System

Abstract

A system is provided.SOLUTION: Means for the user to upload a merchandise image, means for the server to pass the merchandise image to the generated AI model and perform background elimination and size adjustment, means for presenting the generated preview image to the user and asking for confirmation, means for the user to input a merchandise name, means for the server to activate a merchandise information auto-completion AI and automatically reflect detailed information in an entry form based on the merchandise name, means for the user to confirm / correct and save the automatically generated merchandise information, means for the user to request automatic generation of a merchandise introduction, and means for the server to pass the collected merchandise information to the generated AI model, means for generating and presenting a product description to a 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] Operating an e-commerce site requires a wide range of tasks, including product registration, promotion, and site analysis, which can take up a significant amount of time and effort for site operators. As a result, the site operator may neglect their primary goal of delivering products to consumers. The goal of this project is to solve this issue and provide an environment where site operators can focus on providing high-quality products and improving services. [Means for solving the problem]

[0005] The present invention solves the above-mentioned problems by the following means.

[0006] A means for users to upload product images;

[0007] A means for the server to pass product images to a generative AI model for background removal and resizing;

[0008] means for presenting the generated preview image to a user and requesting confirmation;

[0009] A means for the user to input a product name;

[0010] The server activates the product information auto-completion AI and automatically reflects detailed information in the input form based on the product name.

[0011] A means for users to check, modify and save automatically generated product information;

[0012] A means for users to request automatic generation of product descriptions;

[0013] The server passes the collected product information to the AI ​​model, generates product descriptions, and presents them to the user.

[0014] Systems including this can automate and optimize these tedious tasks, significantly reducing the burden on e-commerce site operators, allowing them to more effectively deliver products to consumers and focus on improving their services.

[0015] "User" refers to the operator or administrator of the EC site, who is the entity that registers, edits, and manages product information through the system.

[0016] "Server" refers to a computer system that processes data based on user instructions and provides various automated functions using generative AI models.

[0017] A "generative AI model" refers to an algorithm or program that uses artificial intelligence technology to automatically perform advanced data processing such as image editing and text generation.

[0018] "Uploading product images" refers to an operation in which a user takes a photo of a product using a terminal and sends the image file to a server.

[0019] "Background removal" refers to an image editing process that automatically removes unwanted background from product images to highlight the product itself.

[0020] "Sizing" refers to the process of automatically resizing product images to fit the display size appropriate for e-commerce sites.

[0021] "Preview" refers to the content that is temporarily displayed to allow the user to check the edited image or text generated by the server.

[0022] "Product name" refers to the name of the product that the user wishes to register.

[0023] "Product information automatic completion AI" refers to an AI system that automatically retrieves detailed product information from past data or external databases based on the product name and reflects it in the form entered by the user.

[0024] "Product description" refers to text automatically created by a generative AI model to explain the features and benefits of a product. [Brief explanation of the drawings]

[0025] [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

[0026] 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.

[0027] First, the terms used in the following description will be explained.

[0028] 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).

[0029] 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.

[0030] 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.

[0031] 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.

[0032] 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."

[0033] [First embodiment]

[0034] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0035] 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.

[0036] 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).

[0037] 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.

[0038] 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.

[0039] 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.

[0040] 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.

[0041] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0042] 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.

[0043] 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.

[0044] 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.

[0045] 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."

[0046] The present invention is a system that reduces the time and effort required for tasks such as product promotion and website analysis, and provides an environment in which operators can concentrate on providing high-quality products and improving services. Below, an embodiment of this system, "Assistant EC," is explained, along with program processing flows and specific examples.

[0047] Automate product image editing

[0048] The user uploads a product image from their device. The uploaded image is sent to the server, where it is resized and background-removed using a generative AI model, and the edited image is presented to the user as a preview. For example, if a user uploads an image of a shirt, the background is automatically changed to white and the image is resized to the appropriate size.

[0049] Product information auto-completion

[0050] When a user accesses the product information input form and enters the product name, the information is sent to the server. The server then activates the product information auto-completion AI, which retrieves relevant information from existing databases and external databases and reflects it in the input form. For example, if a user enters "smartwatch," the product description, specifications, price, etc. are automatically entered in the input form.

[0051] Automatic generation of product descriptions

[0052] After completing product information registration, the user requests automatic generation of a product description. This request is sent to the server, which passes the collected product information to the AI ​​model to generate the product description. The generated description is then sent to the user's device, where the user can review and approve the content. For example, a description such as "The latest smartwatch with high-performance heart rate measurement" is automatically generated.

[0053] Promotion optimization

[0054] The server automatically sets promotion targets based on user data and behavioral history. It uses generative AI models to optimize targeting and proposes promotion strategies that respond in real time. For example, it could display smartwatch ads during times when many female users in their 20s visit.

[0055] When a user requests the creation of a landing page, the server activates an AI generative model to automatically generate an effective landing page based on product features and keywords. For example, a landing page for a new smartwatch product is generated, and the page includes compelling copy and design.

[0056] Site optimization

[0057] The server collects and analyzes Google Analytics data and uses a generative AI model to automatically generate site improvement proposals. For example, if the time spent on a particular page is short, it will suggest updating the content or changing the layout. The server also monitors user behavior and suggests optimal page structure and navigation improvements. For example, if a particular category is frequently accessed, it will suggest adding that category to the main menu.

[0058] As described above, this system, "Assistant EC," automates and optimizes many of the tasks involved in operating an e-commerce site, allowing users to focus on strategic operations and improving services. Furthermore, by using this system, e-commerce sites can maintain their competitiveness and achieve sustainable growth.

[0059] The processing flow will be explained below.

[0060] Automate product image editing

[0061] Step 1:

[0062] The user selects a product image from the device and clicks the upload button.

[0063] Step 2:

[0064] The terminal transmits the selected image file to the server.

[0065] Step 3:

[0066] The server passes the received product images to the generative AI model.

[0067] Step 4:

[0068] The generative AI model removes backgrounds and resizes images.

[0069] Step 5:

[0070] The server temporarily stores the edited image and generates a preview image.

[0071] Step 6:

[0072] The server transmits the preview image to the terminal.

[0073] Step 7:

[0074] The user checks the preview on the terminal and presses the "Confirm" button.

[0075] Product information auto-completion

[0076] Step 1:

[0077] The user accesses the product information entry form.

[0078] Step 2:

[0079] The terminal acquires the product information input form from the server and displays it.

[0080] Step 3:

[0081] The user inputs the product name.

[0082] Step 4:

[0083] The terminal transmits the input product name to the server.

[0084] Step 5:

[0085] The server launches an AI that automatically completes product information and retrieves information by comparing it with past databases and external databases.

[0086] Step 6:

[0087] The server reflects the obtained detailed information in the input form.

[0088] Step 7:

[0089] The server sends the input form back to the terminal.

[0090] Step 8:

[0091] The user checks the auto-completed information and corrects it if necessary.

[0092] Step 9:

[0093] After checking, the user clicks the "Save" button.

[0094] Step 10:

[0095] The terminal transmits the final product information to the server.

[0096] Step 11:

[0097] The server stores the received product information in a database.

[0098] Automatic generation of product descriptions

[0099] Step 1:

[0100] After completing the product information registration process, the user clicks the "Auto-generate introduction" button.

[0101] Step 2:

[0102] The terminal sends this request to the server.

[0103] Step 3:

[0104] The server passes the collected product information to the generative AI model.

[0105] Step 4:

[0106] The generative AI model generates product descriptions.

[0107] Step 5:

[0108] The server temporarily stores the generated introduction and presents it to the user.

[0109] Step 6:

[0110] The server transmits the generated introduction to the terminal.

[0111] Step 7:

[0112] The user can review and approve the testimonial on their device and make any necessary corrections.

[0113] Step 8:

[0114] The user approves the final testimonial and sends it to the server.

[0115] Step 9:

[0116] The server stores the approved testimonials in a database.

[0117] Promotion optimization

[0118] Step 1:

[0119] The server collects user data and behavioral history.

[0120] Step 2:

[0121] The server uses generative AI models to optimize promotion targeting.

[0122] Step 3:

[0123] The server will suggest the best target audience and promotion strategy.

[0124] Step 4:

[0125] The user reviews and approves the proposed targeting and promotion strategy.

[0126] Step 5:

[0127] The server prepares to execute the promotion strategy based on the approved strategy.

[0128] Site optimization

[0129] Step 1:

[0130] The server collects data for Google Analytics.

[0131] Step 2:

[0132] The server analyzes the data using a generative AI model.

[0133] Step 3:

[0134] The server automatically generates suggestions for improving the site.

[0135] Step 4:

[0136] The server presents improvement suggestions to the user.

[0137] Step 5:

[0138] The user reviews and approves the proposed improvements.

[0139] Step 6:

[0140] The server implements the proposals to update the site layout and content based on the approved improvements.

[0141] These steps will enable the "Assistant EC" system to function efficiently, reduce the workload of EC site operators, and improve the quality of service provided.

[0142] Example 1

[0143] 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."

[0144] Modern e-commerce site operations require a wide range of tasks, including editing product images, completing product information, creating product descriptions, optimizing promotion targeting, generating landing pages, and proposing site improvements. These tasks place a heavy burden on site operators. This makes it difficult for site operators to devote sufficient time to providing high-quality products and improving services, which is what they should be focusing on. To solve this problem, a system is needed to automate and streamline these tasks.

[0145] 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.

[0146] In this invention, the server includes: a means for a user to upload product images; a means for the server to pass the product images to a generative AI model and perform background removal and size adjustment; a means for presenting the generated preview image to the user and requesting confirmation; a means for the user to input a product name; a means for the server to activate a product information auto-completion AI and automatically update an input form with detailed information based on the product name; a means for the user to confirm, correct, and save the automatically generated product information; a means for the user to request automatic generation of a product description; a means for the server to pass collected product information to a generative AI model, generate a product description, and present it to the user; a means for the server to automatically set promotion targets based on user data and behavioral history and optimize targeting using a generative AI model; a means for the user to request creation of a landing page; a means for the server to automatically generate a landing page based on product features and keywords using a generative AI model; and a means for the server to automatically generate site improvement plans using Google Analytics data and present them to the user. This enables a wide range of e-commerce site operations to be performed efficiently and automatically.

[0147] "User" refers to the person who operates the e-commerce site or the person who uses the system.

[0148] "Terminal" refers to electronic devices such as personal computers, smartphones, and tablets operated by users.

[0149] "Server" refers to a computer system that receives and processes data sent by users.

[0150] "Product image" refers to an image file that visually represents a product sold on an e-commerce site.

[0151] A "generative AI model" refers to an artificial intelligence model that automatically handles specific tasks based on machine learning or deep learning.

[0152] "Background removal" refers to the process of automatically removing the background from a product image.

[0153] "Resizing" refers to the process of automatically resizing product images to the appropriate dimensions.

[0154] The term "preview image" refers to an image that is temporarily displayed to allow the user to check the product image processed by the server.

[0155] "Product information input form" refers to a web form for entering product information such as product name, description, and specifications.

[0156] "Product information automatic completion AI" refers to an artificial intelligence model that automatically completes detailed information based on the product name entered.

[0157] "Product description" refers to text that explains the features and benefits of a product.

[0158] "Promotion target" refers to a group of users who are the target of promotional activities.

[0159] "Targeting optimization" refers to the process of selecting and implementing the most effective promotional strategies for a specific user demographic.

[0160] "Landing page" refers to a dedicated web page related to a specific product or campaign.

[0161] "Google Analytics" refers to a tool for analyzing website traffic and user behavior.

[0162] "Site Improvement Suggestions" means suggestions for improving a website to improve user experience or conversion rates.

[0163] This invention is a system for automatically editing product images, automatically completing product information, automatically generating product descriptions, optimizing promotions, and optimizing websites. This system automates and optimizes various tasks so that users can concentrate on providing products and improving services.

[0164] Automate product image editing

[0165] The user uploads a product image from their device. The uploaded image is sent to the server. The server uses a generative AI model (e.g., the "Remove.bg API" for background removal and the "OpenCV" library for resizing) to remove the background and resize the image. The edited image is presented to the user as a preview, and the user can confirm the results. An example of a specific prompt is as follows: "Remove the background to white and generate an appropriately resized image."

[0166] Product information auto-completion

[0167] The user enters the product name into a product information input form. The entered product name information is sent to the server, which then activates an AI that automatically completes product information (e.g., the "GPT-4" model). The server retrieves related information from existing databases or external databases (e.g., Amazon Product Advertising API) and reflects it in the input form. For example, if "smartwatch" is entered, the product description, specifications, price, etc. will be automatically entered in the input form. An example of a prompt sentence is "Enter the product name 'smartwatch'. Detailed information will be automatically completed."

[0168] Automatic generation of product descriptions

[0169] After completing the product information registration, the user requests the automatic generation of a product description. This request is sent to the server, which passes the collected product information to a generative AI model (e.g., "GPT-4") to generate a product description. This generated description is presented to the user, who then confirms and approves the content. For example, a description for "the latest smartwatch with high-performance heart rate measurement" is generated. An example of a prompt would be "Please automatically generate a product description for a smartwatch. Please particularly emphasize the heart rate measurement function."

[0170] Promotion optimization

[0171] The server automatically sets promotion targets based on user data and behavioral history. It uses a generative AI model (e.g., "Segmentation AI Model") to optimize targeting and propose promotion strategies that respond in real time. For example, it displays ads for smartwatches during times when many female users in their 20s visit. Also, when a user requests the creation of a landing page, the server uses a generative AI model to automatically generate an effective landing page based on product features and keywords. For example, a landing page for a new smartwatch product is generated. This page includes attractive copy and design. An example of a prompt sentence is, "Please generate a landing page for our new smartwatch product."

[0172] Site optimization

[0173] The server collects and analyzes Google Analytics data and uses a generative AI model (e.g., "Data Analysis AI") to automatically generate site improvement proposals. For example, if the time spent on a particular page is short, it will suggest updating the content or changing the layout. The server also monitors user behavior and suggests appropriate page structure and navigation improvement proposals. For example, if a particular category is frequently accessed, it will suggest adding that category to the main menu. An example prompt is, "Generate site improvement proposals based on Google Analytics data."

[0174] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0175] Automate product image editing

[0176] Step 1:

[0177] The user uploads a product image from their device.

[0178] Specific operation: The user clicks the upload button in the browser and selects the product image file.

[0179] Input: Product image file

[0180] Output: Product image files are sent to the server

[0181] Step 2:

[0182] The server receives the product image.

[0183] Specific operation: The server receives the HTTP request and saves the image file.

[0184] Input: Product image file

[0185] Output: Product images stored in server storage

[0186] Step 3:

[0187] The server uses the generative AI model to remove backgrounds and resize images.

[0188] Specific operation: The server calls the "Remove.bg API" to remove the background and resizes the image using "OpenCV".

[0189] Input: Product images stored in server storage

[0190] Output: Background removed and resized product image

[0191] Step 4:

[0192] The server presents the edited image to the user as a preview.

[0193] Specific operation: The server sends the edited image to the user's device as an HTTP response, and a preview is displayed in the browser.

[0194] Input: Background removed and resized product image

[0195] Output: A preview image that is displayed in the user's browser.

[0196] Product information auto-completion

[0197] Step 1:

[0198] The user enters the product name in the product information input form.

[0199] Specific behavior: The user types the product name into the form field and clicks the submit button.

[0200] Input: Product name

[0201] Output: Product name sent to the server as an HTTP request

[0202] Step 2:

[0203] The server receives the product name and activates the product information auto-completion AI.

[0204] How it works: The server analyzes the product name and sends a query to the GPT-4 model.

[0205] Input: Product name received as an HTTP request

[0206] Output: Query passed to product information auto-completion AI

[0207] Step 3:

[0208] The server retrieves relevant information from existing databases or external databases and reflects it in the input form.

[0209] Specific operation: The server executes a database query and sends the retrieved data to the user terminal in JSON format.

[0210] Input: Product name query, related information in the database

[0211] Output: Detailed information in JSON format (product description, specifications, price, etc.)

[0212] Step 4:

[0213] The user checks, edits, and saves the automatically generated product information.

[0214] Specific behavior: The user checks the details, makes any necessary corrections, and clicks the save button.

[0215] Input: Auto-completed product information

[0216] Output: Product information confirmed and corrected by the user is saved on the server

[0217] Automatic generation of product descriptions

[0218] Step 1:

[0219] After completing the registration of product information, the user requests automatic generation of a product description.

[0220] Specific operation: The user clicks the "Automatically generate introduction" button.

[0221] Input: User action (button click)

[0222] Output: A testimonial generation request is sent to the server

[0223] Step 2:

[0224] The server passes the collected product information to a generative AI model, which then generates a product description.

[0225] How it works: The server sends product information to the GPT-4 model and receives the generated description.

[0226] Input: Collected product information

[0227] Output: Generated product description

[0228] Step 3:

[0229] The server presents the generated testimonial to the user.

[0230] Specific operation: The server sends the generated introduction to the user's terminal as an HTTP response and displays it in the browser.

[0231] Input: Generated product description

[0232] Output: Product description displayed in the user's browser

[0233] Promotion optimization

[0234] Step 1:

[0235] The server automatically sets promotion targets based on user data and behavioral history.

[0236] Specific operation: The server extracts user data from the database and analyzes it.

[0237] Input: User data, behavioral history

[0238] Output: Promotion target data as analysis results

[0239] Step 2:

[0240] The server uses the generative AI model to optimize targeting.

[0241] Specific operation: The server uses the "Segmentation AI Model" to optimize promotion targets.

[0242] Input: Promotion target data

[0243] Output: Optimized targeting information

[0244] Step 3:

[0245] The server proposes a promotion strategy to the user that responds in real time.

[0246] Specific operation: The server generates a promotion strategy based on the optimized targeting information and proposes it to the user.

[0247] Input: Optimized targeting information

[0248] Output: Promotion strategies presented to the user

[0249] Step 4:

[0250] A user requests the creation of a landing page.

[0251] Specific operation: The user enters the required information into the landing page creation request form and clicks the submit button.

[0252] Input: Information entered in the landing page creation request form

[0253] Output: A landing page creation request is sent to the server

[0254] Step 5:

[0255] The server uses a generative AI model to automatically generate landing pages based on product features and keywords.

[0256] How it works: The server sends the request to the GPT-4 model and receives the generated landing page.

[0257] Input: Product features and keywords

[0258] Output: Auto-generated landing page

[0259] Site optimization

[0260] Step 1:

[0261] The server collects and analyzes Google Analytics data.

[0262] Specific operation: The server calls the Google Analytics API and retrieves the data.

[0263] Input: Google Analytics data

[0264] Output: Parsed site performance data

[0265] Step 2:

[0266] The server uses the generative AI model to automatically generate site improvement proposals.

[0267] Specific operation: The server uses "Data Analysis AI" to generate site improvement proposals based on the analysis data.

[0268] Input: Site performance data

[0269] Output: Generated site improvement proposals

[0270] Step 3:

[0271] The server monitors user behavior and suggests optimal page structure and navigation improvements.

[0272] Specific operation: The server monitors user behavior in real time and generates improvement suggestions.

[0273] Input: User behavior data

[0274] Output: Generated navigation improvement suggestions

[0275] (Application example 1)

[0276] 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."

[0277] E-commerce site operators need to efficiently manage a large number of products and quickly carry out high-quality promotions. This requires a great deal of time and effort for tasks such as image editing, information entry, and promotion setup. A system is needed to solve this issue and allow operators to focus on more strategic tasks.

[0278] 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.

[0279] In this invention, the server includes a means for users to upload product images, a means for passing the product images to a generative AI model to remove backgrounds and adjust the size, a means for presenting the generated preview image to the user for confirmation, and a means for the server to analyze user data and behavioral history and optimize promotion strategies. This allows e-commerce site operators to significantly reduce the effort and time required for image editing, information input, and promotion settings, enabling them to efficiently provide high-quality products and improve services.

[0280] definition statement

[0281] "User" refers to an individual or organization that uses the system as an operator or administrator of an EC site.

[0282] A "server" is a computer system that handles the entire system, including image editing, data analysis, information supplementation, and launching generative AI models.

[0283] "Product images" are graphic data such as product photos and illustrations posted on e-commerce sites.

[0284] A "generative AI model" is an artificial intelligence framework that uses techniques such as machine learning and deep learning to automatically handle specific tasks.

[0285] "Background removal" is the process of removing the background from a product image and cutting out only the product.

[0286] "Size adjustment" is the operation of changing the size of product images to a size suitable for posting on an e-commerce site.

[0287] A "preview image" is a trial image shown to the user after background removal and resizing have been performed by the generative AI model.

[0288] "Product Information Automatic Completion AI" is an artificial intelligence system that automatically infers and inputs detailed product information based on the product name.

[0289] A "product description" is a sentence generated to explain the features and advantages of a product.

[0290] A "promotion strategy" is a plan or method for effectively implementing advertisements or campaigns aimed at a specific user demographic.

[0291] "Google Analytics" is a tool for collecting and analyzing website traffic data.

[0292] "Site Improvement Suggestions" are specific suggestions for improving the usability and quality of the content of a website.

[0293] MODE FOR CARRYING OUT THE INVENTION

[0294] The present invention provides a system that allows an EC site operator to efficiently edit product images, input product information, and set promotion strategies. A specific embodiment of this system is described below.

[0295] System Overview

[0296] The system for implementing this invention includes a user terminal, a server, a generative AI model, an AI for automatically completing product information, Google Analytics, etc. Its main functions include automatic editing of product images, automatic completion of product information, automatic generation of product descriptions, optimization of promotion strategies, and optimization of the website.

[0297] Automate product image editing

[0298] Users upload product images using their devices. The uploaded images are sent to a server, which uses a generative AI model to remove backgrounds and resize the images. The edited images are then presented to the user as preview images. For example, if a user uploads an image of a shirt, the background is automatically changed to white and the image is resized to the appropriate size.

[0299] Product information auto-completion

[0300] When a user accesses the product information input form and enters the product name, the information is sent to the server. The server then activates the product information auto-completion AI, which retrieves relevant information from existing databases and external databases and automatically reflects it in the input form. For example, if a user enters "smartwatch," the product description, specifications, price, etc. are automatically entered.

[0301] Automatic generation of product descriptions

[0302] After completing the product information registration, the user requests the automatic generation of a product description. This request is sent to the server, which passes the collected product information to the generative AI model to generate the product description. The generated description is then sent to the user's device, where the user can review and approve the content. As a specific example, a description such as "The latest smartwatch with high-performance heart rate measurement" is automatically generated.

[0303] Promotion optimization

[0304] The server automatically sets promotion targets based on user data and behavioral history. It uses a generative AI model to optimize targeting and proposes promotion strategies that respond in real time. For example, one possible method is to display smartwatch ads during times when many female users in their 20s visit. When a user requests the creation of a landing page, the server activates the AI ​​generative model and automatically generates an effective landing page based on product features and keywords.

