the generative model. This data is input to the generative model.

[0505] 6. The server runs generative AI models to generate design elements. For example, the image generation model generates a high-resolution wallpaper based on "nature scenery," "blue and green," and the user's emotional data of "joy." The icon generation model generates a nature-themed icon set that evokes joy. The sound generation model generates a cheerful nature-inspired ringtone.

[0506] 7. The generated design elements are stored in a temporary database, and access URLs for each design element are generated. The server sends a response containing these URLs in JSON format to the terminal.

[0507] 8. The device parses the received JSON response and extracts the URL and data of each design element.

[0508] 9. The device will update the UI to allow the user to preview the generated design elements. The user can review the previews, select the design elements they like, and download or apply them to their phone.

[0509] For example, if a user inputs "nature scenery" and "blue and green" and the emotion engine recognizes "joy," the server will analyze this and generate a bright and attractive blue sky and forest themed wallpaper, as well as a set of leaves and flowers icons that evoke a natural feeling and express joy. In this way, users can easily enjoy mobile phone designs that reflect their preferences and emotions.

[0510] The above is a detailed description of the embodiment of the present invention, which is a system that allows users to customize the design of their mobile phone to reflect their preferences, images, and even emotions. This system allows users to easily enjoy unique and attractive designs.

[0511] The processing flow will be explained below.

[0512] Step 1:

[0513] The user launches the customization application on their device. The application displays a user interface (UI) that includes fields for inputting preferences and images. The user enters specific keywords (e.g., "nature landscapes," "blue and green tones") and can optionally upload reference images.

[0514] Step 2:

[0515] The device temporarily stores the user's input data and waits for the user to press the "Send" button after completing the input. When the user presses the "Send" button, the device passes the preference and image data to the emotion engine.

[0516] Step 3:

[0517] The device's built-in emotion engine analyzes user emotions in real time based on user input and interface usage. For example, it recognizes emotions by analyzing keywords, phrases, touch gestures, and even the speed and strength of input. The recognized emotion data (e.g., "happiness" or "surprise") is stored together with preference and image data.

[0518] Step 4:

[0519] The device converts the preferences, image data, and emotion data into JSON format and generates a request to send it to the server, using an internet connection.

[0520] Step 5:

[0521] The server parses the received JSON data, extracts data from each field, and uses a natural language processing (NLP) engine to understand the user's preferences, imagery, and even emotions. For example, it analyzes the keywords "nature scenery" and "blue and green," and the emotion "joy."

[0522] Step 6:

[0523] The server converts the analysis results into a data format appropriate for the generative model, and this data is then input into the generative model.

[0524] Step 7:

[0525] The server runs generative AI models to generate design elements. For example, the image generation model generates high-resolution wallpapers based on "natural scenery," "blue and green," and the emotion "joy." The icon generation model creates an icon set that evokes feelings of nature and joy, and the audio generation model creates ringtones based on upbeat natural sounds.

[0526] Step 8:

[0527] The generated design elements are stored in a temporary database. The server generates a URL for each design element and sends a response containing the URL in JSON format to the device.

[0528] Step 9:

[0529] The device parses the received JSON response and extracts the URL and data of each design element.

[0530] Step 10:

[0531] The device will update the UI to allow the user to preview the generated design elements, and the user can review the previews, select the design elements they like, and download or apply them to their mobile phone.

[0532] For example, if a user inputs "nature scenery" and "blue and green" and the emotion engine recognizes "joy," the server will analyze this and generate a bright and attractive blue sky and forest themed wallpaper, an icon set of leaves and flowers that evokes a sense of nature and expresses joy, and a ringtone based on bright natural sounds. In this way, users can easily enjoy a mobile phone design that reflects their preferences and emotions.

[0533] Example 2

[0534] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0535] Conventional customization systems mainly generate designs based on direct user input, making it difficult to reflect the user's emotions or temporary moods. Furthermore, the diversity of generated design elements and the provision of unique designs are limited. This makes it difficult for users to obtain design elements that perfectly match their actual requirements, resulting in low satisfaction.

[0536] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for receiving preferences, image data, and emotion data input by the user, means for analyzing the received preferences, image data, and emotion data and passing the input data to the generative model, and means for providing the user with design elements (wallpaper, icons, sounds, etc.) generated by the generative model. This makes it possible to generate diverse and unique design elements that reflect the user's emotions and preferences in real time.

[0537] "User" refers to an individual who uses the system to customize designs.

[0538] "Preferences and image data" refers to information including keywords specified by the user and reference images uploaded by the user.

[0539] "Emotional data" refers to data that indicates a user's emotional state obtained by analyzing user input and interface usage.

[0540] "Analysis" refers to the process of understanding and interpreting the meaning of received preference, image, and emotional data.

[0541] A "generative model" refers to an algorithm or AI model that automatically generates design elements based on input data.

[0542] "Design elements" refer to decorative items such as wallpaper, icons, and sounds created by generative models.

[0543] "Serving" refers to the process of making the generated design elements available to users.

[0544] "Means" refers to a method or apparatus for performing a particular function or process.

[0545] A "system" refers to a set of hardware and software that generates and provides design elements that reflect the user's preferences and emotions.

[0546] "Preview" means a display feature that allows a user to visually preview generated design elements before downloading or applying them.

[0547] This invention relates to a system that allows users to enjoy designing and customizing mobile phones, and generates original design elements by combining the user's preferences, image data, and an emotion engine that recognizes emotions. The processing of the program required to implement this invention will be described.

[0548] Program Overview

[0549] A user launches the customization application using their mobile device. The application displays a user interface (UI) and provides fields for the user to input preferences and images. In this section, the user can enter specific keywords (e.g., "nature landscape," "blue and green tones") and upload reference images, if desired.

[0550] The device's built-in emotion engine analyzes and recognizes the user's emotions (e.g., joy, sadness, surprise, etc.) in real time based on the user's input and interface usage, and the recognized emotion data is stored together with preference and image data.

[0551] When the user presses the "Send" button, the device converts the preferences, image data, and emotion data into JSON format and generates a send request to the server. This transmission is performed over an internet connection, and the server parses the received JSON data. On the server side, a natural language processing (NLP) engine is used to extract the data from each field and understand the user's preferences, images, and emotions. For example, if the keyword "nature scene" and the emotion "joy" are recognized, the design will work to generate a bright and attractive nature scene.

[0552] The server converts the analysis results into the appropriate data format to be passed to the generative model. This data is input into the generative AI model. The server runs the generative AI model to generate design elements. For example, the image generation model generates a high-resolution wallpaper based on "nature scenery," "blue and green," and "joy," the icon generation model generates a joyful icon set with a nature theme, and the sound generation model generates an upbeat nature-inspired ringtone.

[0553] The generated design elements are stored in a temporary database, and an access URL for each design element is generated. The server sends a response containing these URLs in JSON format to the device. The device parses the received JSON response and extracts the URL and data for each design element. The device then updates the UI so that the user can preview the generated design elements. The user can check the displayed previews, select the design element they like, and download or apply it to their mobile phone.

[0554] Specific examples

[0555] For example, consider a case where a user inputs "nature scenery" and "blue and green," and the emotion engine recognizes "joy." This data is converted into JSON format and sent to the server. The server analyzes it and provides data for the image generation model and icon generation model to operate. The server can then generate a bright and attractive wallpaper with a blue sky and forest theme, or an icon set of leaves and flowers that evokes a natural feeling and expresses joy. This allows users to easily enjoy mobile phone designs that reflect their preferences and emotions.

[0556] Prompt Sentence Examples

[0557] "Natural Landscapes, Blue and Green, Joy"

[0558] This invention makes it possible to generate personalized designs that take into account the user's emotions, something that was not possible with conventional customization systems.

[0559] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0560] Program processing flow

[0561] Step 1:

[0562] A user launches a customization application using their device. The application displays a user interface (UI) and provides fields for the user to input preferences and images. This can be presented as text fields or image upload fields, where the user can enter specific keywords (e.g., "nature landscapes," "blue and green tones") or upload a reference image.

[0563] Input: Keywords entered by users and uploaded image data.

[0564] Output: Input preferences and image data.

[0565] Step 2:

[0566] The device's built-in emotion engine monitors user input and interface usage in real time to analyze and recognize emotions, often using facial recognition cameras and sensors to recognize emotions such as happiness, sadness, and surprise.

[0567] Input: User facial expressions and operation logs.

[0568] Output: Parsed emotion data.

[0569] Step 3:

[0570] When the user presses the "Send" button, the device converts the entered preferences, image data, and emotion data into JSON format, which is then generated as a send request to the server and sent over the internet connection.

[0571] Input: User preferences, image data, and emotional data.

[0572] Output: JSON formatted data.

[0573] Step 4:

[0574] The server parses the received JSON data. A natural language processing (NLP) engine is used to process and understand the data in each field. For example, if the keyword "nature scene" and the emotion "joy" are recognized, the design will be steered to generate a bright and attractive nature scene.

[0575] Input: JSON data.

[0576] Output: Parsed keywords and sentiment data.

[0577] Step 5:

[0578] The server converts the analysis results into an appropriate data format to be passed to the generative model, where a prompt sentence is formed to be input to the generative AI model.

[0579] Input: Analysis result data.

[0580] Output: A prompt sentence for the generative AI model.

[0581] Step 6:

[0582] The server runs generative AI models to generate design elements: an image generation model might produce a high-resolution wallpaper, an icon generation model might produce a joyful icon set with a nature theme, and a sound generation model might generate a cheerful ringtone.

[0583] Input: A prompt sentence for the generative AI model.

[0584] Output: Generated design elements (wallpapers, icons, sounds, etc.).

[0585] Step 7:

[0586] The generated design elements are stored in a temporary database, and access URLs for each design element are generated. The server sends a response containing these URLs in JSON format to the terminal.

[0587] Input: Generated design elements.

[0588] Output: A JSON response containing the access URL.

[0589] Step 8:

[0590] The device parses the received JSON response and extracts the URL and data of each design element, allowing access to the generated design elements.

[0591] Input: JSON response.

[0592] Output: URLs and data of design elements.

[0593] Step 9:

[0594] The device updates the user interface to allow the user to preview the generated design elements, and the user reviews the previews, selects the design element that suits their preference, and downloads or applies it to their mobile phone.

[0595] Input: URL or data of the design element.

[0596] Output: Updated UI and preview screen.

[0597] Through these processing steps, personalized design elements that reflect the user's emotions and preferences are generated and presented.

[0598] (Application example 2)

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

[0600] Conventional methods for customizing store displays and decorations have difficulty in instantly generating specific design proposals that reflect a user's preferences, images, and even emotions. In particular, it has been difficult for users to instantly obtain appropriate display proposals based on emotion recognition using specific keywords and reference images. Therefore, there is a need for a method to efficiently and effectively customize in-store decorations.

[0601] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0602] In this invention, the server includes means for receiving preferences and image data input by a user, means for analyzing the received preferences and image data and passing the input data to a generative model, means for providing the user with design elements (wallpaper, icons, sounds, etc.) generated by the generative model, means for collecting and analyzing user emotion data using emotion recognition means, means for reflecting the analyzed emotion data in design generation, and means for previewing the generated design elements for customizing store decorations and displays. This makes it possible to easily customize store decorations and displays in accordance with the user's preferences and emotions.

[0603] "User" means any individual or entity that uses the System to create or customize Design Elements.

[0604] "Preferences and image data" refers to design-related keywords, themes, reference images, etc. specified by the user.

[0605] A "generative model" is a machine learning model that generates design elements based on user preferences, image data, and emotional data.

[0606] "Design elements" are visual and auditory elements such as wallpaper, icons, and sounds generated by generative models.

[0607] "Emotion recognition means" is a technology for collecting and analyzing emotional data from user input and reference images.

[0608] "Preview" is a function that displays the generated design elements in advance, allowing the user to check their contents.

[0609] A "display plan" is a specific design plan used for store decoration and display.

[0610] This invention relates to a system for customizing store decorations and displays based on user preferences, images, and emotions. The system involves the cooperation of a smartphone, an emotion recognition engine, a generative AI model, and a server.

[0611] First, a store employee launches the application on their smartphone. The application displays a user interface with fields for inputting the store's theme and decorative image. For example, they can enter keywords such as "spring flowers" or "warm atmosphere" and upload images they would like to use as inspiration.

[0612] The smartphone is equipped with an emotion recognition engine (e.g., Microsoft Azure's Face API) that analyzes the staff member's emotions (e.g., joy, excitement, relaxation, etc.) in real time from the input content and reference images, thereby collecting emotional data along with preference and image data.

[0613] When a staff member presses the "send" button, the device converts preferences, image data, and emotion data into JSON format and sends it over the internet to a server. The server then analyzes the received JSON data and uses Google's Natural Language API to understand the keywords and emotions entered. It then uses a generative AI model such as OpenAI's DALL-E to generate display ideas that fit the store's theme.

[0614] The generated display proposals are temporarily stored on the server, and an access URL for each proposal is generated. A response containing these URLs is sent in JSON format to the smartphone. The smartphone analyzes the received JSON response and displays a preview of the generated display proposals. Staff can then check these previews, select the display proposal that best suits their needs, and apply it to the actual exhibition.

[0615] As a concrete example, the following prompt sentences can be fed into a generative AI model:

[0616] Example prompts: "Spring flowers," "Warm atmosphere," "Relaxing," "Image: sakura_field.jpg"

[0617] Result: "A display concept themed around a spring flower field. Bright pinks and greens dominate, with relaxing, soft lighting."

[0618] As described above, by using this system, it is possible to easily customize store decorations and displays to suit the user's preferences and emotions.

[0619] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0620] Step 1:

[0621] Users enter the store's theme and decorative image and upload reference images.

[0622] Input: Keywords (e.g., "spring flowers" or "warm atmosphere") and reference images

[0623] Output: Keywords and image data

[0624] Specific operations: Launch the smartphone application, enter keywords in the fields displayed in the user interface, and upload reference images.

[0625] Step 2:

[0626] An emotion recognition means analyzes emotions from the user's input and the reference image.

[0627] Input: Keywords, reference images

[0628] Output: Emotion data (e.g., joy, excitement, relaxation)

[0629] How it works: The smartphone's built-in emotion recognition engine (for example, Microsoft Azure's Face API) analyzes keywords and reference images to recognize emotions in real time.

[0630] Step 3:

[0631] The device converts preference, image data, and emotion data into JSON format and sends it to the server.

[0632] Input: Keywords, reference images, emotion data

[0633] Output: JSON format data

[0634] Specific operation: The smartphone compiles the input keywords, reference images, and analyzed emotion data into a single JSON data set and sends it to a server via the Internet.

[0635] Step 4:

[0636] The server analyzes the received JSON data and understands keywords and sentiment data.

[0637] Input: JSON format data

[0638] Output: Parsed keywords and sentiment data

[0639] Specific operation: The server uses Google's Natural Language API to extract and analyze keywords and sentiment data from the JSON data.

[0640] Step 5:

[0641] The server inputs the analysis data into the generated AI model and generates a display proposal.

[0642] Input: Analyzed keywords, sentiment data

[0643] Output: Generated display plan and its access URL

[0644] How it works: The server inputs the analyzed keywords and emotion data into a generative AI model such as OpenAI's DALL-E, and generates display ideas that fit the store's theme. The generated display ideas are temporarily saved and an access URL is generated.

[0645] Step 6:

[0646] The server sends the URL of the generated display plan in JSON format to the terminal.

[0647] Input: Generated display plan and its access URL

[0648] Output: Response data in JSON format

[0649] Specific operation: The server creates a JSON format response including the access URL for the generated display proposal and sends it to the smartphone.

[0650] Step 7:

[0651] The device analyzes the received JSON response and displays a preview of the proposed display.

[0652] Input: Response data in JSON format

[0653] Output: Preview of the display

[0654] Specific operation: The smartphone parses the JSON response and displays the generated display proposals on the preview screen. The user checks the preview and selects a display proposal.

[0655] As a concrete example, the following prompt sentence is input into the generative AI model, and a display proposal is generated:

[0656] Example prompts: "Spring flowers," "Warm atmosphere," "Relaxing," "Image: sakura_field.jpg"

[0657] As a result, the generated display proposal is "A display concept based on a spring flower field theme, with bright pink and green as the main colors and soft, relaxing lighting."

[0658] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0659] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0660] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0661] [Third embodiment]

[0662] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0663] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0664] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0665] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0666] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0667] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0668] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0669] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0670] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0671] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0672] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0673] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0674] This invention relates to a system that allows users to enjoy designing and customizing mobile phones, and generates original design elements using a generative AI model based on preferences and image data entered by the user. The program processing required to implement this invention is described below.

[0675] 1. First, the user launches the customization application on their device. The application displays an interface, and the user inputs information about their preferred keywords and images (e.g., natural landscapes, blue and green tones). This input data is temporarily stored on the device.

[0676] 2. When the user presses the "Send" button, the device converts this input data into JSON format and sends it to the server, using an internet connection.

[0677] 3. The server analyzes the received JSON data to understand the user's preferences and impressions. Specifically, it uses a natural language processing (NLP) engine to analyze the meaning of the input and converts it into the appropriate data format to be passed to the generative model.

[0678] 4. The server passes the analyzed data to a generative AI model, which then generates original design elements (wallpapers, icons, ringtones, etc.) based on the data. For example, the keywords "natural scenery" and "blue and green" could generate a wallpaper depicting a blue sky and green forest, as well as a set of nature-themed leaf icons.

[0679] 5. The generated design elements are stored in a temporary database, and access URLs for each design element are generated. The server sends a response containing these URLs in JSON format to the terminal.

[0680] 6. The device parses the received JSON response and displays a preview of the generated design elements to the user, allowing the user to select the design elements they like and download or apply them.

[0681] For example, if a user inputs "nature scenery" and "blue and green," the server will analyze this and generate wallpaper with a blue sky and forest theme, as well as an icon set of leaves and flowers that evoke nature. This allows users to easily enjoy a mobile phone design that reflects their preferences and individuality.

[0682] The above is a detailed description of the embodiment of the present invention, which is a system that can easily reflect the user's preferences and ideas in the design of a mobile phone. This system makes it easy to enjoy unique and attractive designs.

[0683] The processing flow will be explained below.

[0684] Step 1:

[0685] A user launches a customization application on their device. The application displays a user interface (UI) containing input fields where the user can enter keywords that reflect their preferences or image (e.g., "nature landscapes" or "blue and green tones") and optionally upload reference images.

[0686] Step 2:

[0687] The terminal temporarily stores the data entered by the user and waits for the user to press the "Send" button after completing the input. When the user presses the "Send" button, the terminal converts the input data into JSON format and generates a send request to the server.

[0688] Step 3:

[0689] The device sends the generated request and sends JSON data including the user's input data to the server, using an internet connection.

[0690] Step 4:

[0691] The server parses the received JSON data. It extracts data from each field and uses a natural language processing (NLP) engine to understand the user's preferences and impressions. Specifically, it analyzes keywords such as "natural scenery" and "blue and green" and extracts their meanings.

[0692] Step 5:

[0693] The server converts the analysis results into an appropriate data format to be passed to the generative model. This data is then input to the generative model.

[0694] Step 6:

[0695] The server runs generative AI models to generate design elements: for example, an image generation model generates high-resolution wallpapers based on "nature landscapes" and "blue and green" data, an icon generation model generates a nature-themed icon set, and an audio generation model generates ringtones based on nature sounds.

[0696] Step 7:

[0697] The server stores the generated design elements in a temporary database and generates an access URL for each design element (wallpaper, icon set, ringtone), which allows the generated design element to be identified.

[0698] Step 8:

[0699] The server creates a JSON response containing the access URL for the generated design element and sends this response to the terminal.

[0700] Step 9:

[0701] The device parses the received JSON response and extracts the URL and data of each design element.

[0702] Step 10:

[0703] The device will update the UI to allow the user to preview the generated design elements, and the user can review the previews, select the design elements they like, and download or apply them to their mobile phone.

[0704] Example 1

[0705] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0706] Previously, when users customized the design of their mobile phones, they had no choice but to choose from existing templates or limited options, making it difficult to reflect the individual user's preferences and image. Furthermore, there was a lack of a way to easily generate different design elements (wallpapers, icons, sounds, etc.) under a unified theme. Another problem was the lack of a system that allowed users to preview the generated design elements before selecting them.

[0707] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0708] In this invention, the server includes means for converting received preference and image data into JSON format and sending it to the server, means for the server to analyze the received JSON data and convert it into a data format suitable for the generative AI model, and means for storing design elements (wallpaper, icons, sounds, etc.) generated by the generative AI model in a database and generating an access URL. This makes it possible to easily generate original design elements that reflect the user's preferences and images with a unified theme, and to preview and select the generated design elements.

[0709] "User" refers to a person who uses the system to design and customize a mobile phone.

[0710] "Preference and image data" refers to information such as requests, keywords, and images regarding individual designs that users input into the system.

[0711] "JSON format" is an abbreviation for JavaScript Object Notation, and is a lightweight data exchange format for expressing data in text format.

[0712] "Server" refers to a computer system that receives, analyzes, and processes data sent from a user's terminal.

[0713] A "generative AI model" refers to an algorithm or program that uses artificial intelligence technology to generate design elements (wallpaper, icons, sounds, etc.) from data entered by the user.

[0714] "Data format" refers to the rules and conventions that define the structure and format in which data is represented.

[0715] "Design Elements" means individual design components used to customize a mobile phone, such as system-generated wallpaper, icons, sounds, etc.