[0305] Site optimization

[0306] The server collects and analyzes Google Analytics data and uses a generative AI model to automatically generate site improvement proposals. For example, if the time spent on a particular page is short, it will suggest updating the content or changing the layout. The server also monitors user behavior and suggests optimal page structure and navigation improvements. For example, if a particular category is frequently accessed, it will suggest adding that category to the main menu.

[0307] Prompt Sentence Examples

[0308] An example of a prompt for automatically editing product images is, "Please change the background of uploaded product images to white and resize them to appropriate retail sizes."

[0309] As described above, this system allows e-commerce site operators to significantly reduce the effort required for image editing, information input, and promotion settings, allowing them to focus on strategic operations and improving services.

[0310] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0311] Program processing steps

[0312] Step 1:

[0313] The user uploads a product image from the terminal. The input of this step is the product image file selected by the user from the terminal, and the output is the raw data transferred to the server.

[0314] Step 2:

[0315] The server passes the received product image data to the generative AI model, which removes the background and adjusts the size. The input for this step is the product image data sent to the server, and the generative AI model removes the background and adjusts the size, resulting in an edited preview image.

[0316] Step 3:

[0317] The server presents the generated preview image to the user for confirmation. The input to this step is the preview image after background removal and resizing, and the output is a confirmation screen presented to the user. The user can confirm or make corrections.

[0318] Step 4:

[0319] The user inputs the product name from the terminal and sends it to the server. The input of this step is the product name input by the user from the terminal, and the output is the product name data passed to the server.

[0320] Step 5:

[0321] The server launches the product information auto-completion AI, which automatically fills in the input form with detailed information based on the product name. The input for this step is the product name data, the product information auto-completion AI retrieves related information from the database, and the output is form data with complete product information filled in.

[0322] Step 6:

[0323] The user confirms, modifies, and saves the automatically generated product information. The input of this step is the form data with the product information filled in, and the output is the final product information confirmed and modified by the user.

[0324] Step 7:

[0325] The user requests automatic generation of a product description and sends a request to the server. The input of this step is the automatic generation request sent from the user's terminal, and the output is the request data passed to the server.

[0326] Step 8:

[0327] The server passes the collected product information to the generative AI model to generate a product description. The input of this step is the collected product information, the generative AI model creates a product description, and the output is the generated product description.

[0328] Step 9:

[0329] The server presents the generated product description to the user, who then confirms and approves the content. The input to this step is the generated product description, and the output is text data for confirmation that is presented to the user. The user can confirm or make corrections.

[0330] Step 10:

[0331] The server analyzes user data and behavioral history to optimize the promotion strategy. The input of this step is user data and behavioral history, and the output is an optimized promotion strategy after data analysis using a generative AI model.

[0332] Step 11:

[0333] The server collects and analyzes Google Analytics data and automatically generates site improvement proposals. The input for this step is traffic data from Google Analytics, and the output is proposed site improvement proposals after data analysis using a generative AI model.

[0334] By implementing each of the above steps, e-commerce site operators can efficiently provide high-quality products and improve their services.

[0335] 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.

[0336] The present invention is a system that reduces the time and effort required for tasks such as product promotion and website analysis, and provides an environment in which operators can concentrate on providing high-quality products and improving services. Furthermore, by combining it with an emotion engine that recognizes user emotions, the quality of the user experience can be improved. Below, we will explain an embodiment in which this system, "Assistant EC," is combined with an emotion engine.

[0337] Automate product image editing

[0338] A user uploads a product image from their device. At this time, an emotion engine is built into the device, which recognizes emotions from the user's facial expressions and voice. The uploaded image and emotion data are sent to the server. The server uses a generative AI model to remove the background and adjust the size of the image, and presents the edited image to the user as a preview. For example, if a user uploads an image of a shirt and shows a dissatisfied expression, the background color and brightness are automatically adjusted and the preview is displayed again.

[0339] Product information auto-completion

[0340] When a user accesses a product information input form and enters a product name, the information is sent to the server. The server then activates the product information auto-completion AI, which retrieves relevant information from existing and external databases and reflects it in the input form. The emotion engine continues to monitor the user's facial expressions and reactions, analyzing whether the user is satisfied or dissatisfied. For example, if the user shows a confused expression while entering "smartwatch," the engine automatically adjusts to provide a more detailed explanation or alternative suggestions.

[0341] Automatic generation of product descriptions

[0342] After completing product information registration, the user requests automatic generation of a product description. This request is sent to the server, which passes the collected product information to the generative AI model to generate the product description. The generated description is then sent to the device along with the user's emotional data, and the user confirms and approves the content. For example, when a description such as "The latest smartwatch with high-performance heart rate measurement" is generated, if the user shows a happy expression, approval will proceed. If the user shows a dissatisfied expression, a regeneration will be suggested.

[0343] Promotion optimization

[0344] The server automatically sets promotion targets based on user data and behavioral history. It uses a generative AI model to optimize targeting and leverages emotional data collected through an emotion engine to propose promotion strategies that respond in real time. For example, when displaying smartwatch ads during times when many female users in their 20s visit, the server analyzes their emotional responses to past ads and selects the most appealing method.

[0345] Additionally, when a user requests the creation of a landing page, the server activates an AI generative model to automatically generate an effective landing page based on product features and keywords. The emotion engine analyzes the user's reactions while viewing a preview of the landing page. For example, a landing page for a new smartwatch product is generated, containing appealing copy and design. If the user expresses satisfaction after viewing the preview, the page is published as is. If the user expresses dissatisfaction, the page is re-adjusted.

[0346] Site optimization

[0347] The server collects and analyzes data from Google Analytics and uses a generative AI model to automatically generate suggestions for improving the site. The emotion engine monitors users' reactions while they browse the site and provides the accumulated data to the generative AI model. For example, if the time spent on a particular page is short, it suggests updating the content or changing the layout. The server also monitors user behavior and suggests optimal page structure and navigation improvements. For example, if a particular category is frequently accessed, it suggests adding that category to the main menu.

[0348] These steps enable users, devices, and servers to work together efficiently using the emotion engine to carry out the processes of product registration, information supplementation, promotion, and site improvement. By incorporating emotion recognition, it is possible to enhance the user experience and improve the efficiency and quality of e-commerce site operations.

[0349] The processing flow will be explained below.

[0350] Automate product image editing

[0351] Step 1:

[0352] The user selects a product image from the device and clicks the upload button.

[0353] Step 2:

[0354] The terminal transmits the selected image file to the server, and simultaneously collects and transmits the user's facial expression data using an emotion engine.

[0355] Step 3:

[0356] The server passes the received product images to the generative AI model.

[0357] Step 4:

[0358] The generative AI model removes backgrounds and resizes images.

[0359] Step 5:

[0360] The server temporarily stores the edited image and generates a preview image.

[0361] Step 6:

[0362] The server analyzes the user's facial expression data along with the preview image and presents it to the user.

[0363] Step 7:

[0364] The user checks the preview on the terminal and presses the "Confirm" button.

[0365] Step 8:

[0366] If the user's facial expression indicates dissatisfaction, the server calls the generative AI model again and re-edits the image.

[0367] Product information auto-completion

[0368] Step 1:

[0369] The user accesses the product information entry form.

[0370] Step 2:

[0371] The terminal acquires the product information input form from the server and displays it.

[0372] Step 3:

[0373] The user inputs the product name.

[0374] Step 4:

[0375] The terminal transmits the input product name to the server, and also collects and transmits the user's facial expression data using an emotion engine.

[0376] Step 5:

[0377] The server launches an AI that automatically completes product information and retrieves information by comparing it with past databases and external databases.

[0378] Step 6:

[0379] The server reflects the obtained detailed information in the input form and resends it to the user.

[0380] Step 7:

[0381] The terminal displays an input form to the user and continuously collects the user's facial expression data using an emotion engine.

[0382] Step 8:

[0383] The user checks the auto-completed information and corrects it if necessary.

[0384] Step 9:

[0385] After checking, the user clicks the "Save" button.

[0386] Step 10:

[0387] The terminal transmits the final product information to the server, and also transmits the final facial expression data of the user.

[0388] Step 11:

[0389] The server stores the received product information and facial expression data in a database and improves the accuracy of the information based on the results of analyzing the facial expression data.

[0390] Automatic generation of product descriptions

[0391] Step 1:

[0392] After completing the product information registration process, the user clicks the "Auto-generate introduction" button.

[0393] Step 2:

[0394] The terminal sends this request to the server, and the emotion engine collects and transmits the user's facial expression data at the same time.

[0395] Step 3:

[0396] The server passes the collected product information to the generative AI model.

[0397] Step 4:

[0398] The generative AI model generates product descriptions.

[0399] Step 5:

[0400] The server analyzes the user's facial expression data along with the generated introduction and presents it to the user.

[0401] Step 6:

[0402] The user checks the introduction on the device and reacts with facial expressions.

[0403] Step 7:

[0404] If the user shows a satisfied expression, they approve the testimonial and press the "Confirm" button. If the expression shows dissatisfaction, the server calls the AI ​​model again to regenerate the testimonial.

[0405] Step 8:

[0406] The user approves the final testimonial and sends it to the server.

[0407] Step 9:

[0408] The server stores the approved testimonials in a database.

[0409] Promotion optimization

[0410] Step 1:

[0411] The server collects user data and behavioral history, and also collects emotion data through an emotion engine.

[0412] Step 2:

[0413] The server uses generative AI models to optimize promotion targeting.

[0414] Step 3:

[0415] The server analyzes the sentiment data and suggests optimal target demographics and promotion strategies.

[0416] Step 4:

[0417] The user reviews and approves the proposed targeting and promotion strategy.

[0418] Step 5:

[0419] The server prepares to execute the promotion strategy based on the approved strategy.

[0420] Step 6:

[0421] During the promotion, the emotion engine continuously monitors user reactions and adjusts the promotion content in real time as needed.

[0422] Site optimization

[0423] Step 1:

[0424] The server collects data for Google Analytics.

[0425] Step 2:

[0426] The server collects user emotion data through an emotion engine.

[0427] Step 3:

[0428] The server analyzes the data using a generative AI model.

[0429] Step 4:

[0430] The server automatically generates site improvement proposals and presents them to the user.

[0431] Step 5:

[0432] The user reviews and approves the proposed improvements.

[0433] Step 6:

[0434] The server implements the proposals to update the site layout and content based on the approved improvements.

[0435] Step 7:

[0436] The server continuously analyzes data from the emotion engine and generates additional improvement suggestions to maximize user satisfaction.

[0437] Through these specific processing steps, the "Assistant EC" system can utilize user emotional data to streamline EC site operations and improve the quality of the user experience.

[0438] Example 2

[0439] 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."

[0440] In conventional e-commerce site operations, editing product images, completing product information, promoting products, and optimizing the site takes a lot of time and effort, making it difficult for operators to focus on providing high-quality products and improving services. In addition, because the site does not take user emotions into consideration, there is also the issue of the quality of the user experience not being sufficiently improved.

[0441] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: a means for a user to upload a product image; a means for a terminal to recognize emotions using an emotion engine that analyzes the user's facial expressions and voice data; a means for the terminal to transmit image data and emotion data to the server; a means for the server to pass the product image to a generative AI model and perform background removal and size adjustment; a means for presenting a generated preview image to the user and suggesting re-editing based on the emotion data; a means for a user to input a product name; a means for the server to start a product information automatic completion AI and automatically reflect detailed information in an input form based on the product name; a means for the emotion engine to monitor the user's emotion data and adjust the completion information; a means for a user to confirm, correct, and save the automatically generated product information; a means for a user to request automatic generation of a product description; a means for the server to pass collected product information to a generative AI model, generate a product description, and present it to the user; and a means for suggesting re-generation based on the generated description and emotion data. This allows for efficient product registration, information supplementation, promotion, and site improvement, and makes it possible to provide a high-quality user experience that takes user emotions into consideration.

[0442] "User" means any person or entity that uses the System to upload Product Images and enter, review and edit Product Information.

[0443] "Device" refers to an electronic device operated by a user, such as a computer, tablet, or smartphone, which includes an emotion engine and an image upload function.

[0444] An "emotion engine" is a software or hardware function that analyzes a user's facial expressions and voice data and recognizes their emotional state in real time.

[0445] A "server" is a remote computer system that performs editing of product images, completing product information, generating product descriptions, and other related processing.

[0446] A "generative AI model" is a machine learning model used to automatically perform tasks such as background removal, sizing, and product description generation.

[0447] A "preview image" is a product image edited using a generative AI model, and is an image that can be reviewed and re-edited by the user.

[0448] "Product Information Automatic Completion AI" is an artificial intelligence technology that automatically reflects detailed information in input forms based on the product name entered.

[0449] "Re-editing suggestions" are suggestions for re-editing using a generative AI model when the emotion engine detects a dissatisfied expression or reaction from the user.

[0450] A "product description" is a text description that is automatically generated by a generative AI model based on product information.

[0451] "Site Improvement Suggestions" are suggestions for improving the structure and content of a website generated by the server based on Google Analytics data and user sentiment data.

[0452] This invention is a system that reduces the time and effort required for tasks such as product promotion and website analysis, and provides an environment where operators can concentrate on providing high-quality products and improving services. In particular, it is characterized by improving the quality of the user experience by combining it with an emotion engine that recognizes user emotions.

[0453] Automate product image editing

[0454] The user uploads a product image from their device. The device is equipped with an emotion engine that captures the user's facial expressions and voice and recognizes their emotions. The uploaded image and emotion data are sent from the device to a server. The server uses a generative AI model to remove the background and resize the image. The edited image is presented to the user as a preview, and re-editing may be suggested based on the emotion data.

[0455] For example, if a user shows dissatisfaction after uploading a picture of a shirt, a prompt such as "Upload a picture of a shirt" could be used to automatically adjust the background color and brightness and display the preview again.

[0456] Product information auto-completion

[0457] When a user accesses the product information input form and enters the product name, the entered information is sent from the device to the server. The server then activates the product information auto-completion AI, retrieves relevant information from existing databases and external databases, and reflects it in the input form. During this time, the emotion engine continues to monitor the user's facial expressions and reactions, analyzing whether the user is satisfied or dissatisfied.

[0458] For example, if the user shows a confused expression while typing "smartwatch," the server automatically adjusts to provide a more detailed explanation or alternatives, such as a prompt that reads, "Complete the smartwatch information."

[0459] Automatic generation of product descriptions

[0460] After completing product information registration, the user requests automatic generation of a product description. This request is sent from the device to the server. The server passes the collected product information to a generative AI model to generate a product description. The generated description is then sent to the device along with emotional data, and the user checks and approves the content. Regeneration may also be suggested based on the user's emotional data.

[0461] A specific example of a prompt sentence is "Generate a description of a smartwatch."

[0462] Promotion optimization

[0463] The server automatically sets promotion targets based on user data and behavioral history, and optimizes targeting using a generative AI model. It utilizes emotional data collected through the emotion engine to propose promotion strategies that respond in real time. For example, when displaying smartwatch ads during times when many female users in their 20s visit, it analyzes their emotional responses to past ads and selects the most appealing method.

[0464] Additionally, when a user requests the creation of a landing page, the server activates a generative AI model to automatically generate an effective landing page based on product features and keywords. The emotion engine analyzes the user's reactions while displaying a preview of the landing page. An example of a prompt sentence would be "Optimize the promotional strategy for smartwatches."

[0465] Site optimization

[0466] The server collects and analyzes Google Analytics data and uses a generative AI model to automatically generate site improvement recommendations. The emotion engine monitors users' reactions while browsing the site and provides the data to the generative AI model. For example, if a particular category is frequently accessed, it will suggest adding that category to the main menu. Content updates and layout changes will also be suggested.

[0467] A specific example of a prompt might be "Improve time spent on site."

[0468] This allows users, devices, and servers to work together efficiently using the emotion engine, and executes the processes of product registration, information supplementation, promotion, and site improvement, improving the user experience as well as the efficiency and quality of e-commerce site operations.

[0469] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0470] Step 1:

[0471] A user uploads a product image.

[0472] Input: Product image taken or selected by the user

[0473] How it works: The user accesses the image upload screen in a web browser or a dedicated app, selects an image file, and presses the upload button.

[0474] Output: Uploaded product image data

[0475] Step 2:

[0476] The device analyzes the user's facial expressions and voice data.

[0477] Input: Upload product images and receive user's facial expressions and voice

[0478] How it works: The device's camera and microphone capture the user's face and voice, and the emotion engine analyzes this data in real time.

[0479] Output: User emotion data (e.g., satisfied, dissatisfied, confused)

[0480] Step 3:

[0481] The terminal transmits image data and emotion data to the server.

[0482] Input: Product image data, user emotion data

[0483] How it works: The device sends product image data and emotion data to the server using an HTTP request.

[0484] Output: Product image data and emotion data received by the server

[0485] Step 4:

[0486] The server passes product images to a generative AI model for background removal and resizing.

[0487] Input: Product image data

[0488] How it works: The server inputs product image data into the generative AI model, performs background removal and resizing.

[0489] Output: Edited product image data

[0490] Step 5:

[0491] The server presents the generated preview image to the user.

[0492] Input: Edited product image data

[0493] Operation: The server sends the edited image to the device, which displays it as a preview screen.

[0494] Output: Preview image that the user sees

[0495] Step 6:

[0496] Suggest re-editing based on emotional data.

[0497] Input: Emotion data when the user checks the preview image

[0498] How it works: The emotion engine analyzes the user's emotion and sends a re-editing request to the server. If the emotion is dissatisfied, the re-editing process is executed.

[0499] Output: Re-edited product image data

[0500] Step 7:

[0501] The user inputs the product name.

[0502] Input: Product name entered by the user

[0503] How it works: The user enters the product name into the input screen of a web form or dedicated app.

[0504] Output: Entered product name data

[0505] Step 8:

[0506] The server launches an AI that automatically completes product information, and automatically reflects detailed information in the input form based on the product name.

[0507] Input: Product name data entered by the user

[0508] How it works: The server uses product information auto-completion AI to retrieve relevant details from an internal database or external API and populate the input form.

[0509] Output: Auto-completed product information data

[0510] Step 9:

[0511] The emotion engine monitors the user's emotion data and adjusts the complementary information.

[0512] Input: Emotional data while the user is reviewing the form

[0513] How it works: The emotion engine analyzes the user's emotions in real time, and if they indicate dissatisfaction or confusion, it requests the server to re-adjust the details.

[0514] Output: Adjusted product information data

[0515] Step 10:

[0516] The user checks, edits, and saves the automatically generated product information.

[0517] Input: Auto-completed product information data

[0518] Action: The user reviews the form, corrects the information as needed, and clicks the save button.

[0519] Output: Saved product information data

[0520] Step 11:

[0521] A user requests automatic generation of a product description.

[0522] Input: Data after product information registration is complete

[0523] How it works: The user clicks the auto-generate product description button, sending a request to the server.

[0524] Output: Product description generation request data

[0525] Step 12:

[0526] The server passes the collected product information to a generative AI model, which then generates a product description.

[0527] Input: Saved product information data

[0528] How it works: The server inputs product information into a generative AI model, which then generates a product description.

[0529] Output: Generated product description data

[0530] Step 13:

[0531] We propose regeneration based on the generated product description and emotion data.

[0532] Input: Product description data, user emotion data

[0533] How it works: The server sends the generated product description and the user's emotion data to the device, where the user confirms it. The emotion engine analyzes the user's reaction and suggests regenerating the expression if it is dissatisfied.

[0534] Output: Revised product description data or regeneration suggestions

[0535] Step 14:

[0536] The server automatically generates site improvement proposals using Google Analytics data and sentiment data and presents them to the user.

[0537] Input: Google Analytics data, user sentiment data

[0538] How it works: The server analyzes the collected data and uses a generative AI model to generate site improvement suggestions.

[0539] Output: Generated site improvement proposal data

[0540] (Application example 2)

[0541] 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."

[0542] In conventional e-commerce site operations, each process, such as editing product images, automatically completing product information, and generating product descriptions, requires a lot of time and effort. Furthermore, there is a lack of efforts to improve services that incorporate user emotions, resulting in a poor user experience. This makes it difficult for site operators to focus on providing high-quality products and improving services. It is necessary to solve these problems and improve the efficiency of e-commerce site operations and the user experience.

[0543] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for a user to upload a product image; a terminal including an emotion recognition engine that recognizes emotions from the user's facial expressions and voice; means for the server to pass the product image to a generative AI model and perform background removal and size adjustment; means for using the user's emotion data to reflect the adjustment results when editing the image; means for presenting a generated preview image to the user and requesting confirmation; means for the user to input a product name; means for the server to start a product information automatic completion AI and automatically reflect detailed information in an input form based on the product name; means for the emotion recognition engine to monitor the user's reaction and automatically provide detailed information or alternatives when detecting a dissatisfied expression, means for the user to confirm, correct, and save the automatically generated product information; means for the user to request automatic generation of a product description; means for the server to pass collected product information to the generative AI model, generate a product description, and present it to the user; and means for the emotion recognition engine to analyze the user's reaction and suggest regeneration when the user shows a dissatisfied expression. This will enable the efficient execution of each process of product registration, information supplementation, promotion, and site improvement, enhancing the user experience and improving the efficiency and quality of e-commerce site operations.

[0544] "User" means a person who uses the system to upload product images and enter, confirm, modify and save product information.

[0545] An "emotion recognition engine" is software or hardware that analyzes a user's facial expressions and voice to recognize emotions.

[0546] A "terminal" is a device used by a user, such as a computer, smartphone, or tablet.

[0547] A "server" is a computer system that processes product images, product information, automatically generated product descriptions, etc., and stores and provides data.

[0548] A "generative AI model" is a model that uses artificial intelligence technology to automatically perform processes such as removing backgrounds from images, adjusting size, completing product information, and generating product descriptions.

[0549] "Background removal" is a process that removes unnecessary background parts from an image.

[0550] "Resizing" is the process of changing the dimensions of an image to meet specific criteria.

[0551] A "preview image" is a temporary display version of an image that is presented to the user after editing.

[0552] "Product Information Automatic Completion AI" is an artificial intelligence that automatically completes related detailed information based on input information such as product names.

[0553] "Passing to the generative AI model" means inputting the data collected by the server into the generative AI model.

[0554] A "product description" is text that explains the features and benefits of a product.

[0555] "Promotion" is a marketing activity to promote the sale of a product.

[0556] The present invention relates to a system for improving the efficiency of product promotion and site analysis in the operation of an e-commerce site, and for improving the user experience. Specific embodiments of the present invention will be described below.