[0716] "Database" refers to a management system for storing generated design elements, a data repository for easy access and retrieval.

[0717] "Access URL" refers to the unique resource locator required for a user to access a generated design element.

[0718] "Preview" refers to the function of displaying generated design elements so that the user can check them before selecting them.

[0719] This invention relates to a system that allows users to enjoy designing and customizing mobile phones. The purpose is to generate original design elements using a generative AI model based on user input preferences and image data. The program processing required to implement this invention is described in detail below.

[0720] The user first launches the customization application using the device. This application displays a user interface, allowing the user to input information about keywords and images they like. For example, the user may input keywords such as "nature landscape, blue and green." This input data is temporarily stored on the device.

[0721] Next, when the user presses the "Send" button, the device converts this input data into JSON format. The converted JSON data is sent over the internet to a server. The server parses the received JSON data and uses a natural language processing (NLP) engine to understand the meaning of the input data. This analysis converts it into the appropriate data format to be passed to the generative AI model.

[0722] Once the analysis is complete, the server passes the data to a generative AI model, which then generates original design elements (wallpaper, icons, audio, etc.) based on the received data. Examples of generative AI models used include OpenAI's GPT-3 and DALL-E. For example, the keywords "natural scenery" and "blue and green" generate a wallpaper depicting a blue sky and green forest, as well as a set of nature-themed leaf icons.

[0723] The generated design elements are stored in a temporary database, and an access URL for each design element is generated. The server sends a response containing these URLs in JSON format to the device. The device parses the received JSON response and displays a preview of the generated design elements to the user. The user can view the preview, select the design element they like, and download or apply it.

[0724] For example, if a user inputs "nature scenery" and "blue and green," the server analyzes this and passes it to a generative AI model. The generative AI model generates a wallpaper with a blue sky and forest theme, or an icon set of leaves and flowers that evokes nature. The device previews the generated designs, and the user can select the design they like and download or apply it.

[0725] An example prompt might be, "Use a generative AI model to create design elements with blue skies and forests, based on a natural landscape theme. Please use a blue and green color scheme."

[0726] This system allows users to easily enjoy designing their mobile phones to reflect their own tastes and personalities.

[0727] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0728] Step 1:

[0729] User Input Processing

[0730] The user launches the customization application using the device. The application displays a user interface, and the user inputs information about their preferred keywords and images. For example, they input "nature landscape, blue and green." This input data is temporarily stored on the device.

[0731] Input: Data entered into the user interface (e.g., nature scenes, blue and green)

[0732] Output: Input data temporarily saved on the device

[0733] Step 2:

[0734] Converting data to JSON and sending it

[0735] When the user presses the "Send" button, the device converts the saved input data into JSON format, for example, {"keywords": ["nature landscape", "blue and green"]}. The device then sends the converted JSON data to the server via the Internet.

[0736] Input: Temporarily saved input data

[0737] Output: JSON data sent to the server

[0738] Step 3:

[0739] Parsing JSON Data

[0740] The server parses the received JSON data. Using a natural language processing (NLP) engine, the server analyzes the meaning of the input data and converts it into an appropriate data format (e.g., a prompt) to be passed to the generative AI model. For example, the input data "Nature landscape, blue and green" is converted into the prompt "Design with a nature landscape theme. Color scheme: blue and green."

[0741] Input: Received JSON data

[0742] Output: Prompt sentence to be passed to the generative AI model

[0743] Step 4:

[0744] Passing data to generative AI models

[0745] The server passes the prompt to the generative AI model, which then generates original design elements (wallpaper, icons, audio, etc.) based on the prompt. For example, a prompt like "A design themed around natural landscapes. The color scheme is blue and green" generates a wallpaper depicting a blue sky and green forest, as well as a set of leafy icons.

[0746] Input: prompt statement

[0747] Output: Generated design elements

[0748] Step 5:

[0749] Save design elements and generate URLs

[0750] The server stores the generated design elements (e.g., wallpaper, icon set) in a temporary database. Access URLs for each design element are generated, and a response containing these URLs is returned in JSON format. For example, {"wallpaper": "https: / / example.com / wallpaper1", "icons": "https: / / example.com / icons1"} is generated.

[0751] Input: Generated design elements

[0752] Output: Design elements stored in the database and a generated access URL

[0753] Step 6:

[0754] Response analysis and preview display

[0755] The device analyzes the JSON response received from the server. Based on the access URL of the analyzed design element, a preview of the generated design element is displayed on the user interface. The user can view the preview, select the design element they like, and download or apply it. For example, the user can download wallpaper and set it as the home screen.

[0756] Input: JSON response received from the server

[0757] Output: A preview of the design elements displayed in the user interface

[0758] (Application example 1)

[0759] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0760] Conventional food delivery systems have difficulty providing food designs that reflect the user's taste and appearance preferences, making it difficult to increase user satisfaction. In addition, there are limited ways for users to customize the taste and appearance of food to their own preferences, creating a need for a more personalized experience.

[0761] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0762] In this invention, the server includes a means for receiving preferences and image data input by the user, a means for analyzing the received preferences and image data and passing the input data to the generative model, and a means for providing the user with design elements (wallpaper, icons, audio, presentation designs, etc.) generated by the generative model, thereby enabling the design of dishes that reflect the user's taste and appearance preferences.

[0763] "User-entered preference and image data" refers to information that a user inputs into an application to express their taste preferences and visual image.

[0764] The "means of receiving" is the mechanism by which the application receives preferences and image data input by the user.

[0765] The "means of analyzing and passing input data to the generative model" refers to a mechanism that analyzes the received data and sends the analysis results to the generative AI model as input data.

[0766] A "generative model" is an artificial intelligence system that generates original design elements (e.g., wallpaper, icons, sounds, presentation designs, etc.) based on analyzed data.

[0767] "Generated design elements" are specific elements of a design created by a generative AI model based on a user's preferences and imagination.

[0768] "Means for providing to users" refers to the mechanism by which the generated design elements are shown to users or provided in a usable form.

[0769] "Wallpaper, icons, sounds, presentation designs, etc." refers to visual and auditory design elements customized based on user preferences and used in smartphone and food delivery presentations.

[0770] The "means for displaying a preview to the user" is a mechanism for displaying the generated design elements in a form that allows the user to visually confirm them.

[0771] This invention is a system that uses a generative AI model to generate original design elements (wallpaper, icons, sounds, presentation designs, etc.) based on preferences and image data entered by the user. Specific examples of this system are described below.

[0772] 1. Overall system configuration

[0773] This system consists of a user device, a server, a generative AI model, and a database. The user device can be a smartphone, tablet, or computer, while the server is often installed on a cloud service. The generative AI model used is, for example, OpenAI's DALL-E.

[0774] 2. User Interface

[0775] The user launches the customization application using a terminal and enters information about their preferred flavors and visual image, for example, the following prompt:

[0776] "Prepare spicy dishes in a modern art style using red and yellow hues."

[0777] When the user presses the "Send" button, the device converts the input data into JSON format and sends it to the server, using an internet connection.

[0778] 3. Server Processing

[0779] The server parses the received JSON data to understand the user's preferences and impressions. Specifically, it uses a natural language processing (NLP) engine to analyze the meaning of the input and converts it into the appropriate data format to be passed to the generative AI model (for example, using the Google NLP API).

[0780] 4. Design generation using generative AI models

[0781] The server passes the analyzed data to a generative AI model, which then generates original design elements based on the received data. For example, using the keywords "spicy," "red and yellow," and "modern art," it generates a modern art-style presentation design for spicy food, based on red and yellow tones.

[0782] 5. Providing design elements

[0783] The generated design elements are stored in a temporary database, and access URLs for each design element are generated. The server sends a response containing these URLs in JSON format to the terminal. The user terminal parses the received JSON response and displays a preview of the generated design elements to the user.

[0784] 6. Specific Examples

[0785] For example, if a user inputs "spicy," "red and yellow," and "modern art," the server analyzes the input and generates a modern art-style presentation design for spicy food based on red and yellow tones. These generated design elements are stored in a database, and users can browse the design themes and select their favorite design.

[0786] This system makes it possible to provide personalized dish designs that reflect the user's preferences and ideas.

[0787] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0788] Step 1:

[0789] The user launches the customization application on the device and inputs information about the desired taste and visual image. The user inputs specific keywords and phrases. For example, the prompt "Please present a spicy dish in red and yellow tones in a modern art style" is used. The input here is text data.

[0790] Step 2:

[0791] When the user types something in and hits the submit button, the device converts the input data into JSON format, which makes it easier to send over the network. The converted JSON data looks like this:

[0792] {

[0793] "taste": "spicy",

[0794] "color_scheme": "red and yellow",

[0795] "presentation": "modern art"

[0796] }

[0797] This JSON data is sent to the server.

[0798] Step 3:

[0799] The server parses the received JSON data. Based on the received data, it uses a natural language processing (NLP) engine (e.g., Google NLP API) to understand the user's preferences and impressions. The result of the parsing is converted into an appropriate data format to be passed to the generative model. For example, the parsed data looks like this:

[0800] {

[0801] "parsed_taste": "spicy",

[0802] "parsed_color_scheme": ["red", "yellow"],

[0803] "parsed_presentation": "modern art"

[0804] }

[0805] This parsed data is passed to a generative AI model.

[0806] Step 4:

[0807] The server passes the analyzed data to a generative AI model. The generative AI model (e.g., OpenAI's DALL-E) generates original design elements based on this data. For example, using the keywords "spicy," "red and yellow," and "modern art," it generates a modern art-style presentation design for spicy food based on red and yellow tones. This generative process generates concrete design data.

[0808] Step 5:

[0809] The generated design elements are stored in a temporary database. A unique access URL is generated for each generated design element. The server sends a response containing these URLs in JSON format to the terminal. Example response from the server:

[0810] {

[0811] "design_url": "https: / / example.com / designs / 12345"

[0812] }

[0813] This allows the terminal to access the generated design elements.

[0814] Step 6:

[0815] The terminal parses the received JSON response and displays a preview of the generated design elements to the user. The user can view the preview, select the design element they like, and download or apply it. Specifically, the generated design elements are displayed on the application interface. The user can also rate the design elements.

[0816] Through the above processing steps, users can easily enjoy personalized food designs that reflect their own preferences and images.

[0817] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0818] This invention relates to a system that allows users to enjoy designing and customizing mobile phones, and generates original design elements by combining user preferences, image data, and an emotion engine that recognizes emotions. The program processing required to implement this invention is described below.

[0819] 1. First, the user launches the customization application on their device. The application displays a user interface (UI) that includes fields for inputting preferences and images. The user enters specific keywords (e.g., "nature landscape," "blue and green tones") and can upload reference images if desired.

[0820] 2. The device is equipped with an emotion engine that analyzes and recognizes user emotions (e.g., joy, sadness, surprise, etc.) in real time based on user input and interface usage. The recognized emotion data is stored together with preference and image data.