[0557] The system that realizes this invention consists of a user's device, an emotion recognition engine, a generative AI model, and a server. The user uploads product images from their device, which has an emotion recognition engine built into it. The emotion recognition engine analyzes the user's facial expressions and voice to collect emotional data. This data and the product images are sent to the server.

[0558] The server includes the following means:

[0559] 1. Product Image Processing:

[0560] The server passes the uploaded product image to a generative AI model, which removes the background and adjusts the size. The server then reflects the adjustment results based on the user's emotional data collected by the emotion recognition engine, and presents the generated preview image to the user. For example, if the user shows a dissatisfied expression, the system automatically adjusts the background color and brightness and displays the preview again.

[0561] 2. Product information auto-completion:

[0562] When a user enters a product name, the server activates an AI that automatically completes product information, retrieving related information from existing and external databases and displaying it in the input form. The emotion recognition engine monitors the user's reactions, and if the user shows a confused expression, it automatically provides more detailed information or alternatives. For example, if the user shows a confused expression while typing "smartwatch," it provides complementary information such as sports functions, heart rate monitor, and blood oxygen monitor.

[0563] 3. Automatically generate product descriptions:

[0564] After a user completes the product information registration process, they can request the automatic generation of a product description. The server passes the collected product information to a generative AI model, which generates the product description. The generated description is sent to the device along with the user's emotional data, and the user confirms and approves the content. If the user shows a dissatisfied expression, the server will suggest regenerating the description. For example, if a description such as "The latest smartwatch with high-performance heart rate measurement" is generated and the user shows a happy expression, approval will proceed.

[0565] 4. Promotion optimization:

[0566] The server automatically sets promotion targets based on user data and behavioral history. It uses a generative AI model to optimize targeting and an emotion recognition engine to leverage emotional data collected to propose promotion strategies that respond in real time. For example, it displays smartwatch ads during times when many female users in their 20s visit, analyzing their emotional responses to past ads to select the most appealing method.

[0567] 5. Site Optimization:

[0568] The server collects and analyzes data from Google Analytics and uses a generative AI model to automatically generate site improvement recommendations. The emotion recognition engine monitors users' reactions while browsing the site and provides the collected data to the generative AI model. For example, if the time spent on a particular page is short, it will suggest updating the content or changing the layout.

[0569] Examples of specific examples and prompts

[0570] 1. Example:

[0571] Automatic editing of product images: If a user uploads an image of a t-shirt with a confused face, the image will automatically be brightened and the background will be white.

[0572] Auto-complete product information: If you type "smartwatch" and a confused face is detected, the app will suggest additional information such as "sports features, heart rate monitor, blood oxygen monitor."

[0573] Automatic generation of product description: Generate a description such as "The latest smartwatch with high-performance heart rate measurement" and approve it by checking the user's happy expression.

[0574] 2. Example prompt:

[0575] Product Image Editing: "Remove backgrounds and brighten product images uploaded with emotion data."

[0576] Product information auto-completion: "Please automatically complete related information for the product name 'Smartwatch' based on sentiment data."

[0577] Automated product description generation: "Generate compelling product descriptions based on product information, taking into account emotional responses."

[0578] As described above, the system for implementing the present invention utilizes a generative AI model and an emotion recognition engine to improve the efficiency of e-commerce site operations and the user experience.

[0579] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0580] Step 1:

[0581] The user uploads product images from the terminal.

[0582] The device is equipped with an emotion recognition engine that analyzes the user's facial expressions and voice data to collect emotional data, which is then sent to a server along with product images.

[0583] Input: User-uploaded product images, emotion data

[0584] Output: Product images and emotion data are sent to the server.

[0585] Step 2:

[0586] The server passes product images to a generative AI model for background removal and resizing.

[0587] The adjustment results are reflected based on the emotional data collected by the emotion recognition engine.

[0588] Input: Product images and emotion data sent to the server

[0589] Output: Background removed and resized product image

[0590] How it works: The generative AI model removes the background from the product image and scales it to the specified size.

[0591] Step 3:

[0592] The server presents the generated preview image to the user and asks for confirmation.

[0593] Input: Background-removed and resized product image

[0594] Output: A preview image is sent to the user's device.

[0595] Specific operation: The server prepares a preview image and sends the data to the device.

[0596] Step 4:

[0597] The user inputs the product name.

[0598] Input: Product name entered by the user

[0599] Output: Product name sent to server

[0600] Step 5:

[0601] The server launches an AI that automatically completes product information, automatically filling in the input form with detailed information based on the product name.

[0602] An emotion recognition engine monitors the user's reactions and automatically provides more information or alternatives if the user shows signs of confusion.

[0603] Input: Entered product name, user emotion data

[0604] Output: Auto-completed product information

[0605] Specific operation: Product information auto-completion AI searches and inputs related information based on the product name, and the emotion recognition engine adjusts the completion content based on the user's reaction.

[0606] Step 6:

[0607] The user checks, edits and saves the automatically generated product information.

[0608] Input: Auto-completed product information

[0609] Output: Saved product information

[0610] Specific action: The user checks the displayed information, corrects any omissions or errors, and saves the information.

[0611] Step 7:

[0612] The user requests automatic generation of a product description.

[0613] Input: User request

[0614] Output: Request information is sent to the server

[0615] Step 8:

[0616] The server passes the collected product information to a generative AI model to generate a product description.

[0617] An emotion recognition engine analyzes the user's reactions and suggests regeneration if the user shows a dissatisfied expression.

[0618] Input: Collected product information, user emotion data

[0619] Output: Generated product description

[0620] Specific operation: The generative AI model automatically generates product descriptions from product information and adjusts them to the optimal text by referencing user sentiment data.

[0621] Step 9:

[0622] The server automatically sets promotion targets based on user data and behavioral history, and optimizes targeting using a generative AI model.

[0623] Propose targeted promotions based on past emotional responses.

[0624] Input: User data, behavioral history, and past emotional responses

[0625] Output: Optimized promotion plan

[0626] How it works: The generative AI model analyzes the data and creates a promotion plan that users will respond to most.

[0627] Step 10:

[0628] The server collects and analyzes Google Analytics data and uses a generative AI model to automatically generate site improvement proposals.

[0629] An emotion recognition engine monitors users' reactions while browsing the site and makes suggestions for improvements based on page visit time and frequency.

[0630] Input: Google Analytics data, user sentiment data

[0631] Output: Generated site improvement proposals

[0632] How it works: The generative AI model analyzes the collected data and makes suggestions for improving the site structure and content by referring to the sentiment data.

[0633] 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.

[0634] 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.

[0635] 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.

[0636] [Second embodiment]

[0637] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0638] 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.

[0639] 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).

[0640] 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.

[0641] 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.

[0642] 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).

[0643] 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.

[0644] 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.

[0645] 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.

[0646] 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.

[0647] 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.

[0648] 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."

[0649] The present invention is a system that reduces the time and effort required for tasks such as product promotion and website analysis, and provides an environment in which operators can concentrate on providing high-quality products and improving services. Below, an embodiment of this system, "Assistant EC," is explained, along with program processing flows and specific examples.

[0650] Automate product image editing

[0651] The user uploads a product image from their device. The uploaded image is sent to the server, where it is resized and background-removed using a generative AI model, and the edited image is presented to the user as a preview. For example, if a user uploads an image of a shirt, the background is automatically changed to white and the image is resized to the appropriate size.

[0652] Product information auto-completion

[0653] When a user accesses the product information input form and enters the product name, the information is sent to the server. The server then activates the product information auto-completion AI, which retrieves relevant information from existing databases and external databases and reflects it in the input form. For example, if a user enters "smartwatch," the product description, specifications, price, etc. are automatically entered in the input form.

[0654] Automatic generation of product descriptions

[0655] After completing product information registration, the user requests automatic generation of a product description. This request is sent to the server, which passes the collected product information to the AI ​​model to generate the product description. The generated description is then sent to the user's device, where the user can review and approve the content. For example, a description such as "The latest smartwatch with high-performance heart rate measurement" is automatically generated.

[0656] Promotion optimization

[0657] The server automatically sets promotion targets based on user data and behavioral history. It uses generative AI models to optimize targeting and proposes promotion strategies that respond in real time. For example, it could display smartwatch ads during times when many female users in their 20s visit.

[0658] When a user requests the creation of a landing page, the server activates an AI generative model to automatically generate an effective landing page based on product features and keywords. For example, a landing page for a new smartwatch product is generated, and the page includes compelling copy and design.

[0659] Site optimization

[0660] The server collects and analyzes Google Analytics data and uses a generative AI model to automatically generate site improvement proposals. For example, if the time spent on a particular page is short, it will suggest updating the content or changing the layout. The server also monitors user behavior and suggests optimal page structure and navigation improvements. For example, if a particular category is frequently accessed, it will suggest adding that category to the main menu.

[0661] As described above, this system, "Assistant EC," automates and optimizes many of the tasks involved in operating an e-commerce site, allowing users to focus on strategic operations and improving services. Furthermore, by using this system, e-commerce sites can maintain their competitiveness and achieve sustainable growth.

[0662] The processing flow will be explained below.

[0663] Automate product image editing

[0664] Step 1:

[0665] The user selects a product image from the device and clicks the upload button.

[0666] Step 2:

[0667] The terminal transmits the selected image file to the server.

[0668] Step 3:

[0669] The server passes the received product images to the generative AI model.

[0670] Step 4:

[0671] The generative AI model removes backgrounds and resizes images.

[0672] Step 5:

[0673] The server temporarily stores the edited image and generates a preview image.

[0674] Step 6:

[0675] The server transmits the preview image to the terminal.

[0676] Step 7:

[0677] The user checks the preview on the terminal and presses the "Confirm" button.

[0678] Product information auto-completion

[0679] Step 1:

[0680] The user accesses the product information entry form.

[0681] Step 2:

[0682] The terminal acquires the product information input form from the server and displays it.

[0683] Step 3:

[0684] The user inputs the product name.

[0685] Step 4:

[0686] The terminal transmits the input product name to the server.

[0687] Step 5:

[0688] The server launches an AI that automatically completes product information and retrieves information by comparing it with past databases and external databases.

[0689] Step 6:

[0690] The server reflects the obtained detailed information in the input form.

[0691] Step 7:

[0692] The server sends the input form back to the terminal.

[0693] Step 8:

[0694] The user checks the auto-completed information and corrects it if necessary.

[0695] Step 9:

[0696] After checking, the user clicks the "Save" button.

[0697] Step 10:

[0698] The terminal transmits the final product information to the server.

[0699] Step 11:

[0700] The server stores the received product information in a database.

[0701] Automatic generation of product descriptions

[0702] Step 1:

[0703] After completing the product information registration process, the user clicks the "Auto-generate introduction" button.

[0704] Step 2:

[0705] The terminal sends this request to the server.

[0706] Step 3:

[0707] The server passes the collected product information to the generative AI model.

[0708] Step 4:

[0709] The generative AI model generates product descriptions.

[0710] Step 5:

[0711] The server temporarily stores the generated introduction and presents it to the user.

[0712] Step 6:

[0713] The server transmits the generated introduction to the terminal.

[0714] Step 7:

[0715] The user can review and approve the testimonial on their device and make any necessary corrections.

[0716] Step 8:

[0717] The user approves the final testimonial and sends it to the server.

[0718] Step 9:

[0719] The server stores the approved testimonials in a database.

[0720] Promotion optimization

[0721] Step 1:

[0722] The server collects user data and behavioral history.

[0723] Step 2:

[0724] The server uses generative AI models to optimize promotion targeting.

[0725] Step 3:

[0726] The server will suggest the best target audience and promotion strategy.

[0727] Step 4:

[0728] The user reviews and approves the proposed targeting and promotion strategy.

[0729] Step 5:

[0730] The server prepares to execute the promotion strategy based on the approved strategy.

[0731] Site optimization

[0732] Step 1:

[0733] The server collects data for Google Analytics.

[0734] Step 2:

[0735] The server analyzes the data using a generative AI model.

[0736] Step 3:

[0737] The server automatically generates suggestions for improving the site.

[0738] Step 4:

[0739] The server presents improvement suggestions to the user.

[0740] Step 5:

[0741] The user reviews and approves the proposed improvements.

[0742] Step 6:

[0743] The server implements the proposals to update the site layout and content based on the approved improvements.

[0744] These steps will enable the "Assistant EC" system to function efficiently, reduce the workload of EC site operators, and improve the quality of service provided.

[0745] Example 1

[0746] 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."

[0747] Modern e-commerce site operations require a wide range of tasks, including editing product images, completing product information, creating product descriptions, optimizing promotion targeting, generating landing pages, and proposing site improvements. These tasks place a heavy burden on site operators. This makes it difficult for site operators to devote sufficient time to providing high-quality products and improving services, which is what they should be focusing on. To solve this problem, a system is needed to automate and streamline these tasks.

[0748] 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.

[0749] In this invention, the server includes: a means for a user to upload product images; a means for the server to pass the product images to a generative AI model and perform background removal and size adjustment; a means for presenting the generated preview image to the user and requesting confirmation; a means for the user to input a product name; a means for the server to activate a product information auto-completion AI and automatically update an input form with detailed information based on the product name; a means for the user to confirm, correct, and save the automatically generated product information; a means for the user to request automatic generation of a product description; a means for the server to pass collected product information to a generative AI model, generate a product description, and present it to the user; a means for the server to automatically set promotion targets based on user data and behavioral history and optimize targeting using a generative AI model; a means for the user to request creation of a landing page; a means for the server to automatically generate a landing page based on product features and keywords using a generative AI model; and a means for the server to automatically generate site improvement plans using Google Analytics data and present them to the user. This enables a wide range of e-commerce site operations to be performed efficiently and automatically.

[0750] "User" refers to the person who operates the e-commerce site or the person who uses the system.

[0751] "Terminal" refers to electronic devices such as personal computers, smartphones, and tablets operated by users.

[0752] "Server" refers to a computer system that receives and processes data sent by users.

[0753] "Product image" refers to an image file that visually represents a product sold on an e-commerce site.

[0754] A "generative AI model" refers to an artificial intelligence model that automatically handles specific tasks based on machine learning or deep learning.

[0755] "Background removal" refers to the process of automatically removing the background from a product image.

[0756] "Resizing" refers to the process of automatically resizing product images to the appropriate dimensions.

[0757] The term "preview image" refers to an image that is temporarily displayed to allow the user to check the product image processed by the server.

[0758] "Product information input form" refers to a web form for entering product information such as product name, description, and specifications.

[0759] "Product information automatic completion AI" refers to an artificial intelligence model that automatically completes detailed information based on the product name entered.

[0760] "Product description" refers to text that explains the features and benefits of a product.

[0761] "Promotion target" refers to a group of users who are the target of promotional activities.

[0762] "Targeting optimization" refers to the process of selecting and implementing the most effective promotional strategies for a specific user demographic.

[0763] "Landing page" refers to a dedicated web page related to a specific product or campaign.

[0764] "Google Analytics" refers to a tool for analyzing website traffic and user behavior.

[0765] "Site Improvement Suggestions" means suggestions for improving a website to improve user experience or conversion rates.

[0766] This invention is a system for automatically editing product images, automatically completing product information, automatically generating product descriptions, optimizing promotions, and optimizing websites. This system automates and optimizes various tasks so that users can concentrate on providing products and improving services.

[0767] Automate product image editing

[0768] The user uploads a product image from their device. The uploaded image is sent to the server. The server uses a generative AI model (e.g., the "Remove.bg API" for background removal and the "OpenCV" library for resizing) to remove the background and resize the image. The edited image is presented to the user as a preview, and the user can confirm the results. An example of a specific prompt is as follows: "Remove the background to white and generate an appropriately resized image."

[0769] Product information auto-completion

[0770] The user enters the product name into a product information input form. The entered product name information is sent to the server, which then activates an AI that automatically completes product information (e.g., the "GPT-4" model). The server retrieves related information from existing databases or external databases (e.g., Amazon Product Advertising API) and reflects it in the input form. For example, if "smartwatch" is entered, the product description, specifications, price, etc. will be automatically entered in the input form. An example of a prompt sentence is "Enter the product name 'smartwatch'. Detailed information will be automatically completed."

[0771] Automatic generation of product descriptions

[0772] After completing the product information registration, the user requests the automatic generation of a product description. This request is sent to the server, which passes the collected product information to a generative AI model (e.g., "GPT-4") to generate a product description. This generated description is presented to the user, who then confirms and approves the content. For example, a description for "the latest smartwatch with high-performance heart rate measurement" is generated. An example of a prompt would be "Please automatically generate a product description for a smartwatch. Please particularly emphasize the heart rate measurement function."

[0773] Promotion optimization

[0774] The server automatically sets promotion targets based on user data and behavioral history. It uses a generative AI model (e.g., "Segmentation AI Model") to optimize targeting and propose promotion strategies that respond in real time. For example, it displays ads for smartwatches during times when many female users in their 20s visit. Also, when a user requests the creation of a landing page, the server uses a generative AI model to automatically generate an effective landing page based on product features and keywords. For example, a landing page for a new smartwatch product is generated. This page includes attractive copy and design. An example of a prompt sentence is, "Please generate a landing page for our new smartwatch product."

[0775] Site optimization

[0776] The server collects and analyzes Google Analytics data and uses a generative AI model (e.g., "Data Analysis AI") to automatically generate site improvement proposals. For example, if the time spent on a particular page is short, it will suggest updating the content or changing the layout. The server also monitors user behavior and suggests appropriate page structure and navigation improvement proposals. For example, if a particular category is frequently accessed, it will suggest adding that category to the main menu. An example prompt is, "Generate site improvement proposals based on Google Analytics data."

[0777] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0778] Automate product image editing

[0779] Step 1:

[0780] The user uploads a product image from their device.

[0781] Specific operation: The user clicks the upload button in the browser and selects the product image file.

[0782] Input: Product image file

[0783] Output: Product image files are sent to the server

[0784] Step 2:

[0785] The server receives the product image.

[0786] Specific operation: The server receives the HTTP request and saves the image file.

[0787] Input: Product image file

[0788] Output: Product images stored in server storage

[0789] Step 3:

[0790] The server uses the generative AI model to remove backgrounds and resize images.

[0791] Specific operation: The server calls the "Remove.bg API" to remove the background and resizes the image using "OpenCV".

[0792] Input: Product images stored in server storage

[0793] Output: Background removed and resized product image

[0794] Step 4:

[0795] The server presents the edited image to the user as a preview.

[0796] Specific operation: The server sends the edited image to the user's device as an HTTP response, and a preview is displayed in the browser.

[0797] Input: Background removed and resized product image

[0798] Output: A preview image that is displayed in the user's browser.

[0799] Product information auto-completion

[0800] Step 1:

[0801] The user enters the product name in the product information input form.

[0802] Specific behavior: The user types the product name into the form field and clicks the submit button.

[0803] Input: Product name

[0804] Output: Product name sent to the server as an HTTP request

[0805] Step 2:

[0806] The server receives the product name and activates the product information auto-completion AI.

[0807] How it works: The server analyzes the product name and sends a query to the GPT-4 model.

[0808] Input: Product name received as an HTTP request

[0809] Output: Query passed to product information auto-completion AI

[0810] Step 3:

[0811] The server retrieves relevant information from existing databases or external databases and reflects it in the input form.

[0812] Specific operation: The server executes a database query and sends the retrieved data to the user terminal in JSON format.

[0813] Input: Product name query, related information in the database

[0814] Output: Detailed information in JSON format (product description, specifications, price, etc.)

[0815] Step 4:

[0816] The user checks, edits, and saves the automatically generated product information.

[0817] Specific behavior: The user checks the details, makes any necessary corrections, and clicks the save button.

[0818] Input: Auto-completed product information

[0819] Output: Product information confirmed and corrected by the user is saved on the server

[0820] Automatic generation of product descriptions

[0821] Step 1:

[0822] After completing the registration of product information, the user requests automatic generation of a product description.

[0823] Specific operation: The user clicks the "Automatically generate introduction" button.

[0824] Input: User action (button click)

[0825] Output: A testimonial generation request is sent to the server

[0826] Step 2:

[0827] The server passes the collected product information to a generative AI model, which then generates a product description.

[0828] How it works: The server sends product information to the GPT-4 model and receives the generated description.

[0829] Input: Collected product information

[0830] Output: Generated product description

[0831] Step 3:

[0832] The server presents the generated testimonial to the user.

[0833] Specific operation: The server sends the generated introduction to the user's terminal as an HTTP response and displays it in the browser.

[0834] Input: Generated product description

[0835] Output: Product description displayed in the user's browser

[0836] Promotion optimization

[0837] Step 1:

[0838] The server automatically sets promotion targets based on user data and behavioral history.

[0839] Specific operation: The server extracts user data from the database and analyzes it.

[0840] Input: User data, behavioral history

[0841] Output: Promotion target data as analysis results

[0842] Step 2:

[0843] The server uses the generative AI model to optimize targeting.

[0844] Specific operation: The server uses the "Segmentation AI Model" to optimize promotion targets.

[0845] Input: Promotion target data

[0846] Output: Optimized targeting information

[0847] Step 3:

[0848] The server proposes a promotion strategy to the user that responds in real time.

[0849] Specific operation: The server generates a promotion strategy based on the optimized targeting information and proposes it to the user.

[0850] Input: Optimized targeting information

[0851] Output: Promotion strategies presented to the user

[0852] Step 4:

[0853] A user requests the creation of a landing page.

[0854] Specific operation: The user enters the required information into the landing page creation request form and clicks the submit button.

[0855] Input: Information entered in the landing page creation request form

[0856] Output: A landing page creation request is sent to the server

[0857] Step 5:

[0858] The server uses a generative AI model to automatically generate landing pages based on product features and keywords.

[0859] How it works: The server sends the request to the GPT-4 model and receives the generated landing page.

[0860] Input: Product features and keywords

[0861] Output: Auto-generated landing page

[0862] Site optimization

[0863] Step 1:

[0864] The server collects and analyzes Google Analytics data.

[0865] Specific operation: The server calls the Google Analytics API and retrieves the data.

[0866] Input: Google Analytics data

[0867] Output: Parsed site performance data

[0868] Step 2:

[0869] The server uses the generative AI model to automatically generate site improvement proposals.

[0870] Specific operation: The server uses "Data Analysis AI" to generate site improvement proposals based on the analysis data.

[0871] Input: Site performance data

[0872] Output: Generated site improvement proposals

[0873] Step 3:

[0874] The server monitors user behavior and suggests optimal page structure and navigation improvements.

[0875] Specific operation: The server monitors user behavior in real time and generates improvement suggestions.

[0876] Input: User behavior data

[0877] Output: Generated navigation improvement suggestions

[0878] (Application example 1)

[0879] 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."

[0880] E-commerce site operators need to efficiently manage a large number of products and quickly carry out high-quality promotions. This requires a great deal of time and effort for tasks such as image editing, information entry, and promotion setup. A system is needed to solve this issue and allow operators to focus on more strategic tasks.

[0881] 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.

[0882] In this invention, the server includes a means for users to upload product images, a means for passing the product images to a generative AI model to remove backgrounds and adjust the size, a means for presenting the generated preview image to the user for confirmation, and a means for the server to analyze user data and behavioral history and optimize promotion strategies. This allows e-commerce site operators to significantly reduce the effort and time required for image editing, information input, and promotion settings, enabling them to efficiently provide high-quality products and improve services.

[0883] definition statement

[0884] "User" refers to an individual or organization that uses the system as an operator or administrator of an EC site.

[0885] A "server" is a computer system that handles the entire system, including image editing, data analysis, information supplementation, and launching generative AI models.

[0886] "Product images" are graphic data such as product photos and illustrations posted on e-commerce sites.

[0887] A "generative AI model" is an artificial intelligence framework that uses techniques such as machine learning and deep learning to automatically handle specific tasks.

[0888] "Background removal" is the process of removing the background from a product image and cutting out only the product.

[0889] "Size adjustment" is the operation of changing the size of product images to a size suitable for posting on an e-commerce site.

[0890] A "preview image" is a trial image shown to the user after background removal and resizing have been performed by the generative AI model.

[0891] "Product Information Automatic Completion AI" is an artificial intelligence system that automatically infers and inputs detailed product information based on the product name.

[0892] A "product description" is a sentence generated to explain the features and advantages of a product.

[0893] A "promotion strategy" is a plan or method for effectively implementing advertisements or campaigns aimed at a specific user demographic.

[0894] "Google Analytics" is a tool for collecting and analyzing website traffic data.

[0895] "Site Improvement Suggestions" are specific suggestions for improving the usability and quality of the content of a website.

[0896] MODE FOR CARRYING OUT THE INVENTION

[0897] The present invention provides a system that allows an EC site operator to efficiently edit product images, input product information, and set promotion strategies. A specific embodiment of this system is described below.

[0898] System Overview

[0899] The system for implementing this invention includes a user terminal, a server, a generative AI model, an AI for automatically completing product information, Google Analytics, etc. Its main functions include automatic editing of product images, automatic completion of product information, automatic generation of product descriptions, optimization of promotion strategies, and optimization of the website.

[0900] Automate product image editing

[0901] Users upload product images using their devices. The uploaded images are sent to a server, which uses a generative AI model to remove backgrounds and resize the images. The edited images are then presented to the user as preview images. For example, if a user uploads an image of a shirt, the background is automatically changed to white and the image is resized to the appropriate size.

[0902] Product information auto-completion

[0903] When a user accesses the product information input form and enters the product name, the information is sent to the server. The server then activates the product information auto-completion AI, which retrieves relevant information from existing databases and external databases and automatically reflects it in the input form. For example, if a user enters "smartwatch," the product description, specifications, price, etc. are automatically entered.

[0904] Automatic generation of product descriptions

[0905] After completing the product information registration, the user requests the automatic generation of a product description. This request is sent to the server, which passes the collected product information to the generative AI model to generate the product description. The generated description is then sent to the user's device, where the user can review and approve the content. As a specific example, a description such as "The latest smartwatch with high-performance heart rate measurement" is automatically generated.

[0906] Promotion optimization

[0907] The server automatically sets promotion targets based on user data and behavioral history. It uses a generative AI model to optimize targeting and proposes promotion strategies that respond in real time. For example, one possible method is to display smartwatch ads during times when many female users in their 20s visit. When a user requests the creation of a landing page, the server activates the AI ​​generative model and automatically generates an effective landing page based on product features and keywords.

[0908] Site optimization

[0909] The server collects and analyzes Google Analytics data and uses a generative AI model to automatically generate site improvement proposals. For example, if the time spent on a particular page is short, it will suggest updating the content or changing the layout. The server also monitors user behavior and suggests optimal page structure and navigation improvements. For example, if a particular category is frequently accessed, it will suggest adding that category to the main menu.

[0910] Prompt Sentence Examples

[0911] An example of a prompt for automatically editing product images is, "Please change the background of uploaded product images to white and resize them to appropriate retail sizes."

[0912] As described above, this system allows e-commerce site operators to significantly reduce the effort required for image editing, information input, and promotion settings, allowing them to focus on strategic operations and improving services.

[0913] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0914] Program processing steps

[0915] Step 1:

[0916] The user uploads a product image from the terminal. The input of this step is the product image file selected by the user from the terminal, and the output is the raw data transferred to the server.

[0917] Step 2:

[0918] The server passes the received product image data to the generative AI model, which removes the background and adjusts the size. The input for this step is the product image data sent to the server, and the generative AI model removes the background and adjusts the size, resulting in an edited preview image.

[0919] Step 3:

[0920] The server presents the generated preview image to the user for confirmation. The input to this step is the preview image after background removal and resizing, and the output is a confirmation screen presented to the user. The user can confirm or make corrections.

[0921] Step 4:

[0922] The user inputs the product name from the terminal and sends it to the server. The input of this step is the product name input by the user from the terminal, and the output is the product name data passed to the server.

[0923] Step 5:

[0924] The server launches the product information auto-completion AI, which automatically fills in the input form with detailed information based on the product name. The input for this step is the product name data, the product information auto-completion AI retrieves related information from the database, and the output is form data with complete product information filled in.

[0925] Step 6:

[0926] The user confirms, modifies, and saves the automatically generated product information. The input of this step is the form data with the product information filled in, and the output is the final product information confirmed and modified by the user.

[0927] Step 7:

[0928] The user requests automatic generation of a product description and sends a request to the server. The input of this step is the automatic generation request sent from the user's terminal, and the output is the request data passed to the server.

[0929] Step 8:

[0930] The server passes the collected product information to the generative AI model to generate a product description. The input of this step is the collected product information, the generative AI model creates a product description, and the output is the generated product description.

[0931] Step 9:

[0932] The server presents the generated product description to the user, who then confirms and approves the content. The input to this step is the generated product description, and the output is text data for confirmation that is presented to the user. The user can confirm or make corrections.

[0933] Step 10:

[0934] The server analyzes user data and behavioral history to optimize the promotion strategy. The input of this step is user data and behavioral history, and the output is an optimized promotion strategy after data analysis using a generative AI model.

[0935] Step 11:

[0936] The server collects and analyzes Google Analytics data and automatically generates site improvement proposals. The input for this step is traffic data from Google Analytics, and the output is proposed site improvement proposals after data analysis using a generative AI model.

[0937] By implementing each of the above steps, e-commerce site operators can efficiently provide high-quality products and improve their services.

[0938] 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.

[0939] The present invention is a system that reduces the time and effort required for tasks such as product promotion and website analysis, and provides an environment in which operators can concentrate on providing high-quality products and improving services. Furthermore, by combining it with an emotion engine that recognizes user emotions, the quality of the user experience can be improved. Below, we will explain an embodiment in which this system, "Assistant EC," is combined with an emotion engine.

[0940] Automate product image editing

[0941] A user uploads a product image from their device. At this time, an emotion engine is built into the device, which recognizes emotions from the user's facial expressions and voice. The uploaded image and emotion data are sent to the server. The server uses a generative AI model to remove the background and adjust the size of the image, and presents the edited image to the user as a preview. For example, if a user uploads an image of a shirt and shows a dissatisfied expression, the background color and brightness are automatically adjusted and the preview is displayed again.

[0942] Product information auto-completion

[0943] When a user accesses a product information input form and enters a product name, the information is sent to the server. The server then activates the product information auto-completion AI, which retrieves relevant information from existing and external databases and reflects it in the input form. The emotion engine continues to monitor the user's facial expressions and reactions, analyzing whether the user is satisfied or dissatisfied. For example, if the user shows a confused expression while entering "smartwatch," the engine automatically adjusts to provide a more detailed explanation or alternative suggestions.

[0944] Automatic generation of product descriptions

[0945] After completing product information registration, the user requests automatic generation of a product description. This request is sent to the server, which passes the collected product information to the generative AI model to generate the product description. The generated description is then sent to the device along with the user's emotional data, and the user confirms and approves the content. For example, when a description such as "The latest smartwatch with high-performance heart rate measurement" is generated, if the user shows a happy expression, approval will proceed. If the user shows a dissatisfied expression, a regeneration will be suggested.

[0946] Promotion optimization

[0947] The server automatically sets promotion targets based on user data and behavioral history. It uses a generative AI model to optimize targeting and leverages emotional data collected through an emotion engine to propose promotion strategies that respond in real time. For example, when displaying smartwatch ads during times when many female users in their 20s visit, the server analyzes their emotional responses to past ads and selects the most appealing method.

[0948] Additionally, when a user requests the creation of a landing page, the server activates an AI generative model to automatically generate an effective landing page based on product features and keywords. The emotion engine analyzes the user's reactions while viewing a preview of the landing page. For example, a landing page for a new smartwatch product is generated, containing appealing copy and design. If the user expresses satisfaction after viewing the preview, the page is published as is. If the user expresses dissatisfaction, the page is re-adjusted.

[0949] Site optimization

[0950] The server collects and analyzes data from Google Analytics and uses a generative AI model to automatically generate suggestions for improving the site. The emotion engine monitors users' reactions while they browse the site and provides the accumulated data to the generative AI model. For example, if the time spent on a particular page is short, it suggests updating the content or changing the layout. The server also monitors user behavior and suggests optimal page structure and navigation improvements. For example, if a particular category is frequently accessed, it suggests adding that category to the main menu.

[0951] These steps enable users, devices, and servers to work together efficiently using the emotion engine to carry out the processes of product registration, information supplementation, promotion, and site improvement. By incorporating emotion recognition, it is possible to enhance the user experience and improve the efficiency and quality of e-commerce site operations.

[0952] The processing flow will be explained below.

[0953] Automate product image editing

[0954] Step 1:

[0955] The user selects a product image from the device and clicks the upload button.

[0956] Step 2:

[0957] The terminal transmits the selected image file to the server, and simultaneously collects and transmits the user's facial expression data using an emotion engine.

[0958] Step 3:

[0959] The server passes the received product images to the generative AI model.

[0960] Step 4:

[0961] The generative AI model removes backgrounds and resizes images.

[0962] Step 5:

[0963] The server temporarily stores the edited image and generates a preview image.

[0964] Step 6:

[0965] The server analyzes the user's facial expression data along with the preview image and presents it to the user.

[0966] Step 7:

[0967] The user checks the preview on the terminal and presses the "Confirm" button.

[0968] Step 8:

[0969] If the user's facial expression indicates dissatisfaction, the server calls the generative AI model again and re-edits the image.

[0970] Product information auto-completion

[0971] Step 1:

[0972] The user accesses the product information entry form.

[0973] Step 2:

[0974] The terminal acquires the product information input form from the server and displays it.

[0975] Step 3:

[0976] The user inputs the product name.

[0977] Step 4:

[0978] The terminal transmits the input product name to the server, and also collects and transmits the user's facial expression data using an emotion engine.

[0979] Step 5:

[0980] The server launches an AI that automatically completes product information and retrieves information by comparing it with past databases and external databases.

[0981] Step 6:

[0982] The server reflects the obtained detailed information in the input form and resends it to the user.

[0983] Step 7:

[0984] The terminal displays an input form to the user and continuously collects the user's facial expression data using an emotion engine.

[0985] Step 8:

[0986] The user checks the auto-completed information and corrects it if necessary.

[0987] Step 9:

[0988] After checking, the user clicks the "Save" button.

[0989] Step 10:

[0990] The terminal transmits the final product information to the server, and also transmits the final facial expression data of the user.

[0991] Step 11:

[0992] The server stores the received product information and facial expression data in a database and improves the accuracy of the information based on the results of analyzing the facial expression data.

[0993] Automatic generation of product descriptions

[0994] Step 1:

[0995] After completing the product information registration process, the user clicks the "Auto-generate introduction" button.

[0996] Step 2:

[0997] The terminal sends this request to the server, and the emotion engine collects and transmits the user's facial expression data at the same time.

[0998] Step 3:

[0999] The server passes the collected product information to the generative AI model.

[1000] Step 4:

[1001] The generative AI model generates product descriptions.

[1002] Step 5:

[1003] The server analyzes the user's facial expression data along with the generated introduction and presents it to the user.

[1004] Step 6:

[1005] The user checks the introduction on the device and reacts with facial expressions.

[1006] Step 7:

[1007] If the user shows a satisfied expression, they approve the testimonial and press the "Confirm" button. If the expression shows dissatisfaction, the server calls the AI ​​model again to regenerate the testimonial.

[1008] Step 8:

[1009] The user approves the final testimonial and sends it to the server.

[1010] Step 9:

[1011] The server stores the approved testimonials in a database.

[1012] Promotion optimization

[1013] Step 1:

[1014] The server collects user data and behavioral history, and also collects emotion data through an emotion engine.

[1015] Step 2:

[1016] The server uses generative AI models to optimize promotion targeting.

[1017] Step 3:

[1018] The server analyzes the sentiment data and suggests optimal target demographics and promotion strategies.

[1019] Step 4:

[1020] The user reviews and approves the proposed targeting and promotion strategy.

[1021] Step 5:

[1022] The server prepares to execute the promotion strategy based on the approved strategy.

[1023] Step 6:

[1024] During the promotion, the emotion engine continuously monitors user reactions and adjusts the promotion content in real time as needed.

[1025] Site optimization

[1026] Step 1:

[1027] The server collects data for Google Analytics.

[1028] Step 2:

[1029] The server collects user emotion data through an emotion engine.

[1030] Step 3:

[1031] The server analyzes the data using a generative AI model.

[1032] Step 4:

[1033] The server automatically generates site improvement proposals and presents them to the user.

[1034] Step 5:

[1035] The user reviews and approves the proposed improvements.

[1036] Step 6:

[1037] The server implements the proposals to update the site layout and content based on the approved improvements.

[1038] Step 7:

[1039] The server continuously analyzes data from the emotion engine and generates additional improvement suggestions to maximize user satisfaction.

[1040] Through these specific processing steps, the "Assistant EC" system can utilize user emotional data to streamline EC site operations and improve the quality of the user experience.

[1041] Example 2

[1042] 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."

[1043] In conventional e-commerce site operations, editing product images, completing product information, promoting products, and optimizing the site takes a lot of time and effort, making it difficult for operators to focus on providing high-quality products and improving services. In addition, because the site does not take user emotions into consideration, there is also the issue of the quality of the user experience not being sufficiently improved.

[1044] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: a means for a user to upload a product image; a means for a terminal to recognize emotions using an emotion engine that analyzes the user's facial expressions and voice data; a means for the terminal to transmit image data and emotion data to the server; a means for the server to pass the product image to a generative AI model and perform background removal and size adjustment; a means for presenting a generated preview image to the user and suggesting re-editing based on the emotion data; a means for a user to input a product name; a means for the server to start a product information automatic completion AI and automatically reflect detailed information in an input form based on the product name; a means for the emotion engine to monitor the user's emotion data and adjust the completion information; a means for a user to confirm, correct, and save the automatically generated product information; a means for a user to request automatic generation of a product description; a means for the server to pass collected product information to a generative AI model, generate a product description, and present it to the user; and a means for suggesting re-generation based on the generated description and emotion data. This allows for efficient product registration, information supplementation, promotion, and site improvement, and makes it possible to provide a high-quality user experience that takes user emotions into consideration.

[1045] "User" means any person or entity that uses the System to upload Product Images and enter, review and edit Product Information.

[1046] "Device" refers to an electronic device operated by a user, such as a computer, tablet, or smartphone, which includes an emotion engine and an image upload function.

[1047] An "emotion engine" is a software or hardware function that analyzes a user's facial expressions and voice data and recognizes their emotional state in real time.

[1048] A "server" is a remote computer system that performs editing of product images, completing product information, generating product descriptions, and other related processing.

[1049] A "generative AI model" is a machine learning model used to automatically perform tasks such as background removal, sizing, and product description generation.

[1050] A "preview image" is a product image edited using a generative AI model, and is an image that can be reviewed and re-edited by the user.

[1051] "Product Information Automatic Completion AI" is an artificial intelligence technology that automatically reflects detailed information in input forms based on the product name entered.

[1052] "Re-editing suggestions" are suggestions for re-editing using a generative AI model when the emotion engine detects a dissatisfied expression or reaction from the user.

[1053] A "product description" is a text description that is automatically generated by a generative AI model based on product information.

[1054] "Site Improvement Suggestions" are suggestions for improving the structure and content of a website generated by the server based on Google Analytics data and user sentiment data.

[1055] This invention is a system that reduces the time and effort required for tasks such as product promotion and website analysis, and provides an environment where operators can concentrate on providing high-quality products and improving services. In particular, it is characterized by improving the quality of the user experience by combining it with an emotion engine that recognizes user emotions.

[1056] Automate product image editing

[1057] The user uploads a product image from their device. The device is equipped with an emotion engine that captures the user's facial expressions and voice and recognizes their emotions. The uploaded image and emotion data are sent from the device to a server. The server uses a generative AI model to remove the background and resize the image. The edited image is presented to the user as a preview, and re-editing may be suggested based on the emotion data.

[1058] For example, if a user shows dissatisfaction after uploading a picture of a shirt, a prompt such as "Upload a picture of a shirt" could be used to automatically adjust the background color and brightness and display the preview again.

[1059] Product information auto-completion

[1060] When a user accesses the product information input form and enters the product name, the entered information is sent from the device to the server. The server then activates the product information auto-completion AI, retrieves relevant information from existing databases and external databases, and reflects it in the input form. During this time, the emotion engine continues to monitor the user's facial expressions and reactions, analyzing whether the user is satisfied or dissatisfied.

[1061] For example, if the user shows a confused expression while typing "smartwatch," the server automatically adjusts to provide a more detailed explanation or alternatives, such as a prompt that reads, "Complete the smartwatch information."

[1062] Automatic generation of product descriptions

[1063] After completing product information registration, the user requests automatic generation of a product description. This request is sent from the device to the server. The server passes the collected product information to a generative AI model to generate a product description. The generated description is then sent to the device along with emotional data, and the user checks and approves the content. Regeneration may also be suggested based on the user's emotional data.

[1064] A specific example of a prompt sentence is "Generate a description of a smartwatch."

[1065] Promotion optimization

[1066] The server automatically sets promotion targets based on user data and behavioral history, and optimizes targeting using a generative AI model. It utilizes emotional data collected through the emotion engine to propose promotion strategies that respond in real time. For example, when displaying smartwatch ads during times when many female users in their 20s visit, it analyzes their emotional responses to past ads and selects the most appealing method.

[1067] Additionally, when a user requests the creation of a landing page, the server activates a generative AI model to automatically generate an effective landing page based on product features and keywords. The emotion engine analyzes the user's reactions while displaying a preview of the landing page. An example of a prompt sentence would be "Optimize the promotional strategy for smartwatches."

[1068] Site optimization

[1069] The server collects and analyzes Google Analytics data and uses a generative AI model to automatically generate site improvement recommendations. The emotion engine monitors users' reactions while browsing the site and provides the data to the generative AI model. For example, if a particular category is frequently accessed, it will suggest adding that category to the main menu. Content updates and layout changes will also be suggested.

[1070] A specific example of a prompt might be "Improve time spent on site."

[1071] This allows users, devices, and servers to work together efficiently using the emotion engine, and executes the processes of product registration, information supplementation, promotion, and site improvement, improving the user experience as well as the efficiency and quality of e-commerce site operations.

[1072] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1073] Step 1:

[1074] A user uploads a product image.

[1075] Input: Product image taken or selected by the user

[1076] How it works: The user accesses the image upload screen in a web browser or a dedicated app, selects an image file, and presses the upload button.

[1077] Output: Uploaded product image data

[1078] Step 2:

[1079] The device analyzes the user's facial expressions and voice data.

[1080] Input: Upload product images and receive user's facial expressions and voice

[1081] How it works: The device's camera and microphone capture the user's face and voice, and the emotion engine analyzes this data in real time.

[1082] Output: User emotion data (e.g., satisfied, dissatisfied, confused)

[1083] Step 3:

[1084] The terminal transmits image data and emotion data to the server.

[1085] Input: Product image data, user emotion data

[1086] How it works: The device sends product image data and emotion data to the server using an HTTP request.

[1087] Output: Product image data and emotion data received by the server

[1088] Step 4:

[1089] The server passes product images to a generative AI model for background removal and resizing.

[1090] Input: Product image data

[1091] How it works: The server inputs product image data into the generative AI model, performs background removal and resizing.

[1092] Output: Edited product image data

[1093] Step 5:

[1094] The server presents the generated preview image to the user.

[1095] Input: Edited product image data

[1096] Operation: The server sends the edited image to the device, which displays it as a preview screen.

[1097] Output: Preview image that the user sees

[1098] Step 6:

[1099] Suggest re-editing based on emotional data.

[1100] Input: Emotion data when the user checks the preview image

[1101] How it works: The emotion engine analyzes the user's emotion and sends a re-editing request to the server. If the emotion is dissatisfied, the re-editing process is executed.

[1102] Output: Re-edited product image data

[1103] Step 7:

[1104] The user inputs the product name.

[1105] Input: Product name entered by the user

[1106] How it works: The user enters the product name into the input screen of a web form or dedicated app.

[1107] Output: Entered product name data

[1108] Step 8:

[1109] The server launches an AI that automatically completes product information, and automatically reflects detailed information in the input form based on the product name.

[1110] Input: Product name data entered by the user

[1111] How it works: The server uses product information auto-completion AI to retrieve relevant details from an internal database or external API and populate the input form.

[1112] Output: Auto-completed product information data

[1113] Step 9:

[1114] The emotion engine monitors the user's emotion data and adjusts the complementary information.

[1115] Input: Emotional data while the user is reviewing the form

[1116] How it works: The emotion engine analyzes the user's emotions in real time, and if they indicate dissatisfaction or confusion, it requests the server to re-adjust the details.

[1117] Output: Adjusted product information data

[1118] Step 10:

[1119] The user checks, edits, and saves the automatically generated product information.

[1120] Input: Auto-completed product information data

[1121] Action: The user reviews the form, corrects the information as needed, and clicks the save button.

[1122] Output: Saved product information data

[1123] Step 11:

[1124] A user requests automatic generation of a product description.

[1125] Input: Data after product information registration is complete

[1126] How it works: The user clicks the auto-generate product description button, sending a request to the server.

[1127] Output: Product description generation request data

[1128] Step 12:

[1129] The server passes the collected product information to a generative AI model, which then generates a product description.

[1130] Input: Saved product information data

[1131] How it works: The server inputs product information into a generative AI model, which then generates a product description.

[1132] Output: Generated product description data

[1133] Step 13:

[1134] We propose regeneration based on the generated product description and emotion data.