[0821] 3. When the user presses the "Send" button, the device converts the preferences, image data, and emotion data into JSON format and generates a send request to the server, using an internet connection.

[0822] 4. The server parses the received JSON data. It extracts data from each field and uses a natural language processing (NLP) engine to understand the user's preferences, impressions, and even emotions. For example, if the emotion "joy" is recognized along with the keyword "nature scene," the design will work to generate a bright and vibrant nature scene.

[0823] 5. The server converts the analysis results into an appropriate data format to be passed to the generative model. This data is input to the generative model.

[0824] 6. The server runs generative AI models to generate design elements. For example, the image generation model generates a high-resolution wallpaper based on "nature scenery," "blue and green," and the user's emotional data of "joy." The icon generation model generates a nature-themed icon set that evokes joy. The sound generation model generates a cheerful nature-inspired ringtone.

[0825] 7. The generated design elements are stored in a temporary database, and access URLs for each design element are generated. The server sends a response containing these URLs in JSON format to the terminal.

[0826] 8. The device parses the received JSON response and extracts the URL and data of each design element.

[0827] 9. The device will update the UI to allow the user to preview the generated design elements. The user can review the previews, select the design elements they like, and download or apply them to their phone.

[0828] For example, if a user inputs "nature scenery" and "blue and green" and the emotion engine recognizes "joy," the server will analyze this and generate a bright and attractive blue sky and forest themed wallpaper, as well as a set of leaves and flowers icons that evoke a natural feeling and express joy. In this way, users can easily enjoy mobile phone designs that reflect their preferences and emotions.

[0829] The above is a detailed description of the embodiment of the present invention, which is a system that allows users to customize the design of their mobile phone to reflect their preferences, images, and even emotions. This system allows users to easily enjoy unique and attractive designs.

[0830] The processing flow will be explained below.

[0831] Step 1:

[0832] The user launches the customization application on their device. The application displays a user interface (UI) that includes fields for inputting preferences and images. The user enters specific keywords (e.g., "nature landscapes," "blue and green tones") and can optionally upload reference images.

[0833] Step 2:

[0834] The device temporarily stores the user's input data and waits for the user to press the "Send" button after completing the input. When the user presses the "Send" button, the device passes the preference and image data to the emotion engine.

[0835] Step 3:

[0836] The device's built-in emotion engine analyzes user emotions in real time based on user input and interface usage. For example, it recognizes emotions by analyzing keywords, phrases, touch gestures, and even the speed and strength of input. The recognized emotion data (e.g., "happiness" or "surprise") is stored together with preference and image data.

[0837] Step 4:

[0838] The device converts the preferences, image data, and emotion data into JSON format and generates a request to send it to the server, using an internet connection.

[0839] Step 5:

[0840] The server parses the received JSON data, extracts data from each field, and uses a natural language processing (NLP) engine to understand the user's preferences, imagery, and even emotions. For example, it analyzes the keywords "nature scenery" and "blue and green," and the emotion "joy."

[0841] Step 6:

[0842] The server converts the analysis results into a data format appropriate for the generative model, and this data is then input into the generative model.

[0843] Step 7:

[0844] The server runs generative AI models to generate design elements. For example, the image generation model generates high-resolution wallpapers based on "natural scenery," "blue and green," and the emotion "joy." The icon generation model creates an icon set that evokes feelings of nature and joy, and the audio generation model creates ringtones based on upbeat natural sounds.

[0845] Step 8:

[0846] The generated design elements are stored in a temporary database. The server generates a URL for each design element and sends a response containing the URL in JSON format to the device.

[0847] Step 9:

[0848] The device parses the received JSON response and extracts the URL and data of each design element.

[0849] Step 10:

[0850] The device will update the UI to allow the user to preview the generated design elements, and the user can review the previews, select the design elements they like, and download or apply them to their mobile phone.

[0851] For example, if a user inputs "nature scenery" and "blue and green" and the emotion engine recognizes "joy," the server will analyze this and generate a bright and attractive blue sky and forest themed wallpaper, an icon set of leaves and flowers that evokes a sense of nature and expresses joy, and a ringtone based on bright natural sounds. In this way, users can easily enjoy a mobile phone design that reflects their preferences and emotions.

[0852] Example 2

[0853] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0854] Conventional customization systems mainly generate designs based on direct user input, making it difficult to reflect the user's emotions or temporary moods. Furthermore, the diversity of generated design elements and the provision of unique designs are limited. This makes it difficult for users to obtain design elements that perfectly match their actual requirements, resulting in low satisfaction.

[0855] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for receiving preferences, image data, and emotion data input by the user, means for analyzing the received preferences, image data, and emotion data and passing the input data to the generative model, and means for providing the user with design elements (wallpaper, icons, sounds, etc.) generated by the generative model. This makes it possible to generate diverse and unique design elements that reflect the user's emotions and preferences in real time.

[0856] "User" refers to an individual who uses the system to customize designs.

[0857] "Preferences and image data" refers to information including keywords specified by the user and reference images uploaded by the user.

[0858] "Emotional data" refers to data that indicates a user's emotional state obtained by analyzing user input and interface usage.

[0859] "Analysis" refers to the process of understanding and interpreting the meaning of received preference, image, and emotional data.

[0860] A "generative model" refers to an algorithm or AI model that automatically generates design elements based on input data.

[0861] "Design elements" refer to decorative items such as wallpaper, icons, and sounds created by generative models.

[0862] "Serving" refers to the process of making the generated design elements available to users.

[0863] "Means" refers to a method or apparatus for performing a particular function or process.

[0864] A "system" refers to a set of hardware and software that generates and provides design elements that reflect the user's preferences and emotions.

[0865] "Preview" means a display feature that allows a user to visually preview generated design elements before downloading or applying them.

[0866] This invention relates to a system that allows users to enjoy designing and customizing mobile phones, and generates original design elements by combining the user's preferences, image data, and an emotion engine that recognizes emotions. The processing of the program required to implement this invention will be described.

[0867] Program Overview

[0868] A user launches the customization application using their mobile device. The application displays a user interface (UI) and provides fields for the user to input preferences and images. In this section, the user can enter specific keywords (e.g., "nature landscape," "blue and green tones") and upload reference images, if desired.

[0869] The device's built-in emotion engine analyzes and recognizes the user's emotions (e.g., joy, sadness, surprise, etc.) in real time based on the user's input and interface usage, and the recognized emotion data is stored together with preference and image data.

[0870] When the user presses the "Send" button, the device converts the preferences, image data, and emotion data into JSON format and generates a send request to the server. This transmission is performed over an internet connection, and the server parses the received JSON data. On the server side, a natural language processing (NLP) engine is used to extract the data from each field and understand the user's preferences, images, and emotions. For example, if the keyword "nature scene" and the emotion "joy" are recognized, the design will work to generate a bright and attractive nature scene.

[0871] The server converts the analysis results into the appropriate data format to be passed to the generative model. This data is input into the generative AI model. The server runs the generative AI model to generate design elements. For example, the image generation model generates a high-resolution wallpaper based on "nature scenery," "blue and green," and "joy," the icon generation model generates a joyful icon set with a nature theme, and the sound generation model generates an upbeat nature-inspired ringtone.

[0872] The generated design elements are stored in a temporary database, and an access URL for each design element is generated. The server sends a response containing these URLs in JSON format to the device. The device parses the received JSON response and extracts the URL and data for each design element. The device then updates the UI so that the user can preview the generated design elements. The user can check the displayed previews, select the design element they like, and download or apply it to their mobile phone.

[0873] Specific examples

[0874] For example, consider a case where a user inputs "nature scenery" and "blue and green," and the emotion engine recognizes "joy." This data is converted into JSON format and sent to the server. The server analyzes it and provides data for the image generation model and icon generation model to operate. The server can then generate a bright and attractive wallpaper with a blue sky and forest theme, or an icon set of leaves and flowers that evokes a natural feeling and expresses joy. This allows users to easily enjoy mobile phone designs that reflect their preferences and emotions.

[0875] Prompt Sentence Examples

[0876] "Natural Landscapes, Blue and Green, Joy"

[0877] This invention makes it possible to generate personalized designs that take into account the user's emotions, something that was not possible with conventional customization systems.

[0878] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0879] Program processing flow

[0880] Step 1:

[0881] A user launches a customization application using their device. The application displays a user interface (UI) and provides fields for the user to input preferences and images. This can be presented as text fields or image upload fields, where the user can enter specific keywords (e.g., "nature landscapes," "blue and green tones") or upload a reference image.

[0882] Input: Keywords entered by users and uploaded image data.

[0883] Output: Input preferences and image data.

[0884] Step 2:

[0885] The device's built-in emotion engine monitors user input and interface usage in real time to analyze and recognize emotions, often using facial recognition cameras and sensors to recognize emotions such as happiness, sadness, and surprise.

[0886] Input: User facial expressions and operation logs.

[0887] Output: Parsed emotion data.

[0888] Step 3:

[0889] When the user presses the "Send" button, the device converts the entered preferences, image data, and emotion data into JSON format, which is then generated as a send request to the server and sent over the internet connection.

[0890] Input: User preferences, image data, and emotional data.

[0891] Output: JSON formatted data.

[0892] Step 4:

[0893] The server parses the received JSON data. A natural language processing (NLP) engine is used to process and understand the data in each field. For example, if the keyword "nature scene" and the emotion "joy" are recognized, the design will be steered to generate a bright and attractive nature scene.

[0894] Input: JSON data.

[0895] Output: Parsed keywords and sentiment data.

[0896] Step 5:

[0897] The server converts the analysis results into an appropriate data format to be passed to the generative model, where a prompt sentence is formed to be input to the generative AI model.

[0898] Input: Analysis result data.

[0899] Output: A prompt sentence for the generative AI model.

[0900] Step 6:

[0901] The server runs generative AI models to generate design elements: an image generation model might produce a high-resolution wallpaper, an icon generation model might produce a joyful icon set with a nature theme, and a sound generation model might generate a cheerful ringtone.

[0902] Input: A prompt sentence for the generative AI model.

[0903] Output: Generated design elements (wallpapers, icons, sounds, etc.).

[0904] Step 7:

[0905] The generated design elements are stored in a temporary database, and access URLs for each design element are generated. The server sends a response containing these URLs in JSON format to the terminal.

[0906] Input: Generated design elements.

[0907] Output: A JSON response containing the access URL.

[0908] Step 8:

[0909] The device parses the received JSON response and extracts the URL and data of each design element, allowing access to the generated design elements.

[0910] Input: JSON response.

[0911] Output: URLs and data of design elements.

[0912] Step 9:

[0913] The device updates the user interface to allow the user to preview the generated design elements, and the user reviews the previews, selects the design element that suits their preference, and downloads or applies it to their mobile phone.

[0914] Input: URL or data of the design element.

[0915] Output: Updated UI and preview screen.

[0916] Through these processing steps, personalized design elements that reflect the user's emotions and preferences are generated and presented.