[1135] Input: Product description data, user emotion data

[1136] How it works: The server sends the generated product description and the user's emotion data to the device, where the user confirms it. The emotion engine analyzes the user's reaction and suggests regenerating the expression if it is dissatisfied.

[1137] Output: Revised product description data or regeneration suggestions

[1138] Step 14:

[1139] The server automatically generates site improvement proposals using Google Analytics data and sentiment data and presents them to the user.

[1140] Input: Google Analytics data, user sentiment data

[1141] How it works: The server analyzes the collected data and uses a generative AI model to generate site improvement suggestions.

[1142] Output: Generated site improvement proposal data

[1143] (Application example 2)

[1144] 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."

[1145] In conventional e-commerce site operations, each process, such as editing product images, automatically completing product information, and generating product descriptions, requires a lot of time and effort. Furthermore, there is a lack of efforts to improve services that incorporate user emotions, resulting in a poor user experience. This makes it difficult for site operators to focus on providing high-quality products and improving services. It is necessary to solve these problems and improve the efficiency of e-commerce site operations and the user experience.

[1146] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for a user to upload a product image; a terminal including an emotion recognition engine that recognizes emotions from the user's facial expressions and voice; means for the server to pass the product image to a generative AI model and perform background removal and size adjustment; means for using the user's emotion data to reflect the adjustment results when editing the image; means for presenting a generated preview image to the user and requesting confirmation; means for the user to input a product name; means for the server to start a product information automatic completion AI and automatically reflect detailed information in an input form based on the product name; means for the emotion recognition engine to monitor the user's reaction and automatically provide detailed information or alternatives when detecting a dissatisfied expression, means for the user to confirm, correct, and save the automatically generated product information; means for the user to request automatic generation of a product description; means for the server to pass collected product information to the generative AI model, generate a product description, and present it to the user; and means for the emotion recognition engine to analyze the user's reaction and suggest regeneration when the user shows a dissatisfied expression. This will enable the efficient execution of each process of product registration, information supplementation, promotion, and site improvement, enhancing the user experience and improving the efficiency and quality of e-commerce site operations.

[1147] "User" means a person who uses the system to upload product images and enter, confirm, modify and save product information.

[1148] An "emotion recognition engine" is software or hardware that analyzes a user's facial expressions and voice to recognize emotions.

[1149] A "terminal" is a device used by a user, such as a computer, smartphone, or tablet.

[1150] A "server" is a computer system that processes product images, product information, automatically generated product descriptions, etc., and stores and provides data.

[1151] A "generative AI model" is a model that uses artificial intelligence technology to automatically perform processes such as removing backgrounds from images, adjusting size, completing product information, and generating product descriptions.

[1152] "Background removal" is a process that removes unnecessary background parts from an image.

[1153] "Resizing" is the process of changing the dimensions of an image to meet specific criteria.

[1154] A "preview image" is a temporary display version of an image that is presented to the user after editing.

[1155] "Product Information Automatic Completion AI" is an artificial intelligence that automatically completes related detailed information based on input information such as product names.

[1156] "Passing to the generative AI model" means inputting the data collected by the server into the generative AI model.

[1157] A "product description" is text that explains the features and benefits of a product.

[1158] "Promotion" is a marketing activity to promote the sale of a product.

[1159] The present invention relates to a system for improving the efficiency of product promotion and site analysis in the operation of an e-commerce site, and for improving the user experience. Specific embodiments of the present invention will be described below.

[1160] The system that realizes this invention consists of a user's device, an emotion recognition engine, a generative AI model, and a server. The user uploads product images from their device, which has an emotion recognition engine built into it. The emotion recognition engine analyzes the user's facial expressions and voice to collect emotional data. This data and the product images are sent to the server.

[1161] The server includes the following means:

[1162] 1. Product Image Processing:

[1163] The server passes the uploaded product image to a generative AI model, which removes the background and adjusts the size. The server then reflects the adjustment results based on the user's emotional data collected by the emotion recognition engine, and presents the generated preview image to the user. For example, if the user shows a dissatisfied expression, the system automatically adjusts the background color and brightness and displays the preview again.

[1164] 2. Product information auto-completion:

[1165] When a user enters a product name, the server activates an AI that automatically completes product information, retrieving related information from existing and external databases and displaying it in the input form. The emotion recognition engine monitors the user's reactions, and if the user shows a confused expression, it automatically provides more detailed information or alternatives. For example, if the user shows a confused expression while typing "smartwatch," it provides complementary information such as sports functions, heart rate monitor, and blood oxygen monitor.

[1166] 3. Automatically generate product descriptions:

[1167] After a user completes the product information registration process, they can request the automatic generation of a product description. The server passes the collected product information to a generative AI model, which generates the product description. The generated description is sent to the device along with the user's emotional data, and the user confirms and approves the content. If the user shows a dissatisfied expression, the server will suggest regenerating the description. For example, if a description such as "The latest smartwatch with high-performance heart rate measurement" is generated and the user shows a happy expression, approval will proceed.

[1168] 4. Promotion optimization:

[1169] The server automatically sets promotion targets based on user data and behavioral history. It uses a generative AI model to optimize targeting and an emotion recognition engine to leverage emotional data collected to propose promotion strategies that respond in real time. For example, it displays smartwatch ads during times when many female users in their 20s visit, analyzing their emotional responses to past ads to select the most appealing method.

[1170] 5. Site Optimization:

[1171] The server collects and analyzes data from Google Analytics and uses a generative AI model to automatically generate site improvement recommendations. The emotion recognition engine monitors users' reactions while browsing the site and provides the collected data to the generative AI model. For example, if the time spent on a particular page is short, it will suggest updating the content or changing the layout.

[1172] Examples of specific examples and prompts

[1173] 1. Example:

[1174] Automatic editing of product images: If a user uploads an image of a t-shirt with a confused face, the image will automatically be brightened and the background will be white.

[1175] Auto-complete product information: If you type "smartwatch" and a confused face is detected, the app will suggest additional information such as "sports features, heart rate monitor, blood oxygen monitor."

[1176] Automatic generation of product description: Generate a description such as "The latest smartwatch with high-performance heart rate measurement" and approve it by checking the user's happy expression.

[1177] 2. Example prompt:

[1178] Product Image Editing: "Remove backgrounds and brighten product images uploaded with emotion data."

[1179] Product information auto-completion: "Please automatically complete related information for the product name 'Smartwatch' based on sentiment data."

[1180] Automated product description generation: "Generate compelling product descriptions based on product information, taking into account emotional responses."

[1181] As described above, the system for implementing the present invention utilizes a generative AI model and an emotion recognition engine to improve the efficiency of e-commerce site operations and the user experience.

[1182] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1183] Step 1:

[1184] The user uploads product images from the terminal.

[1185] The device is equipped with an emotion recognition engine that analyzes the user's facial expressions and voice data to collect emotional data, which is then sent to a server along with product images.

[1186] Input: User-uploaded product images, emotion data

[1187] Output: Product images and emotion data are sent to the server.

[1188] Step 2:

[1189] The server passes product images to a generative AI model for background removal and resizing.

[1190] The adjustment results are reflected based on the emotional data collected by the emotion recognition engine.

[1191] Input: Product images and emotion data sent to the server

[1192] Output: Background removed and resized product image

[1193] How it works: The generative AI model removes the background from the product image and scales it to the specified size.

[1194] Step 3:

[1195] The server presents the generated preview image to the user and asks for confirmation.

[1196] Input: Background-removed and resized product image

[1197] Output: A preview image is sent to the user's device.

[1198] Specific operation: The server prepares a preview image and sends the data to the device.

[1199] Step 4:

[1200] The user inputs the product name.

[1201] Input: Product name entered by the user

[1202] Output: Product name sent to server

[1203] Step 5:

[1204] The server launches an AI that automatically completes product information, automatically filling in the input form with detailed information based on the product name.

[1205] An emotion recognition engine monitors the user's reactions and automatically provides more information or alternatives if the user shows signs of confusion.

[1206] Input: Entered product name, user emotion data

[1207] Output: Auto-completed product information

[1208] Specific operation: Product information auto-completion AI searches and inputs related information based on the product name, and the emotion recognition engine adjusts the completion content based on the user's reaction.

[1209] Step 6:

[1210] The user checks, edits and saves the automatically generated product information.

[1211] Input: Auto-completed product information

[1212] Output: Saved product information

[1213] Specific action: The user checks the displayed information, corrects any omissions or errors, and saves the information.

[1214] Step 7:

[1215] The user requests automatic generation of a product description.

[1216] Input: User request

[1217] Output: Request information is sent to the server

[1218] Step 8:

[1219] The server passes the collected product information to a generative AI model to generate a product description.

[1220] An emotion recognition engine analyzes the user's reactions and suggests regeneration if the user shows a dissatisfied expression.

[1221] Input: Collected product information, user emotion data

[1222] Output: Generated product description

[1223] Specific operation: The generative AI model automatically generates product descriptions from product information and adjusts them to the optimal text by referencing user sentiment data.

[1224] Step 9:

[1225] The server automatically sets promotion targets based on user data and behavioral history, and optimizes targeting using a generative AI model.

[1226] Propose targeted promotions based on past emotional responses.

[1227] Input: User data, behavioral history, and past emotional responses

[1228] Output: Optimized promotion plan

[1229] How it works: The generative AI model analyzes the data and creates a promotion plan that users will respond to most.

[1230] Step 10:

[1231] The server collects and analyzes Google Analytics data and uses a generative AI model to automatically generate site improvement proposals.

[1232] An emotion recognition engine monitors users' reactions while browsing the site and makes suggestions for improvements based on page visit time and frequency.

[1233] Input: Google Analytics data, user sentiment data

[1234] Output: Generated site improvement proposals

[1235] How it works: The generative AI model analyzes the collected data and makes suggestions for improving the site structure and content by referring to the sentiment data.

[1236] 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.

[1237] 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.

[1238] 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.

[1239] [Third embodiment]

[1240] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[1241] 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.

[1242] 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).

[1243] 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.

[1244] 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.

[1245] 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).

[1246] 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.

[1247] 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.

[1248] 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.

[1249] 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.

[1250] 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.

[1251] 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."

[1252] The present invention is a system that reduces the time and effort required for tasks such as product promotion and website analysis, and provides an environment in which operators can concentrate on providing high-quality products and improving services. Below, an embodiment of this system, "Assistant EC," is explained, along with program processing flows and specific examples.

[1253] Automate product image editing

[1254] The user uploads a product image from their device. The uploaded image is sent to the server, where it is resized and background-removed using a generative AI model, and the edited image is presented to the user as a preview. For example, if a user uploads an image of a shirt, the background is automatically changed to white and the image is resized to the appropriate size.

[1255] Product information auto-completion

[1256] When a user accesses the product information input form and enters the product name, the information is sent to the server. The server then activates the product information auto-completion AI, which retrieves relevant information from existing databases and external databases and reflects it in the input form. For example, if a user enters "smartwatch," the product description, specifications, price, etc. are automatically entered in the input form.

[1257] Automatic generation of product descriptions

[1258] After completing product information registration, the user requests automatic generation of a product description. This request is sent to the server, which passes the collected product information to the AI ​​model to generate the product description. The generated description is then sent to the user's device, where the user can review and approve the content. For example, a description such as "The latest smartwatch with high-performance heart rate measurement" is automatically generated.

[1259] Promotion optimization

[1260] The server automatically sets promotion targets based on user data and behavioral history. It uses generative AI models to optimize targeting and proposes promotion strategies that respond in real time. For example, it could display smartwatch ads during times when many female users in their 20s visit.

[1261] When a user requests the creation of a landing page, the server activates an AI generative model to automatically generate an effective landing page based on product features and keywords. For example, a landing page for a new smartwatch product is generated, and the page includes compelling copy and design.

[1262] Site optimization

[1263] The server collects and analyzes Google Analytics data and uses a generative AI model to automatically generate site improvement proposals. For example, if the time spent on a particular page is short, it will suggest updating the content or changing the layout. The server also monitors user behavior and suggests optimal page structure and navigation improvements. For example, if a particular category is frequently accessed, it will suggest adding that category to the main menu.

[1264] As described above, this system, "Assistant EC," automates and optimizes many of the tasks involved in operating an e-commerce site, allowing users to focus on strategic operations and improving services. Furthermore, by using this system, e-commerce sites can maintain their competitiveness and achieve sustainable growth.

[1265] The processing flow will be explained below.

[1266] Automate product image editing

[1267] Step 1:

[1268] The user selects a product image from the device and clicks the upload button.

[1269] Step 2:

[1270] The terminal transmits the selected image file to the server.

[1271] Step 3:

[1272] The server passes the received product images to the generative AI model.

[1273] Step 4:

[1274] The generative AI model removes backgrounds and resizes images.

[1275] Step 5:

[1276] The server temporarily stores the edited image and generates a preview image.

[1277] Step 6:

[1278] The server transmits the preview image to the terminal.

[1279] Step 7:

[1280] The user checks the preview on the terminal and presses the "Confirm" button.

[1281] Product information auto-completion

[1282] Step 1:

[1283] The user accesses the product information entry form.

[1284] Step 2:

[1285] The terminal acquires the product information input form from the server and displays it.

[1286] Step 3:

[1287] The user inputs the product name.

[1288] Step 4:

[1289] The terminal transmits the input product name to the server.

[1290] Step 5:

[1291] The server launches an AI that automatically completes product information and retrieves information by comparing it with past databases and external databases.

[1292] Step 6:

[1293] The server reflects the obtained detailed information in the input form.

[1294] Step 7:

[1295] The server sends the input form back to the terminal.

[1296] Step 8:

[1297] The user checks the auto-completed information and corrects it if necessary.

[1298] Step 9:

[1299] After checking, the user clicks the "Save" button.

[1300] Step 10:

[1301] The terminal transmits the final product information to the server.

[1302] Step 11:

[1303] The server stores the received product information in a database.

[1304] Automatic generation of product descriptions

[1305] Step 1:

[1306] After completing the product information registration process, the user clicks the "Auto-generate introduction" button.

[1307] Step 2:

[1308] The terminal sends this request to the server.

[1309] Step 3:

[1310] The server passes the collected product information to the generative AI model.

[1311] Step 4:

[1312] The generative AI model generates product descriptions.

[1313] Step 5:

[1314] The server temporarily stores the generated introduction and presents it to the user.

[1315] Step 6:

[1316] The server transmits the generated introduction to the terminal.

[1317] Step 7:

[1318] The user can review and approve the testimonial on their device and make any necessary corrections.

[1319] Step 8:

[1320] The user approves the final testimonial and sends it to the server.

[1321] Step 9:

[1322] The server stores the approved testimonials in a database.

[1323] Promotion optimization

[1324] Step 1:

[1325] The server collects user data and behavioral history.

[1326] Step 2:

[1327] The server uses generative AI models to optimize promotion targeting.

[1328] Step 3:

[1329] The server will suggest the best target audience and promotion strategy.

[1330] Step 4:

[1331] The user reviews and approves the proposed targeting and promotion strategy.

[1332] Step 5:

[1333] The server prepares to execute the promotion strategy based on the approved strategy.

[1334] Site optimization

[1335] Step 1:

[1336] The server collects data for Google Analytics.

[1337] Step 2:

[1338] The server analyzes the data using a generative AI model.

[1339] Step 3:

[1340] The server automatically generates suggestions for improving the site.

[1341] Step 4:

[1342] The server presents improvement suggestions to the user.

[1343] Step 5:

[1344] The user reviews and approves the proposed improvements.

[1345] Step 6:

[1346] The server implements the proposals to update the site layout and content based on the approved improvements.

[1347] These steps will enable the "Assistant EC" system to function efficiently, reduce the workload of EC site operators, and improve the quality of service provided.

[1348] Example 1

[1349] 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."

[1350] Modern e-commerce site operations require a wide range of tasks, including editing product images, completing product information, creating product descriptions, optimizing promotion targeting, generating landing pages, and proposing site improvements. These tasks place a heavy burden on site operators. This makes it difficult for site operators to devote sufficient time to providing high-quality products and improving services, which is what they should be focusing on. To solve this problem, a system is needed to automate and streamline these tasks.

[1351] 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.

[1352] In this invention, the server includes: a means for a user to upload product images; a means for the server to pass the product images to a generative AI model and perform background removal and size adjustment; a means for presenting the generated preview image to the user and requesting confirmation; a means for the user to input a product name; a means for the server to activate a product information auto-completion AI and automatically update an input form with detailed information based on the product name; a means for the user to confirm, correct, and save the automatically generated product information; a means for the user to request automatic generation of a product description; a means for the server to pass collected product information to a generative AI model, generate a product description, and present it to the user; a means for the server to automatically set promotion targets based on user data and behavioral history and optimize targeting using a generative AI model; a means for the user to request creation of a landing page; a means for the server to automatically generate a landing page based on product features and keywords using a generative AI model; and a means for the server to automatically generate site improvement plans using Google Analytics data and present them to the user. This enables a wide range of e-commerce site operations to be performed efficiently and automatically.

[1353] "User" refers to the person who operates the e-commerce site or the person who uses the system.

[1354] "Terminal" refers to electronic devices such as personal computers, smartphones, and tablets operated by users.

[1355] "Server" refers to a computer system that receives and processes data sent by users.

[1356] "Product image" refers to an image file that visually represents a product sold on an e-commerce site.

[1357] A "generative AI model" refers to an artificial intelligence model that automatically handles specific tasks based on machine learning or deep learning.

[1358] "Background removal" refers to the process of automatically removing the background from a product image.

[1359] "Resizing" refers to the process of automatically resizing product images to the appropriate dimensions.

[1360] The term "preview image" refers to an image that is temporarily displayed to allow the user to check the product image processed by the server.

[1361] "Product information input form" refers to a web form for entering product information such as product name, description, and specifications.

[1362] "Product information automatic completion AI" refers to an artificial intelligence model that automatically completes detailed information based on the product name entered.

[1363] "Product description" refers to text that explains the features and benefits of a product.

[1364] "Promotion target" refers to a group of users who are the target of promotional activities.

[1365] "Targeting optimization" refers to the process of selecting and implementing the most effective promotional strategies for a specific user demographic.

[1366] "Landing page" refers to a dedicated web page related to a specific product or campaign.

[1367] "Google Analytics" refers to a tool for analyzing website traffic and user behavior.

[1368] "Site Improvement Suggestions" means suggestions for improving a website to improve user experience or conversion rates.

[1369] This invention is a system for automatically editing product images, automatically completing product information, automatically generating product descriptions, optimizing promotions, and optimizing websites. This system automates and optimizes various tasks so that users can concentrate on providing products and improving services.

[1370] Automate product image editing

[1371] The user uploads a product image from their device. The uploaded image is sent to the server. The server uses a generative AI model (e.g., the "Remove.bg API" for background removal and the "OpenCV" library for resizing) to remove the background and resize the image. The edited image is presented to the user as a preview, and the user can confirm the results. An example of a specific prompt is as follows: "Remove the background to white and generate an appropriately resized image."

[1372] Product information auto-completion

[1373] The user enters the product name into a product information input form. The entered product name information is sent to the server, which then activates an AI that automatically completes product information (e.g., the "GPT-4" model). The server retrieves related information from existing databases or external databases (e.g., Amazon Product Advertising API) and reflects it in the input form. For example, if "smartwatch" is entered, the product description, specifications, price, etc. will be automatically entered in the input form. An example of a prompt sentence is "Enter the product name 'smartwatch'. Detailed information will be automatically completed."

[1374] Automatic generation of product descriptions

[1375] After completing the product information registration, the user requests the automatic generation of a product description. This request is sent to the server, which passes the collected product information to a generative AI model (e.g., "GPT-4") to generate a product description. This generated description is presented to the user, who then confirms and approves the content. For example, a description for "the latest smartwatch with high-performance heart rate measurement" is generated. An example of a prompt would be "Please automatically generate a product description for a smartwatch. Please particularly emphasize the heart rate measurement function."

[1376] Promotion optimization

[1377] The server automatically sets promotion targets based on user data and behavioral history. It uses a generative AI model (e.g., "Segmentation AI Model") to optimize targeting and propose promotion strategies that respond in real time. For example, it displays ads for smartwatches during times when many female users in their 20s visit. Also, when a user requests the creation of a landing page, the server uses a generative AI model to automatically generate an effective landing page based on product features and keywords. For example, a landing page for a new smartwatch product is generated. This page includes attractive copy and design. An example of a prompt sentence is, "Please generate a landing page for our new smartwatch product."

[1378] Site optimization

[1379] The server collects and analyzes Google Analytics data and uses a generative AI model (e.g., "Data Analysis AI") to automatically generate site improvement proposals. For example, if the time spent on a particular page is short, it will suggest updating the content or changing the layout. The server also monitors user behavior and suggests appropriate page structure and navigation improvement proposals. For example, if a particular category is frequently accessed, it will suggest adding that category to the main menu. An example prompt is, "Generate site improvement proposals based on Google Analytics data."

[1380] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1381] Automate product image editing

[1382] Step 1:

[1383] The user uploads a product image from their device.

[1384] Specific operation: The user clicks the upload button in the browser and selects the product image file.

[1385] Input: Product image file

[1386] Output: Product image files are sent to the server

[1387] Step 2:

[1388] The server receives the product image.

[1389] Specific operation: The server receives the HTTP request and saves the image file.

[1390] Input: Product image file

[1391] Output: Product images stored in server storage

[1392] Step 3:

[1393] The server uses the generative AI model to remove backgrounds and resize images.

[1394] Specific operation: The server calls the "Remove.bg API" to remove the background and resizes the image using "OpenCV".

[1395] Input: Product images stored in server storage

[1396] Output: Background removed and resized product image

[1397] Step 4:

[1398] The server presents the edited image to the user as a preview.

[1399] Specific operation: The server sends the edited image to the user's device as an HTTP response, and a preview is displayed in the browser.

[1400] Input: Background removed and resized product image

[1401] Output: A preview image that is displayed in the user's browser.

[1402] Product information auto-completion

[1403] Step 1:

[1404] The user enters the product name in the product information input form.

[1405] Specific behavior: The user types the product name into the form field and clicks the submit button.

[1406] Input: Product name

[1407] Output: Product name sent to the server as an HTTP request

[1408] Step 2:

[1409] The server receives the product name and activates the product information auto-completion AI.

[1410] How it works: The server analyzes the product name and sends a query to the GPT-4 model.

[1411] Input: Product name received as an HTTP request

[1412] Output: Query passed to product information auto-completion AI

[1413] Step 3:

[1414] The server retrieves relevant information from existing databases or external databases and reflects it in the input form.

[1415] Specific operation: The server executes a database query and sends the retrieved data to the user terminal in JSON format.

[1416] Input: Product name query, related information in the database

[1417] Output: Detailed information in JSON format (product description, specifications, price, etc.)