[0917] (Application example 2)

[0918] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0919] Conventional methods for customizing store displays and decorations have difficulty in instantly generating specific design proposals that reflect a user's preferences, images, and even emotions. In particular, it has been difficult for users to instantly obtain appropriate display proposals based on emotion recognition using specific keywords and reference images. Therefore, there is a need for a method to efficiently and effectively customize in-store decorations.

[0920] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[0921] In this invention, the server includes means for receiving preferences and image data input by a user, means for analyzing the received preferences and image data and passing the input data to a generative model, means for providing the user with design elements (wallpaper, icons, sounds, etc.) generated by the generative model, means for collecting and analyzing user emotion data using emotion recognition means, means for reflecting the analyzed emotion data in design generation, and means for previewing the generated design elements for customizing store decorations and displays. This makes it possible to easily customize store decorations and displays in accordance with the user's preferences and emotions.

[0922] "User" means any individual or entity that uses the System to create or customize Design Elements.

[0923] "Preferences and image data" refers to design-related keywords, themes, reference images, etc. specified by the user.

[0924] A "generative model" is a machine learning model that generates design elements based on user preferences, image data, and emotional data.

[0925] "Design elements" are visual and auditory elements such as wallpaper, icons, and sounds generated by generative models.

[0926] "Emotion recognition means" is a technology for collecting and analyzing emotional data from user input and reference images.

[0927] "Preview" is a function that displays the generated design elements in advance, allowing the user to check their contents.

[0928] A "display plan" is a specific design plan used for store decoration and display.

[0929] This invention relates to a system for customizing store decorations and displays based on user preferences, images, and emotions. The system involves the cooperation of a smartphone, an emotion recognition engine, a generative AI model, and a server.

[0930] First, a store employee launches the application on their smartphone. The application displays a user interface with fields for inputting the store's theme and decorative image. For example, they can enter keywords such as "spring flowers" or "warm atmosphere" and upload images they would like to use as inspiration.

[0931] The smartphone is equipped with an emotion recognition engine (e.g., Microsoft Azure's Face API) that analyzes the staff member's emotions (e.g., joy, excitement, relaxation, etc.) in real time from the input content and reference images, thereby collecting emotional data along with preference and image data.

[0932] When a staff member presses the "send" button, the device converts preferences, image data, and emotion data into JSON format and sends it over the internet to a server. The server then analyzes the received JSON data and uses Google's Natural Language API to understand the keywords and emotions entered. It then uses a generative AI model such as OpenAI's DALL-E to generate display ideas that fit the store's theme.

[0933] The generated display proposals are temporarily stored on the server, and an access URL for each proposal is generated. A response containing these URLs is sent in JSON format to the smartphone. The smartphone analyzes the received JSON response and displays a preview of the generated display proposals. Staff can then check these previews, select the display proposal that best suits their needs, and apply it to the actual exhibition.

[0934] As a concrete example, the following prompt sentences can be fed into a generative AI model:

[0935] Example prompts: "Spring flowers," "Warm atmosphere," "Relaxing," "Image: sakura_field.jpg"

[0936] Result: "A display concept themed around a spring flower field. Bright pinks and greens dominate, with relaxing, soft lighting."

[0937] As described above, by using this system, it is possible to easily customize store decorations and displays to suit the user's preferences and emotions.

[0938] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0939] Step 1:

[0940] Users enter the store's theme and decorative image and upload reference images.

[0941] Input: Keywords (e.g., "spring flowers" or "warm atmosphere") and reference images

[0942] Output: Keywords and image data

[0943] Specific operations: Launch the smartphone application, enter keywords in the fields displayed in the user interface, and upload reference images.

[0944] Step 2:

[0945] An emotion recognition means analyzes emotions from the user's input and the reference image.

[0946] Input: Keywords, reference images

[0947] Output: Emotion data (e.g., joy, excitement, relaxation)

[0948] How it works: The smartphone's built-in emotion recognition engine (for example, Microsoft Azure's Face API) analyzes keywords and reference images to recognize emotions in real time.

[0949] Step 3:

[0950] The device converts preference, image data, and emotion data into JSON format and sends it to the server.

[0951] Input: Keywords, reference images, emotion data

[0952] Output: JSON format data

[0953] Specific operation: The smartphone compiles the input keywords, reference images, and analyzed emotion data into a single JSON data set and sends it to a server via the Internet.

[0954] Step 4:

[0955] The server analyzes the received JSON data and understands keywords and sentiment data.

[0956] Input: JSON format data

[0957] Output: Parsed keywords and sentiment data

[0958] Specific operation: The server uses Google's Natural Language API to extract and analyze keywords and sentiment data from the JSON data.

[0959] Step 5:

[0960] The server inputs the analysis data into the generated AI model and generates a display proposal.

[0961] Input: Analyzed keywords, sentiment data

[0962] Output: Generated display plan and its access URL

[0963] How it works: The server inputs the analyzed keywords and emotion data into a generative AI model such as OpenAI's DALL-E, and generates display ideas that fit the store's theme. The generated display ideas are temporarily saved and an access URL is generated.

[0964] Step 6:

[0965] The server sends the URL of the generated display plan in JSON format to the terminal.

[0966] Input: Generated display plan and its access URL

[0967] Output: Response data in JSON format

[0968] Specific operation: The server creates a JSON format response including the access URL for the generated display proposal and sends it to the smartphone.

[0969] Step 7:

[0970] The device analyzes the received JSON response and displays a preview of the proposed display.

[0971] Input: Response data in JSON format

[0972] Output: Preview of the display

[0973] Specific operation: The smartphone parses the JSON response and displays the generated display proposals on the preview screen. The user checks the preview and selects a display proposal.

[0974] As a concrete example, the following prompt sentence is input into the generative AI model, and a display proposal is generated:

[0975] Example prompts: "Spring flowers," "Warm atmosphere," "Relaxing," "Image: sakura_field.jpg"

[0976] As a result, the generated display proposal is "A display concept based on a spring flower field theme, with bright pink and green as the main colors and soft, relaxing lighting."

[0977] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0978] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0979] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[0980] [Fourth embodiment]

[0981] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0982] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0983] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0984] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0985] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0986] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0987] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0988] The control object 443 includes a display device, LEDs in the eyes, and motors for driving the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0989] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0990] The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0991] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0992] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0993] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[0994] This invention relates to a system that allows users to enjoy designing and customizing mobile phones, and generates original design elements using a generative AI model based on preferences and image data entered by the user. The program processing required to implement this invention is described below.

[0995] 1. First, the user launches the customization application on their device. The application displays an interface, and the user inputs information about their preferred keywords and images (e.g., natural landscapes, blue and green tones). This input data is temporarily stored on the device.

[0996] 2. When the user presses the "Send" button, the device converts this input data into JSON format and sends it to the server, using an internet connection.

[0997] 3. The server analyzes the received JSON data to understand the user's preferences and impressions. Specifically, it uses a natural language processing (NLP) engine to analyze the meaning of the input and converts it into the appropriate data format to be passed to the generative model.

[0998] 4. The server passes the analyzed data to a generative AI model, which then generates original design elements (wallpapers, icons, ringtones, etc.) based on the data. For example, the keywords "natural scenery" and "blue and green" could generate a wallpaper depicting a blue sky and green forest, as well as a set of nature-themed leaf icons.

[0999] 5. The generated design elements are stored in a temporary database, and access URLs for each design element are generated. The server sends a response containing these URLs in JSON format to the terminal.

[1000] 6. The device parses the received JSON response and displays a preview of the generated design elements to the user, allowing the user to select the design elements they like and download or apply them.

[1001] For example, if a user inputs "nature scenery" and "blue and green," the server will analyze this and generate wallpaper with a blue sky and forest theme, as well as an icon set of leaves and flowers that evoke nature. This allows users to easily enjoy a mobile phone design that reflects their preferences and individuality.

[1002] The above is a detailed description of the embodiment of the present invention, which is a system that can easily reflect the user's preferences and ideas in the design of a mobile phone. This system makes it easy to enjoy unique and attractive designs.

[1003] The processing flow will be explained below.

[1004] Step 1:

[1005] A user launches a customization application on their device. The application displays a user interface (UI) containing input fields where the user can enter keywords that reflect their preferences or image (e.g., "nature landscapes" or "blue and green tones") and optionally upload reference images.

[1006] Step 2:

[1007] The terminal temporarily stores the data entered by the user and waits for the user to press the "Send" button after completing the input. When the user presses the "Send" button, the terminal converts the input data into JSON format and generates a send request to the server.

[1008] Step 3:

[1009] The device sends the generated request and sends JSON data including the user's input data to the server, using an internet connection.

[1010] Step 4:

[1011] The server parses the received JSON data. It extracts data from each field and uses a natural language processing (NLP) engine to understand the user's preferences and impressions. Specifically, it analyzes keywords such as "natural scenery" and "blue and green" and extracts their meanings.

[1012] Step 5:

[1013] The server converts the analysis results into an appropriate data format to be passed to the generative model. This data is then input to the generative model.

[1014] Step 6:

[1015] The server runs generative AI models to generate design elements: for example, an image generation model generates high-resolution wallpapers based on "nature landscapes" and "blue and green" data, an icon generation model generates a nature-themed icon set, and an audio generation model generates ringtones based on nature sounds.

[1016] Step 7:

[1017] The server stores the generated design elements in a temporary database and generates an access URL for each design element (wallpaper, icon set, ringtone), which allows the generated design element to be identified.

[1018] Step 8:

[1019] The server creates a JSON response containing the access URL for the generated design element and sends this response to the terminal.

[1020] Step 9:

[1021] The device parses the received JSON response and extracts the URL and data of each design element.

[1022] Step 10:

[1023] The device will update the UI to allow the user to preview the generated design elements, and the user can review the previews, select the design elements they like, and download or apply them to their mobile phone.

[1024] Example 1

[1025] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1026] Previously, when users customized the design of their mobile phones, they had no choice but to choose from existing templates or limited options, making it difficult to reflect the individual user's preferences and image. Furthermore, there was a lack of a way to easily generate different design elements (wallpapers, icons, sounds, etc.) under a unified theme. Another problem was the lack of a system that allowed users to preview the generated design elements before selecting them.

[1027] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1028] In this invention, the server includes means for converting received preference and image data into JSON format and sending it to the server, means for the server to analyze the received JSON data and convert it into a data format suitable for the generative AI model, and means for storing design elements (wallpaper, icons, sounds, etc.) generated by the generative AI model in a database and generating an access URL. This makes it possible to easily generate original design elements that reflect the user's preferences and images with a unified theme, and to preview and select the generated design elements.

[1029] "User" refers to a person who uses the system to design and customize a mobile phone.

[1030] "Preference and image data" refers to information such as requests, keywords, and images regarding individual designs that users input into the system.

[1031] "JSON format" is an abbreviation for JavaScript Object Notation, and is a lightweight data exchange format for expressing data in text format.

[1032] "Server" refers to a computer system that receives, analyzes, and processes data sent from a user's terminal.

[1033] A "generative AI model" refers to an algorithm or program that uses artificial intelligence technology to generate design elements (wallpaper, icons, sounds, etc.) from data entered by the user.

[1034] "Data format" refers to the rules and conventions that define the structure and format in which data is represented.

[1035] "Design Elements" means individual design components used to customize a mobile phone, such as system-generated wallpaper, icons, sounds, etc.

[1036] "Database" refers to a management system for storing generated design elements, a data repository for easy access and retrieval.

[1037] "Access URL" refers to the unique resource locator required for a user to access a generated design element.

[1038] "Preview" refers to the function of displaying generated design elements so that the user can check them before selecting them.

[1039] This invention relates to a system that allows users to enjoy designing and customizing mobile phones. The purpose is to generate original design elements using a generative AI model based on user input preferences and image data. The program processing required to implement this invention is described in detail below.

[1040] The user first launches the customization application using the device. This application displays a user interface, allowing the user to input information about keywords and images they like. For example, the user may input keywords such as "nature landscape, blue and green." This input data is temporarily stored on the device.

[1041] Next, when the user presses the "Send" button, the device converts this input data into JSON format. The converted JSON data is sent over the internet to a server. The server parses the received JSON data and uses a natural language processing (NLP) engine to understand the meaning of the input data. This analysis converts it into the appropriate data format to be passed to the generative AI model.

[1042] Once the analysis is complete, the server passes the data to a generative AI model, which then generates original design elements (wallpaper, icons, audio, etc.) based on the received data. Examples of generative AI models used include OpenAI's GPT-3 and DALL-E. For example, the keywords "natural scenery" and "blue and green" generate a wallpaper depicting a blue sky and green forest, as well as a set of nature-themed leaf icons.

[1043] The generated design elements are stored in a temporary database, and an access URL for each design element is generated. The server sends a response containing these URLs in JSON format to the device. The device parses the received JSON response and displays a preview of the generated design elements to the user. The user can view the preview, select the design element they like, and download or apply it.

[1044] For example, if a user inputs "nature scenery" and "blue and green," the server analyzes this and passes it to a generative AI model. The generative AI model generates a wallpaper with a blue sky and forest theme, or an icon set of leaves and flowers that evokes nature. The device previews the generated designs, and the user can select the design they like and download or apply it.

[1045] An example prompt might be, "Use a generative AI model to create design elements with blue skies and forests, based on a natural landscape theme. Please use a blue and green color scheme."

[1046] This system allows users to easily enjoy designing their mobile phones to reflect their own tastes and personalities.

[1047] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1048] Step 1:

[1049] User Input Processing

[1050] The user launches the customization application using the device. The application displays a user interface, and the user inputs information about their preferred keywords and images. For example, they input "nature landscape, blue and green." This input data is temporarily stored on the device.

[1051] Input: Data entered into the user interface (e.g., nature scenes, blue and green)

[1052] Output: Input data temporarily saved on the device

[1053] Step 2:

[1054] Converting data to JSON and sending it

[1055] When the user presses the "Send" button, the device converts the saved input data into JSON format, for example, {"keywords": ["nature landscape", "blue and green"]}. The device then sends the converted JSON data to the server via the Internet.

[1056] Input: Temporarily saved input data

[1057] Output: JSON data sent to the server

[1058] Step 3:

[1059] Parsing JSON Data

[1060] The server parses the received JSON data. Using a natural language processing (NLP) engine, the server analyzes the meaning of the input data and converts it into an appropriate data format (e.g., a prompt) to be passed to the generative AI model. For example, the input data "Nature landscape, blue and green" is converted into the prompt "Design with a nature landscape theme. Color scheme: blue and green."

[1061] Input: Received JSON data

[1062] Output: Prompt sentence to be passed to the generative AI model

[1063] Step 4:

[1064] Passing data to generative AI models

[1065] The server passes the prompt to the generative AI model, which then generates original design elements (wallpaper, icons, audio, etc.) based on the prompt. For example, a prompt like "A design themed around natural landscapes. The color scheme is blue and green" generates a wallpaper depicting a blue sky and green forest, as well as a set of leafy icons.

[1066] Input: prompt statement

[1067] Output: Generated design elements

[1068] Step 5:

[1069] Save design elements and generate URLs

[1070] The server stores the generated design elements (e.g., wallpaper, icon set) in a temporary database. Access URLs for each design element are generated, and a response containing these URLs is returned in JSON format. For example, {"wallpaper": "https: / / example.com / wallpaper1", "icons": "https: / / example.com / icons1"} is generated.

[1071] Input: Generated design elements

[1072] Output: Design elements stored in the database and a generated access URL

[1073] Step 6:

[1074] Response analysis and preview display

[1075] The device analyzes the JSON response received from the server. Based on the access URL of the analyzed design element, a preview of the generated design element is displayed on the user interface. The user can view the preview, select the design element they like, and download or apply it. For example, the user can download wallpaper and set it as the home screen.

[1076] Input: JSON response received from the server

[1077] Output: A preview of the design elements displayed in the user interface

[1078] (Application example 1)

[1079] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1080] Conventional food delivery systems have difficulty providing food designs that reflect the user's taste and appearance preferences, making it difficult to increase user satisfaction. In addition, there are limited ways for users to customize the taste and appearance of food to their own preferences, creating a need for a more personalized experience.

[1081] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1082] In this invention, the server includes a means for receiving preferences and image data input by the user, a means for analyzing the received preferences and image data and passing the input data to the generative model, and a means for providing the user with design elements (wallpaper, icons, audio, presentation designs, etc.) generated by the generative model, thereby enabling the design of dishes that reflect the user's taste and appearance preferences.

[1083] "User-entered preference and image data" refers to information that a user inputs into an application to express their taste preferences and visual image.

[1084] The "means of receiving" is the mechanism by which the application receives preferences and image data input by the user.

[1085] The "means of analyzing and passing input data to the generative model" refers to a mechanism that analyzes the received data and sends the analysis results to the generative AI model as input data.

[1086] A "generative model" is an artificial intelligence system that generates original design elements (e.g., wallpaper, icons, sounds, presentation designs, etc.) based on analyzed data.

[1087] "Generated design elements" are specific elements of a design created by a generative AI model based on a user's preferences and imagination.

[1088] "Means for providing to users" refers to the mechanism by which the generated design elements are shown to users or provided in a usable form.

[1089] "Wallpaper, icons, sounds, presentation designs, etc." refers to visual and auditory design elements customized based on user preferences and used in smartphone and food delivery presentations.

[1090] The "means for displaying a preview to the user" is a mechanism for displaying the generated design elements in a form that allows the user to visually confirm them.

[1091] This invention is a system that uses a generative AI model to generate original design elements (wallpaper, icons, sounds, presentation designs, etc.) based on preferences and image data entered by the user. Specific examples of this system are described below.

[1092] 1. Overall system configuration

[1093] This system consists of a user device, a server, a generative AI model, and a database. The user device can be a smartphone, tablet, or computer, while the server is often installed on a cloud service. The generative AI model used is, for example, OpenAI's DALL-E.

[1094] 2. User Interface

[1095] The user launches the customization application using a terminal and enters information about their preferred flavors and visual image, for example, the following prompt:

[1096] "Prepare spicy dishes in a modern art style using red and yellow hues."

[1097] When the user presses the "Send" button, the device converts the input data into JSON format and sends it to the server, using an internet connection.

[1098] 3. Server Processing

[1099] The server parses the received JSON data to understand the user's preferences and impressions. Specifically, it uses a natural language processing (NLP) engine to analyze the meaning of the input and converts it into the appropriate data format to be passed to the generative AI model (for example, using the Google NLP API).

[1100] 4. Design generation using generative AI models

[1101] The server passes the analyzed data to a generative AI model, which then generates original design elements based on the received data. For example, using the keywords "spicy," "red and yellow," and "modern art," it generates a modern art-style presentation design for spicy food, based on red and yellow tones.

[1102] 5. Providing design elements

[1103] The generated design elements are stored in a temporary database, and access URLs for each design element are generated. The server sends a response containing these URLs in JSON format to the terminal. The user terminal parses the received JSON response and displays a preview of the generated design elements to the user.

[1104] 6. Specific Examples

[1105] For example, if a user inputs "spicy," "red and yellow," and "modern art," the server analyzes the input and generates a modern art-style presentation design for spicy food based on red and yellow tones. These generated design elements are stored in a database, and users can browse the design themes and select their favorite design.

[1106] This system makes it possible to provide personalized dish designs that reflect the user's preferences and ideas.

[1107] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1108] Step 1:

[1109] The user launches the customization application on the device and inputs information about the desired taste and visual image. The user inputs specific keywords and phrases. For example, the prompt "Please present a spicy dish in red and yellow tones in a modern art style" is used. The input here is text data.

[1110] Step 2:

[1111] When the user types something in and hits the submit button, the device converts the input data into JSON format, which makes it easier to send over the network. The converted JSON data looks like this:

[1112] {

[1113] "taste": "spicy",

[1114] "color_scheme": "red and yellow",

[1115] "presentation": "modern art"

[1116] }

[1117] This JSON data is sent to the server.

[1118] Step 3:

[1119] The server parses the received JSON data. Based on the received data, it uses a natural language processing (NLP) engine (e.g., Google NLP API) to understand the user's preferences and impressions. The result of the parsing is converted into an appropriate data format to be passed to the generative model. For example, the parsed data looks like this:

[1120] {

[1121] "parsed_taste": "spicy",

[1122] "parsed_color_scheme": ["red", "yellow"],

[1123] "parsed_presentation": "modern art"

[1124] }

[1125] This parsed data is passed to a generative AI model.

[1126] Step 4:

[1127] The server passes the analyzed data to a generative AI model. The generative AI model (e.g., OpenAI's DALL-E) generates original design elements based on this data. For example, using the keywords "spicy," "red and yellow," and "modern art," it generates a modern art-style presentation design for spicy food based on red and yellow tones. This generative process generates concrete design data.

[1128] Step 5:

[1129] The generated design elements are stored in a temporary database. A unique access URL is generated for each generated design element. The server sends a response containing these URLs in JSON format to the terminal. Example response from the server:

[1130] {

[1131] "design_url": "https: / / example.com / designs / 12345"

[1132] }

[1133] This allows the terminal to access the generated design elements.

[1134] Step 6:

[1135] The terminal parses the received JSON response and displays a preview of the generated design elements to the user. The user can view the preview, select the design element they like, and download or apply it. Specifically, the generated design elements are displayed on the application interface. The user can also rate the design elements.

[1136] Through the above processing steps, users can easily enjoy personalized food designs that reflect their own preferences and images.