[1418] Step 4:

[1419] The user checks, edits, and saves the automatically generated product information.

[1420] Specific behavior: The user checks the details, makes any necessary corrections, and clicks the save button.

[1421] Input: Auto-completed product information

[1422] Output: Product information confirmed and corrected by the user is saved on the server

[1423] Automatic generation of product descriptions

[1424] Step 1:

[1425] After completing the registration of product information, the user requests automatic generation of a product description.

[1426] Specific operation: The user clicks the "Automatically generate introduction" button.

[1427] Input: User action (button click)

[1428] Output: A testimonial generation request is sent to the server

[1429] Step 2:

[1430] The server passes the collected product information to a generative AI model, which then generates a product description.

[1431] How it works: The server sends product information to the GPT-4 model and receives the generated description.

[1432] Input: Collected product information

[1433] Output: Generated product description

[1434] Step 3:

[1435] The server presents the generated testimonial to the user.

[1436] Specific operation: The server sends the generated introduction to the user's terminal as an HTTP response and displays it in the browser.

[1437] Input: Generated product description

[1438] Output: Product description displayed in the user's browser

[1439] Promotion optimization

[1440] Step 1:

[1441] The server automatically sets promotion targets based on user data and behavioral history.

[1442] Specific operation: The server extracts user data from the database and analyzes it.

[1443] Input: User data, behavioral history

[1444] Output: Promotion target data as analysis results

[1445] Step 2:

[1446] The server uses the generative AI model to optimize targeting.

[1447] Specific operation: The server uses the "Segmentation AI Model" to optimize promotion targets.

[1448] Input: Promotion target data

[1449] Output: Optimized targeting information

[1450] Step 3:

[1451] The server proposes a promotion strategy to the user that responds in real time.

[1452] Specific operation: The server generates a promotion strategy based on the optimized targeting information and proposes it to the user.

[1453] Input: Optimized targeting information

[1454] Output: Promotion strategies presented to the user

[1455] Step 4:

[1456] A user requests the creation of a landing page.

[1457] Specific operation: The user enters the required information into the landing page creation request form and clicks the submit button.

[1458] Input: Information entered in the landing page creation request form

[1459] Output: A landing page creation request is sent to the server

[1460] Step 5:

[1461] The server uses a generative AI model to automatically generate landing pages based on product features and keywords.

[1462] How it works: The server sends the request to the GPT-4 model and receives the generated landing page.

[1463] Input: Product features and keywords

[1464] Output: Auto-generated landing page

[1465] Site optimization

[1466] Step 1:

[1467] The server collects and analyzes Google Analytics data.

[1468] Specific operation: The server calls the Google Analytics API and retrieves the data.

[1469] Input: Google Analytics data

[1470] Output: Parsed site performance data

[1471] Step 2:

[1472] The server uses the generative AI model to automatically generate site improvement proposals.

[1473] Specific operation: The server uses "Data Analysis AI" to generate site improvement proposals based on the analysis data.

[1474] Input: Site performance data

[1475] Output: Generated site improvement proposals

[1476] Step 3:

[1477] The server monitors user behavior and suggests optimal page structure and navigation improvements.

[1478] Specific operation: The server monitors user behavior in real time and generates improvement suggestions.

[1479] Input: User behavior data

[1480] Output: Generated navigation improvement suggestions

[1481] (Application example 1)

[1482] 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."

[1483] E-commerce site operators need to efficiently manage a large number of products and quickly carry out high-quality promotions. This requires a great deal of time and effort for tasks such as image editing, information entry, and promotion setup. A system is needed to solve this issue and allow operators to focus on more strategic tasks.

[1484] 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.

[1485] In this invention, the server includes a means for users to upload product images, a means for passing the product images to a generative AI model to remove backgrounds and adjust the size, a means for presenting the generated preview image to the user for confirmation, and a means for the server to analyze user data and behavioral history and optimize promotion strategies. This allows e-commerce site operators to significantly reduce the effort and time required for image editing, information input, and promotion settings, enabling them to efficiently provide high-quality products and improve services.

[1486] definition statement

[1487] "User" refers to an individual or organization that uses the system as an operator or administrator of an EC site.

[1488] A "server" is a computer system that handles the entire system, including image editing, data analysis, information supplementation, and launching generative AI models.

[1489] "Product images" are graphic data such as product photos and illustrations posted on e-commerce sites.

[1490] A "generative AI model" is an artificial intelligence framework that uses techniques such as machine learning and deep learning to automatically handle specific tasks.

[1491] "Background removal" is the process of removing the background from a product image and cutting out only the product.

[1492] "Size adjustment" is the operation of changing the size of product images to a size suitable for posting on an e-commerce site.

[1493] A "preview image" is a trial image shown to the user after background removal and resizing have been performed by the generative AI model.

[1494] "Product Information Automatic Completion AI" is an artificial intelligence system that automatically infers and inputs detailed product information based on the product name.

[1495] A "product description" is a sentence generated to explain the features and advantages of a product.

[1496] A "promotion strategy" is a plan or method for effectively implementing advertisements or campaigns aimed at a specific user demographic.

[1497] "Google Analytics" is a tool for collecting and analyzing website traffic data.

[1498] "Site Improvement Suggestions" are specific suggestions for improving the usability and quality of the content of a website.

[1499] MODE FOR CARRYING OUT THE INVENTION

[1500] The present invention provides a system that allows an EC site operator to efficiently edit product images, input product information, and set promotion strategies. A specific embodiment of this system is described below.

[1501] System Overview

[1502] The system for implementing this invention includes a user terminal, a server, a generative AI model, an AI for automatically completing product information, Google Analytics, etc. Its main functions include automatic editing of product images, automatic completion of product information, automatic generation of product descriptions, optimization of promotion strategies, and optimization of the website.

[1503] Automate product image editing

[1504] Users upload product images using their devices. The uploaded images are sent to a server, which uses a generative AI model to remove backgrounds and resize the images. The edited images are then presented to the user as preview images. For example, if a user uploads an image of a shirt, the background is automatically changed to white and the image is resized to the appropriate size.

[1505] Product information auto-completion

[1506] When a user accesses the product information input form and enters the product name, the information is sent to the server. The server then activates the product information auto-completion AI, which retrieves relevant information from existing databases and external databases and automatically reflects it in the input form. For example, if a user enters "smartwatch," the product description, specifications, price, etc. are automatically entered.

[1507] Automatic generation of product descriptions

[1508] After completing the product information registration, the user requests the automatic generation of a product description. This request is sent to the server, which passes the collected product information to the generative AI model to generate the product description. The generated description is then sent to the user's device, where the user can review and approve the content. As a specific example, a description such as "The latest smartwatch with high-performance heart rate measurement" is automatically generated.

[1509] Promotion optimization

[1510] The server automatically sets promotion targets based on user data and behavioral history. It uses a generative AI model to optimize targeting and proposes promotion strategies that respond in real time. For example, one possible method is to display smartwatch ads during times when many female users in their 20s visit. When a user requests the creation of a landing page, the server activates the AI ​​generative model and automatically generates an effective landing page based on product features and keywords.

[1511] Site optimization

[1512] The server collects and analyzes Google Analytics data and uses a generative AI model to automatically generate site improvement proposals. For example, if the time spent on a particular page is short, it will suggest updating the content or changing the layout. The server also monitors user behavior and suggests optimal page structure and navigation improvements. For example, if a particular category is frequently accessed, it will suggest adding that category to the main menu.

[1513] Prompt Sentence Examples

[1514] An example of a prompt for automatically editing product images is, "Please change the background of uploaded product images to white and resize them to appropriate retail sizes."

[1515] As described above, this system allows e-commerce site operators to significantly reduce the effort required for image editing, information input, and promotion settings, allowing them to focus on strategic operations and improving services.

[1516] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1517] Program processing steps

[1518] Step 1:

[1519] The user uploads a product image from the terminal. The input of this step is the product image file selected by the user from the terminal, and the output is the raw data transferred to the server.

[1520] Step 2:

[1521] The server passes the received product image data to the generative AI model, which removes the background and adjusts the size. The input for this step is the product image data sent to the server, and the generative AI model removes the background and adjusts the size, resulting in an edited preview image.

[1522] Step 3:

[1523] The server presents the generated preview image to the user for confirmation. The input to this step is the preview image after background removal and resizing, and the output is a confirmation screen presented to the user. The user can confirm or make corrections.

[1524] Step 4:

[1525] The user inputs the product name from the terminal and sends it to the server. The input of this step is the product name input by the user from the terminal, and the output is the product name data passed to the server.

[1526] Step 5:

[1527] The server launches the product information auto-completion AI, which automatically fills in the input form with detailed information based on the product name. The input for this step is the product name data, the product information auto-completion AI retrieves related information from the database, and the output is form data with complete product information filled in.

[1528] Step 6:

[1529] The user confirms, modifies, and saves the automatically generated product information. The input of this step is the form data with the product information filled in, and the output is the final product information confirmed and modified by the user.

[1530] Step 7:

[1531] The user requests automatic generation of a product description and sends a request to the server. The input of this step is the automatic generation request sent from the user's terminal, and the output is the request data passed to the server.

[1532] Step 8:

[1533] The server passes the collected product information to the generative AI model to generate a product description. The input of this step is the collected product information, the generative AI model creates a product description, and the output is the generated product description.

[1534] Step 9:

[1535] The server presents the generated product description to the user, who then confirms and approves the content. The input to this step is the generated product description, and the output is text data for confirmation that is presented to the user. The user can confirm or make corrections.

[1536] Step 10:

[1537] The server analyzes user data and behavioral history to optimize the promotion strategy. The input of this step is user data and behavioral history, and the output is an optimized promotion strategy after data analysis using a generative AI model.

[1538] Step 11:

[1539] The server collects and analyzes Google Analytics data and automatically generates site improvement proposals. The input for this step is traffic data from Google Analytics, and the output is proposed site improvement proposals after data analysis using a generative AI model.

[1540] By implementing each of the above steps, e-commerce site operators can efficiently provide high-quality products and improve their services.

[1541] 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.

[1542] The present invention is a system that reduces the time and effort required for tasks such as product promotion and website analysis, and provides an environment in which operators can concentrate on providing high-quality products and improving services. Furthermore, by combining it with an emotion engine that recognizes user emotions, the quality of the user experience can be improved. Below, we will explain an embodiment in which this system, "Assistant EC," is combined with an emotion engine.

[1543] Automate product image editing

[1544] A user uploads a product image from their device. At this time, an emotion engine is built into the device, which recognizes emotions from the user's facial expressions and voice. The uploaded image and emotion data are sent to the server. The server uses a generative AI model to remove the background and adjust the size of the image, and presents the edited image to the user as a preview. For example, if a user uploads an image of a shirt and shows a dissatisfied expression, the background color and brightness are automatically adjusted and the preview is displayed again.

[1545] Product information auto-completion

[1546] When a user accesses a product information input form and enters a product name, the information is sent to the server. The server then activates the product information auto-completion AI, which retrieves relevant information from existing and external databases and reflects it in the input form. The emotion engine continues to monitor the user's facial expressions and reactions, analyzing whether the user is satisfied or dissatisfied. For example, if the user shows a confused expression while entering "smartwatch," the engine automatically adjusts to provide a more detailed explanation or alternative suggestions.

[1547] Automatic generation of product descriptions

[1548] After completing product information registration, the user requests automatic generation of a product description. This request is sent to the server, which passes the collected product information to the generative AI model to generate the product description. The generated description is then sent to the device along with the user's emotional data, and the user confirms and approves the content. For example, when a description such as "The latest smartwatch with high-performance heart rate measurement" is generated, if the user shows a happy expression, approval will proceed. If the user shows a dissatisfied expression, a regeneration will be suggested.

[1549] Promotion optimization

[1550] The server automatically sets promotion targets based on user data and behavioral history. It uses a generative AI model to optimize targeting and leverages emotional data collected through an emotion engine to propose promotion strategies that respond in real time. For example, when displaying smartwatch ads during times when many female users in their 20s visit, the server analyzes their emotional responses to past ads and selects the most appealing method.

[1551] Additionally, when a user requests the creation of a landing page, the server activates an AI generative model to automatically generate an effective landing page based on product features and keywords. The emotion engine analyzes the user's reactions while viewing a preview of the landing page. For example, a landing page for a new smartwatch product is generated, containing appealing copy and design. If the user expresses satisfaction after viewing the preview, the page is published as is. If the user expresses dissatisfaction, the page is re-adjusted.

[1552] Site optimization

[1553] The server collects and analyzes data from Google Analytics and uses a generative AI model to automatically generate suggestions for improving the site. The emotion engine monitors users' reactions while they browse the site and provides the accumulated data to the generative AI model. For example, if the time spent on a particular page is short, it suggests updating the content or changing the layout. The server also monitors user behavior and suggests optimal page structure and navigation improvements. For example, if a particular category is frequently accessed, it suggests adding that category to the main menu.

[1554] These steps enable users, devices, and servers to work together efficiently using the emotion engine to carry out the processes of product registration, information supplementation, promotion, and site improvement. By incorporating emotion recognition, it is possible to enhance the user experience and improve the efficiency and quality of e-commerce site operations.

[1555] The processing flow will be explained below.

[1556] Automate product image editing

[1557] Step 1:

[1558] The user selects a product image from the device and clicks the upload button.

[1559] Step 2:

[1560] The terminal transmits the selected image file to the server, and simultaneously collects and transmits the user's facial expression data using an emotion engine.

[1561] Step 3:

[1562] The server passes the received product images to the generative AI model.

[1563] Step 4:

[1564] The generative AI model removes backgrounds and resizes images.

[1565] Step 5:

[1566] The server temporarily stores the edited image and generates a preview image.

[1567] Step 6:

[1568] The server analyzes the user's facial expression data along with the preview image and presents it to the user.

[1569] Step 7:

[1570] The user checks the preview on the terminal and presses the "Confirm" button.

[1571] Step 8:

[1572] If the user's facial expression indicates dissatisfaction, the server calls the generative AI model again and re-edits the image.

[1573] Product information auto-completion

[1574] Step 1:

[1575] The user accesses the product information entry form.

[1576] Step 2:

[1577] The terminal acquires the product information input form from the server and displays it.

[1578] Step 3:

[1579] The user inputs the product name.

[1580] Step 4:

[1581] The terminal transmits the input product name to the server, and also collects and transmits the user's facial expression data using an emotion engine.

[1582] Step 5:

[1583] The server launches an AI that automatically completes product information and retrieves information by comparing it with past databases and external databases.

[1584] Step 6:

[1585] The server reflects the obtained detailed information in the input form and resends it to the user.

[1586] Step 7:

[1587] The terminal displays an input form to the user and continuously collects the user's facial expression data using an emotion engine.

[1588] Step 8:

[1589] The user checks the auto-completed information and corrects it if necessary.

[1590] Step 9:

[1591] After checking, the user clicks the "Save" button.

[1592] Step 10:

[1593] The terminal transmits the final product information to the server, and also transmits the final facial expression data of the user.

[1594] Step 11:

[1595] The server stores the received product information and facial expression data in a database and improves the accuracy of the information based on the results of analyzing the facial expression data.

[1596] Automatic generation of product descriptions

[1597] Step 1:

[1598] After completing the product information registration process, the user clicks the "Auto-generate introduction" button.

[1599] Step 2:

[1600] The terminal sends this request to the server, and the emotion engine collects and transmits the user's facial expression data at the same time.

[1601] Step 3:

[1602] The server passes the collected product information to the generative AI model.

[1603] Step 4:

[1604] The generative AI model generates product descriptions.

[1605] Step 5:

[1606] The server analyzes the user's facial expression data along with the generated introduction and presents it to the user.

[1607] Step 6:

[1608] The user checks the introduction on the device and reacts with facial expressions.

[1609] Step 7:

[1610] If the user shows a satisfied expression, they approve the testimonial and press the "Confirm" button. If the expression shows dissatisfaction, the server calls the AI ​​model again to regenerate the testimonial.

[1611] Step 8:

[1612] The user approves the final testimonial and sends it to the server.

[1613] Step 9:

[1614] The server stores the approved testimonials in a database.

[1615] Promotion optimization

[1616] Step 1:

[1617] The server collects user data and behavioral history, and also collects emotion data through an emotion engine.

[1618] Step 2:

[1619] The server uses generative AI models to optimize promotion targeting.

[1620] Step 3:

[1621] The server analyzes the sentiment data and suggests optimal target demographics and promotion strategies.

[1622] Step 4:

[1623] The user reviews and approves the proposed targeting and promotion strategy.

[1624] Step 5:

[1625] The server prepares to execute the promotion strategy based on the approved strategy.

[1626] Step 6:

[1627] During the promotion, the emotion engine continuously monitors user reactions and adjusts the promotion content in real time as needed.

[1628] Site optimization

[1629] Step 1:

[1630] The server collects data for Google Analytics.

[1631] Step 2:

[1632] The server collects user emotion data through an emotion engine.

[1633] Step 3:

[1634] The server analyzes the data using a generative AI model.

[1635] Step 4:

[1636] The server automatically generates site improvement proposals and presents them to the user.

[1637] Step 5:

[1638] The user reviews and approves the proposed improvements.

[1639] Step 6:

[1640] The server implements the proposals to update the site layout and content based on the approved improvements.

[1641] Step 7:

[1642] The server continuously analyzes data from the emotion engine and generates additional improvement suggestions to maximize user satisfaction.

[1643] Through these specific processing steps, the "Assistant EC" system can utilize user emotional data to streamline EC site operations and improve the quality of the user experience.

[1644] Example 2

[1645] 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."

[1646] In conventional e-commerce site operations, editing product images, completing product information, promoting products, and optimizing the site takes a lot of time and effort, making it difficult for operators to focus on providing high-quality products and improving services. In addition, because the site does not take user emotions into consideration, there is also the issue of the quality of the user experience not being sufficiently improved.

[1647] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: a means for a user to upload a product image; a means for a terminal to recognize emotions using an emotion engine that analyzes the user's facial expressions and voice data; a means for the terminal to transmit image data and emotion data to the server; a means for the server to pass the product image to a generative AI model and perform background removal and size adjustment; a means for presenting a generated preview image to the user and suggesting re-editing based on the emotion data; a means for a user to input a product name; a means for the server to start a product information automatic completion AI and automatically reflect detailed information in an input form based on the product name; a means for the emotion engine to monitor the user's emotion data and adjust the completion information; a means for a user to confirm, correct, and save the automatically generated product information; a means for a user to request automatic generation of a product description; a means for the server to pass collected product information to a generative AI model, generate a product description, and present it to the user; and a means for suggesting re-generation based on the generated description and emotion data. This allows for efficient product registration, information supplementation, promotion, and site improvement, and makes it possible to provide a high-quality user experience that takes user emotions into consideration.

[1648] "User" means any person or entity that uses the System to upload Product Images and enter, review and edit Product Information.

[1649] "Device" refers to an electronic device operated by a user, such as a computer, tablet, or smartphone, which includes an emotion engine and an image upload function.

[1650] An "emotion engine" is a software or hardware function that analyzes a user's facial expressions and voice data and recognizes their emotional state in real time.

[1651] A "server" is a remote computer system that performs editing of product images, completing product information, generating product descriptions, and other related processing.

[1652] A "generative AI model" is a machine learning model used to automatically perform tasks such as background removal, sizing, and product description generation.

[1653] A "preview image" is a product image edited using a generative AI model, and is an image that can be reviewed and re-edited by the user.

[1654] "Product Information Automatic Completion AI" is an artificial intelligence technology that automatically reflects detailed information in input forms based on the product name entered.

[1655] "Re-editing suggestions" are suggestions for re-editing using a generative AI model when the emotion engine detects a dissatisfied expression or reaction from the user.

[1656] A "product description" is a text description that is automatically generated by a generative AI model based on product information.

[1657] "Site Improvement Suggestions" are suggestions for improving the structure and content of a website generated by the server based on Google Analytics data and user sentiment data.

[1658] This invention is a system that reduces the time and effort required for tasks such as product promotion and website analysis, and provides an environment where operators can concentrate on providing high-quality products and improving services. In particular, it is characterized by improving the quality of the user experience by combining it with an emotion engine that recognizes user emotions.

[1659] Automate product image editing

[1660] The user uploads a product image from their device. The device is equipped with an emotion engine that captures the user's facial expressions and voice and recognizes their emotions. The uploaded image and emotion data are sent from the device to a server. The server uses a generative AI model to remove the background and resize the image. The edited image is presented to the user as a preview, and re-editing may be suggested based on the emotion data.

[1661] For example, if a user shows dissatisfaction after uploading a picture of a shirt, a prompt such as "Upload a picture of a shirt" could be used to automatically adjust the background color and brightness and display the preview again.

[1662] Product information auto-completion

[1663] When a user accesses the product information input form and enters the product name, the entered information is sent from the device to the server. The server then activates the product information auto-completion AI, retrieves relevant information from existing databases and external databases, and reflects it in the input form. During this time, the emotion engine continues to monitor the user's facial expressions and reactions, analyzing whether the user is satisfied or dissatisfied.

[1664] For example, if the user shows a confused expression while typing "smartwatch," the server automatically adjusts to provide a more detailed explanation or alternatives, such as a prompt that reads, "Complete the smartwatch information."

[1665] Automatic generation of product descriptions

[1666] After completing product information registration, the user requests automatic generation of a product description. This request is sent from the device to the server. The server passes the collected product information to a generative AI model to generate a product description. The generated description is then sent to the device along with emotional data, and the user checks and approves the content. Regeneration may also be suggested based on the user's emotional data.

[1667] A specific example of a prompt sentence is "Generate a description of a smartwatch."

[1668] Promotion optimization

[1669] The server automatically sets promotion targets based on user data and behavioral history, and optimizes targeting using a generative AI model. It utilizes emotional data collected through the emotion engine to propose promotion strategies that respond in real time. For example, when displaying smartwatch ads during times when many female users in their 20s visit, it analyzes their emotional responses to past ads and selects the most appealing method.

[1670] Additionally, when a user requests the creation of a landing page, the server activates a generative AI model to automatically generate an effective landing page based on product features and keywords. The emotion engine analyzes the user's reactions while displaying a preview of the landing page. An example of a prompt sentence would be "Optimize the promotional strategy for smartwatches."

[1671] Site optimization

[1672] The server collects and analyzes Google Analytics data and uses a generative AI model to automatically generate site improvement recommendations. The emotion engine monitors users' reactions while browsing the site and provides the data to the generative AI model. For example, if a particular category is frequently accessed, it will suggest adding that category to the main menu. Content updates and layout changes will also be suggested.

[1673] A specific example of a prompt might be "Improve time spent on site."