[1137] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1138] This invention relates to a system that allows users to enjoy designing and customizing mobile phones, and generates original design elements by combining user preferences, image data, and an emotion engine that recognizes emotions. The program processing required to implement this invention is described below.

[1139] 1. First, the user launches the customization application on their device. The application displays a user interface (UI) that includes fields for inputting preferences and images. The user enters specific keywords (e.g., "nature landscape," "blue and green tones") and can upload reference images if desired.

[1140] 2. The device is equipped with an emotion engine that analyzes and recognizes user emotions (e.g., joy, sadness, surprise, etc.) in real time based on user input and interface usage. The recognized emotion data is stored together with preference and image data.

[1141] 3. When the user presses the "Send" button, the device converts the preferences, image data, and emotion data into JSON format and generates a send request to the server, using an internet connection.

[1142] 4. The server parses the received JSON data. It extracts data from each field and uses a natural language processing (NLP) engine to understand the user's preferences, impressions, and even emotions. For example, if the emotion "joy" is recognized along with the keyword "nature scene," the design will work to generate a bright and vibrant nature scene.

[1143] 5. The server converts the analysis results into an appropriate data format to be passed to the generative model. This data is input to the generative model.

[1144] 6. The server runs generative AI models to generate design elements. For example, the image generation model generates a high-resolution wallpaper based on "nature scenery," "blue and green," and the user's emotional data of "joy." The icon generation model generates a nature-themed icon set that evokes joy. The sound generation model generates a cheerful nature-inspired ringtone.

[1145] 7. The generated design elements are stored in a temporary database, and access URLs for each design element are generated. The server sends a response containing these URLs in JSON format to the terminal.

[1146] 8. The device parses the received JSON response and extracts the URL and data of each design element.

[1147] 9. The device will update the UI to allow the user to preview the generated design elements. The user can review the previews, select the design elements they like, and download or apply them to their phone.

[1148] For example, if a user inputs "nature scenery" and "blue and green" and the emotion engine recognizes "joy," the server will analyze this and generate a bright and attractive blue sky and forest themed wallpaper, as well as a set of leaves and flowers icons that evoke a natural feeling and express joy. In this way, users can easily enjoy mobile phone designs that reflect their preferences and emotions.

[1149] The above is a detailed description of the embodiment of the present invention, which is a system that allows users to customize the design of their mobile phone to reflect their preferences, images, and even emotions. This system allows users to easily enjoy unique and attractive designs.

[1150] The processing flow will be explained below.

[1151] Step 1:

[1152] The user launches the customization application on their device. The application displays a user interface (UI) that includes fields for inputting preferences and images. The user enters specific keywords (e.g., "nature landscapes," "blue and green tones") and can optionally upload reference images.

[1153] Step 2:

[1154] The device temporarily stores the user's input data and waits for the user to press the "Send" button after completing the input. When the user presses the "Send" button, the device passes the preference and image data to the emotion engine.

[1155] Step 3:

[1156] The device's built-in emotion engine analyzes user emotions in real time based on user input and interface usage. For example, it recognizes emotions by analyzing keywords, phrases, touch gestures, and even the speed and strength of input. The recognized emotion data (e.g., "happiness" or "surprise") is stored together with preference and image data.

[1157] Step 4:

[1158] The device converts the preferences, image data, and emotion data into JSON format and generates a request to send it to the server, using an internet connection.

[1159] Step 5:

[1160] The server parses the received JSON data, extracts data from each field, and uses a natural language processing (NLP) engine to understand the user's preferences, imagery, and even emotions. For example, it analyzes the keywords "nature scenery" and "blue and green," and the emotion "joy."

[1161] Step 6:

[1162] The server converts the analysis results into a data format appropriate for the generative model, and this data is then input into the generative model.

[1163] Step 7:

[1164] The server runs generative AI models to generate design elements. For example, the image generation model generates high-resolution wallpapers based on "natural scenery," "blue and green," and the emotion "joy." The icon generation model creates an icon set that evokes feelings of nature and joy, and the audio generation model creates ringtones based on upbeat natural sounds.

[1165] Step 8:

[1166] The generated design elements are stored in a temporary database. The server generates a URL for each design element and sends a response containing the URL in JSON format to the device.

[1167] Step 9:

[1168] The device parses the received JSON response and extracts the URL and data of each design element.

[1169] Step 10:

[1170] The device will update the UI to allow the user to preview the generated design elements, and the user can review the previews, select the design elements they like, and download or apply them to their mobile phone.

[1171] For example, if a user inputs "nature scenery" and "blue and green" and the emotion engine recognizes "joy," the server will analyze this and generate a bright and attractive blue sky and forest themed wallpaper, an icon set of leaves and flowers that evokes a sense of nature and expresses joy, and a ringtone based on bright natural sounds. In this way, users can easily enjoy a mobile phone design that reflects their preferences and emotions.

[1172] Example 2

[1173] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1174] Conventional customization systems mainly generate designs based on direct user input, making it difficult to reflect the user's emotions or temporary moods. Furthermore, the diversity of generated design elements and the provision of unique designs are limited. This makes it difficult for users to obtain design elements that perfectly match their actual requirements, resulting in low satisfaction.

[1175] The identification process by the identification processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means. In this invention, the server includes means for receiving preferences, image data, and emotion data input by the user, means for analyzing the received preferences, image data, and emotion data and passing the input data to the generative model, and means for providing the user with design elements (wallpaper, icons, sounds, etc.) generated by the generative model. This makes it possible to generate diverse and unique design elements that reflect the user's emotions and preferences in real time.

[1176] "User" refers to an individual who uses the system to customize designs.

[1177] "Preferences and image data" refers to information including keywords specified by the user and reference images uploaded by the user.

[1178] "Emotional data" refers to data that indicates a user's emotional state obtained by analyzing user input and interface usage.

[1179] "Analysis" refers to the process of understanding and interpreting the meaning of received preference, image, and emotional data.

[1180] A "generative model" refers to an algorithm or AI model that automatically generates design elements based on input data.

[1181] "Design elements" refer to decorative items such as wallpaper, icons, and sounds created by generative models.

[1182] "Serving" refers to the process of making the generated design elements available to users.

[1183] "Means" refers to a method or apparatus for performing a particular function or process.

[1184] A "system" refers to a set of hardware and software that generates and provides design elements that reflect the user's preferences and emotions.

[1185] "Preview" means a display feature that allows a user to visually preview generated design elements before downloading or applying them.

[1186] This invention relates to a system that allows users to enjoy designing and customizing mobile phones, and generates original design elements by combining the user's preferences, image data, and an emotion engine that recognizes emotions. The processing of the program required to implement this invention will be described.

[1187] Program Overview

[1188] A user launches the customization application using their mobile device. The application displays a user interface (UI) and provides fields for the user to input preferences and images. In this section, the user can enter specific keywords (e.g., "nature landscape," "blue and green tones") and upload reference images, if desired.

[1189] The device's built-in emotion engine analyzes and recognizes the user's emotions (e.g., joy, sadness, surprise, etc.) in real time based on the user's input and interface usage, and the recognized emotion data is stored together with preference and image data.

[1190] When the user presses the "Send" button, the device converts the preferences, image data, and emotion data into JSON format and generates a send request to the server. This transmission is performed over an internet connection, and the server parses the received JSON data. On the server side, a natural language processing (NLP) engine is used to extract the data from each field and understand the user's preferences, images, and emotions. For example, if the keyword "nature scene" and the emotion "joy" are recognized, the design will work to generate a bright and attractive nature scene.

[1191] The server converts the analysis results into the appropriate data format to be passed to the generative model. This data is input into the generative AI model. The server runs the generative AI model to generate design elements. For example, the image generation model generates a high-resolution wallpaper based on "nature scenery," "blue and green," and "joy," the icon generation model generates a joyful icon set with a nature theme, and the sound generation model generates an upbeat nature-inspired ringtone.

[1192] The generated design elements are stored in a temporary database, and an access URL for each design element is generated. The server sends a response containing these URLs in JSON format to the device. The device parses the received JSON response and extracts the URL and data for each design element. The device then updates the UI so that the user can preview the generated design elements. The user can check the displayed previews, select the design element they like, and download or apply it to their mobile phone.

[1193] Specific examples

[1194] For example, consider a case where a user inputs "nature scenery" and "blue and green," and the emotion engine recognizes "joy." This data is converted into JSON format and sent to the server. The server analyzes it and provides data for the image generation model and icon generation model to operate. The server can then generate a bright and attractive wallpaper with a blue sky and forest theme, or an icon set of leaves and flowers that evokes a natural feeling and expresses joy. This allows users to easily enjoy mobile phone designs that reflect their preferences and emotions.

[1195] Prompt Sentence Examples

[1196] "Natural Landscapes, Blue and Green, Joy"

[1197] This invention makes it possible to generate personalized designs that take into account the user's emotions, something that was not possible with conventional customization systems.

[1198] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1199] Program processing flow

[1200] Step 1:

[1201] A user launches a customization application using their device. The application displays a user interface (UI) and provides fields for the user to input preferences and images. This can be presented as text fields or image upload fields, where the user can enter specific keywords (e.g., "nature landscapes," "blue and green tones") or upload a reference image.

[1202] Input: Keywords entered by users and uploaded image data.

[1203] Output: Input preferences and image data.

[1204] Step 2:

[1205] The device's built-in emotion engine monitors user input and interface usage in real time to analyze and recognize emotions, often using facial recognition cameras and sensors to recognize emotions such as happiness, sadness, and surprise.

[1206] Input: User facial expressions and operation logs.

[1207] Output: Parsed emotion data.

[1208] Step 3:

[1209] When the user presses the "Send" button, the device converts the entered preferences, image data, and emotion data into JSON format, which is then generated as a send request to the server and sent over the internet connection.

[1210] Input: User preferences, image data, and emotional data.

[1211] Output: JSON formatted data.

[1212] Step 4:

[1213] The server parses the received JSON data. A natural language processing (NLP) engine is used to process and understand the data in each field. For example, if the keyword "nature scene" and the emotion "joy" are recognized, the design will be steered to generate a bright and attractive nature scene.

[1214] Input: JSON data.

[1215] Output: Parsed keywords and sentiment data.

[1216] Step 5:

[1217] The server converts the analysis results into an appropriate data format to be passed to the generative model, where a prompt sentence is formed to be input to the generative AI model.

[1218] Input: Analysis result data.

[1219] Output: A prompt sentence for the generative AI model.

[1220] Step 6:

[1221] The server runs generative AI models to generate design elements: an image generation model might produce a high-resolution wallpaper, an icon generation model might produce a joyful icon set with a nature theme, and a sound generation model might generate a cheerful ringtone.

[1222] Input: A prompt sentence for the generative AI model.

[1223] Output: Generated design elements (wallpapers, icons, sounds, etc.).

[1224] Step 7:

[1225] The generated design elements are stored in a temporary database, and access URLs for each design element are generated. The server sends a response containing these URLs in JSON format to the terminal.