[1674] This allows users, devices, and servers to work together efficiently using the emotion engine, and executes the processes of product registration, information supplementation, promotion, and site improvement, improving the user experience as well as the efficiency and quality of e-commerce site operations.

[1675] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1676] Step 1:

[1677] A user uploads a product image.

[1678] Input: Product image taken or selected by the user

[1679] How it works: The user accesses the image upload screen in a web browser or a dedicated app, selects an image file, and presses the upload button.

[1680] Output: Uploaded product image data

[1681] Step 2:

[1682] The device analyzes the user's facial expressions and voice data.

[1683] Input: Upload product images and receive user's facial expressions and voice

[1684] How it works: The device's camera and microphone capture the user's face and voice, and the emotion engine analyzes this data in real time.

[1685] Output: User emotion data (e.g., satisfied, dissatisfied, confused)

[1686] Step 3:

[1687] The terminal transmits image data and emotion data to the server.

[1688] Input: Product image data, user emotion data

[1689] How it works: The device sends product image data and emotion data to the server using an HTTP request.

[1690] Output: Product image data and emotion data received by the server

[1691] Step 4:

[1692] The server passes product images to a generative AI model for background removal and resizing.

[1693] Input: Product image data

[1694] How it works: The server inputs product image data into the generative AI model, performs background removal and resizing.

[1695] Output: Edited product image data

[1696] Step 5:

[1697] The server presents the generated preview image to the user.

[1698] Input: Edited product image data

[1699] Operation: The server sends the edited image to the device, which displays it as a preview screen.

[1700] Output: Preview image that the user sees

[1701] Step 6:

[1702] Suggest re-editing based on emotional data.

[1703] Input: Emotion data when the user checks the preview image

[1704] How it works: The emotion engine analyzes the user's emotion and sends a re-editing request to the server. If the emotion is dissatisfied, the re-editing process is executed.

[1705] Output: Re-edited product image data

[1706] Step 7:

[1707] The user inputs the product name.

[1708] Input: Product name entered by the user

[1709] How it works: The user enters the product name into the input screen of a web form or dedicated app.

[1710] Output: Entered product name data

[1711] Step 8:

[1712] The server launches an AI that automatically completes product information, and automatically reflects detailed information in the input form based on the product name.

[1713] Input: Product name data entered by the user

[1714] How it works: The server uses product information auto-completion AI to retrieve relevant details from an internal database or external API and populate the input form.

[1715] Output: Auto-completed product information data

[1716] Step 9:

[1717] The emotion engine monitors the user's emotion data and adjusts the complementary information.

[1718] Input: Emotional data while the user is reviewing the form

[1719] How it works: The emotion engine analyzes the user's emotions in real time, and if they indicate dissatisfaction or confusion, it requests the server to re-adjust the details.

[1720] Output: Adjusted product information data

[1721] Step 10:

[1722] The user checks, edits, and saves the automatically generated product information.

[1723] Input: Auto-completed product information data

[1724] Action: The user reviews the form, corrects the information as needed, and clicks the save button.

[1725] Output: Saved product information data

[1726] Step 11:

[1727] A user requests automatic generation of a product description.

[1728] Input: Data after product information registration is complete

[1729] How it works: The user clicks the auto-generate product description button, sending a request to the server.

[1730] Output: Product description generation request data

[1731] Step 12:

[1732] The server passes the collected product information to a generative AI model, which then generates a product description.

[1733] Input: Saved product information data

[1734] How it works: The server inputs product information into a generative AI model, which then generates a product description.

[1735] Output: Generated product description data

[1736] Step 13:

[1737] We propose regeneration based on the generated product description and emotion data.

[1738] Input: Product description data, user emotion data

[1739] How it works: The server sends the generated product description and the user's emotion data to the device, where the user confirms it. The emotion engine analyzes the user's reaction and suggests regenerating the expression if it is dissatisfied.

[1740] Output: Revised product description data or regeneration suggestions

[1741] Step 14:

[1742] The server automatically generates site improvement proposals using Google Analytics data and sentiment data and presents them to the user.

[1743] Input: Google Analytics data, user sentiment data

[1744] How it works: The server analyzes the collected data and uses a generative AI model to generate site improvement suggestions.

[1745] Output: Generated site improvement proposal data

[1746] (Application example 2)

[1747] 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."

[1748] In conventional e-commerce site operations, each process, such as editing product images, automatically completing product information, and generating product descriptions, requires a lot of time and effort. Furthermore, there is a lack of efforts to improve services that incorporate user emotions, resulting in a poor user experience. This makes it difficult for site operators to focus on providing high-quality products and improving services. It is necessary to solve these problems and improve the efficiency of e-commerce site operations and the user experience.

[1749] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes: means for a user to upload a product image; a terminal including an emotion recognition engine that recognizes emotions from the user's facial expressions and voice; means for the server to pass the product image to a generative AI model and perform background removal and size adjustment; means for using the user's emotion data to reflect the adjustment results when editing the image; means for presenting a generated preview image to the user and requesting confirmation; means for the user to input a product name; means for the server to start a product information automatic completion AI and automatically reflect detailed information in an input form based on the product name; means for the emotion recognition engine to monitor the user's reaction and automatically provide detailed information or alternatives when detecting a dissatisfied expression, means for the user to confirm, correct, and save the automatically generated product information; means for the user to request automatic generation of a product description; means for the server to pass collected product information to the generative AI model, generate a product description, and present it to the user; and means for the emotion recognition engine to analyze the user's reaction and suggest regeneration when the user shows a dissatisfied expression. This will enable the efficient execution of each process of product registration, information supplementation, promotion, and site improvement, enhancing the user experience and improving the efficiency and quality of e-commerce site operations.

[1750] "User" means a person who uses the system to upload product images and enter, confirm, modify and save product information.

[1751] An "emotion recognition engine" is software or hardware that analyzes a user's facial expressions and voice to recognize emotions.

[1752] A "terminal" is a device used by a user, such as a computer, smartphone, or tablet.

[1753] A "server" is a computer system that processes product images, product information, automatically generated product descriptions, etc., and stores and provides data.

[1754] A "generative AI model" is a model that uses artificial intelligence technology to automatically perform processes such as removing backgrounds from images, adjusting size, completing product information, and generating product descriptions.

[1755] "Background removal" is a process that removes unnecessary background parts from an image.

[1756] "Resizing" is the process of changing the dimensions of an image to meet specific criteria.

[1757] A "preview image" is a temporary display version of an image that is presented to the user after editing.

[1758] "Product Information Automatic Completion AI" is an artificial intelligence that automatically completes related detailed information based on input information such as product names.

[1759] "Passing to the generative AI model" means inputting the data collected by the server into the generative AI model.

[1760] A "product description" is text that explains the features and benefits of a product.

[1761] "Promotion" is a marketing activity to promote the sale of a product.

[1762] The present invention relates to a system for improving the efficiency of product promotion and site analysis in the operation of an e-commerce site, and for improving the user experience. Specific embodiments of the present invention will be described below.

[1763] The system that realizes this invention consists of a user's device, an emotion recognition engine, a generative AI model, and a server. The user uploads product images from their device, which has an emotion recognition engine built into it. The emotion recognition engine analyzes the user's facial expressions and voice to collect emotional data. This data and the product images are sent to the server.

[1764] The server includes the following means:

[1765] 1. Product Image Processing:

[1766] The server passes the uploaded product image to a generative AI model, which removes the background and adjusts the size. The server then reflects the adjustment results based on the user's emotional data collected by the emotion recognition engine, and presents the generated preview image to the user. For example, if the user shows a dissatisfied expression, the system automatically adjusts the background color and brightness and displays the preview again.

[1767] 2. Product information auto-completion:

[1768] When a user enters a product name, the server activates an AI that automatically completes product information, retrieving related information from existing and external databases and displaying it in the input form. The emotion recognition engine monitors the user's reactions, and if the user shows a confused expression, it automatically provides more detailed information or alternatives. For example, if the user shows a confused expression while typing "smartwatch," it provides complementary information such as sports functions, heart rate monitor, and blood oxygen monitor.

[1769] 3. Automatically generate product descriptions:

[1770] After a user completes the product information registration process, they can request the automatic generation of a product description. The server passes the collected product information to a generative AI model, which generates the product description. The generated description is sent to the device along with the user's emotional data, and the user confirms and approves the content. If the user shows a dissatisfied expression, the server will suggest regenerating the description. For example, if a description such as "The latest smartwatch with high-performance heart rate measurement" is generated and the user shows a happy expression, approval will proceed.

[1771] 4. Promotion optimization:

[1772] The server automatically sets promotion targets based on user data and behavioral history. It uses a generative AI model to optimize targeting and an emotion recognition engine to leverage emotional data collected to propose promotion strategies that respond in real time. For example, it displays smartwatch ads during times when many female users in their 20s visit, analyzing their emotional responses to past ads to select the most appealing method.

[1773] 5. Site Optimization:

[1774] The server collects and analyzes data from Google Analytics and uses a generative AI model to automatically generate site improvement recommendations. The emotion recognition engine monitors users' reactions while browsing the site and provides the collected data to the generative AI model. For example, if the time spent on a particular page is short, it will suggest updating the content or changing the layout.

[1775] Examples of specific examples and prompts

[1776] 1. Example:

[1777] Automatic editing of product images: If a user uploads an image of a t-shirt with a confused face, the image will automatically be brightened and the background will be white.

[1778] Auto-complete product information: If you type "smartwatch" and a confused face is detected, the app will suggest additional information such as "sports features, heart rate monitor, blood oxygen monitor."

[1779] Automatic generation of product description: Generate a description such as "The latest smartwatch with high-performance heart rate measurement" and approve it by checking the user's happy expression.

[1780] 2. Example prompt:

[1781] Product Image Editing: "Remove backgrounds and brighten product images uploaded with emotion data."

[1782] Product information auto-completion: "Please automatically complete related information for the product name 'Smartwatch' based on sentiment data."

[1783] Automated product description generation: "Generate compelling product descriptions based on product information, taking into account emotional responses."

[1784] As described above, the system for implementing the present invention utilizes a generative AI model and an emotion recognition engine to improve the efficiency of e-commerce site operations and the user experience.

[1785] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1786] Step 1:

[1787] The user uploads product images from the terminal.

[1788] The device is equipped with an emotion recognition engine that analyzes the user's facial expressions and voice data to collect emotional data, which is then sent to a server along with product images.

[1789] Input: User-uploaded product images, emotion data

[1790] Output: Product images and emotion data are sent to the server.

[1791] Step 2:

[1792] The server passes product images to a generative AI model for background removal and resizing.

[1793] The adjustment results are reflected based on the emotional data collected by the emotion recognition engine.

[1794] Input: Product images and emotion data sent to the server

[1795] Output: Background removed and resized product image

[1796] How it works: The generative AI model removes the background from the product image and scales it to the specified size.

[1797] Step 3:

[1798] The server presents the generated preview image to the user and asks for confirmation.

[1799] Input: Background-removed and resized product image

[1800] Output: A preview image is sent to the user's device.

[1801] Specific operation: The server prepares a preview image and sends the data to the device.

[1802] Step 4:

[1803] The user inputs the product name.

[1804] Input: Product name entered by the user

[1805] Output: Product name sent to server

[1806] Step 5:

[1807] The server launches an AI that automatically completes product information, automatically filling in the input form with detailed information based on the product name.

[1808] An emotion recognition engine monitors the user's reactions and automatically provides more information or alternatives if the user shows signs of confusion.

[1809] Input: Entered product name, user emotion data

[1810] Output: Auto-completed product information

[1811] Specific operation: Product information auto-completion AI searches and inputs related information based on the product name, and the emotion recognition engine adjusts the completion content based on the user's reaction.

[1812] Step 6:

[1813] The user checks, edits and saves the automatically generated product information.

[1814] Input: Auto-completed product information

[1815] Output: Saved product information

[1816] Specific action: The user checks the displayed information, corrects any omissions or errors, and saves the information.

[1817] Step 7:

[1818] The user requests automatic generation of a product description.

[1819] Input: User request

[1820] Output: Request information is sent to the server

[1821] Step 8:

[1822] The server passes the collected product information to a generative AI model to generate a product description.

[1823] An emotion recognition engine analyzes the user's reactions and suggests regeneration if the user shows a dissatisfied expression.

[1824] Input: Collected product information, user emotion data

[1825] Output: Generated product description

[1826] Specific operation: The generative AI model automatically generates product descriptions from product information and adjusts them to the optimal text by referencing user sentiment data.

[1827] Step 9:

[1828] The server automatically sets promotion targets based on user data and behavioral history, and optimizes targeting using a generative AI model.

[1829] Propose targeted promotions based on past emotional responses.

[1830] Input: User data, behavioral history, and past emotional responses

[1831] Output: Optimized promotion plan

[1832] How it works: The generative AI model analyzes the data and creates a promotion plan that users will respond to most.

[1833] Step 10:

[1834] The server collects and analyzes Google Analytics data and uses a generative AI model to automatically generate site improvement proposals.

[1835] An emotion recognition engine monitors users' reactions while browsing the site and makes suggestions for improvements based on page visit time and frequency.

[1836] Input: Google Analytics data, user sentiment data

[1837] Output: Generated site improvement proposals

[1838] How it works: The generative AI model analyzes the collected data and makes suggestions for improving the site structure and content by referring to the sentiment data.

[1839] 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.

[1840] 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.

[1841] 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.

[1842] [Fourth embodiment]

[1843] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1844] 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.

[1845] 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).

[1846] 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.

[1847] 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.

[1848] 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).

[1849] 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.

[1850] 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.

[1851] 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.

[1852] 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.

[1853] 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.

[1854] 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.

[1855] 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."

[1856] The present invention is a system that reduces the time and effort required for tasks such as product promotion and website analysis, and provides an environment in which operators can concentrate on providing high-quality products and improving services. Below, an embodiment of this system, "Assistant EC," is explained, along with program processing flows and specific examples.

[1857] Automate product image editing

[1858] The user uploads a product image from their device. The uploaded image is sent to the server, where it is resized and background-removed using a generative AI model, and the edited image is presented to the user as a preview. For example, if a user uploads an image of a shirt, the background is automatically changed to white and the image is resized to the appropriate size.

[1859] Product information auto-completion

[1860] When a user accesses the product information input form and enters the product name, the information is sent to the server. The server then activates the product information auto-completion AI, which retrieves relevant information from existing databases and external databases and reflects it in the input form. For example, if a user enters "smartwatch," the product description, specifications, price, etc. are automatically entered in the input form.

[1861] Automatic generation of product descriptions

[1862] After completing product information registration, the user requests automatic generation of a product description. This request is sent to the server, which passes the collected product information to the AI ​​model to generate the product description. The generated description is then sent to the user's device, where the user can review and approve the content. For example, a description such as "The latest smartwatch with high-performance heart rate measurement" is automatically generated.

[1863] Promotion optimization

[1864] The server automatically sets promotion targets based on user data and behavioral history. It uses generative AI models to optimize targeting and proposes promotion strategies that respond in real time. For example, it could display smartwatch ads during times when many female users in their 20s visit.

[1865] When a user requests the creation of a landing page, the server activates an AI generative model to automatically generate an effective landing page based on product features and keywords. For example, a landing page for a new smartwatch product is generated, and the page includes compelling copy and design.

[1866] Site optimization

[1867] The server collects and analyzes Google Analytics data and uses a generative AI model to automatically generate site improvement proposals. For example, if the time spent on a particular page is short, it will suggest updating the content or changing the layout. The server also monitors user behavior and suggests optimal page structure and navigation improvements. For example, if a particular category is frequently accessed, it will suggest adding that category to the main menu.

[1868] As described above, this system, "Assistant EC," automates and optimizes many of the tasks involved in operating an e-commerce site, allowing users to focus on strategic operations and improving services. Furthermore, by using this system, e-commerce sites can maintain their competitiveness and achieve sustainable growth.

[1869] The processing flow will be explained below.

[1870] Automate product image editing

[1871] Step 1:

[1872] The user selects a product image from the device and clicks the upload button.

[1873] Step 2:

[1874] The terminal transmits the selected image file to the server.

[1875] Step 3:

[1876] The server passes the received product images to the generative AI model.

[1877] Step 4:

[1878] The generative AI model removes backgrounds and resizes images.

[1879] Step 5:

[1880] The server temporarily stores the edited image and generates a preview image.

[1881] Step 6:

[1882] The server transmits the preview image to the terminal.

[1883] Step 7:

[1884] The user checks the preview on the terminal and presses the "Confirm" button.

[1885] Product information auto-completion

[1886] Step 1:

[1887] The user accesses the product information entry form.

[1888] Step 2:

[1889] The terminal acquires the product information input form from the server and displays it.

[1890] Step 3:

[1891] The user inputs the product name.

[1892] Step 4:

[1893] The terminal transmits the input product name to the server.

[1894] Step 5:

[1895] The server launches an AI that automatically completes product information and retrieves information by comparing it with past databases and external databases.

[1896] Step 6:

[1897] The server reflects the obtained detailed information in the input form.

[1898] Step 7:

[1899] The server sends the input form back to the terminal.

[1900] Step 8:

[1901] The user checks the auto-completed information and corrects it if necessary.

[1902] Step 9:

[1903] After checking, the user clicks the "Save" button.

[1904] Step 10:

[1905] The terminal transmits the final product information to the server.

[1906] Step 11:

[1907] The server stores the received product information in a database.

[1908] Automatic generation of product descriptions

[1909] Step 1:

[1910] After completing the product information registration process, the user clicks the "Auto-generate introduction" button.

[1911] Step 2:

[1912] The terminal sends this request to the server.

[1913] Step 3:

[1914] The server passes the collected product information to the generative AI model.

[1915] Step 4:

[1916] The generative AI model generates product descriptions.

[1917] Step 5:

[1918] The server temporarily stores the generated introduction and presents it to the user.

[1919] Step 6:

[1920] The server transmits the generated introduction to the terminal.

[1921] Step 7:

[1922] The user can review and approve the testimonial on their device and make any necessary corrections.

[1923] Step 8:

[1924] The user approves the final testimonial and sends it to the server.

[1925] Step 9:

[1926] The server stores the approved testimonials in a database.

[1927] Promotion optimization

[1928] Step 1:

[1929] The server collects user data and behavioral history.

[1930] Step 2:

[1931] The server uses generative AI models to optimize promotion targeting.

[1932] Step 3:

[1933] The server will suggest the best target audience and promotion strategy.

[1934] Step 4:

[1935] The user reviews and approves the proposed targeting and promotion strategy.

[1936] Step 5:

[1937] The server prepares to execute the promotion strategy based on the approved strategy.

[1938] Site optimization

[1939] Step 1:

[1940] The server collects data for Google Analytics.

[1941] Step 2:

[1942] The server analyzes the data using a generative AI model.

[1943] Step 3:

[1944] The server automatically generates suggestions for improving the site.

[1945] Step 4:

[1946] The server presents improvement suggestions to the user.

[1947] Step 5:

[1948] The user reviews and approves the proposed improvements.

[1949] Step 6:

[1950] The server implements the proposals to update the site layout and content based on the approved improvements.

[1951] These steps will enable the "Assistant EC" system to function efficiently, reduce the workload of EC site operators, and improve the quality of service provided.

[1952] Example 1

[1953] 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."

[1954] Modern e-commerce site operations require a wide range of tasks, including editing product images, completing product information, creating product descriptions, optimizing promotion targeting, generating landing pages, and proposing site improvements. These tasks place a heavy burden on site operators. This makes it difficult for site operators to devote sufficient time to providing high-quality products and improving services, which is what they should be focusing on. To solve this problem, a system is needed to automate and streamline these tasks.

[1955] 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.

[1956] In this invention, the server includes: a means for a user to upload product images; a means for the server to pass the product images to a generative AI model and perform background removal and size adjustment; a means for presenting the generated preview image to the user and requesting confirmation; a means for the user to input a product name; a means for the server to activate a product information auto-completion AI and automatically update an input form with detailed information based on the product name; a means for the user to confirm, correct, and save the automatically generated product information; a means for the user to request automatic generation of a product description; a means for the server to pass collected product information to a generative AI model, generate a product description, and present it to the user; a means for the server to automatically set promotion targets based on user data and behavioral history and optimize targeting using a generative AI model; a means for the user to request creation of a landing page; a means for the server to automatically generate a landing page based on product features and keywords using a generative AI model; and a means for the server to automatically generate site improvement plans using Google Analytics data and present them to the user. This enables a wide range of e-commerce site operations to be performed efficiently and automatically.

[1957] "User" refers to the person who operates the e-commerce site or the person who uses the system.

[1958] "Terminal" refers to electronic devices such as personal computers, smartphones, and tablets operated by users.

[1959] "Server" refers to a computer system that receives and processes data sent by users.

[1960] "Product image" refers to an image file that visually represents a product sold on an e-commerce site.

[1961] A "generative AI model" refers to an artificial intelligence model that automatically handles specific tasks based on machine learning or deep learning.

[1962] "Background removal" refers to the process of automatically removing the background from a product image.

[1963] "Resizing" refers to the process of automatically resizing product images to the appropriate dimensions.

[1964] The term "preview image" refers to an image that is temporarily displayed to allow the user to check the product image processed by the server.

[1965] "Product information input form" refers to a web form for entering product information such as product name, description, and specifications.

[1966] "Product information automatic completion AI" refers to an artificial intelligence model that automatically completes detailed information based on the product name entered.

[1967] "Product description" refers to text that explains the features and benefits of a product.

[1968] "Promotion target" refers to a group of users who are the target of promotional activities.

[1969] "Targeting optimization" refers to the process of selecting and implementing the most effective promotional strategies for a specific user demographic.

[1970] "Landing page" refers to a dedicated web page related to a specific product or campaign.

[1971] "Google Analytics" refers to a tool for analyzing website traffic and user behavior.

[1972] "Site Improvement Suggestions" means suggestions for improving a website to improve user experience or conversion rates.

[1973] This invention is a system for automatically editing product images, automatically completing product information, automatically generating product descriptions, optimizing promotions, and optimizing websites. This system automates and optimizes various tasks so that users can concentrate on providing products and improving services.

[1974] Automate product image editing

[1975] The user uploads a product image from their device. The uploaded image is sent to the server. The server uses a generative AI model (e.g., the "Remove.bg API" for background removal and the "OpenCV" library for resizing) to remove the background and resize the image. The edited image is presented to the user as a preview, and the user can confirm the results. An example of a specific prompt is as follows: "Remove the background to white and generate an appropriately resized image."

[1976] Product information auto-completion

[1977] The user enters the product name into a product information input form. The entered product name information is sent to the server, which then activates an AI that automatically completes product information (e.g., the "GPT-4" model). The server retrieves related information from existing databases or external databases (e.g., Amazon Product Advertising API) and reflects it in the input form. For example, if "smartwatch" is entered, the product description, specifications, price, etc. will be automatically entered in the input form. An example of a prompt sentence is "Enter the product name 'smartwatch'. Detailed information will be automatically completed."