[1226] Input: Generated design elements.

[1227] Output: A JSON response containing the access URL.

[1228] Step 8:

[1229] The device parses the received JSON response and extracts the URL and data of each design element, allowing access to the generated design elements.

[1230] Input: JSON response.

[1231] Output: URLs and data of design elements.

[1232] Step 9:

[1233] The device updates the user interface to allow the user to preview the generated design elements, and the user reviews the previews, selects the design element that suits their preference, and downloads or applies it to their mobile phone.

[1234] Input: URL or data of the design element.

[1235] Output: Updated UI and preview screen.

[1236] Through these processing steps, personalized design elements that reflect the user's emotions and preferences are generated and presented.

[1237] (Application example 2)

[1238] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1239] Conventional methods for customizing store displays and decorations have difficulty in instantly generating specific design proposals that reflect a user's preferences, images, and even emotions. In particular, it has been difficult for users to instantly obtain appropriate display proposals based on emotion recognition using specific keywords and reference images. Therefore, there is a need for a method to efficiently and effectively customize in-store decorations.

[1240] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 2 is realized by the following means.

[1241] In this invention, the server includes means for receiving preferences and image data input by a user, means for analyzing the received preferences and image data and passing the input data to a generative model, means for providing the user with design elements (wallpaper, icons, sounds, etc.) generated by the generative model, means for collecting and analyzing user emotion data using emotion recognition means, means for reflecting the analyzed emotion data in design generation, and means for previewing the generated design elements for customizing store decorations and displays. This makes it possible to easily customize store decorations and displays in accordance with the user's preferences and emotions.

[1242] "User" means any individual or entity that uses the System to create or customize Design Elements.

[1243] "Preferences and image data" refers to design-related keywords, themes, reference images, etc. specified by the user.

[1244] A "generative model" is a machine learning model that generates design elements based on user preferences, image data, and emotional data.

[1245] "Design elements" are visual and auditory elements such as wallpaper, icons, and sounds generated by generative models.

[1246] "Emotion recognition means" is a technology for collecting and analyzing emotional data from user input and reference images.

[1247] "Preview" is a function that displays the generated design elements in advance, allowing the user to check their contents.

[1248] A "display plan" is a specific design plan used for store decoration and display.

[1249] This invention relates to a system for customizing store decorations and displays based on user preferences, images, and emotions. The system involves the cooperation of a smartphone, an emotion recognition engine, a generative AI model, and a server.

[1250] First, a store employee launches the application on their smartphone. The application displays a user interface with fields for inputting the store's theme and decorative image. For example, they can enter keywords such as "spring flowers" or "warm atmosphere" and upload images they would like to use as inspiration.

[1251] The smartphone is equipped with an emotion recognition engine (e.g., Microsoft Azure's Face API) that analyzes the staff member's emotions (e.g., joy, excitement, relaxation, etc.) in real time from the input content and reference images, thereby collecting emotional data along with preference and image data.

[1252] When a staff member presses the "send" button, the device converts preferences, image data, and emotion data into JSON format and sends it over the internet to a server. The server then analyzes the received JSON data and uses Google's Natural Language API to understand the keywords and emotions entered. It then uses a generative AI model such as OpenAI's DALL-E to generate display ideas that fit the store's theme.

[1253] The generated display proposals are temporarily stored on the server, and an access URL for each proposal is generated. A response containing these URLs is sent in JSON format to the smartphone. The smartphone analyzes the received JSON response and displays a preview of the generated display proposals. Staff can then check these previews, select the display proposal that best suits their needs, and apply it to the actual exhibition.

[1254] As a concrete example, the following prompt sentences can be fed into a generative AI model:

[1255] Example prompts: "Spring flowers," "Warm atmosphere," "Relaxing," "Image: sakura_field.jpg"

[1256] Result: "A display concept themed around a spring flower field. Bright pinks and greens dominate, with relaxing, soft lighting."

[1257] As described above, by using this system, it is possible to easily customize store decorations and displays to suit the user's preferences and emotions.

[1258] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1259] Step 1:

[1260] Users enter the store's theme and decorative image and upload reference images.

[1261] Input: Keywords (e.g., "spring flowers" or "warm atmosphere") and reference images

[1262] Output: Keywords and image data

[1263] Specific operations: Launch the smartphone application, enter keywords in the fields displayed in the user interface, and upload reference images.

[1264] Step 2:

[1265] An emotion recognition means analyzes emotions from the user's input and the reference image.

[1266] Input: Keywords, reference images

[1267] Output: Emotion data (e.g., joy, excitement, relaxation)

[1268] How it works: The smartphone's built-in emotion recognition engine (for example, Microsoft Azure's Face API) analyzes keywords and reference images to recognize emotions in real time.

[1269] Step 3:

[1270] The device converts preference, image data, and emotion data into JSON format and sends it to the server.

[1271] Input: Keywords, reference images, emotion data

[1272] Output: JSON format data

[1273] Specific operation: The smartphone compiles the input keywords, reference images, and analyzed emotion data into a single JSON data set and sends it to a server via the Internet.

[1274] Step 4:

[1275] The server analyzes the received JSON data and understands keywords and sentiment data.

[1276] Input: JSON format data

[1277] Output: Parsed keywords and sentiment data

[1278] Specific operation: The server uses Google's Natural Language API to extract and analyze keywords and sentiment data from the JSON data.

[1279] Step 5:

[1280] The server inputs the analysis data into the generated AI model and generates a display proposal.

[1281] Input: Analyzed keywords, sentiment data

[1282] Output: Generated display plan and its access URL

[1283] How it works: The server inputs the analyzed keywords and emotion data into a generative AI model such as OpenAI's DALL-E, and generates display ideas that fit the store's theme. The generated display ideas are temporarily saved and an access URL is generated.

[1284] Step 6:

[1285] The server sends the URL of the generated display plan in JSON format to the terminal.

[1286] Input: Generated display plan and its access URL

[1287] Output: Response data in JSON format

[1288] Specific operation: The server creates a JSON format response including the access URL for the generated display proposal and sends it to the smartphone.

[1289] Step 7:

[1290] The device analyzes the received JSON response and displays a preview of the proposed display.

[1291] Input: Response data in JSON format

[1292] Output: Preview of the display

[1293] Specific operation: The smartphone parses the JSON response and displays the generated display proposals on the preview screen. The user checks the preview and selects a display proposal.

[1294] As a concrete example, the following prompt sentence is input into the generative AI model, and a display proposal is generated:

[1295] Example prompts: "Spring flowers," "Warm atmosphere," "Relaxing," "Image: sakura_field.jpg"

[1296] As a result, the generated display proposal is "A display concept based on a spring flower field theme, with bright pink and green as the main colors and soft, relaxing lighting."

[1297] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[1298] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating voice, text data indicating text, and image data indicating an image is also input. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[1299] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1300] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[1301] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[1302] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[1303] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[1304] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1305] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[1306] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[1307] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1308] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1309] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[1310] 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.

[1311] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[1312] The hardware resource for executing a specific process can be any of the following processors: An example of a processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. Another example of a processor is a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[1313] The hardware resource that executes the specific processing may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific processing may be a single processor.

[1314] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[1315] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[1316] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[1317] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[1318] The following is further disclosed regarding the above embodiment.

[1319] (Claim 1)

[1320] means for receiving user-entered preference and image data;

[1321] A means for analyzing the received preference and image data and providing input data to the generative model;

[1322] A means for providing users with design elements (wallpapers, icons, sounds, etc.) generated by the generative model;

[1323] A system including:

[1324] (Claim 2)

[1325] 10. The system of claim 1, further comprising means for generating input data for design generation based on user-specified keywords.

[1326] (Claim 3)

[1327] 10. The system of claim 1, further comprising means for displaying a preview of the generated design elements to a user.

[1328] "Example 1"

[1329] (Claim 1)

[1330] means for receiving user-entered preferences and image data;

[1331] A means to convert the received preference and image data into JSON format and send it to the server;

[1332] A means for the server to analyze the received JSON data and convert it into a data format suitable for the generative AI model;

[1333] A means for storing the design elements (wallpaper, icons, sounds, etc.) generated by the generative AI model in a database and generating an access URL;

[1334] means for displaying a preview of the generated design elements;

[1335] A system including:

[1336] (Claim 2)

[1337] 10. The system of claim 1, further comprising means for generating input data for design generation based on keywords specified by a user.

[1338] (Claim 3)

[1339] 10. The system of claim 1, further comprising means for displaying a preview of the generated design element to a user.

[1340] "Application Example 1"

[1341] (Claim 1)

[1342] means for receiving user-entered preference and image data;

[1343] A means for analyzing the received preference and image data and providing input data to the generative model;

[1344] A means for providing users with design elements (wallpapers, icons, sounds, presentation designs, etc.) generated by the generative model;

[1345] A system including:

[1346] (Claim 2)

[1347] 10. The system of claim 1, further comprising means for generating input data for design generation based on user-specified keywords.

[1348] (Claim 3)

[1349] 10. The system of claim 1, further comprising means for displaying a preview of the generated design elements to a user.

[1350] "Example 2: Combining Emotion Engines"

[1351] (Claim 1)

[1352] means for receiving user-entered preference and image data;

[1353] a means for analyzing the received preference, image, and emotion data and providing the input data to a generative model;

[1354] A means for providing users with design elements (wallpapers, icons, sounds, etc.) generated by the generative model;

[1355] A system including:

[1356] (Claim 2)

[1357] 10. The system of claim 1, further comprising: means for analyzing the emotion data; and means for generating input data for design generation based on the emotion data.

[1358] (Claim 3)

[1359] 10. The system of claim 1, further comprising means for displaying a preview of the generated design elements to a user.

[1360] "Application example 2 when combining emotion engines"

[1361] (Claim 1)

[1362] means for receiving user-entered preference and image data;

[1363] A means for analyzing the received preference and image data and providing input data to the generative model;

[1364] A means for providing users with design elements (wallpapers, icons, sounds, etc.) generated by the generative model;

[1365] a means for collecting and analyzing user emotion data using emotion recognition means;

[1366] A means for reflecting the analyzed emotional data in design generation;

[1367] A means to preview the generated design elements for customizing store décor and displays;

[1368] A system including:

[1369] (Claim 2)

[1370] 10. The system of claim 1, further comprising means for generating input data for design generation based on user-specified keywords.

[1371] (Claim 3)

[1372] 10. The system of claim 1, further comprising means for displaying a preview of the generated design elements to a user. [Explanation of symbols]

[1373] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. means for receiving user-entered preference and image data; A means for analyzing the received preference and image data and providing input data to the generative model; A means for providing users with design elements (wallpapers, icons, sounds, etc.) generated by the generative model; A system including:

2. 10. The system of claim 1, further comprising means for generating input data for design generation based on user-specified keywords.

3. 10. The system of claim 1, further comprising means for displaying a preview of the generated design elements to a user.

Images