[1978] Automatic generation of product descriptions

[1979] After completing the product information registration, the user requests the automatic generation of a product description. This request is sent to the server, which passes the collected product information to a generative AI model (e.g., "GPT-4") to generate a product description. This generated description is presented to the user, who then confirms and approves the content. For example, a description for "the latest smartwatch with high-performance heart rate measurement" is generated. An example of a prompt would be "Please automatically generate a product description for a smartwatch. Please particularly emphasize the heart rate measurement function."

[1980] Promotion optimization

[1981] The server automatically sets promotion targets based on user data and behavioral history. It uses a generative AI model (e.g., "Segmentation AI Model") to optimize targeting and propose promotion strategies that respond in real time. For example, it displays ads for smartwatches during times when many female users in their 20s visit. Also, when a user requests the creation of a landing page, the server uses a generative AI model to automatically generate an effective landing page based on product features and keywords. For example, a landing page for a new smartwatch product is generated. This page includes attractive copy and design. An example of a prompt sentence is, "Please generate a landing page for our new smartwatch product."

[1982] Site optimization

[1983] The server collects and analyzes Google Analytics data and uses a generative AI model (e.g., "Data Analysis AI") to automatically generate site improvement proposals. For example, if the time spent on a particular page is short, it will suggest updating the content or changing the layout. The server also monitors user behavior and suggests appropriate page structure and navigation improvement proposals. For example, if a particular category is frequently accessed, it will suggest adding that category to the main menu. An example prompt is, "Generate site improvement proposals based on Google Analytics data."

[1984] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1985] Automate product image editing

[1986] Step 1:

[1987] The user uploads a product image from their device.

[1988] Specific operation: The user clicks the upload button in the browser and selects the product image file.

[1989] Input: Product image file

[1990] Output: Product image files are sent to the server

[1991] Step 2:

[1992] The server receives the product image.

[1993] Specific operation: The server receives the HTTP request and saves the image file.

[1994] Input: Product image file

[1995] Output: Product images stored in server storage

[1996] Step 3:

[1997] The server uses the generative AI model to remove backgrounds and resize images.

[1998] Specific operation: The server calls the "Remove.bg API" to remove the background and resizes the image using "OpenCV".

[1999] Input: Product images stored in server storage

[2000] Output: Background removed and resized product image

[2001] Step 4:

[2002] The server presents the edited image to the user as a preview.

[2003] Specific operation: The server sends the edited image to the user's device as an HTTP response, and a preview is displayed in the browser.

[2004] Input: Background removed and resized product image

[2005] Output: A preview image that is displayed in the user's browser.

[2006] Product information auto-completion

[2007] Step 1:

[2008] The user enters the product name in the product information input form.

[2009] Specific behavior: The user types the product name into the form field and clicks the submit button.

[2010] Input: Product name

[2011] Output: Product name sent to the server as an HTTP request

[2012] Step 2:

[2013] The server receives the product name and activates the product information auto-completion AI.

[2014] How it works: The server analyzes the product name and sends a query to the GPT-4 model.

[2015] Input: Product name received as an HTTP request

[2016] Output: Query passed to product information auto-completion AI

[2017] Step 3:

[2018] The server retrieves relevant information from existing databases or external databases and reflects it in the input form.

[2019] Specific operation: The server executes a database query and sends the retrieved data to the user terminal in JSON format.

[2020] Input: Product name query, related information in the database

[2021] Output: Detailed information in JSON format (product description, specifications, price, etc.)

[2022] Step 4:

[2023] The user checks, edits, and saves the automatically generated product information.

[2024] Specific behavior: The user checks the details, makes any necessary corrections, and clicks the save button.

[2025] Input: Auto-completed product information

[2026] Output: Product information confirmed and corrected by the user is saved on the server

[2027] Automatic generation of product descriptions

[2028] Step 1:

[2029] After completing the registration of product information, the user requests automatic generation of a product description.

[2030] Specific operation: The user clicks the "Automatically generate introduction" button.

[2031] Input: User action (button click)

[2032] Output: A testimonial generation request is sent to the server

[2033] Step 2:

[2034] The server passes the collected product information to a generative AI model, which then generates a product description.

[2035] How it works: The server sends product information to the GPT-4 model and receives the generated description.

[2036] Input: Collected product information

[2037] Output: Generated product description

[2038] Step 3:

[2039] The server presents the generated testimonial to the user.

[2040] Specific operation: The server sends the generated introduction to the user's terminal as an HTTP response and displays it in the browser.

[2041] Input: Generated product description

[2042] Output: Product description displayed in the user's browser

[2043] Promotion optimization

[2044] Step 1:

[2045] The server automatically sets promotion targets based on user data and behavioral history.

[2046] Specific operation: The server extracts user data from the database and analyzes it.

[2047] Input: User data, behavioral history

[2048] Output: Promotion target data as analysis results

[2049] Step 2:

[2050] The server uses the generative AI model to optimize targeting.

[2051] Specific operation: The server uses the "Segmentation AI Model" to optimize promotion targets.

[2052] Input: Promotion target data

[2053] Output: Optimized targeting information

[2054] Step 3:

[2055] The server proposes a promotion strategy to the user that responds in real time.

[2056] Specific operation: The server generates a promotion strategy based on the optimized targeting information and proposes it to the user.

[2057] Input: Optimized targeting information

[2058] Output: Promotion strategies presented to the user

[2059] Step 4:

[2060] A user requests the creation of a landing page.

[2061] Specific operation: The user enters the required information into the landing page creation request form and clicks the submit button.

[2062] Input: Information entered in the landing page creation request form

[2063] Output: A landing page creation request is sent to the server

[2064] Step 5:

[2065] The server uses a generative AI model to automatically generate landing pages based on product features and keywords.

[2066] How it works: The server sends the request to the GPT-4 model and receives the generated landing page.

[2067] Input: Product features and keywords

[2068] Output: Auto-generated landing page

[2069] Site optimization

[2070] Step 1:

[2071] The server collects and analyzes Google Analytics data.

[2072] Specific operation: The server calls the Google Analytics API and retrieves the data.

[2073] Input: Google Analytics data

[2074] Output: Parsed site performance data

[2075] Step 2:

[2076] The server uses the generative AI model to automatically generate site improvement proposals.

[2077] Specific operation: The server uses "Data Analysis AI" to generate site improvement proposals based on the analysis data.

[2078] Input: Site performance data

[2079] Output: Generated site improvement proposals

[2080] Step 3:

[2081] The server monitors user behavior and suggests optimal page structure and navigation improvements.

[2082] Specific operation: The server monitors user behavior in real time and generates improvement suggestions.

[2083] Input: User behavior data

[2084] Output: Generated navigation improvement suggestions

[2085] (Application example 1)

[2086] 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."

[2087] E-commerce site operators need to efficiently manage a large number of products and quickly carry out high-quality promotions. This requires a great deal of time and effort for tasks such as image editing, information entry, and promotion setup. A system is needed to solve this issue and allow operators to focus on more strategic tasks.

[2088] 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.

[2089] In this invention, the server includes a means for users to upload product images, a means for passing the product images to a generative AI model to remove backgrounds and adjust the size, a means for presenting the generated preview image to the user for confirmation, and a means for the server to analyze user data and behavioral history and optimize promotion strategies. This allows e-commerce site operators to significantly reduce the effort and time required for image editing, information input, and promotion settings, enabling them to efficiently provide high-quality products and improve services.

[2090] definition statement

[2091] "User" refers to an individual or organization that uses the system as an operator or administrator of an EC site.

[2092] A "server" is a computer system that handles the entire system, including image editing, data analysis, information supplementation, and launching generative AI models.

[2093] "Product images" are graphic data such as product photos and illustrations posted on e-commerce sites.

[2094] A "generative AI model" is an artificial intelligence framework that uses techniques such as machine learning and deep learning to automatically handle specific tasks.

[2095] "Background removal" is the process of removing the background from a product image and cutting out only the product.

[2096] "Size adjustment" is the operation of changing the size of product images to a size suitable for posting on an e-commerce site.

[2097] A "preview image" is a trial image shown to the user after background removal and resizing have been performed by the generative AI model.

[2098] "Product Information Automatic Completion AI" is an artificial intelligence system that automatically infers and inputs detailed product information based on the product name.

[2099] A "product description" is a sentence generated to explain the features and advantages of a product.

[2100] A "promotion strategy" is a plan or method for effectively implementing advertisements or campaigns aimed at a specific user demographic.

[2101] "Google Analytics" is a tool for collecting and analyzing website traffic data.

[2102] "Site Improvement Suggestions" are specific suggestions for improving the usability and quality of the content of a website.

[2103] MODE FOR CARRYING OUT THE INVENTION

[2104] The present invention provides a system that allows an EC site operator to efficiently edit product images, input product information, and set promotion strategies. A specific embodiment of this system is described below.

[2105] System Overview

[2106] The system for implementing this invention includes a user terminal, a server, a generative AI model, an AI for automatically completing product information, Google Analytics, etc. Its main functions include automatic editing of product images, automatic completion of product information, automatic generation of product descriptions, optimization of promotion strategies, and optimization of the website.

[2107] Automate product image editing

[2108] Users upload product images using their devices. The uploaded images are sent to a server, which uses a generative AI model to remove backgrounds and resize the images. The edited images are then presented to the user as preview images. For example, if a user uploads an image of a shirt, the background is automatically changed to white and the image is resized to the appropriate size.

[2109] Product information auto-completion

[2110] When a user accesses the product information input form and enters the product name, the information is sent to the server. The server then activates the product information auto-completion AI, which retrieves relevant information from existing databases and external databases and automatically reflects it in the input form. For example, if a user enters "smartwatch," the product description, specifications, price, etc. are automatically entered.

[2111] Automatic generation of product descriptions

[2112] After completing the product information registration, the user requests the automatic generation of a product description. This request is sent to the server, which passes the collected product information to the generative AI model to generate the product description. The generated description is then sent to the user's device, where the user can review and approve the content. As a specific example, a description such as "The latest smartwatch with high-performance heart rate measurement" is automatically generated.

[2113] Promotion optimization

[2114] The server automatically sets promotion targets based on user data and behavioral history. It uses a generative AI model to optimize targeting and proposes promotion strategies that respond in real time. For example, one possible method is to display smartwatch ads during times when many female users in their 20s visit. When a user requests the creation of a landing page, the server activates the AI ​​generative model and automatically generates an effective landing page based on product features and keywords.

[2115] Site optimization

[2116] The server collects and analyzes Google Analytics data and uses a generative AI model to automatically generate site improvement proposals. For example, if the time spent on a particular page is short, it will suggest updating the content or changing the layout. The server also monitors user behavior and suggests optimal page structure and navigation improvements. For example, if a particular category is frequently accessed, it will suggest adding that category to the main menu.

[2117] Prompt Sentence Examples

[2118] An example of a prompt for automatically editing product images is, "Please change the background of uploaded product images to white and resize them to appropriate retail sizes."

[2119] As described above, this system allows e-commerce site operators to significantly reduce the effort required for image editing, information input, and promotion settings, allowing them to focus on strategic operations and improving services.

[2120] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[2121] Program processing steps

[2122] Step 1:

[2123] The user uploads a product image from the terminal. The input of this step is the product image file selected by the user from the terminal, and the output is the raw data transferred to the server.

[2124] Step 2:

[2125] The server passes the received product image data to the generative AI model, which removes the background and adjusts the size. The input for this step is the product image data sent to the server, and the generative AI model removes the background and adjusts the size, resulting in an edited preview image.

[2126] Step 3:

[2127] The server presents the generated preview image to the user for confirmation. The input to this step is the preview image after background removal and resizing, and the output is a confirmation screen presented to the user. The user can confirm or make corrections.

[2128] Step 4:

[2129] The user inputs the product name from the terminal and sends it to the server. The input of this step is the product name input by the user from the terminal, and the output is the product name data passed to the server.

[2130] Step 5:

[2131] The server launches the product information auto-completion AI, which automatically fills in the input form with detailed information based on the product name. The input for this step is the product name data, the product information auto-completion AI retrieves related information from the database, and the output is form data with complete product information filled in.

[2132] Step 6:

[2133] The user confirms, modifies, and saves the automatically generated product information. The input of this step is the form data with the product information filled in, and the output is the final product information confirmed and modified by the user.

[2134] Step 7:

[2135] The user requests automatic generation of a product description and sends a request to the server. The input of this step is the automatic generation request sent from the user's terminal, and the output is the request data passed to the server.

[2136] Step 8:

[2137] The server passes the collected product information to the generative AI model to generate a product description. The input of this step is the collected product information, the generative AI model creates a product description, and the output is the generated product description.

[2138] Step 9:

[2139] The server presents the generated product description to the user, who then confirms and approves the content. The input to this step is the generated product description, and the output is text data for confirmation that is presented to the user. The user can confirm or make corrections.

[2140] Step 10:

[2141] The server analyzes user data and behavioral history to optimize the promotion strategy. The input of this step is user data and behavioral history, and the output is an optimized promotion strategy after data analysis using a generative AI model.

[2142] Step 11:

[2143] The server collects and analyzes Google Analytics data and automatically generates site improvement proposals. The input for this step is traffic data from Google Analytics, and the output is proposed site improvement proposals after data analysis using a generative AI model.

[2144] By implementing each of the above steps, e-commerce site operators can efficiently provide high-quality products and improve their services.

[2145] 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.

[2146] The present invention is a system that reduces the time and effort required for tasks such as product promotion and website analysis, and provides an environment in which operators can concentrate on providing high-quality products and improving services. Furthermore, by combining it with an emotion engine that recognizes user emotions, the quality of the user experience can be improved. Below, we will explain an embodiment in which this system, "Assistant EC," is combined with an emotion engine.

[2147] Automate product image editing

[2148] A user uploads a product image from their device. At this time, an emotion engine is built into the device, which recognizes emotions from the user's facial expressions and voice. The uploaded image and emotion data are sent to the server. The server uses a generative AI model to remove the background and adjust the size of the image, and presents the edited image to the user as a preview. For example, if a user uploads an image of a shirt and shows a dissatisfied expression, the background color and brightness are automatically adjusted and the preview is displayed again.

[2149] Product information auto-completion

[2150] When a user accesses a product information input form and enters a product name, the information is sent to the server. The server then activates the product information auto-completion AI, which retrieves relevant information from existing and external databases and reflects it in the input form. The emotion engine continues to monitor the user's facial expressions and reactions, analyzing whether the user is satisfied or dissatisfied. For example, if the user shows a confused expression while entering "smartwatch," the engine automatically adjusts to provide a more detailed explanation or alternative suggestions.

[2151] Automatic generation of product descriptions

[2152] After completing product information registration, the user requests automatic generation of a product description. This request is sent to the server, which passes the collected product information to the generative AI model to generate the product description. The generated description is then sent to the device along with the user's emotional data, and the user confirms and approves the content. For example, when a description such as "The latest smartwatch with high-performance heart rate measurement" is generated, if the user shows a happy expression, approval will proceed. If the user shows a dissatisfied expression, a regeneration will be suggested.

[2153] Promotion optimization

[2154] The server automatically sets promotion targets based on user data and behavioral history. It uses a generative AI model to optimize targeting and leverages emotional data collected through an emotion engine to propose promotion strategies that respond in real time. For example, when displaying smartwatch ads during times when many female users in their 20s visit, the server analyzes their emotional responses to past ads and selects the most appealing method.

[2155] Additionally, when a user requests the creation of a landing page, the server activates an AI generative model to automatically generate an effective landing page based on product features and keywords. The emotion engine analyzes the user's reactions while viewing a preview of the landing page. For example, a landing page for a new smartwatch product is generated, containing appealing copy and design. If the user expresses satisfaction after viewing the preview, the page is published as is. If the user expresses dissatisfaction, the page is re-adjusted.

[2156] Site optimization

[2157] The server collects and analyzes data from Google Analytics and uses a generative AI model to automatically generate suggestions for improving the site. The emotion engine monitors users' reactions while they browse the site and provides the accumulated data to the generative AI model. For example, if the time spent on a particular page is short, it suggests updating the content or changing the layout. The server also monitors user behavior and suggests optimal page structure and navigation improvements. For example, if a particular category is frequently accessed, it suggests adding that category to the main menu.

[2158] These steps enable users, devices, and servers to work together efficiently using the emotion engine to carry out the processes of product registration, information supplementation, promotion, and site improvement. By incorporating emotion recognition, it is possible to enhance the user experience and improve the efficiency and quality of e-commerce site operations.

[2159] The processing flow will be explained below.

[2160] Automate product image editing

[2161] Step 1:

[2162] The user selects a product image from the device and clicks the upload button.

[2163] Step 2:

[2164] The terminal transmits the selected image file to the server, and simultaneously collects and transmits the user's facial expression data using an emotion engine.

[2165] Step 3:

[2166] The server passes the received product images to the generative AI model.

[2167] Step 4:

[2168] The generative AI model removes backgrounds and resizes images.

[2169] Step 5:

[2170] The server temporarily stores the edited image and generates a preview image.

[2171] Step 6:

[2172] The server analyzes the user's facial expression data along with the preview image and presents it to the user.

[2173] Step 7:

[2174] The user checks the preview on the terminal and presses the "Confirm" button.

[2175] Step 8:

[2176] If the user's facial expression indicates dissatisfaction, the server calls the generative AI model again and re-edits the image.

[2177] Product information auto-completion

[2178] Step 1:

[2179] The user accesses the product information entry form.

[2180] Step 2:

[2181] The terminal acquires the product information input form from the server and displays it.

[2182] Step 3:

[2183] The user inputs the product name.

[2184] Step 4:

[2185] The terminal transmits the input product name to the server, and also collects and transmits the user's facial expression data using an emotion engine.

[2186] Step 5:

[2187] The server launches an AI that automatically completes product information and retrieves information by comparing it with past databases and external databases.

[2188] Step 6:

[2189] The server reflects the obtained detailed information in the input form and resends it to the user.

[2190] Step 7:

[2191] The terminal displays an input form to the user and continuously collects the user's facial expression data using an emotion engine.

[2192] Step 8:

[2193] The user checks the auto-completed information and corrects it if necessary.

[2194] Step 9:

[2195] After checking, the user clicks the "Save" button.

[2196] Step 10:

[2197] The terminal transmits the final product information to the server, and also transmits the final facial expression data of the user.

[2198] Step 11:

[2199] The server stores the received product information and facial expression data in a database and improves the accuracy of the information based on the results of analyzing the facial expression data.

[2200] Automatic generation of product descriptions

[2201] Step 1:

[2202] After completing the product information registration process, the user clicks the "Auto-generate introduction" button.

[2203] Step 2:

[2204] The terminal sends this request to the server, and the emotion engine collects and transmits the user's facial expression data at the same time.

[2205] Step 3:

[2206] The server passes the collected product information to the generative AI model.

[2207] Step 4:

[2208] The generative AI model generates product descriptions.

[2209] Step 5:

[2210] The server analyzes the user's facial expression data along with the generated introduction and presents it to the user.

[2211] Step 6:

[2212] The user checks the introduction on the device and reacts with facial expressions.

[2213] Step 7:

[2214] If the user shows a satisfied expression, they approve the testimonial and press the "Confirm" button. If the expression shows dissatisfaction, the server calls the AI ​​model again to regenerate the testimonial.

[2215] Step 8:

[2216] The user approves the final testimonial and sends it to the server.

[2217] Step 9:

[2218] The server stores the approved testimonials in a database.

[2219] Promotion optimization

[2220] Step 1:

[2221] The server collects user data and behavioral history, and also collects emotion data through an emotion engine.

[2222] Step 2:

[2223] The server uses generative AI models to optimize promotion targeting.

[2224] Step 3:

[2225] The server analyzes the sentiment data and suggests optimal target demographics and promotion strategies.

[2226] Step 4:

[2227] The user reviews and approves the proposed targeting and promotion strategy.

[2228] Step 5:

[2229] The server prepares to execute the promotion strategy based on the approved strategy.

[2230] Step 6:

[2231] During the promotion, the emotion engine continuously monitors user reactions and adjusts the promotion content in real time as needed.

[2232] Site optimization

[2233] Step 1:

[2234] The server collects data for Google Analytics.

[2235] Step 2:

[2236] The server collects user emotion data through an emotion engine.

[2237] Step 3:

[2238] The server analyzes the data using a generative AI model.

[2239] Step 4:

[2240] The server automatically generates site improvement proposals and presents them to the user.

[2241] Step 5:

[2242] The user reviews and approves the proposed improvements.

[2243] Step 6:

[2244] The server implements the proposals to update the site layout and content based on the approved improvements.

[2245] Step 7:

[2246] The server continuously analyzes data from the emotion engine and generates additional improvement suggestions to maximize user satisfaction.

[2247] Through these specific processing steps, the "Assistant EC" system can utilize user emotional data to streamline EC site operations and improve the quality of the user experience.

[2248] Example 2

[2249] 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."

[2250] In conventional e-commerce site operations, editing product images, completing product information, promoting products, and optimizing the site takes a lot of time and effort, making it difficult for operators to focus on providing high-quality products and improving services. In addition, because the site does not take user emotions into consideration, there is also the issue of the quality of the user experience not being sufficiently improved.

[2251] The identification process by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means. In this invention, the server includes: a means for a user to upload a product image; a means for a terminal to recognize emotions using an emotion engine that analyzes the user's facial expressions and voice data; a means for the terminal to transmit image data and emotion data to the server; a means for the server to pass the product image to a generative AI model and perform background removal and size adjustment; a means for presenting a generated preview image to the user and suggesting re-editing based on the emotion data; a means for a user to input a product name; a means for the server to start a product information automatic completion AI and automatically reflect detailed information in an input form based on the product name; a means for the emotion engine to monitor the user's emotion data and adjust the completion information; a means for a user to confirm, correct, and save the automatically generated product information; a means for a user to request automatic generation of a product description; a means for the server to pass collected product information to a generative AI model, generate a product description, and present it to the us...

Claims

1. a means for users to upload product images; The server passes the product images to the generative AI model for background removal and resizing; means for presenting the generated preview image to a user and requesting confirmation; A means for a user to input a product name; The server starts the product information auto-completion AI and automatically reflects detailed information in the input form based on the product name; A means for users to check, modify, and save automatically generated product information; A means for a user to request automatic generation of product descriptions; A means for the server to pass the collected product information to the generation AI model, generate a product description, and present it to the user; A system including:

2. The system according to claim 1, wherein after a user completes the product information registration process, a product description is automatically generated using a generative AI model, and the user confirms and approves it.

3. 2. The system according to claim 1, wherein the server automatically generates site improvement proposals using Google Analytics data and presents them to the user.

Images